CN117137166A - Device and method for detecting spreading uniformity in blending process based on line laser scanning - Google Patents

Abstract

The invention provides a device and a method for detecting spreading uniformity in a blending process based on line laser scanning. The invention provides a technical means for the fine control of cigarette production and the process evaluation of blending process, and also provides data support for the quality stabilization and process improvement of finished tobacco shreds.

Description

Device and method for detecting spreading uniformity in blending process based on line laser scanning

Technical Field

The invention belongs to the technical field of wire making equipment, and particularly relates to a wire laser scanning-based device and a wire laser scanning-based method for detecting the spreading uniformity in a blending process.

Background

In the tobacco shred production process, the process task of the blending procedure is to accurately and uniformly blend and mix various blending materials (including cut tobacco, expanded tobacco shred, expanded stem shred, thin shred and recycled tobacco shred) according to the product formula requirement, and the uniformity of blending and mixing of various materials directly influences the stability of the internal quality of the cigarette product.

There are few methods available in the tobacco industry for detecting the spreading uniformity of cut tobacco blending processes. The invention discloses a device for detecting the spreading uniformity of a blending material and a detection method thereof in the Chinese patent application with publication number of CN201210133888.7, wherein a square area is arranged on a blending collecting conveyor belt, the tobacco shred amount falling into each square area in the blending spreading process is calculated, and meanwhile, whether the blending material spreading process is uniform or not is judged by combining a human eye visual evaluation method. In addition, the Chinese patent application with publication number of CN201710586301.0 discloses a detection and evaluation method for on-line tobacco shred mixing uniformity of cigarettes, which establishes a method for on-line evaluation uniformity by utilizing the difference of the sugar-alkali ratios of on-line tobacco shreds according to the principle that the sugar-alkali ratios of the on-line tobacco shreds of the cigarettes can better reflect the internal quality of the cigarettes, but the operation process of the method is complex, the evaluation result depends on a prediction model constructed by simulating the tobacco shreds, and the method needs to be re-constructed whenever the formula structure of the tobacco shreds changes, thereby wasting time and labor; meanwhile, the method is mainly used for evaluating the uniformity of the process of mixing the wires and discharging the wires from the cabinet, relates to the procedures of perfuming, mixing the wires and the like, and cannot be directly used for representing the spreading uniformity of the blending process.

In view of the drawbacks of the two methods, it is necessary to develop an apparatus and method for online, continuous and objective detection of the spreading uniformity of blended materials.

Disclosure of Invention

In order to solve the technical problems in the prior art, the invention provides a device and a method for detecting the spreading uniformity in the blending process based on line laser scanning, which provide a technical means for the fine control of cigarette production and the process evaluation of the blending process, and also provide data support for the quality stability and process improvement of finished tobacco shreds. The invention has wide market prospect in cigarette production, low input cost and high return.

The technical scheme adopted by the invention is as follows:

the method for detecting the spreading uniformity in the blending process based on line laser scanning is characterized by comprising the following steps of:

s1: definition data:

defining the direction coordinate of the conveyor belt as x, wherein the value range is 1-100, and the step length is 1;

defining the length direction coordinate of the conveying belt as y, wherein the value range is 1-1800, and the step length is 1; defining a coordinate in the height direction of tobacco shreds on a conveying belt as z, wherein a single coordinate corresponds to a certain height value, and 1800 x 100 tobacco shreds are in total;

s2, partitioning data:

dividing 1800 x 100 data into 180 x 10 uniform data blocks, sequentially taking the numbers 1,2,3 and the shapes of grid sequence numbers and rows in the length direction and the width direction of the grid, and forming grid sequence numbers, rows, columns and shapes: obtaining the sample height value of the corresponding grid, and forming csv format output;

because the detection area of the original acquired data is too small, the data is selectively segmented for processing, and the fluctuation of the data is reduced; if the complete data block cannot be formed, performing discarding treatment;

s3, dividing the grid:

dividing the conveyor belt into equal m parts in the width direction and equal n parts in the length direction, thereby forming m x n grids, wherein each rectangular grid is equivalent to one detection domain; the larger the values of m and n are, the data are biased to the microscopic angle to judge uniformity, the smaller the values are, and the data are biased to the macroscopic angle to judge uniformity;

under the condition that certain data blocks cannot be divided completely, the maximum difference between the maximum value and the minimum value is ensured to be 1;

s4, processing data:

in the three-dimensional model, carrying out Gaussian smoothing treatment on the data after grid segmentation, and finally generating a fitting curved surface in a three-dimensional state to form visualized data display; let σ=35, σ be the standard deviation in the algorithm, the larger the numerical value is, the higher the smoothness is, and the numerical value is variable; uniformity in the width direction x-axis may not be considered in general, as the physical notion of "high in the middle and low on both sides" would be present;

the image is easy to be interfered by external environment in the processes of acquisition, transmission and conversion, the quality of the acquired image is influenced, the extraction of useful information is interfered, and in order to effectively eliminate or reduce the influence, gaussian smoothing is adopted for processing, and a Gaussian smoothing processing formula (1) is as follows:

wherein: (x, y) is a pixel point, g (x, y) is a gray value of an output image, sigma is a standard deviation, and e is a mathematical constant;

s5, analyzing the fluctuation of the thermodynamic diagram:

detecting whether the volatility of the segmented data is reduced relative to the original data, and if the volatility of the segmented data is not reduced, readjusting the values of m and n; if yes, carrying out the next step of grid height average value and standard deviation generation operation; we define the grid size after grid segmentation, i.e. the mean value, to be 0.05;

according to the data, the conveyer belt is uniform in the length direction, and if the value of m is more than or equal to 3, the normal physical imagination that the middle is high and the two sides are low is presented in the width direction, so that the conveyer belt is also uniform in the width direction; for the standard deviation of the grid heights after grid segmentation, whether obvious differences exist among different grids or not is judged, if the highest average value minus the lowest average value is smaller than the standard deviation value, the cut tobacco is uniform, and otherwise, the cut tobacco is nonuniform;

s6, analyzing variance:

6.1, squaring and summing;

the sum of squares is the sum of squares SS of the deviations of all observations from the total average _T The calculation formula is shown as (2),

wherein X represents a coordinate value in the width direction of the conveyor belt, G represents a total of all data, and N represents the total data number;

the sum of squares between groups is the sum of the squares of the deviations of the average per group from the total average and the product of the number of data of the group SS _B The calculation formula is shown as (3),

wherein,for the average per group>T is the total mean value of the data _i For each group of data sums, n _i For the number of data sets;

the sum of squares within the group is the sum of squares SS of the deviations between the values tested and the group average _W The calculation formula is shown as (4),

SS _W ＝∑SS _i (4)

wherein the SS _i SS as dispersion of average number in group _T ＝SS _B +SS _W For calculation before verification;

6.2, calculating the degree of freedom;

df _T ＝N-1 (5)

df _B ＝k-1 (6)

df _W ＝k(n-1)＝N-k (7)

wherein df is _T N is the total data number, df, for the overall degree of freedom _B For the degree of freedom between groups, k is the number of data between groups, df _W Is the degree of freedom in the group;

6.3, calculating the mean square;

wherein MS is _B For inter-group variance, SS _B As sum of squares, df between groups _B For inter-group degrees of freedom, MS _W For intra-group variance, SS _W As the sum of squares in the group, df _W Is the degree of freedom in the group;

6.4, calculating a final total data variance P;

wherein: MS (MS) _B For inter-group variance, MS _W Is the intra-group variance;

calculating the bias derivative to obtain P= 0.937054; p represents the actual data variance, and since 1800×100 data is equally divided into 10 grids of 180×10, the variance calculation formula is referred to above, so as to obtain the final total data variance P as 0.937054; as the variance value P is more than 0.05, the difference among different grids is not obvious, and the uniform distribution state of tobacco shreds on a conveying belt is reflected;

s7, generating a box diagram:

the box-type diagram establishes coordinate system data by taking a grid serial number as an X axis and a grid height as a Y value; the difference between different grids is displayed by generating a box type graph, and the uniformity of tobacco shreds is reflected.

Spreading uniformity detection device based on line laser scanning's blending process, characterized by comprising:

the line laser is arranged above the blending material conveying belt, and the surface texture of the blending material particles is enhanced by laser beams emitted by the line laser;

the camera is arranged above the blending material conveying belt and used for acquiring real-time height data of the surface of the blending material;

and the information processing computer is electrically connected with the camera, receives the real-time height data of the surface of the blended material obtained by the camera and evaluates the spreading uniformity through an analysis variance algorithm.

Further, the device also comprises a bracket, wherein the bracket is erected above the material conveying belt, and the line laser and the camera are installed on the bracket.

Compared with the prior art, the invention has the beneficial effects that:

the invention provides a device and a method for detecting the spreading uniformity in the blending process based on a line laser scanning technology aiming at the wire making blending process, and the method is accurate and reliable, and simple and accurate in equipment; real-time online detection of uniformity of the blending materials can be realized; the product is simple and convenient to install, high in flexibility and high in measurement accuracy.

Drawings

FIG. 1 is a schematic view of the structure of the device of the present invention.

FIG. 2 is a box diagram of the tobacco shred uniformity according to the invention.

Detailed Description

The following describes the detailed implementation of the embodiments of the present invention with reference to the drawings. It should be understood that the detailed description and specific examples, while indicating and illustrating the invention, are not intended to limit the invention.

It should be noted that, without conflict, the embodiments of the present invention and features of the embodiments may be combined with each other.

The invention will be described in detail below with reference to the drawings in connection with exemplary embodiments.

Referring to fig. 1 and 2, the method for detecting the evenness of the blending process pavement based on line laser scanning comprises the following steps:

s1: definition data:

defining the direction coordinate of the conveyor belt as x, wherein the value range is 1-100, and the step length is 1;

defining the length direction coordinate of the conveying belt as y, wherein the value range is 1-1800, and the step length is 1; defining a coordinate in the height direction of tobacco shreds on a conveying belt as z, wherein a single coordinate corresponds to a certain height value, and 1800 x 100 tobacco shreds are in total;

s2, partitioning data:

dividing 1800 x 100 data into 180 x 10 uniform data blocks, sequentially taking the numbers 1,2,3 and the shapes of grid sequence numbers and rows in the length direction and the width direction of the grid, and forming grid sequence numbers, rows, columns and shapes: obtaining the sample height value of the corresponding grid, and forming csv format output;

because the detection area of the original acquired data is too small, the data is selectively segmented for processing, and the fluctuation of the data is reduced; if the complete data block cannot be formed, performing discarding treatment;

s3, dividing the grid:

dividing the conveyor belt into equal m parts in the width direction and equal n parts in the length direction, thereby forming m x n grids, wherein each rectangular grid is equivalent to one detection domain; the larger the values of m and n are, the data are biased to the microscopic angle to judge uniformity, the smaller the values are, and the data are biased to the macroscopic angle to judge uniformity;

if some of the data blocks cannot be divided, the difference between the maximum and the minimum is 1 as much as possible, for example, the data blocks in the width direction are 10 data blocks, m takes 3, and the values are 4,3,3 respectively.

S4, processing data:

in the three-dimensional model, carrying out Gaussian smoothing treatment on the data after grid segmentation, and finally generating a fitting curved surface in a three-dimensional state to form visualized data display; let σ=35, σ be the standard deviation in the algorithm, the larger the numerical value is, the higher the smoothness is, and the numerical value is variable; uniformity in the width direction x-axis may not be considered in general, as the physical notion of "high in the middle and low on both sides" would be present;

the image is easy to be interfered by external environment in the processes of acquisition, transmission and conversion, the quality of the acquired image is influenced, the extraction of useful information is interfered, and in order to effectively eliminate or reduce the influence, gaussian smoothing is adopted for processing, and a Gaussian smoothing processing formula (1) is as follows:

wherein: (x, y) is a pixel point, g (x, y) is a gray value of an output image, sigma is a standard deviation, and e is a mathematical constant;

s5, analyzing the fluctuation of the thermodynamic diagram:

detecting whether the volatility of the segmented data is reduced relative to the original data, and if the volatility of the segmented data is not reduced, readjusting the values of m and n; if yes, carrying out the next step of grid height average value and standard deviation generation operation; we define the grid size after grid segmentation, i.e. the mean value, to be 0.05;

according to the data, the conveyer belt is uniform in the length direction, and if the value of m is more than or equal to 3, the normal physical imagination that the middle is high and the two sides are low is presented in the width direction, so that the conveyer belt is also uniform in the width direction; for the standard deviation of the grid heights after grid segmentation, whether obvious differences exist among different grids or not is judged, if the highest average value minus the lowest average value is smaller than the standard deviation value, the cut tobacco 5 is represented to be uniform, and otherwise, the cut tobacco 5 is represented to be nonuniform;

s6, analyzing variance:

6.1, squaring and summing;

the sum of squares is the sum of squares SS of the deviations of all observations from the total average _T The calculation formula is shown as (2),

wherein X represents a coordinate value in the width direction of the conveyor belt, G represents a total of all data, and N represents the total data number;

the sum of squares between groups is the sum of the squares of the deviations of the average per group from the total average and the product of the number of data of the group SS _B The calculation formula is shown as (3),

wherein,for the average per group>T is the total mean value of the data _i For each group of data sums, n _i For the set of dataA number of;

the sum of squares within the group is the sum of squares SS of the deviations between the values tested and the group average _W The calculation formula is shown as (4),

SS _W ＝∑SS _i (4)

wherein the SS _i SS as dispersion of average number in group _T ＝SS _B +SS _W For calculation before verification;

6.2, calculating the degree of freedom;

df _T ＝N-1 (5)

df _B ＝k-1 (6)

df _W ＝k(n-1)＝N-k (7)

wherein df is _T N is the total data number, df, for the overall degree of freedom _B For the degree of freedom between groups, k is the number of data between groups, df _W Is the degree of freedom in the group;

6.3, calculating the mean square;

wherein MS is _B For inter-group variance, SS _B As sum of squares, df between groups _B For inter-group degrees of freedom, MS _W For intra-group variance, SS _W As the sum of squares in the group, df _W Is the degree of freedom in the group;

6.4, calculating a final total data variance P;

wherein: MS (MS) _B For inter-group variance, MS _W Is the intra-group variance;

calculating the bias derivative to obtain P= 0.937054; p represents the actual data variance, and since 1800×100 data is equally divided into 10 grids of 180×10, the variance calculation formula is referred to above, so as to obtain the final total data variance P as 0.937054; as the variance value P is more than 0.05, the difference among different grids is not obvious, and the uniform distribution state of tobacco shreds on a conveying belt is reflected;

s7, generating a box diagram:

the box-type diagram establishes coordinate system data by taking a grid serial number as an X axis and a grid height as a Y value; the difference between different grids is displayed by generating a box-type diagram, and the uniformity of the tobacco shreds 5 is reflected.

Referring to fig. 1, a device for detecting the uniformity of a batch material in a blending process based on line laser scanning according to the present invention includes:

the line laser 1 is arranged above the blending material conveying belt 3, and the surface texture of the blending material particles is enhanced by the laser beam 2 emitted by the line laser 1;

a camera which is arranged above the blending material conveying belt 3 and acquires real-time height data of the surface of the blending material;

and the information processing computer is electrically connected with the camera, receives the real-time height data of the surface of the blended material obtained by the camera and evaluates the spreading uniformity through an analysis variance algorithm.

In one embodiment, the device further comprises a bracket 4, wherein the bracket 4 is erected above the material conveying belt 3, and the line laser 1 and the camera are mounted on the bracket 4.

While embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the invention.

Claims (3)

1. The method for detecting the spreading uniformity in the blending process based on line laser scanning is characterized by comprising the following steps of:

s1: definition data:

defining the direction coordinate of the conveyor belt as x, wherein the value range is 1-100, and the step length is 1;

defining the length direction coordinate of the conveying belt as y, wherein the value range is 1-1800, and the step length is 1; defining a coordinate in the height direction of tobacco shreds on a conveying belt as z, wherein a single coordinate corresponds to a certain height value, and 1800 x 100 tobacco shreds are in total;

s2, partitioning data:

dividing 1800 x 100 data into 180 x 10 uniform data blocks, sequentially taking the numbers 1,2,3 and the shapes of grid sequence numbers and rows in the length direction and the width direction of the grid, and forming grid sequence numbers, rows, columns and shapes: obtaining the sample height value of the corresponding grid, and forming csv format output;

because the detection area of the original acquired data is too small, the data is selectively segmented for processing, and the fluctuation of the data is reduced; if the complete data block cannot be formed, performing discarding treatment;

s3, dividing the grid:

dividing the conveyor belt into equal m parts in the width direction and equal n parts in the length direction, thereby forming m x n grids, wherein each rectangular grid is equivalent to one detection domain; the larger the values of m and n are, the data are biased to the microscopic angle to judge uniformity, the smaller the values are, and the data are biased to the macroscopic angle to judge uniformity;

under the condition that certain data blocks cannot be divided completely, the maximum difference between the maximum value and the minimum value is ensured to be 1;

s4, processing data:

in the three-dimensional model, carrying out Gaussian smoothing treatment on the data after grid segmentation, and finally generating a fitting curved surface in a three-dimensional state to form visualized data display; let σ=35, σ be the standard deviation in the algorithm, the larger the numerical value is, the higher the smoothness is, and the numerical value is variable; uniformity in the width direction x-axis may not be considered in general, as the physical notion of "high in the middle and low on both sides" would be present;

the image is easy to be interfered by external environment in the processes of acquisition, transmission and conversion, the quality of the acquired image is influenced, the extraction of useful information is interfered, and in order to effectively eliminate or reduce the influence, gaussian smoothing is adopted for processing, and a Gaussian smoothing processing formula (1) is as follows:

wherein: (x, y) is a pixel point, g (x, y) is a gray value of an output image, sigma is a standard deviation, and e is a mathematical constant;

s5, analyzing the fluctuation of the thermodynamic diagram:

detecting whether the volatility of the segmented data is reduced relative to the original data, and if the volatility of the segmented data is not reduced, readjusting the values of m and n; if yes, carrying out the next step of grid height average value and standard deviation generation operation; we define the grid size after grid segmentation, i.e. the mean value, to be 0.05;

according to the data, the conveyer belt is uniform in the length direction, and if the value of m is more than or equal to 3, the normal physical imagination that the middle is high and the two sides are low is presented in the width direction, so that the conveyer belt is also uniform in the width direction; for the standard deviation of the grid heights after grid segmentation, whether obvious differences exist among different grids or not is judged, if the highest average value minus the lowest average value is smaller than the standard deviation value, the cut tobacco is uniform, and otherwise, the cut tobacco is nonuniform;

s6, analyzing variance:

6.1, squaring and summing;

the sum of squares is the sum of squares SS of the deviations of all observations from the total average _T The calculation formula is shown as (2),

wherein X represents a coordinate value in the width direction of the conveyor belt, G represents a total of all data, and N represents the total data number;

the sum of squares between groups is the sum of the squares of the deviations of the average per group from the total average and the product of the number of data of the group SS _B The calculation formula is shown as (3),

wherein,for the average per group>T is the total mean value of the data _i For each group of data sums, n _i For the number of data sets;

the sum of squares within the group is the sum of squares SS of the deviations between the values tested and the group average _W The calculation formula is shown as (4),

SS _W ＝∑SS _i (4)

wherein the SS _i SS as dispersion of average number in group _T ＝SS _B +SS _W For calculation before verification;

6.2, calculating the degree of freedom;

df _T ＝N-1 (5)

df _B ＝k-1 (6)

df _W ＝k(n-1)＝N-k (7)

wherein df is _T N is the total data number, df, for the overall degree of freedom _B For the degree of freedom between groups, k is the number of data between groups, df _W Is the degree of freedom in the group;

6.3, calculating the mean square;

wherein MS is _B Is a group ofInter variance, SS _B As sum of squares, df between groups _B For inter-group degrees of freedom, MS _W For intra-group variance, SS _W As the sum of squares in the group, df _W Is the degree of freedom in the group;

6.4, calculating a final total data variance P;

wherein: MS (MS) _B For inter-group variance, MS _W Is the intra-group variance;

calculating the bias derivative to obtain P= 0.937054; p represents the actual data variance, and since 1800×100 data is equally divided into 10 grids of 180×10, the variance calculation formula is referred to above, so as to obtain the final total data variance P as 0.937054; as the variance value P is more than 0.05, the difference among different grids is not obvious, and the uniform distribution state of tobacco shreds on a conveying belt is reflected;

s7, generating a box diagram:

the box-type diagram establishes coordinate system data by taking a grid serial number as an X axis and a grid height as a Y value; the difference between different grids is displayed by generating a box type graph, and the uniformity of tobacco shreds is reflected.

2. Spreading uniformity detection device based on line laser scanning's blending process, characterized by comprising:

the line laser is arranged above the blending material conveying belt, and the surface texture of the blending material particles is enhanced by laser beams emitted by the line laser;

the camera is arranged above the blending material conveying belt and used for acquiring real-time height data of the surface of the blending material;

and the information processing computer is electrically connected with the camera, receives the real-time height data of the surface of the blended material obtained by the camera and evaluates the spreading uniformity through an analysis variance algorithm.

3. The device for detecting the evenness of the material in the blending process based on the line laser scanning according to claim 2, further comprising a bracket, wherein the bracket is erected above the material conveying belt, and the line laser and the camera are installed on the bracket.