CN117634948A - Method and system for evaluating quality of silk manufacturing process - Google Patents
Method and system for evaluating quality of silk manufacturing process Download PDFInfo
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Abstract
The invention discloses a method and a system for evaluating the quality of a silk manufacturing process, wherein the method comprises the following steps: detecting the quality of the tobacco shreds, and recording quality detection data results; establishing a process quality evaluation model; verifying the accuracy of the quality detection data result through a process quality evaluation model; evaluating the verified quality detection data result by using a random forest regression model and a sigma level evaluation formula to obtain a variable affecting key tobacco shred indexes and corresponding key tobacco shred indexes, and further obtaining a final tobacco shred process quality evaluation result; according to the method, the accuracy of the process quality evaluation model is verified by establishing the process quality evaluation model and using a verification method, the variables affecting the key quality indexes are screened by using statistical methods such as a random forest regression model, a sigma level evaluation formula and the like, the influence degree is estimated, the scientificity and the reliability of an evaluation system are verified, and the evaluation system is optimized, so that a better application prospect is brought.
Description
Technical Field
The invention relates to the technical field of tobacco manufacturing evaluation, in particular to a method and a system for evaluating the quality of a shredding process.
Background
Under the background of 'two-in-one' of tobacco industry, tobacco enterprises are gradually popularizing and applying a cloud platform-based intelligent process quality control system, the system is based on hardware resources provided by a cloud platform, a process quality closed loop control mode is taken as a thought, an industrial big data analysis method is taken as a means, a scientific and objective whole-batch process quality evaluation system is established through construction research of an intelligent industrial big data analysis platform, the data value of each factory production process is mined, the capability of carrying out force evaluation, process control analysis evaluation and early warning on production technical standards of the production factories is enhanced, the lean manufacturing and quality guarantee level of each factory is continuously improved, the deep fusion of process quality control and informatization construction is promoted, the visualization and traceability of the whole evaluation process are realized, and the traditional process quality control mode is promoted to be converted into an intelligent and whole-process lean control mode. After the tobacco is processed, the quality of the process and the processed tobacco is required to be judged, and the expert evaluation method is generally adopted in the industry to carry out weighting evaluation on parameters in the tobacco processing process.
Therefore, there is an urgent need to develop a method, a system, an electronic device and a storage medium for evaluating the quality of a wire manufacturing process to solve the above-mentioned problems.
Disclosure of Invention
The invention aims to provide a novel technical scheme of a method and a system for evaluating the quality of a silk manufacturing process.
According to a first aspect of the present invention, there is provided a method for evaluating the quality of a wire making process, the method comprising:
step S1: detecting the quality of the tobacco shreds, and recording quality detection data results;
step S2: establishing a process quality evaluation model;
s3, verifying the accuracy of the quality detection data result through the process quality evaluation model;
step S4: and evaluating the verified quality detection data result by using a random forest regression model and a sigma level evaluation formula to obtain a variable affecting the key tobacco shred index and a corresponding key tobacco shred index, and further obtaining a final tobacco shred process quality evaluation result.
Optionally, in the step S1, quality detection is performed on the produced tobacco shred, including color property detection of the tobacco shred, curl degree detection of the tobacco shred, water content detection of the tobacco shred, and appearance detection of the tobacco shred.
Optionally, in the step S2, the process quality evaluation model is obtained by training a training set and a verification set which are established in advance;
the training set is used for training parameters of the process quality evaluation model, and the data of the training set are parameter conditions of different manufacturing processes of the tobacco shred manufacturing equipment;
the validation set is used to adjust the hyper-parameters of the model and to make a preliminary assessment of the model's ability, the data of the validation set being a portion of the data randomly selected from the original data set.
Optionally, in the step S3, a method of performing accuracy verification on the quality detection data result by using the process quality evaluation model is a five-fold cross verification method.
Optionally, in the step S4, a prediction function of the random forest regression model is formulated as:
wherein p represents p decision trees, x represents an input sample, c represents the number of leaf nodes of the decision tree, cx represents the predicted value of the kth leaf node of the p trees, kI represents the number of leaf nodes of the ith tree of the decision tree, and Rkj represents the predicted value of the jth sample.
Optionally, in the step S4, the sigma level evaluation formula is expressed as:
wherein N represents the total number of samples, U represents the total number of samples, u=1, 2, 3 … … N, U represents the average value of the total samples, a represents the measurement value, and X1 represents the average value of the samples;
the standard deviation formula S is expressed as:
wherein m represents the number of samples; p represents the sample number, p=1, 2, 3 … … m, X1 represents the sample average, c represents the measurement time, X1 represents the sample average, and X represents the number of samples.
According to a second aspect of the present invention, there is provided a system for evaluating the quality of a wire making process, the system comprising:
the quality detection module is configured to detect the quality of the prepared cut tobacco and record quality detection data results;
the model building module is configured to build a process quality evaluation model;
the verification module is configured to verify the accuracy of the quality detection data result through the process quality evaluation model;
the combined evaluation module is configured to evaluate the verified quality detection data result by using a random forest regression model and a sigma level evaluation formula so as to obtain a variable affecting a key tobacco shred index and a corresponding key tobacco shred index, and further obtain a final tobacco shred process quality evaluation result.
According to a third aspect of the present invention, there is provided an electronic device comprising a memory and a processor, the memory storing a computer program, the processor implementing the steps in a method for evaluating the quality of a wire-making process according to the first aspect of the present invention as described above when the computer program is executed by the processor.
According to a fourth aspect of the present invention, there is provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of a method for evaluating the quality of a wire manufacturing process according to the first aspect of the present invention described above.
According to a fifth aspect of the present invention, there is provided a computer program product comprising a computer program which, when executed by a processor, implements the steps of a method for evaluating the quality of a wire making process according to the first aspect of the present invention described above.
According to one embodiment of the present disclosure, the following beneficial effects are provided:
according to the quality evaluation method for the silk making process, the process quality evaluation model is established, the accuracy of the process quality evaluation model is verified by the verification method, the variables influencing the key quality indexes are screened by the statistical methods such as the random forest regression model and the sigma level evaluation formula, and the influence degree is estimated, so that the weight is deduced, the scientificity and the reliability of an evaluation system are verified, and the evaluation system is optimized, so that a better use prospect is brought.
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 schematic flow chart of a method for evaluating quality of a filament manufacturing process according to an embodiment;
FIG. 2 is a schematic structural diagram of a system for evaluating quality of a filament manufacturing process according to an embodiment;
fig. 3 is a schematic diagram of an electronic device.
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.
Example 1:
referring to fig. 1, the embodiment provides a method for evaluating the quality of a filament manufacturing process, which includes:
step S1: detecting the quality of the tobacco shreds, and recording quality detection data results;
optionally, in step S1, the quality detection of the produced tobacco shred includes color property detection of the tobacco shred, curl degree test of the tobacco shred, water content test of the tobacco shred, and appearance detection of the tobacco shred.
Specifically, tobacco shred processing is to subject tobacco leaves to a series of processing steps to dry, ferment and ripen the tobacco leaves so as to prepare tobacco products suitable for smoking, and the following is a related description of the basic steps of tobacco shred processing:
preparing tobacco leaves refers to selecting high-quality tobacco leaves, and removing rotten leaves, impurities and parts which do not meet the standard; and classifying the tobacco leaves according to the requirements of being applied to different tobacco products.
Picking tobacco leaves: picking tobacco leaves when the tobacco leaves are ripe; the picked tobacco leaves need to be stored in a specific place for subsequent treatment;
ordering and grading: sorting and grading the picked tobacco leaves according to quality and size; grading the tobacco leaves by using visual or mechanical equipment;
shearing: cutting off stems and veins of tobacco leaves, and retaining leaf parts of the tobacco leaves; the shearing can be performed manually or by using special mechanical equipment;
soaking: humidifying the dried tobacco leaves to restore the elasticity and softness of the tobacco leaves; the wetting treatment can be accomplished by spraying water, soaking or steaming;
fermentation: stacking wet tobacco leaves together, and fermenting under proper temperature and humidity conditions; in the fermentation process, chemical substances in tobacco leaves can change, and the taste and smell of the tobacco leaves are affected;
and (3) drying: the fermented tobacco leaves need to be dried to remove redundant water; the drying process is generally accomplished by natural airing, drying or using specific drying equipment;
and (3) storing: storing the dried tobacco leaves in a proper place to ensure the quality and taste of the tobacco leaves; the correct storage condition may be that the tobacco fully develops its characteristics;
processing and packaging: making the treated tobacco leaves into tobacco shreds of different types, such as cigarettes and tobacco residues, according to the requirements; the processing includes grinding, cutting and mixing;
packaging and storing: finally, the cut tobacco is put into the corresponding tobacco product package; after packaging is complete, the tobacco product needs to be stored in a suitable environment to maintain its taste and quality.
The specific steps of tobacco shred processing are summarized as follows:
a1, flue-cured tobacco, namely spreading tobacco leaves, and placing the tobacco leaves in curing equipment for curing;
a2, taking out the baked tobacco shreds, cutting the tobacco shreds by using a cutting machine, and cutting the tobacco leaves into tobacco shreds;
a3, putting the cut tobacco into a frying box for frying, slowly frying with small fire, waiting for the cut tobacco to turn into golden yellow and reddish, and stopping frying;
a4, flavoring and seasoning the cut tobacco, and placing the cut tobacco in a sealed container for fermentation.
Specific tobacco processing steps may involve specific processing techniques and equipment.
Quality detection is required to be carried out on different types of manufactured tobacco shreds so as to ensure the product quality, and the quality is specific to the tobacco shreds:
detecting color properties of tobacco shreds: the color property of the tobacco shreds is observed and judged by naked eyes, and the color change of the tobacco shreds and the quality condition of the tobacco shreds during processing are judged according to the observation of a master with abundant experience;
and (3) testing the crimping degree of tobacco shreds: the method for detecting the curl degree of the tobacco shreds adopts a rotary cage method, the manufactured sample tobacco shreds are placed in the rotary cage, when the rotary cage rotates, the tobacco shreds slide down along the cage wall, the time required by the tobacco shreds from a sample inlet to a sample outlet is calculated, and the curl degree of the tobacco shreds is calculated;
and (3) testing the water content of the tobacco shreds: the method for testing the moisture content of the tobacco shreds comprises the steps of measuring and calculating the moisture content of the tobacco shreds through a drying method, pre-weighing the tobacco shreds, recording data, placing the tobacco shreds in a drying box for heating and drying, weighing the dried tobacco shreds again after drying, and calculating the moisture content of the tobacco shreds;
and (3) appearance detection of cut tobacco: and observing the prepared appearance by an observation method, and analyzing the quality of the tobacco shreds.
Step S2: establishing a process quality evaluation model;
optionally, in step S2 of the method for evaluating quality of a filament manufacturing process of this embodiment, the process quality evaluation model is obtained by training a training set and a verification set that are established in advance;
the training set is used for training parameters of the process quality evaluation model, and the data of the training set are parameter conditions of different manufacturing processes of the tobacco shred manufacturing equipment;
the verification set is used for adjusting the super parameters of the model and for carrying out preliminary assessment on the capacity of the model, and the data of the verification set is a part of data randomly selected from the original data set.
Specifically, in this embodiment, the training set is used for the data sample of model fitting, and the training set is given and is the most main data source in the model training process, and is used for training the parameters of the model; the training set data are parameter conditions of different manufacturing time of the equipment, and various conditions are substituted into the training set for training;
the verification set is a sample set independently reserved in the model training process and used for adjusting the super parameters of the model and carrying out preliminary evaluation on the capacity of the model, and the verification set is used for helping a machine learning algorithm to better know data in the training process and facilitating better processing of test data after the training is finished;
the verification set is a numerical value existing when a single parameter condition is simulated, and the quality influence of the tobacco shred quality is obtained through continuous calculation verification of a module by substituting data, wherein the verification set is a part of data randomly selected from the original data set and is used for verifying the representation of a model on unseen data.
Step S3, verifying the accuracy of the quality detection data result through a process quality evaluation model;
optionally, in step S3 of the method for evaluating quality of a filament manufacturing process of this embodiment, the method for performing accuracy verification on the quality detection data result by using the process quality evaluation model is a five-fold cross verification method.
Specifically, in this embodiment, the quality detection data result is brought into the process quality evaluation model, and the accuracy of the process quality is verified by using the five-fold cross verification method.
Substituting the data into the evaluation model to obtain accuracy of process quality, wherein the cross verification method is a part of the interior of the model, is a verification mode method, and is verified before the random forest regression model and sigma level evaluation;
in this embodiment, the quality detection data result is substituted into the evaluation model, and the cross-validation method is used for attempting to use different training sets, set division to perform multiple groups of different training on the model, so as to cope with the fact that the single test result is too unilateral and the training data is insufficient.
The cross-validation method roughly divides the data set into k parts which are equal and disjoint, and the calculation formula is as follows:
W＝W1∪W2∪...∪Wk
Wi≠Wo(i≠o) (4)
1 data are selected to be substituted into test, and k-1 parts of additional data are trained to obtain an average value of error as an average value, and the average value is calculated through continuous substitution analysis.
However, five-fold cross-validation is a commonly used machine learning model evaluation method for estimating the performance of the model and reducing randomness caused by different data divisions, and is the following steps of five-fold cross-validation:
data preparation: collecting and sorting your data set, ensuring that the data set has been separated into a training set and a testing set; ensuring that the data is preprocessed, including feature engineering, data cleaning, missing value processing and the like;
dividing the data set: dividing the entire data set into five equally sized subsets, also known as folds; typically, you can use random sampling to ensure that each subset is randomly selected;
model training and evaluation: selecting a machine learning model and then training the model using four subsets thereof; evaluating the performance of the model using the remaining subset; repeating the steps for five times, wherein each time, different subsets are used as test sets, and the other four subsets are used as training sets;
performance metrics: for each iteration of model training and testing, recording performance metrics of the model, such as accuracy, precision, recall, F1 score, etc., and loss functions that need to be optimized;
summarizing the results: averaging the performance metrics in the five iterations to obtain an average performance assessment of the model; calculating standard deviation of performance measurement to know variation of performance;
parameter tuning: if you want to improve the performance of the model, we can use the results of the five-fold cross-validation for super-parametric tuning or feature selection.
Further, the training steps are as follows:
b1, dividing a data set, and dividing the data into 5 subsets for training a model;
b2, training a model, namely training a model by using 4 subsets, and training a data set;
and B3, verifying the data in the model through a verification set to obtain the quality of the cut tobacco.
Step S4: and evaluating the verified quality detection data result by using a random forest regression model and a sigma level evaluation formula to obtain a variable affecting the key tobacco shred index and a corresponding key tobacco shred index, and further obtaining a final tobacco shred process quality evaluation result.
Verification evaluation example:
in order to improve the tobacco quality, a tobacco company carries out comprehensive quality evaluation on tobacco in the production process, and the specific steps are as follows:
and (3) raw material quality evaluation: tobacco leaves with different producing areas, varieties and harvesting time are selected as raw materials, and the quality of the raw materials is evaluated by observing indexes such as leaf structures, colors, aroma and the like of the tobacco leaves, so that the results show that part of the tobacco leaves are good in quality and have higher production values;
and (3) tobacco shred processing and evaluation: the tobacco leaves are subjected to evaluation of links such as slicing, baking, alcoholizing, crushing, mixing, rolling and the like, wherein the evaluation content comprises process parameter setting, equipment performance, operation technology and the like, and the result shows that a certain optimization space exists in part of processing links, and the quality of tobacco shreds can be improved by adjusting the process parameters and improving the operation technology;
evaluation of tobacco shred color and appearance: the color, uniformity, fiber structure and the like of the produced tobacco shreds are observed, and the result shows that the tobacco shreds are full and uniform in color, clear in fiber structure and neat in appearance shape;
evaluation of tobacco aroma and taste: the aroma and the taste of the tobacco shreds are evaluated by tissue professionals, and the result shows that the tobacco shreds are rich and coordinated in aroma, comfortable in taste and free of stimulation;
and (3) evaluating physical properties of tobacco shreds: the physical properties of the tobacco shred such as density, water content, hygroscopicity, combustibility and the like are detected, and the result shows that a certain gap exists between the physical property indexes of part of the tobacco shred, and the physical property of the tobacco shred can be improved by adjusting the production process and equipment;
safety and sanitation evaluation: the method has the advantages that harmful substances in the tobacco shreds are detected, and the result shows that the content of the harmful substances such as tar, nicotine, carbon monoxide and the like in the tobacco shreds meets the national relevant standards;
and (3) checking a finished product: the quality of the finished tobacco shreds is tested comprehensively, wherein the quality of the finished tobacco shreds meets the standard requirements, and the quality test results show that the finished tobacco shreds are tested in the aspects of uniformity, impurity content, moisture content, aroma, taste and the like;
substituting the data parameters into the evaluation formula through the sigma level evaluation formula to obtain the quality condition of the tobacco shreds, optimizing and adjusting the production process by a tobacco company, improving the quality of the tobacco shreds, and simultaneously, strengthening the quality control of raw materials and the production process, and ensuring the stable and excellent quality of the final product;
through the evaluation of the links, the stability and the excellence of the tobacco shred process quality can be ensured, thereby providing high-quality tobacco products for consumers.
Optionally, in step S4, the method for evaluating quality of a filament manufacturing process according to the embodiment includes the following steps:
wherein p represents p decision trees, x represents an input sample, c represents the number of leaf nodes of the decision tree, cx represents the predicted value of the kth leaf node of the p trees, kI represents the number of leaf nodes of the ith tree of the decision tree, and Rkj represents the predicted value of the jth sample.
Optionally, in step S4, the quality evaluation method of the filament manufacturing process of the present embodiment, the sigma level evaluation formula is expressed as:
wherein N represents the total number of samples, U represents the total number of samples, u=1, 2, 3 … … N, U represents the average value of the total samples, a represents the measurement value, and X1 represents the average value of the samples;
the standard deviation formula S is expressed as:
wherein m represents the number of samples; p represents the sample number, p=1, 2, 3 … … m, X1 represents the sample average, c represents the measurement time, X1 represents the sample average, and X represents the number of samples.
In the method for evaluating the quality of the silk manufacturing process, the random forest regression model and the sigma level evaluation formula in the step S4 are used as final evaluation standards, and the quality of the silk manufacturing process is evaluated by analyzing the verified quality detection data result again.
Specifically, the random forest regression model building step in this embodiment is as follows:
c1, selecting a sample set for tobacco quality data as a training set of a decision tree;
c2, selecting data of a part of tobacco shreds during processing as a feature set;
c3, constructing a decision tree based on the training set and the feature set to reach a preset node;
c4, repeating the steps from C1 to C3, and establishing a plurality of decision trees;
and C5, inputting the data into each decision tree, and calculating to obtain a prediction function formula (1).
Specifically, the sigma level evaluation calculation formula in this embodiment is:
in consideration of the offset: c=1.33 corresponds to a4σ level, with a million fraction defective PPM of 63.3; c=1.67 corresponds to a 5 σ level with a million fraction defective PPM of 0.570; c=2.0 corresponds to a 6σ level with a million fraction defective PPM of 0.0020, c=u, sigma level=3cpk1 when the process is not offset.
The following table is a sigma conversion table
In summary, the quality evaluation method of the silk making process of the embodiment of the invention performs accuracy verification on the process quality evaluation model by establishing the process quality evaluation model and performs estimation on the influence degree by using statistical methods such as a random forest regression model and a sigma level evaluation formula, and the like, deduces weight by sigma level evaluation, verifies scientificity and reliability of an evaluation system, optimizes the evaluation system, and brings better use prospect.
Example 2:
referring to fig. 2, the present embodiment provides a system 1 for evaluating the quality of a filament manufacturing process, the system 1 comprising:
the quality detection module 10 is configured to detect the quality of the tobacco shreds and record quality detection data results;
a model building module 20 configured to build a process quality assessment model;
a verification module 30 configured to verify the accuracy of the quality inspection data results by a process quality assessment model;
the combination evaluation module 40 is configured to perform evaluation processing on the verified quality detection data result by using a random forest regression model and a sigma level evaluation formula so as to obtain a variable affecting a key tobacco shred index and a corresponding key tobacco shred index, and further obtain a final tobacco shred process quality evaluation result.
Optionally, the quality detection module 10 in the quality evaluation system of the tobacco shred manufacturing process of the embodiment performs quality detection on the manufactured tobacco shred, including color property detection of the tobacco shred, curl degree test of the tobacco shred, water content test of the tobacco shred and appearance detection of the tobacco shred.
Optionally, the process quality evaluation model in the yarn manufacturing process quality evaluation system of the embodiment is obtained by training a training set and a verification set which are established in advance;
the training set is used for training parameters of the process quality evaluation model, and the data of the training set are parameter conditions of different manufacturing processes of the tobacco shred manufacturing equipment;
the verification set is used for adjusting the super parameters of the model and for carrying out preliminary assessment on the capacity of the model, and the data of the verification set is a part of data randomly selected from the original data set.
Optionally, in the system for evaluating quality of a filament manufacturing process according to this embodiment, the method for verifying accuracy of the quality detection data result by using the process quality evaluation model is a five-fold cross verification method.
Optionally, a prediction function formula of the random forest regression model in the quality evaluation system of the filament manufacturing process in this embodiment is expressed as:
wherein p represents p decision trees, x represents an input sample, c represents the number of leaf nodes of the decision tree, cx represents the predicted value of the kth leaf node of the p trees, kI represents the number of leaf nodes of the ith tree of the decision tree, and Rkj represents the predicted value of the jth sample.
Alternatively, the sigma level evaluation formula in the quality evaluation system of the wire making process of the present embodiment is expressed as:
wherein N represents the total number of samples, U represents the total number of samples, u=1, 2, 3 … … N, U represents the average value of the total samples, a represents the measurement value, and X1 represents the average value of the samples;
the standard deviation formula S is expressed as:
wherein m represents the number of samples; p represents the sample number, p=1, 2, 3 … … m, X1 represents the sample average, c represents the measurement time, X1 represents the sample average, and X represents the number of samples.
Example 3:
the invention discloses an electronic device. The electronic device includes a memory and a processor, the memory storing a computer program, the processor implementing the steps in a method for evaluating the quality of a wire making process according to any one of the disclosed embodiments 1 when executing the computer program.
Fig. 3 is a block diagram of an electronic device according to an embodiment of the present invention, and as shown in fig. 3, the electronic device includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Wherein the processor of the electronic device is configured to provide computing and control capabilities. The memory of the electronic device includes a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The communication interface of the electronic device is used for conducting wired or wireless communication with an external terminal, and the wireless communication can be achieved through WIFI, an operator network, near Field Communication (NFC) or other technologies. The display screen of the electronic equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the electronic equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the electronic equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that the structure shown in fig. 3 is merely a structural diagram of a portion related to the technical solution of the present disclosure, and does not constitute a limitation of the electronic device to which the present application is applied, and that a specific electronic device may include more or less components than those shown in the drawings, or may combine some components, or have different component arrangements.
Example 4:
the embodiment of the invention discloses a computer readable storage medium. A computer-readable storage medium has stored thereon a computer program which, when executed by a processor, implements the steps in a method for evaluating the quality of a wire manufacturing process according to any one of embodiment 1 of the present invention.
Example 5:
the embodiment of the invention discloses a computer program product, which comprises a computer program, wherein the computer program realizes the steps in the method for evaluating the quality of the wire making process according to any one of the embodiment 1 of the invention when being executed by a processor.
Note that the technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be regarded as the scope of the description. The foregoing examples represent only a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the invention. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application is to be determined by the claims appended hereto.
Embodiments of the subject matter and the functional operations described in this specification can be implemented in: digital electronic circuitry, tangibly embodied computer software or firmware, computer hardware including the structures disclosed in this specification and structural equivalents thereof, or a combination of one or more of them. Embodiments of the subject matter described in this specification can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions encoded on a tangible, non-transitory program carrier for execution by, or to control the operation of, data processing apparatus. Alternatively or additionally, the program instructions may be encoded on a manually-generated propagated signal, e.g., a machine-generated electrical, optical, or electromagnetic signal, that is generated to encode and transmit information to suitable receiver apparatus for execution by data processing apparatus. The computer storage medium may be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or a combination of one or more of them.
The processes and logic flows described in this specification can be performed by one or more programmable computers executing one or more computer programs to perform corresponding functions by operating on input data and generating output. The processes and logic flows can also be performed by, and apparatus can also be implemented as, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application-specific integrated circuit).
Computers suitable for executing computer programs include, for example, general purpose and/or special purpose microprocessors, or any other type of central processing unit. Typically, the central processing unit will receive instructions and data from a read only memory and/or a random access memory. The essential elements of a computer include a central processing unit for carrying out or executing instructions and one or more memory devices for storing instructions and data. Typically, a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto-optical disks, or optical disks, etc. However, a computer does not have to have such a device. Furthermore, the computer may be embedded in another device, such as a mobile phone, a Personal Digital Assistant (PDA), a mobile audio or video player, a game console, a Global Positioning System (GPS) receiver, or a portable storage device such as a Universal Serial Bus (USB) flash drive, to name a few.
Computer readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media and memory devices including, for example, semiconductor memory devices (e.g., EPROM, EEPROM, and flash memory devices), magnetic disks (e.g., internal hard disk or removable disks), magneto-optical disks, and CD-ROM and DVD-ROM disks. The processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry.
While this specification contains many specific implementation details, these should not be construed as limitations on the scope of any invention or of what may be claimed, but rather as descriptions of features of specific embodiments of particular inventions. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination in a single embodiment. On the other hand, the various features described in the individual embodiments may also be implemented separately in the various embodiments or in any suitable subcombination. Furthermore, although features may be acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a subcombination or variation of a subcombination.
Similarly, although operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In some cases, multitasking and parallel processing may be advantageous. Moreover, the separation of various system modules and components in the embodiments described above should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.
Thus, particular embodiments of the subject matter have been described. Other embodiments are within the scope of the following claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve desirable results. Furthermore, the processes depicted in the accompanying drawings are not necessarily required to be in the particular order shown, or sequential order, to achieve desirable results. In some implementations, multitasking and parallel processing may be advantageous.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather to enable any modification, equivalent replacement, improvement or the like to be made within the spirit and principles of the invention.
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. The method for evaluating the quality of the silk making process is characterized by comprising the following steps of:
step S1: detecting the quality of the tobacco shreds, and recording quality detection data results;
step S2: establishing a process quality evaluation model;
s3, verifying the accuracy of the quality detection data result through the process quality evaluation model;
step S4: and evaluating the verified quality detection data result by using a random forest regression model and a sigma level evaluation formula to obtain a variable affecting the key tobacco shred index and a corresponding key tobacco shred index, and further obtaining a final tobacco shred process quality evaluation result.
2. The method according to claim 1, wherein in the step S1, the quality detection of the tobacco shred comprises color property detection of the tobacco shred, curl degree detection of the tobacco shred, water content detection of the tobacco shred, and appearance detection of the tobacco shred.
3. The method according to claim 1, wherein in the step S2, the process quality evaluation model is obtained by training a training set and a verification set which are established in advance;
the training set is used for training parameters of the process quality evaluation model, and the data of the training set are parameter conditions of different manufacturing processes of the tobacco shred manufacturing equipment;
the validation set is used to adjust the hyper-parameters of the model and to make a preliminary assessment of the model's ability, the data of the validation set being a portion of the data randomly selected from the original data set.
4. The method according to claim 1, wherein in the step S3, the method for verifying the accuracy of the quality detection data result by the process quality evaluation model is a five-fold cross-verification method.
5. The method according to claim 1, wherein in the step S4, the predictive function formula of the random forest regression model is expressed as:
wherein p represents p decision trees, x represents an input sample, c represents the number of leaf nodes of the decision tree, cx represents the predicted value of the kth leaf node of the p trees, kI represents the number of leaf nodes of the ith tree of the decision tree, and Rkj represents the predicted value of the jth sample.
6. The method according to claim 1, wherein in the step S4, the sigma level evaluation formula is expressed as:
wherein N represents the total number of samples, U represents the total number of samples, u=1, 2, 3 … … N, U represents the average value of the total samples, a represents the measurement value, and X1 represents the average value of the samples;
the standard deviation formula S is expressed as:
wherein m represents the number of samples; p represents the sample number, p=1, 2, 3 … … m, X1 represents the sample average, c represents the measurement time, X1 represents the sample average, and X represents the number of samples.
7. A system for evaluating the quality of a wire making process, the system comprising:
the quality detection module is configured to detect the quality of the prepared cut tobacco and record quality detection data results;
the model building module is configured to build a process quality evaluation model;
the verification module is configured to verify the accuracy of the quality detection data result through the process quality evaluation model;
the combined evaluation module is configured to evaluate the verified quality detection data result by using a random forest regression model and a sigma level evaluation formula so as to obtain a variable affecting a key tobacco shred index and a corresponding key tobacco shred index, and further obtain a final tobacco shred process quality evaluation result.
8. An electronic device comprising a memory and a processor, the memory storing a computer program, the processor implementing the steps of a method for evaluating the quality of a wire-making process according to any one of claims 1 to 6 when the computer program is executed.
9. A computer readable storage medium, characterized in that the computer readable storage medium has stored thereon a computer program which, when executed by a processor, implements the steps of a method for evaluating the quality of a wire manufacturing process according to any one of claims 1 to 6.
10. A computer program product comprising a computer program which, when executed by a processor, implements the steps of a method for evaluating the quality of a wire-making process according to any one of claims 1 to 6.
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