CN117191735A

CN117191735A - Method and apparatus for detecting homogenization of stacked raw materials, and computer-readable storage medium

Info

Publication number: CN117191735A
Application number: CN202311164651.XA
Authority: CN
Inventors: 鹿洪亮; 范坚强; 于德德; 李茂毅; 高忠渊; 郑泉兴; 俞键; 赖炜扬; 黄敏冰; 方钲中
Original assignee: China Tobacco Fujian Industrial Co Ltd
Current assignee: China Tobacco Fujian Industrial Co Ltd
Priority date: 2023-09-11
Filing date: 2023-09-11
Publication date: 2023-12-08

Abstract

The present disclosure relates to a stacking raw material homogenization detection method and apparatus, and a computer-readable storage medium. The method for detecting homogenization of the stacking raw materials comprises the following steps: extracting stacking raw material samples of a plurality of feeding units from stacking raw materials to be detected by adopting three-dimensional sampling; measuring chemical component indexes of raw material samples of each feeding unit; determining a homogenization parameter of the stacking raw material to be detected according to chemical component indexes of raw material samples of a plurality of feeding units, wherein the homogenization parameter comprises at least one of an index range, an index fluctuation standard deviation and an index fluctuation variation coefficient of the chemical component indexes; and determining the homogenization degree of the stacking raw materials to be detected according to the homogenization parameters. The method improves the accuracy of evaluating the homogenization of the stacking raw materials, so that the quality stability of the production process can be ensured.

Description

Method and apparatus for detecting homogenization of stacked raw materials, and computer-readable storage medium

Technical Field

The disclosure relates to the technical field of reconstituted tobacco, in particular to a method and a device for detecting homogenization of stacking raw materials and a computer-readable storage medium.

Background

In the production process of the paper-making reconstituted tobacco, the premise of the in-batch and inter-batch stability of the quality of the finished product is that the production raw materials are stable. The reconstituted tobacco raw materials comprise tobacco stems, crushed tobacco flakes, screening tobacco powder and the like, and have the factors of wide sources, multiple specifications, large annual span, large quality difference, unstable purchasing quantity and the like, and the fluctuation of the internal chemical components of the raw materials can directly influence the quality stability of the production process. In order to control the stability of the quality of reconstituted tobacco products, a large number of analysis and detection of raw materials are needed and the uniformity of the raw material formula is optimized.

Disclosure of Invention

The inventors found through research that: the related technology adopts the average value, the standard deviation of each index and the variation coefficient to improve the accuracy of evaluating the homogenization of the stacking raw materials when evaluating the homogenization of the raw materials.

In view of at least one of the above technical problems, the present disclosure provides a method and apparatus for detecting homogenization of a stacking raw material, and a computer-readable storage medium, which improve accuracy in evaluating homogenization of the stacking raw material, thereby ensuring quality stability of a production process.

According to one aspect of the present disclosure, there is provided a stacked raw material homogenization detection method including:

Extracting stacking raw material samples of a plurality of feeding units from stacking raw materials to be detected by adopting three-dimensional sampling;

measuring chemical component indexes of raw material samples of each feeding unit;

determining a homogenization parameter of the stacking raw material to be detected according to chemical component indexes of raw material samples of a plurality of feeding units, wherein the homogenization parameter comprises at least one of an index range, an index fluctuation standard deviation and an index fluctuation variation coefficient of the chemical component indexes;

and determining the homogenization degree of the stacking raw materials to be detected according to the homogenization parameters.

In some embodiments of the present disclosure, the measuring the chemical composition index of the feedstock sample of each dosing unit comprises:

and measuring the chemical composition index of the raw material sample of each feeding unit by adopting a continuous flow method.

and predicting chemical component indexes of the raw material samples of each feeding unit by adopting a pre-constructed near infrared spectrum model.

In some embodiments of the present disclosure, the stacking raw material homogenization detection method further comprises:

the method comprises the steps of pre-constructing a near infrared spectrum model, wherein the near infrared spectrum model comprises at least one of a stacking raw material near infrared spectrum model in a rotary cup mode and a stacking raw material near infrared spectrum model in an optical fiber mode, and the stacking raw material is at least one reconstituted tobacco stacking raw material selected from tobacco fragments, tobacco stems and tobacco scraps.

In some embodiments of the present disclosure, the pre-constructing the near infrared spectrum model includes:

collecting a near infrared spectrum of a modeling sample;

detecting the chemical component content of the modeled sample, preferably using a continuous flow method;

fitting the near infrared spectrum of the modeled sample with the chemical component content, and establishing a near infrared spectrum model for predicting the stacking raw material.

collecting near infrared spectra of the stacking raw material samples;

the method comprises the steps of preprocessing the spectrum, wherein the preprocessing comprises at least one of vector normalization, standard regular transformation, first derivative, second derivative, multi-element signal correction and spectrum smoothing.

In some embodiments of the disclosure, the chemical composition indicator comprises at least one of total nitrogen, total plant alkali, total sugar, reducing sugar, potassium, chlorine.

In some embodiments of the disclosure, determining the homogenization parameters of the stacked feedstock to be measured according to the chemical composition index of the feedstock samples of the plurality of dosing units includes:

for a chemical composition index, the index limit of the chemical composition index is determined based on the difference between the maximum and minimum values of the chemical composition index for all sampling points of the stack.

In some embodiments of the present disclosure, the homogenization parameters further include at least one of an index fluctuation value and an index fluctuation percentage.

for a chemical composition index, determining an index fluctuation value of the chemical composition index according to the difference between the chemical composition index of a raw material at one position and the average value of the chemical composition indexes of all sampling points of the whole stack;

for a chemical component indicator, determining the indicator fluctuation percentage of the chemical component indicator according to the ratio of the indicator fluctuation value of the chemical component indicator to the average value of the chemical component indicators of all sampling points of the whole stack.

In some embodiments of the present disclosure, determining the degree of homogenization of the stacking stock to be measured based on the homogenization parameters comprises:

and displaying at least one of the index fluctuation value and the index fluctuation percentage through a two-dimensional graph, and determining the homogenization degree of the stacking raw material to be detected.

In some embodiments of the disclosure, the determining the homogenization parameters of the stacked feedstock to be tested according to the chemical composition index of the feedstock samples of the plurality of dosing units includes at least one of the following steps, wherein:

For a chemical component index, determining an index fluctuation standard deviation of the chemical component index according to the standard deviation of the absolute value of the index fluctuation value of the chemical component index;

for a chemical component indicator, determining an index fluctuation coefficient of variation of the chemical component indicator based on a ratio of a standard deviation of an absolute value of an index fluctuation value of the chemical component indicator to an average value of the absolute value of the index fluctuation value of the chemical component indicator.

and determining the homogenization degree of the stacking raw materials to be detected according to at least one of the index fluctuation standard deviation and the index fluctuation variation coefficient.

In some embodiments of the present disclosure, the index fluctuation standard deviation has a higher decision priority than the index fluctuation coefficient of variation.

In some embodiments of the present disclosure, determining the degree of homogenization of the stacking stock to be measured based on the homogenization parameters comprises at least one of the following steps, wherein:

for one chemical component index, under the condition that the index range of the chemical component index is smaller than the preset index range, judging that the chemical component index range of the whole raw materials is small;

For a chemical component index, determining that the homogenization degree of the whole stack of raw materials is good when the standard deviation of index fluctuation of the chemical component index is smaller than the standard deviation of preset index fluctuation;

for a chemical component index, if the index fluctuation coefficient of variation of the chemical component index is smaller than a predetermined index fluctuation coefficient of variation, it is determined that the degree of homogenization of the whole stack of raw materials is good.

According to another aspect of the present disclosure, there is provided a stacked raw material homogenization detection device including:

the sampling module is configured to extract stacking raw material samples of the plurality of feeding units from stacking raw materials to be tested by adopting three-dimensional sampling;

a measurement module configured to measure a chemical composition index of a raw material sample of each dosing unit;

a homogenization parameter determination module configured to determine a homogenization parameter of the raw material to be stacked, according to chemical component indexes of the raw material samples of the plurality of feeding units, wherein the homogenization parameter includes at least one of an index range, an index fluctuation standard deviation, and an index fluctuation variation coefficient of the chemical component indexes;

and the homogenization degree determination module is configured to determine the homogenization degree of the stacking raw material to be tested according to the homogenization parameters.

a memory for storing instructions;

and a processor configured to execute the instructions to cause the stacked raw material homogenization detection device to perform operations for implementing the stacked raw material homogenization detection method described in any of the embodiments above.

According to another aspect of the present disclosure, there is provided a computer readable storage medium storing computer instructions which, when executed by a processor, implement a stacking raw material homogenization detection method as described in any one of the embodiments above.

The method improves the accuracy of evaluating the homogenization of the stacking raw materials, so that the quality stability of the production process can be ensured.

Drawings

In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present disclosure, and other drawings may be obtained according to these drawings without inventive effort to a person of ordinary skill in the art.

Fig. 1 is a schematic view of tobacco stem raw materials of different lengths and different stem diameters.

Fig. 2 is a schematic view of tobacco leaves and tobacco fragment materials in different states.

FIG. 3 is a schematic representation of total alkaloid content in tobacco stems.

Fig. 4 is a schematic representation of the percent fluctuation of total alkaloids from the tobacco stems.

Fig. 5 is a schematic diagram of some embodiments of a stacked feedstock homogenization detection method of the present disclosure.

Fig. 6 is a schematic diagram of three-dimensional stereoscopic sampling of raw silo stacks in some embodiments of the present disclosure.

Fig. 7 is a schematic diagram of further embodiments of the stacked feedstock homogenization detection method of the present disclosure.

Fig. 8 is a schematic representation of total plant alkaloid fluctuation for a category a short tobacco stem stacking feedstock in some embodiments of the present disclosure.

Fig. 9 is a schematic representation of the percentage of total plant alkaloid fluctuation for a category a short tobacco stem stacking feedstock in some embodiments of the present disclosure.

Fig. 10 is a schematic illustration of total plant alkali fluctuation of a B-type short stem stacking feedstock in some embodiments of the present disclosure.

Fig. 11 is a graph showing the percentage of total plant alkali fluctuation of a B-type short stem stacking feedstock in some embodiments of the present disclosure.

Fig. 12 is a schematic view of some embodiments of a stacked feedstock homogenization detection device of the present disclosure.

Fig. 13 is a schematic view of another embodiment of a stacked stock homogeneity detection apparatus of the present disclosure.

Detailed Description

The following description of the technical solutions in the embodiments of the present disclosure will be made clearly and completely with reference to the accompanying drawings in the embodiments of the present disclosure, and it is apparent that the described embodiments are only some embodiments of the present disclosure, not all embodiments. The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the disclosure, its application, or uses. Based on the embodiments in this disclosure, all other embodiments that a person of ordinary skill in the art would obtain without making any inventive effort are within the scope of protection of this disclosure.

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 disclosure unless it is specifically stated otherwise.

Meanwhile, it should be understood that the sizes of the respective parts shown in the drawings are not drawn in actual scale for convenience of description.

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 the exemplary embodiments may have different values.

It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further discussion thereof is necessary in subsequent figures.

The inventors found through research that: the reconstituted tobacco raw materials comprise tobacco stems, crushed tobacco flakes, screened tobacco powder and the like. Fig. 1 is a schematic view of tobacco stem raw materials of different lengths and different stem diameters. Fig. 2 is a schematic view of tobacco leaves and tobacco fragment materials in different states. The reconstituted tobacco has wide sources, multiple specifications, large annual span, large quality difference, unstable purchasing quantity and the like, so that the content of chemical components of the raw materials is greatly fluctuated, the quality is unstable, and the quality stability of a finished product is directly influenced. In order to control the stability of the quality of reconstituted tobacco products, a large number of analysis and detection of raw materials are needed and the uniformity of the raw material formula is optimized.

The related art adopts average value, standard deviation of each index and variation coefficient, and has the problem that homogenization and even discrimination errors cannot be accurately evaluated when raw material homogenization is evaluated. FIG. 3 is a schematic representation of total alkaloid content in tobacco stems. Fig. 4 is a schematic representation of the percent fluctuation of total alkaloids from the tobacco stems. As shown in fig. 3 and 4, the total plant alkali content and the percentage fluctuation of the short tobacco stems can be seen: the total plant alkali content of the whole stack of raw materials is obviously divided into 2 gradients, and the total plant alkali content of the first third of the whole stack is obviously higher than the second third of the whole stack; the total plant alkali content of the first third of the whole stack is 20-25% higher than the average of the whole stack, and the second third of the whole stack is 5-15% lower than the average of the whole stack. The total plant alkali content of the whole raw materials has obvious intra-batch and inter-batch differences, and the initial judgment is that the raw material stack with the specification contains two raw materials and has wrong mixing, but the standard deviation is 0.10 and the variation coefficient is 0.14 according to the raw material homogenization evaluation method of the related technology, so that the problem of the quality of the raw materials cannot be accurately identified.

In order to accurately evaluate the homogenization of the stacking raw materials so as to ensure the quality stability of the production process, a scientific and reasonable new evaluation method is needed to be established. In view of at least one of the above technical problems, the present disclosure provides a stacking raw material homogenization detection method and apparatus, and a computer-readable storage medium. The present disclosure is illustrated by the following examples.

Fig. 5 is a schematic diagram of some embodiments of a stacked feedstock homogenization detection method of the present disclosure. The fig. 5 embodiment may be performed by a stacked feedstock homogenization detection device of the present disclosure. As shown in fig. 5, the method of the embodiment of fig. 5 may include at least one of steps 100 to 400, wherein:

and 100, extracting stacking raw material samples of a plurality of feeding units from stacking raw materials to be detected by adopting three-dimensional sampling.

In some embodiments of the present disclosure, step 100 may include at least one of steps 110 to 120, wherein:

step 110, a minimum feeding unit mode is established.

In some embodiments of the present disclosure, the dosing unit may be 1 bag/bin stock or multiple bags/bins stock.

In some embodiments of the present disclosure, an 8-12 bag or box (not limited to an 8-12 bag or box) is used as a dosing unit.

In some embodiments of the present disclosure, the dosing unit may also be a sampling unit.

And 120, improving sampling representativeness by adopting a three-dimensional positioning sampling mode, wherein the sampling positions cover the upper, middle, lower, front and rear positions of the whole stack, and subdividing the upper, middle, lower, left and right sampling positions of each section. Fig. 6 is a schematic diagram of three-dimensional stereoscopic sampling of raw silo stacks in some embodiments of the present disclosure. As shown in fig. 6, the a direction is the height direction of the entire stack, the B direction is the left-right direction of the entire stack, and the C direction is the front-back direction of the entire stack.

The above embodiments of the present disclosure provide a three-dimensional stereotactic sampling method, thereby improving sampling representativeness.

Step 200, measuring chemical composition index of raw material sample of each feeding unit.

In some embodiments of the disclosure, the chemical composition indicator may be total plant alkali.

In some embodiments of the present disclosure, step 200 may include: and measuring the chemical composition index of the raw material sample of each feeding unit by adopting a continuous flow method.

In some embodiments of the present disclosure, step 200 may include: and predicting chemical component indexes of the raw material samples of each feeding unit by adopting a pre-constructed near infrared spectrum model.

Step 300, determining a homogenization parameter of the stacking raw material to be tested according to chemical component indexes of raw material samples of a plurality of feeding units, wherein the homogenization parameter comprises at least one of an index range, an index fluctuation standard deviation and an index fluctuation variation coefficient of the chemical component indexes.

Step 400, determining the homogenization degree of the stacking raw materials to be tested according to the homogenization parameters.

The above embodiments of the present disclosure propose a method of evaluating homogenization of a raw material for stacking, so that accuracy in evaluating homogenization of raw material for stacking can be improved, and quality stability of a production process can be ensured.

Fig. 7 is a schematic diagram of further embodiments of the stacked feedstock homogenization detection method of the present disclosure. The fig. 7 embodiment may be performed by a stacked feedstock homogenization detection device of the present disclosure. As shown in fig. 7, the stacked raw material homogenization detection method of the embodiment of fig. 7 may further include step 90 in addition to at least one of steps 100 to 400, which may be the same as or similar to the embodiment of fig. 5, in which:

And 90, pre-constructing a near infrared spectrum model, wherein the near infrared spectrum model comprises at least one of a stacking raw material near infrared spectrum model in a rotating cup mode and a stacking raw material near infrared spectrum model in an optical fiber mode, and the stacking raw material is at least one reconstituted tobacco stacking raw material selected from tobacco fragments, tobacco stems and tobacco dust.

In some embodiments of the present disclosure, step 90 may include: the method for establishing the near infrared spectrum sub-model of the reconstituted tobacco raw material comprises the following steps of: 1. adopting a rotating cup mode, collecting near infrared diffuse reflection spectrums of different raw materials of reconstituted tobacco after grinding a sample, and establishing a slice raw material rotating cup mode qualitative and quantitative prediction model through an optimal algorithm according to a fitting relation between a chemical true value and a spectrum curve; 2. the optical fiber mode is adopted to collect near infrared spectrum of raw material samples in an original state, optimize the optimal collection parameters, establish a qualitative and quantitative predictor model of the thin sheet raw material optical fiber mode according to the fitting relation between a chemical true value and a spectral curve, and provide technical support for introducing an online near infrared technology.

In some embodiments of the present disclosure, step 90 may include at least one of steps 91 to 94, wherein:

Step 91, sample collection and preparation.

In some embodiments of the present disclosure, step 91 may include: the reconstituted tobacco raw materials mainly comprise tobacco fragments, tobacco stems and the like, sampling is carried out according to different points from front to back and from left to right according to the upper, middle and lower parts of a stack, 305 samples of the tobacco fragments and 194 samples of the tobacco stems in different areas of 2021-2022 are collected, the fragments are divided into two parts, one part is ground into powder, the powder is sieved by a 40-mesh sieve, and the tobacco stems are ground into powder and sieved by a sieve above 40 meshes.

At step 92, near infrared spectra of the modeled samples are collected.

In some embodiments of the present disclosure, step 92 may include: the sample adopts the scattered reflection of integrating sphere spectrum and the optical fiber diffusion mode to collect the sample spectrum of the tobacco stalk and the fragment after grinding respectively, and the collection parameters are as follows:

and the acquisition module is used for: the optical fiber diffuse reflection module comprises a rotating sample cup integrating sphere spectrum diffuse reflection acquisition module and an optical fiber diffuse reflection module.

In some embodiments of the present disclosure, the acquisition module is a fourier transform near infrared spectrometer.

In some embodiments of the present disclosure, the fourier transform near infrared spectrometer may be an Antaris 2 model number fourier transform near infrared spectrometer of sammer, usa.

Data format: log1/R.

Resolution ratio: 8.0cm-1.

Spectral range: 4000-10000cm < -1 >.

Sample scan times: 68 times.

Scanning background frequency: once every 30 minutes.

Spectrum preservation format: SPA and SPC.

The spectral data obtained from 3 consecutive scans of each sample was averaged.

Step 93, modeling the determination of chemical composition.

In some embodiments of the present disclosure, step 93 may include: the chemical component content of the modeled sample is detected, preferably using a continuous flow method.

In some embodiments of the present disclosure, step 93 may include: the water-soluble sugar (total sugar), total plant alkali, chlorine, potassium, total nitrogen and moisture content of the sample were measured by using a continuous flow analyzer according to the methods specified in industry standards "continuous flow method for measuring water-soluble sugar in YC/T159-2019 tobacco and tobacco products," continuous flow method for measuring YC/T217-2007 tobacco and tobacco products potassium, "continuous flow method for measuring YC/T162-2011 tobacco and tobacco products chlorine," continuous flow method for measuring total nitrogen in YC/T161-2002 tobacco and tobacco products, "continuous flow (potassium thiocyanate) method for measuring YC/T468-2013 tobacco and total plant alkali in tobacco products," continuous flow analyzer "and" YC/T31-1996 tobacco and tobacco products sample preparation and moisture measurement oven method.

At step 94, a near infrared spectrum model is constructed.

In some embodiments of the present disclosure, step 94 may include: and constructing a rotating cup mode and optical fiber mode near infrared spectrum model.

In some embodiments of the present disclosure, step 94 may include at least one of step 941 and step 942, wherein:

step 941, selecting a spectrum area range and preprocessing spectrum data.

In some embodiments of the present disclosure, step 941 may include: from the near infrared original spectrum, the near infrared absorption spectrum of the tobacco leaf fragment sample is 4000-7500 cm ^-1 The area signal is stronger, and the information quantity is abundant. In order to eliminate the influences of high-frequency random noise, baseline drift, sample nonuniformity, light scattering and the like, effective characteristic information contained in a spectrum is fully extracted, the prediction accuracy of a correction model is improved, and necessary pretreatment is needed to be carried out on the spectrum.

In some embodiments of the present disclosure, step 941 may include: in order to eliminate the influence of random noise and reduce the system error, one or more of vector normalization, standard regular transformation, first derivative, second derivative, multi-element signal correction or spectrum smoothing methods can be adopted for the original spectrum of the sample for preprocessing.

In some embodiments of the present disclosure, the collected raw spectra are preprocessed using multivariate signal correction (Multiplicative signal correction, MSC), standard canonical transformation (Standard Normal Variate, SNV), and/or using first derivative, second derivative, in combination with Savitzky-Golay (SG) smoothing filtering (segment length 7; segment spacing 3) or Norris derivative filtering (segment length 5; segment spacing 5), respectively. The result shows that different pretreatment methods have certain influence on the prediction result of the model, and the prediction effect is better when the multi-element signal correction is adopted and the first derivative is adopted to combine with Norris smoothing filtering pretreatment.

In some embodiments of the present disclosure, the calibration model of water-soluble sugars and total alkaloids is optimized by spectral pretreatment, taking the water-soluble sugars and total alkaloids as examples, and the results are shown in table 1. Table 1 shows comparison of parameters related to different pretreatment of tobacco leaf fragments in a model of water-soluble sugar and total plant alkali. And finally, preprocessing the spectrum by adopting the following method to obtain an ideal result by integrating all parameters of the model: (1) correcting and eliminating the difference caused by the non-uniformity of the sample by adopting a multi-element signal; (2) the Norris derivative filtering smooth spectrum with the segment length of 5 and the segment spacing of 5 is adopted, so that high-frequency noise is eliminated, and useful low-frequency information is reserved; (3) the first derivative treatment is adopted to eliminate the influence of baseline drift, so that higher resolution and clearer profile change than the original spectrum are obtained. The optimal spectral pretreatment parameters of other chemical components such as corresponding water-soluble sugar, chlorine, potassium, total nitrogen, moisture and the like are determined by referring to the method.

In some embodiments of the present disclosure, the modeled near infrared absorbance spectrum is in the interval 4000-7500 cm-1, preferably 4256.13-7029.55.

Finally determining the modeling spectrum area to be 4256.13-7029.55 cm ^-1 In this case, the result is the best.

TABLE 1

In some embodiments of the present disclosure, the respective other chemical components may also determine their optimal spectral pretreatment parameters by similar methods.

In step 942, the near infrared spectrum of the modeled sample is fitted to the chemical content to build a near infrared spectrum model that predicts the stacking material.

In some embodiments of the present disclosure, step 942 may include: fitting near infrared spectrums of two tobacco leaf fragment samples obtained by adopting a partial least square method (PLS) in two spectrum acquisition modes of integrating sphere spectrum diffuse reflection and optical fiber diffuse reflection with corresponding chemical component values measured by a continuous flow method, and establishing a near infrared spectrum model for predicting the contents of water-soluble sugar, total plant alkali, chlorine, potassium, total nitrogen and the like in the tobacco leaf fragments.

In some embodiments of the present disclosure, step 942 may include: fitting the near infrared spectrum of the tobacco stalk sample obtained by adopting a partial least square method (PLS) spectrum diffuse reflection spectrum acquisition mode with corresponding chemical component values measured by a continuous flow method, and establishing a near infrared spectrum model for predicting the contents of water-soluble sugar, total plant alkali, chlorine, potassium, total nitrogen and the like in the tobacco stalk.

In some embodiments of the present disclosure, step 942 may comprise at least one of steps (1) to (3), wherein:

and (1) constructing a tobacco leaf fragment near infrared spectrum model in a rotary cup mode.

The technical indexes of the tobacco leaf fragment near infrared spectrum model constructed based on the integrating sphere spectrum diffuse reflection acquisition mode are shown in table 2. Table 2 is a schematic representation of relevant technical indexes of a tobacco leaf fragment near infrared spectrum model constructed based on an integrating sphere spectrum diffuse reflection acquisition mode.

TABLE 2

Step (2), constructing a tobacco leaf fragment near infrared spectrum model in an optical fiber mode

The technical indexes of the tobacco leaf fragment near infrared spectrum model constructed based on the optical fiber diffuse reflection acquisition mode are shown in table 3.

TABLE 3 Table 3

And (3) constructing a tobacco leaf fragment near infrared spectrum model in a rotary cup mode.

The technical indexes of the tobacco stem near infrared spectrum model constructed based on the integrating sphere spectrum diffuse reflection acquisition mode are shown in table 4. Table 4 is a schematic representation of relevant technical indexes of a tobacco stem near infrared spectrum model constructed based on an integrating sphere spectrum diffuse reflection acquisition mode.

TABLE 4 Table 4

In some embodiments of the present disclosure, as shown in fig. 6, reconstituted tobacco raw materials are divided into feeding units according to three-dimensional stereotactic, and the position of each feeding unit in three-dimensional space is recorded through A, B, C three directions.

In some embodiments of the present disclosure, the data at the time of homogenization evaluation is derived from the three-dimensional stereo sampling detection data described above; data were not less than 6 groups.

In some embodiments of the present disclosure, step 200 may include at least one of steps 210 to 230, wherein:

at step 210, near infrared spectra of the stacked feedstock samples are collected.

Step 220, preprocessing the spectrum, wherein the preprocessing comprises at least one of vector normalization, standard regularization transformation, first derivative, second derivative, multi-element signal correction and spectrum smoothing.

And 230, predicting chemical component indexes of the raw material samples of each feeding unit by adopting a pre-constructed near infrared spectrum model.

In some embodiments of the present disclosure, the homogenization parameters may further include at least one of an index fluctuation value and an index fluctuation percentage.

In some embodiments of the present disclosure, step 300 may include at least one of steps 310 to 350, wherein:

step 310, for a chemical composition indicator, determining that the chemical composition indicator is very poor based on the difference between the maximum and minimum values of the chemical composition indicator for all sampling points of the stack.

Step 320, for a chemical composition indicator, determining an indicator fluctuation value of the chemical composition indicator according to a difference between the chemical composition indicator of a position raw material and an average value of the chemical composition indicators of all sampling points of the whole stack.

Step 330, for a chemical component indicator, determining the indicator fluctuation percentage of the chemical component indicator according to the ratio of the indicator fluctuation value of the chemical component indicator to the average value of the chemical component indicators at all sampling points of the whole stack.

Step 340, for a chemical component indicator, determining an indicator fluctuation standard deviation of the chemical component indicator according to the standard deviation of the absolute value of the indicator fluctuation value of the chemical component indicator.

Step 350, for a chemical component indicator, determining an index fluctuation variation coefficient of the chemical component indicator according to a ratio of a standard deviation of an index fluctuation value absolute value of the chemical component indicator and an average value of the index fluctuation value absolute value of the chemical component indicator.

In some embodiments of the present disclosure, step 400 may include: and determining the homogenization degree of the stacking raw materials to be detected according to at least one of the index fluctuation standard deviation and the index fluctuation variation coefficient.

In some embodiments of the present disclosure, step 400 may include at least one of steps 410 through 440, wherein:

step 410, displaying at least one of the index fluctuation value and the index fluctuation percentage through a two-dimensional graph, and determining the homogenization degree of the stacking raw material to be tested.

Step 420, for a chemical component index, determining that the chemical component index of the whole stack of raw materials has a small difference in height when the index difference of the chemical component index is smaller than a predetermined index difference.

Step 430, for a chemical component indicator, determining that the homogenization degree of the whole stack of raw materials is good when the standard deviation of the indicator fluctuation of the chemical component indicator is smaller than the standard deviation of the predetermined indicator fluctuation.

Step 440, for a chemical component index, determining that the homogenization degree of the whole stack of raw materials is good when the index fluctuation coefficient of variation of the chemical component index is smaller than the predetermined index fluctuation coefficient of variation.

In some embodiments of the present disclosure, it is preferred to use total alkaloids that change less during storage as a primary indicator, based on total alkaloids extreme, total alkaloids fluctuation standard deviation, total alkaloids fluctuation coefficient of variation.

In some embodiments of the present disclosure, total plant alkaloid tolerance refers to the maximum of the total plant alkaloid for all sampling points of the entire stack minus the minimum of the total plant alkaloid for all sampling points of the entire stack;

in some embodiments of the present disclosure, the total plant alkaloid fluctuation is the total plant alkaloid of a feedstock at a location minus the total plant alkaloid average at all sampling points of the entire stack;

in some embodiments of the present disclosure, total alkaloid fluctuation = total alkaloid for a feedstock at a location-total alkaloid average for all sampling points of the whole stack;

in some embodiments of the present disclosure, the total plant alkaloid fluctuation may exhibit differences in two-dimensional figures.

In some embodiments of the present disclosure, the total plant alkaloid fluctuation percentage is the total plant alkaloid of a feedstock at a location divided by the total plant alkaloid average for all sampling points of the entire stack.

In some embodiments of the present disclosure, total alkaloid variability = 100 x (total alkaloid for a feedstock-total alkaloid average for all samples of a whole stack) for a location per total alkaloid average for all samples of a whole stack.

In some embodiments of the present disclosure, the total plant alkaloid fluctuation standard deviation refers to the standard deviation of the absolute value of the total plant alkaloid fluctuation value described above;

in some embodiments of the present disclosure, the total plant alkaloid fluctuation coefficient of variation refers to the coefficient of variation of the absolute value of the total plant alkaloid fluctuation value described above.

In some embodiments of the present disclosure, the total plant alkaloid variability = standard deviation of absolute value of total plant alkaloid variability/average of absolute value of total plant alkaloid variability.

In some embodiments of the present disclosure, total plant alkaloids are very poor, representing small overall stack index differences.

In some embodiments of the disclosure, the total plant alkaloid fluctuation standard deviation precedence is higher than the total plant alkaloid fluctuation coefficient of variation.

In some embodiments of the present disclosure, the total plant alkali fluctuation standard deviation is small, representing good homogenization of the whole stack of feedstock.

The invention provides a method for evaluating homogenization of stacking raw materials, which comprises the steps of firstly establishing a minimum feeding unit mode, improving sampling representativeness in a three-dimensional positioning sampling mode, establishing a corresponding mathematical model by utilizing the prominent advantages of rapidness, high efficiency, low cost, no pollution, no damage, simultaneous determination of multiple components and the like of near infrared spectrum, and rapidly detecting the data of conventional chemical components (total nitrogen, total plant alkali, total sugar, reducing sugar, potassium and chlorine) of the raw materials at different positions of a stack. And then based on the standard deviation of index fluctuation and variation coefficient of index fluctuation of the standard chemical composition index of one or more raw materials, other indexes are taken as references to accurately evaluate the homogenization condition of the stacked raw materials, and data support for building a formula is provided for preparing a raw material premixing scheme.

The above embodiments of the present disclosure provide a calculation method for homogenizing stacked raw materials.

The method disclosed by the embodiment of the disclosure has the advantages of strong three-dimensional stereotactic sampling representativeness, rapidness and high efficiency of near infrared spectrum modeling, low cost, no pollution, no damage, simultaneous determination of multiple components, calculation and comparison of the extremely poor index of the conventional chemical components of a plurality of raw materials, standard deviation of index fluctuation, high accuracy of evaluating homogenization of the stacking raw materials by the index fluctuation variation coefficient, and the like.

The present disclosure is further illustrated by the following specific examples.

EXAMPLE 1 homogenization evaluation of raw Material for stacking short tobacco stems of type A

This example analyzed the homogenization of a class a short smoke stack feedstock using the homogenization evaluation of the present disclosure and conducted a comparative analysis of the presently disclosed method and related art evaluation method.

The specific steps may include steps 1 to 5, wherein:

and step 1, adopting a three-dimensional sampling mode, and sampling 53 raw materials in a whole stack.

Step 2, using the established near infrared spectrum model, the total plant alkali content of 53 samples was predicted, and the data are shown in table 4. Table 4 gives an illustration of the total plant alkali content and homogenization parameters for 53 samples.

And 3, calculating total plant alkali fluctuation and total plant alkali fluctuation percentage by adopting the established homogenization evaluation method, and displaying the difference in a two-dimensional graph, wherein fig. 8 and 9 show the difference, and fig. 8 is a schematic diagram of the total plant alkali fluctuation of the type-A short tobacco stalk stacking raw material in some embodiments of the present disclosure. Fig. 9 is a schematic representation of the percentage of total plant alkaloid fluctuation for a category a short tobacco stem stacking feedstock in some embodiments of the present disclosure.

From figures 8 and 9 the total plant alkali content and the percentage fluctuation can be seen: the total plant alkali content of the whole stack of the type A short tobacco stems is obviously divided into 2 gradients, and the total plant alkali content of the first third of the whole stack is obviously higher than the second third of the whole stack; the total plant alkali content of the first third of the whole stack is 20-25% higher than the average of the whole stack, and the second third of the whole stack is 5-15% lower than the average of the whole stack. The uniformity and consistency of the whole stack of raw materials are poor and can be caused by wrong mixing, if the raw materials are directly fed during production, obvious intra-batch and inter-batch differences of the total plant alkali content of the reconstituted tobacco finished product can be caused.

And 4, calculating total plant alkali extreme deviation, total plant alkali fluctuation standard deviation and total plant alkali fluctuation variation coefficient according to the calculated total plant alkali fluctuation and total plant alkali fluctuation percentage by adopting the established homogenization evaluation method, wherein the data are shown in table 5.

TABLE 5

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Step 5, after the above analysis, a homogenization of the short smoke stack a is obtained, see table 6. The homogenization of the a-stack may be determined based on the homogenization parameters shown in table 6.

TABLE 6

Raw material name	Extremely poor	Maximum value	Minimum value	Standard deviation of fluctuation	Coefficient of variation of fluctuation
						A type short smoke stack	0.28	0.85	0.57	6.97	0.55

EXAMPLE 2 homogenization evaluation of raw Material for stacked Aureobasidium pullulans of type B

In this example, the homogenization evaluation described above was used to analyze the homogenization of the B-type short smoke stack feedstock, and a comparative analysis was performed between the presently disclosed homogenization detection method and the related art evaluation method.

The specific steps may include steps 1 to 5, wherein:

and step 1, adopting a three-dimensional sampling mode, and sampling 54 raw materials in a whole stack.

Step 2, using the established near infrared spectrum model, the total plant alkali content of 54 samples was predicted, and the data are shown in table 6. Table 6 gives an indication of the total plant alkali content and homogenization parameters for the 54 samples.

Step 3, calculating total plant alkaloid fluctuation and total plant alkaloid fluctuation percentage by using the established homogenization evaluation method, and displaying the difference in a two-dimensional graph, see fig. 10 and fig. 11, wherein fig. 10 is a schematic diagram of total plant alkaloid fluctuation of the B-type short tobacco stalk stacking raw material in some embodiments of the present disclosure. Fig. 11 is a graph showing the percentage of total plant alkali fluctuation of a B-type short stem stacking feedstock in some embodiments of the present disclosure.

From figures 10 and 11 the total plant alkali content and the percentage fluctuation can be seen: the total plant alkaloid distribution of the B-type short tobacco stem stacks shows a tendency of gradually rising from front to back, and is low: the height is close to 4:6, the fluctuation range of the total plant alkali reaches-28% -20%, and the uniformity and consistency of the whole stack of raw materials are low. If the production is carried out according to the currently adopted material discharged according to the section, the total plant alkali content of the product in the production process is low before and high after the total plant alkali content is high, and the stability of the batch of products cannot be ensured.

(4a) Using the established homogenization evaluation method, total plant alkaloid variation standard deviation, total plant alkaloid variation coefficient were calculated from the calculated total plant alkaloid variation, total plant alkaloid variation percentages, and the data are shown in table 7.

TABLE 7

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Step 5, after the above analysis, a homogenization of the B short smoke stack is obtained, see table 8. The homogenization of the a-stack may be determined based on the homogenization parameters shown in table 8.

TABLE 8

Raw material name	Extremely poor	Maximum value	Minimum value	Standard deviation of fluctuation	Coefficient of variation of fluctuation
						Type B short smoke stack	0.39	0.99	0.60	6.31	0.62

Fig. 12 is a schematic view of some embodiments of a stacked feedstock homogenization detection device of the present disclosure. As shown in fig. 12, the stacked raw material homogenization detection device of the present disclosure may include a sampling module 21, a measuring module 22, a homogenization parameter determination module 23, and a degree of homogenization determination module 24, wherein:

the sampling module 21 is configured to extract a stacking raw material sample of the plurality of feeding units from the stacking raw material to be tested by adopting three-dimensional sampling.

A measurement module 22 configured to measure a chemical composition indicator of the raw material sample of each dosing unit.

In some embodiments of the present disclosure, the measurement module 22 may be configured to measure the chemical composition index of the feedstock sample of each dosing unit using a continuous flow method.

In some embodiments of the present disclosure, the measurement module 22 may be configured to predict the chemical composition index of the feedstock sample for each dosing unit using a pre-constructed near infrared spectral model.

In some embodiments of the present disclosure, the measurement module 22 may be configured to collect near infrared spectra of the stacking feedstock sample; preprocessing the spectrum, wherein the preprocessing comprises at least one of vector normalization, standard regular transformation, first derivative, second derivative, multi-element signal correction and spectrum smoothing; and predicting chemical component indexes of the raw material samples of each feeding unit by adopting a pre-constructed near infrared spectrum model.

A homogenizing parameter determining module 23 configured to determine a homogenizing parameter of the stacked raw material to be measured according to chemical component indexes of the raw material samples of the plurality of feeding units, wherein the homogenizing parameter includes at least one of an index range, an index fluctuation standard deviation, and an index fluctuation variation coefficient of the chemical component indexes.

In some embodiments of the present disclosure, the homogenization parameter determination module 23 may be configured to determine, for one chemical composition indicator, an indicator of the chemical composition indicator that is extremely poor based on the difference between the maximum and minimum values of the chemical composition indicator for all sampling points of the stack.

In some embodiments of the present disclosure, the homogenization parameter determination module 23 may be configured to determine, for a chemical composition indicator, an indicator fluctuation value of the chemical composition indicator based on a difference between the chemical composition indicator of a location feedstock and the chemical composition indicator average of all sampling points of the stack; for a chemical component indicator, determining the indicator fluctuation percentage of the chemical component indicator according to the ratio of the indicator fluctuation value of the chemical component indicator to the average value of the chemical component indicators of all sampling points of the whole stack.

A degree of homogenization determination module 24 is configured to determine a degree of homogenization of the raw stacker material to be tested based on the homogenization parameters.

In some embodiments of the present disclosure, the degree of homogenization determination module 24 may be configured to determine the degree of homogenization of the raw palletized material under test based on at least one of the index fluctuation standard deviation and the index fluctuation coefficient of variation.

In some embodiments of the present disclosure, the degree of homogenization determination module 24 may be configured to determine the degree of homogenization of the raw palletized material to be tested by displaying at least one of the index fluctuation value and the index fluctuation percentage in a two-dimensional graph.

In some embodiments of the present disclosure, the degree of homogenization determination module 24 may be configured to perform at least one of the following operations to determine a degree of homogenization of the palletized raw material to be tested based on the homogenization parameters, wherein: for one chemical component index, under the condition that the index range of the chemical component index is smaller than the preset index range, judging that the chemical component index range of the whole raw materials is small; for a chemical component index, determining that the homogenization degree of the whole stack of raw materials is good when the standard deviation of index fluctuation of the chemical component index is smaller than the standard deviation of preset index fluctuation; for a chemical component index, if the index fluctuation coefficient of variation of the chemical component index is smaller than a predetermined index fluctuation coefficient of variation, it is determined that the degree of homogenization of the whole stack of raw materials is good.

In some embodiments of the present disclosure, as shown in fig. 12, the stacking raw material homogenization detecting device may further include a model building module 20, wherein:

the model construction module 20 is configured to construct a near infrared spectrum model in advance, wherein the near infrared spectrum model comprises at least one of a stacking raw material near infrared spectrum model in a rotating cup mode and a stacking raw material near infrared spectrum model in an optical fiber mode, and the stacking raw material is at least one reconstituted tobacco stacking raw material of tobacco fragments, tobacco stems and tobacco dust.

In some embodiments of the present disclosure, the model building module 20 may be configured to collect near infrared spectra of the modeled samples; detecting the chemical component content of the modeled sample, preferably using a continuous flow method; fitting the near infrared spectrum of the modeled sample with the chemical component content, and establishing a near infrared spectrum model for predicting the stacking raw material.

In some embodiments of the present disclosure, the stacking raw material homogenization detection device of the present disclosure may be configured to perform the stacking raw material homogenization detection method described in any of the embodiments described above (e.g., any of fig. 5-11).

Fig. 13 is a schematic view of another embodiment of a stacked stock homogeneity detection apparatus of the present disclosure. As shown in fig. 13, the stacked raw material homogenization detection device of the present disclosure includes a memory 71 and a processor 72.

The memory 71 is configured to store instructions, and the processor 72 is coupled to the memory 71, the processor 72 being configured to implement the stacked raw material homogenization detection method according to the above-described embodiment (e.g., any of fig. 5 to 11) based on the instructions stored in the memory.

As shown in fig. 13, the stacked raw material homogenization detecting device further includes a communication interface 73 for information exchange with other devices. Meanwhile, the stacked raw material homogenization detecting device further comprises a bus 74, and the processor 72, the communication interface 73 and the memory 71 are in communication with each other through the bus 74.

The memory 71 may comprise a high-speed RAM memory or may further comprise a non-volatile memory (non-volatile memory), such as at least one disk memory. The memory 71 may also be a memory array. The memory 71 may also be partitioned and the blocks may be combined into virtual volumes according to certain rules.

Further, the processor 72 may be a central processing unit CPU, or may be an application specific integrated circuit ASIC, or one or more integrated circuits configured to implement embodiments of the present disclosure.

The invention provides a stacking raw material homogenizing and detecting device, which is a device for evaluating the homogenization of stacking raw materials, and the device is characterized in that a minimum feeding unit mode is firstly established, sampling representativeness is improved through a three-dimensional stereotactic sampling mode, a corresponding mathematical model is established by utilizing the prominent advantages of rapidness, high efficiency, low cost, no pollution, no damage, simultaneous determination of multiple components and the like of near infrared spectrum, and the data of conventional chemical components (total nitrogen, total plant alkali, total sugar, reducing sugar, potassium and chlorine) of raw materials at different positions of a stack are rapidly detected. And then based on the standard deviation of index fluctuation and variation coefficient of index fluctuation of the standard chemical composition index of one or more raw materials, other indexes are taken as references to accurately evaluate the homogenization condition of the stacked raw materials, and data support for building a formula is provided for preparing a raw material premixing scheme.

The above embodiments of the present disclosure provide a computing device for homogenizing a raw material for stacking.

The device disclosed by the embodiment of the disclosure has the advantages of strong three-dimensional stereotactic sampling representativeness, rapidness and high efficiency of near infrared spectrum modeling, low cost, no pollution, no damage, simultaneous determination of multiple components, calculation and comparison of the extremely poor index of the conventional chemical components of a plurality of raw materials, standard deviation of index fluctuation, high accuracy of evaluating homogenization of stacked raw materials by index fluctuation variation coefficients, and the like.

According to another aspect of the present disclosure, there is provided a computer readable storage medium storing computer instructions that when executed by a processor implement a stacking raw material homogenization detection method as described in any of the embodiments above (e.g., any of fig. 5-11).

The computer-readable storage medium of the present disclosure may be embodied as a non-transitory computer-readable storage medium.

It will be apparent to those skilled in the art that embodiments of the present disclosure may be provided as a method, apparatus, or computer program product. Accordingly, the present disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present disclosure may take the form of a computer program product embodied on one or more computer-usable non-transitory storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.

The present disclosure is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

The stacking raw material homogenization detection device, model building module, sampling module, measurement module, homogenization parameter determination module, and homogenization degree determination module described above may be implemented as a general purpose processor, programmable Logic Controller (PLC), digital Signal Processor (DSP), application Specific Integrated Circuit (ASIC), field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, or any suitable combination thereof for performing the functions described in this disclosure.

Those of ordinary skill in the art will appreciate that all or a portion of the steps of a method of an embodiment of the present disclosure may be implemented by hardware, which may be implemented as a general purpose processor, a programmable logic controller, a digital signal processor, an application specific integrated circuit, a field programmable gate array or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any suitable combination thereof, for performing the methods described herein.

Thus far, the present disclosure has been described in detail. In order to avoid obscuring the concepts of the present disclosure, some details known in the art are not described. How to implement the solutions disclosed herein will be fully apparent to those skilled in the art from the above description.

Those of ordinary skill in the art will appreciate that all or a portion of the steps implementing the above embodiments may be implemented by hardware, or may be implemented by a program indicating that the relevant hardware is implemented, where the program may be stored on a non-transitory computer readable storage medium, where the storage medium may be a read-only memory, a magnetic disk or optical disk, etc.

The description of the present disclosure has been presented for purposes of illustration and description, and is not intended to be exhaustive or limited to the disclosure in the form disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiments were chosen and described in order to best explain the principles of the disclosure and the practical application, and to enable others of ordinary skill in the art to understand the disclosure for various embodiments with various modifications as are suited to the particular use contemplated.

Claims

1. A method for detecting homogenization of a stacking material, comprising:

2. The stacked raw material homogenization detection method of claim 1, wherein the measuring the chemical composition index of the raw material sample of each dosing unit includes:

measuring chemical component indexes of raw material samples of each feeding unit by adopting a continuous flow method;

or alternatively, the first and second heat exchangers may be,

3. The stacked raw material homogenization detection method of claim 1 or 2, further comprising:

4. A stacking feedstock homogenization detection method according to claim 3, wherein the pre-constructing a near infrared spectral model comprises:

collecting a near infrared spectrum of a modeling sample;

5. The stacked raw material homogenization detection method of claim 3, further comprising:

collecting near infrared spectra of the stacking raw material samples;

6. The stacked raw material homogenization detection method of claim 1 or 2, wherein:

the chemical composition index comprises at least one of total nitrogen, total plant alkali, total sugar, reducing sugar, potassium and chlorine.

7. The stacked raw material homogenization detection method of claim 1 or 2, wherein the determining the homogenization parameters of the stacked raw material to be measured based on the chemical composition index of the raw material samples of the plurality of feeding units includes:

8. The stacked raw material homogenization detection method of claim 1 or 2, wherein:

the homogenization parameters further include at least one of an index fluctuation value and an index fluctuation percentage.

9. The stacked raw material homogenization detection method of claim 8, wherein the determining the homogenization parameters of the stacked raw material to be measured based on the chemical composition index of the raw material samples of the plurality of feeding units includes:

10. The stacked raw material homogenization detection method of claim 9, wherein determining a degree of homogenization of the stacked raw material to be measured based on the homogenization parameters includes:

11. The stacked raw material homogenization detection method of claim 9, wherein the determining of the homogenization parameters of the stacked raw material to be measured based on the chemical composition index of the raw material samples of the plurality of dosing units includes at least one of the following steps, wherein:

12. The stacked raw material homogenization detection method of claim 1 or 2, wherein determining the degree of homogenization of the stacked raw material to be measured based on the homogenization parameters includes:

13. The stacked raw material homogenization detection method of claim 12, wherein the criterion priority of the index fluctuation standard deviation is higher than the criterion priority of the index fluctuation variation coefficient.

14. The method for detecting homogenization of a raw stacking material according to claim 1 or 2, wherein determining the degree of homogenization of the raw stacking material to be detected based on the homogenization parameters comprises at least one of the following steps, wherein:

15. A stacked raw material homogenization detection device, comprising:

16. A stacked raw material homogenization detection device, comprising:

a memory for storing instructions;

a processor for executing the instructions to cause the stacked feedstock homogenization detection device to perform operations implementing the stacked feedstock homogenization detection method of any one of claims 1-14.

17. A computer readable storage medium storing computer instructions which when executed by a processor implement the stacking feedstock homogenization detection method of any one of claims 1-14.