CN112730148A - In-box lamina density prediction model establishing method and device based on nine-hole sampling method - Google Patents
In-box lamina density prediction model establishing method and device based on nine-hole sampling method Download PDFInfo
- Publication number
- CN112730148A CN112730148A CN202110087764.9A CN202110087764A CN112730148A CN 112730148 A CN112730148 A CN 112730148A CN 202110087764 A CN202110087764 A CN 202110087764A CN 112730148 A CN112730148 A CN 112730148A
- Authority
- CN
- China
- Prior art keywords
- density
- microwave signal
- box
- sample
- value
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 97
- 238000005070 sampling Methods 0.000 title claims abstract description 30
- 241000208125 Nicotiana Species 0.000 claims abstract description 156
- 235000002637 Nicotiana tabacum Nutrition 0.000 claims abstract description 156
- 239000000779 smoke Substances 0.000 claims abstract description 82
- 238000012937 correction Methods 0.000 claims abstract description 57
- 238000001514 detection method Methods 0.000 claims abstract description 34
- 238000012795 verification Methods 0.000 claims description 16
- 238000012360 testing method Methods 0.000 claims description 9
- 238000004590 computer program Methods 0.000 claims description 8
- 238000003860 storage Methods 0.000 claims description 8
- 238000004080 punching Methods 0.000 claims description 4
- 238000004519 manufacturing process Methods 0.000 claims description 3
- 238000004891 communication Methods 0.000 description 6
- 235000019504 cigarettes Nutrition 0.000 description 4
- 238000010586 diagram Methods 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 238000012858 packaging process Methods 0.000 description 2
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 229910052799 carbon Inorganic materials 0.000 description 1
- 239000006185 dispersion Substances 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000011895 specific detection Methods 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N9/00—Investigating density or specific gravity of materials; Analysing materials by determining density or specific gravity
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N22/00—Investigating or analysing materials by the use of microwaves or radio waves, i.e. electromagnetic waves with a wavelength of one millimetre or more
Landscapes
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Electromagnetism (AREA)
- Manufacturing Of Cigar And Cigarette Tobacco (AREA)
Abstract
The embodiment of the invention provides a method and a device for establishing an in-box tobacco lamina density prediction model based on a nine-hole sampling method, wherein the method comprises the following steps: acquiring a sample tobacco flake smoke box with a preset weight gradient, and acquiring a microwave signal value of the sample tobacco flake smoke box by a microwave method; detecting the density value of the sample tobacco flake smoke box by a nine-point detection method; comparing microwave signal values and density values of sample tobacco flake boxes with different weights in a preset weight gradient, and obtaining correlation between the microwave signal values and the density values according to comparison results; when the correlation is larger than a preset threshold value, performing data correction on microwave signal values and density values of different types of sample tobacco flake smoke boxes through the microwave signal values and density values of the sample tobacco flake smoke boxes; and establishing a density value prediction model according to the microwave signal value and the density value after data correction. By adopting the method, the density value of the tobacco flakes in the box can be quickly, accurately and nondestructively detected.
Description
Technical Field
The invention relates to the technical field of density detection in finished tobacco lamina boxes, in particular to a method and a device for establishing a density prediction model of tobacco lamina in a box based on a nine-hole sampling method.
Background
The density deviation rate of the tobacco flakes in the box is one of important quality indexes of a threshing and redrying leaf packaging process, the density deviation rate is obtained through calculating the density of the tobacco flakes in the box and is also an important basis for guiding and optimizing a material homogenizing device in the leaf packaging process, meanwhile, the overlarge density deviation rate of the tobacco flakes in the box is also an important influence factor for causing phenomena of hardening, oil yielding, even mildew, carbon burning and the like in the storage and alcoholization process of the tobacco flakes, and the tobacco flakes are difficult to loosen in the tobacco shred making process.
In the current industry, the density of the tobacco flakes in an industry box is detected by a nine-point static sampling method, wherein according to the experience of related workers, 9 positions specified on a finished product tobacco box are directly sampled and weighed to calculate the density value, and then the corresponding density deviation rate (DVR) is obtained through the density value calculation.
However, although the detection result of this method is highly accurate, there are problems such as low detection efficiency, breakage of the smoke box to be detected, and chipping of the sheet tobacco during the detection process.
Disclosure of Invention
Aiming at the problems in the prior art, the embodiment of the invention provides a method and a device for establishing an in-box tobacco lamina density prediction model based on a nine-hole sampling method.
The embodiment of the invention provides a method for establishing an in-box lamina density prediction model based on a nine-hole sampling method, which comprises the following steps:
acquiring a sample tobacco flake smoke box with a preset weight gradient, and acquiring a microwave signal value of the sample tobacco flake smoke box by a microwave method;
detecting the density value of the sample tobacco flake smoke box by a nine-point detection method;
comparing microwave signal values of the sample tobacco flake boxes with different weights in the preset weight gradient with the density value, and obtaining the correlation between the microwave signal values and the density value according to the comparison result;
when the correlation is larger than a preset threshold value, performing data correction on microwave signal values and density values of different types of sample tobacco flake smoke boxes through the microwave signal values and density values of the sample tobacco flake smoke boxes;
and establishing a density value prediction model according to the microwave signal value and the density value after data correction.
In one embodiment, the method further comprises:
dividing the microwave signal value after data correction and data corresponding to the density value into a correction set and a verification set;
and establishing the density value prediction model through the data of the correction set, and verifying the prediction accuracy of the density value prediction model through the data of the verification set.
In one embodiment, the method further comprises:
and repeatedly detecting the sample tobacco flake smoke box with the preset weight gradient by the microwave method to obtain the variation coefficient of the sample tobacco flake smoke box.
In one embodiment, the method further comprises:
punching the surface of the sample tobacco slice box to obtain a test hole of the sample tobacco slice box;
selecting a middle hole in the test holes, and detecting the density value of the middle hole;
and calculating the average value of the density values of the middle holes to obtain the density value of the sample tobacco flake smoke box.
In one embodiment, the method further comprises:
dividing the sample tobacco flake smoke boxes into different types of sample tobacco flake smoke boxes according to production places;
and performing data correction on the microwave signal values and the density values of different types of sample tobacco flake smoke boxes through the microwave signal values and the density values of the sample tobacco flake smoke boxes.
In one embodiment, the method further comprises:
the sample tobacco flake box with the preset weight gradient comprises finished tobacco flake boxes with 9 weight gradients.
The embodiment of the invention provides an in-box lamina density prediction model establishing device based on a nine-hole sampling method, which comprises the following steps:
the acquisition module is used for acquiring a sample tobacco flake smoke box with a preset weight gradient and acquiring a microwave signal value of the sample tobacco flake smoke box by a microwave method;
the detection module is used for detecting the density value of the sample tobacco flake smoke box by a nine-point detection method;
the comparison module is used for comparing microwave signal values of the sample tobacco flake boxes with different weights in the preset weight gradient with the density value, and obtaining the correlation between the microwave signal values and the density value according to the comparison result;
the data correction module is used for performing data correction on microwave signal values and density values of different types of sample tobacco flake smoke boxes through the microwave signal values and density values of the sample tobacco flake smoke boxes when the correlation is larger than a preset threshold value;
and the establishing module is used for establishing a density value prediction model according to the microwave signal value and the density value after data correction.
In one embodiment, the apparatus further comprises:
the dividing module is used for dividing the microwave signal value after data correction and the data corresponding to the density value into a correction set and a verification set;
and the verification module is used for establishing the density value prediction model through the data of the correction set and verifying the prediction accuracy of the density value prediction model through the data of the verification set.
The embodiment of the invention provides electronic equipment which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the processor executes the program to realize the steps of the in-box tobacco lamina density prediction model establishment method based on the nine-hole sampling method.
An embodiment of the present invention provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the in-box tobacco lamina density prediction model establishment method based on the nine-hole sampling method.
The method and the device for establishing the in-box tobacco lamina density prediction model based on the nine-hole sampling method, provided by the embodiment of the invention, are used for obtaining a sample tobacco lamina box with a preset weight gradient and obtaining a microwave signal value of the sample tobacco lamina box by a microwave method; detecting the density value of the sample tobacco flake smoke box by a nine-point detection method; comparing microwave signal values and density values of sample tobacco flake boxes with different weights in a preset weight gradient, and obtaining correlation between the microwave signal values and the density values according to comparison results; when the correlation is larger than a preset threshold value, performing data correction on microwave signal values and density values of different types of sample tobacco flake smoke boxes through the microwave signal values and density values of the sample tobacco flake smoke boxes; and establishing a density value prediction model according to the microwave signal value and the density value after data correction. Therefore, the method for detecting the density value of the tobacco lamina in the box by applying the microwave can realize the rapid, accurate and nondestructive detection of the density value of the tobacco lamina in the box.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flow chart of a method for establishing an in-box tobacco lamina density prediction model based on a nine-hole sampling method according to an embodiment of the invention;
FIG. 2 is a block diagram of an in-box tobacco lamina density prediction model building device based on nine-hole sampling method in the embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device in an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a schematic flow chart of a method for establishing an in-box tobacco lamina density prediction model based on a nine-hole sampling method according to an embodiment of the present invention, and as shown in fig. 1, an embodiment of the present invention provides a method for establishing an in-box tobacco lamina density prediction model based on a nine-hole sampling method, including:
step S101, obtaining a sample tobacco flake smoke box with a preset weight gradient, and obtaining a microwave signal value of the sample tobacco flake smoke box through a microwave method.
Specifically, a sample lamina tobacco box with a preset weight gradient is obtained, wherein the preset weight gradient may include 9 weight gradients, and 1 box of each weight gradient may be prepared, for example, sample lamina tobacco boxes with 9 weight gradients, such as 160kg, 170kg, 180kg, 190kg, 200kg, 205kg, 210kg, 215kg, and 220kg, may be prepared, and then each sample lamina tobacco box is detected by a microwave density detector according to a preset microwave method, so as to obtain a microwave signal value of the sample lamina tobacco box.
And S102, detecting the density value of the sample tobacco flake smoke box by a nine-point detection method.
Specifically, through the density value of nine spot detection methods detection sample piece cigarette case, specific detection procedure can be, punch to the smoke box surface of sample piece cigarette case, and the quantity that punches can be 9, then can obtain the test hole of sample piece cigarette case to 3 test holes in the middle of selecting 9 test holes, and detect the density value of middle hole through nine spot detection methods, then calculate the average value of middle hole density value, obtain the density value of sample piece cigarette case.
And S103, comparing microwave signal values of the sample tobacco flake boxes with different weights in the preset weight gradient with the density value, and obtaining the correlation between the microwave signal values and the density value according to the comparison result.
Specifically, the microwave signal values and the density values of the sample sheet tobacco boxes with different weights in the preset weight gradient are compared, for example, the microwave signal values of the sample sheet tobacco boxes with 9 different weights and the density values of the sample sheet tobacco boxes with 9 different weights are compared to obtain the correlation between the microwave signal values and the density values, generally speaking, as the density value of the sample sheet tobacco box detected by the nine-point method becomes larger, the microwave signal value of the sample sheet tobacco box tends to become larger, and the correlation coefficient between the microwave signal values and the density values is calculated.
And S104, when the correlation is larger than a preset threshold value, performing data correction on microwave signal values and density values of different types of sample tobacco flake smoke boxes according to the microwave signal values and density values of the sample tobacco flake smoke boxes.
Specifically, whether the correlation between the microwave signal value and the density value is larger than a preset threshold value or not is detected, namely whether the correlation coefficient between the microwave signal value and the density value is larger than the preset threshold value or not is detected, when the correlation is larger than the preset threshold value, the correlation between the microwave signal value and the density value is high enough, data correction is carried out on the microwave signal value and the density value of different types of sample tobacco flake smoke boxes through the microwave signal value and the density value of the sample tobacco flake smoke box, errors in aspects such as nine-point method detection process equipment and personnel and errors between different microwave density detector equipment can be eliminated through correction, and the specific correction step comprises the following steps: the method comprises the steps of dividing a sample tobacco flake smoke box into different types of sample tobacco flake smoke boxes according to the production area, for example, dividing the sample tobacco flake smoke boxes into data of a Fujian Sanming sample and a Yunnan Lijiang sample, correcting detection data of the Yunnan Lijiang sample by taking a nine-point density detection value of the Fujian Sanming sample as a standard, and correcting the detection data of the Fujian Sanming sample by taking a microwave signal value of the Yunnan Lijiang sample as a standard.
And step S105, establishing a density value prediction model according to the microwave signal value and the density value after data correction.
Specifically, after the corrected microwave signal value and the density value are obtained, the corrected microwave signal value and the corrected density value data under different weight gradients are favorably compared, a nine-point method detection density value prediction model is established by utilizing the microwave signal value, and the model can directly calculate the density value data of the tobacco flake smoke box through the microwave signal value.
The method for establishing the in-box tobacco lamina density prediction model based on the nine-hole sampling method, provided by the embodiment of the invention, comprises the steps of obtaining a sample tobacco lamina box with a preset weight gradient, and obtaining a microwave signal value of the sample tobacco lamina box by a microwave method; detecting the density value of the sample tobacco flake smoke box by a nine-point detection method; comparing microwave signal values and density values of sample tobacco flake boxes with different weights in a preset weight gradient, and obtaining correlation between the microwave signal values and the density values according to comparison results; when the correlation is larger than a preset threshold value, performing data correction on microwave signal values and density values of different types of sample tobacco flake smoke boxes through the microwave signal values and density values of the sample tobacco flake smoke boxes; and establishing a density value prediction model according to the microwave signal value and the density value after data correction. Therefore, the method for detecting the density value of the tobacco lamina in the box by applying the microwave can realize the rapid, accurate and nondestructive detection of the density value of the tobacco lamina in the box.
On the basis of the above embodiment, the method for establishing the in-box lamina density prediction model based on the nine-hole sampling method further includes:
dividing the microwave signal value after data correction and data corresponding to the density value into a correction set and a verification set;
and establishing the density value prediction model through the data of the correction set, and verifying the prediction accuracy of the density value prediction model through the data of the verification set.
In the embodiment of the invention, after the microwave signal value and the density value after data correction are obtained, the corrected detection density value and microwave signal value data by the nine-point method are randomly divided into a correction set (modeling set) and a verification set, 80% of samples in the data can be selected to be used for establishing a density value prediction model, and 20% of samples are used for verifying the established density value prediction model.
The embodiment of the invention verifies the established density value prediction model through partial data, thereby ensuring the accuracy of the density value prediction model.
On the basis of the above embodiment, the method for establishing the in-box lamina density prediction model based on the nine-hole sampling method further includes:
and repeatedly detecting the sample tobacco flake smoke box with the preset weight gradient by the microwave method to obtain the variation coefficient of the sample tobacco flake smoke box.
In the embodiment of the invention, before data correction is carried out on microwave signal values and density values of different types of sample sheet tobacco boxes through microwave signal values and density values of the sample sheet tobacco boxes, the coefficient of variation of the sample sheet tobacco boxes needs to be detected, the coefficient of variation is detected, namely the repeatability of the sample sheet tobacco boxes is detected, specifically, the coefficient of variation of the sample sheet tobacco boxes is obtained by comparing the microwave signal values of the sample sheet tobacco boxes with corresponding standard values (the standard values can be obtained through preset standards), and when the coefficient of variation is less than 1%, the repeatability of the sheet tobacco density in the microwave signal value detection box is better.
According to the embodiment of the invention, the variation coefficient of the sample tobacco flake smoke box is detected, so that the dispersion degree of the tobacco flake data in the sample tobacco flake smoke box can be obtained.
Fig. 2 is a device for establishing an in-box lamina density prediction model based on a nine-hole sampling method according to an embodiment of the present invention, including: the system comprises an acquisition module 201, a detection module 202, a comparison module 203, a data correction module 204 and a building module 205, wherein:
the acquisition module 201 is configured to acquire a sample tobacco flake smoke box with a preset weight gradient, and acquire a microwave signal value of the sample tobacco flake smoke box by a microwave method.
A detection module 202 for detecting the density value of the sample tobacco flake smoke box by a nine-point detection method.
A comparison module 203, configured to compare microwave signal values of the sample tobacco flake boxes with different weights in the preset weight gradient with the density value, and obtain a correlation between the microwave signal values and the density value according to a comparison result.
And the data correction module 204 is used for performing data correction on microwave signal values and density values of different types of sample tobacco flake smoke boxes according to the microwave signal values and density values of the sample tobacco flake smoke boxes when the correlation is larger than a preset threshold value.
And the establishing module 205 is configured to establish a density value prediction model according to the microwave signal value and the density value after data correction.
In one embodiment, the apparatus may further comprise:
and the dividing module is used for dividing the data corresponding to the microwave signal value and the density value after data correction into a correction set and a verification set.
And the verification module is used for establishing the density value prediction model through the data of the correction set and verifying the prediction accuracy of the density value prediction model through the data of the verification set.
In one embodiment, the apparatus may further comprise:
and the second detection module is used for repeatedly detecting the sample tobacco flake smoke box with the preset weight gradient by the microwave method to obtain the variation coefficient of the sample tobacco flake smoke box.
In one embodiment, the apparatus may further comprise:
and the punching module is used for punching the surface of the sample sheet tobacco box to obtain the test hole of the sample sheet tobacco box.
And the third detection module is used for selecting the middle hole in the test holes and detecting the density value of the middle hole.
And the calculation module is used for calculating the average value of the density values of the middle holes to obtain the density value of the sample tobacco flake smoke box.
In one embodiment, the apparatus may further comprise:
and the dividing module is used for dividing the sample tobacco flake boxes into different types of sample tobacco flake boxes according to the producing areas.
And the second data correction module is used for performing data correction on the microwave signal values and the density values of different types of sample tobacco flake smoke boxes through the microwave signal values and the density values of the sample tobacco flake smoke boxes.
For specific limitations of the in-box lamina density prediction model establishing device based on the nine-hole sampling method, reference may be made to the above limitations of the in-box lamina density prediction model establishing method based on the nine-hole sampling method, and details are not repeated here. The modules in the in-box tobacco lamina density prediction model establishing device based on the nine-hole sampling method can be wholly or partially realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
Fig. 3 illustrates a physical structure diagram of an electronic device, which may include, as shown in fig. 3: a processor (processor)301, a memory (memory)302, a communication Interface (Communications Interface)303 and a communication bus 304, wherein the processor 301, the memory 302 and the communication Interface 303 complete communication with each other through the communication bus 304. The processor 301 may call logic instructions in the memory 302 to perform the following method: acquiring a sample tobacco flake smoke box with a preset weight gradient, and acquiring a microwave signal value of the sample tobacco flake smoke box by a microwave method; detecting the density value of the sample tobacco flake smoke box by a nine-point detection method; comparing microwave signal values and density values of sample tobacco flake boxes with different weights in a preset weight gradient, and obtaining correlation between the microwave signal values and the density values according to comparison results; when the correlation is larger than a preset threshold value, performing data correction on microwave signal values and density values of different types of sample tobacco flake smoke boxes through the microwave signal values and density values of the sample tobacco flake smoke boxes; and establishing a density value prediction model according to the microwave signal value and the density value after data correction.
Furthermore, the logic instructions in the memory 302 may be implemented in software functional units and stored in a computer readable storage medium when sold or used as a stand-alone product. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In another aspect, an embodiment of the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program is implemented to perform the transmission method provided in the foregoing embodiments when executed by a processor, and for example, the method includes: acquiring a sample tobacco flake smoke box with a preset weight gradient, and acquiring a microwave signal value of the sample tobacco flake smoke box by a microwave method; detecting the density value of the sample tobacco flake smoke box by a nine-point detection method; comparing microwave signal values and density values of sample tobacco flake boxes with different weights in a preset weight gradient, and obtaining correlation between the microwave signal values and the density values according to comparison results; when the correlation is larger than a preset threshold value, performing data correction on microwave signal values and density values of different types of sample tobacco flake smoke boxes through the microwave signal values and density values of the sample tobacco flake smoke boxes; and establishing a density value prediction model according to the microwave signal value and the density value after data correction.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A method for establishing an in-box lamina density prediction model based on a nine-hole sampling method is characterized by comprising the following steps:
acquiring a sample tobacco flake smoke box with a preset weight gradient, and acquiring a microwave signal value of the sample tobacco flake smoke box by a microwave method;
detecting the density value of the sample tobacco flake smoke box by a nine-point detection method;
comparing microwave signal values of the sample tobacco flake boxes with different weights in the preset weight gradient with the density value, and obtaining the correlation between the microwave signal values and the density value according to the comparison result;
when the correlation is larger than a preset threshold value, performing data correction on microwave signal values and density values of different types of sample tobacco flake smoke boxes through the microwave signal values and density values of the sample tobacco flake smoke boxes;
and establishing a density value prediction model according to the microwave signal value and the density value after data correction.
2. The method for building the density prediction model of the tobacco lamina in the box based on the nine-hole sampling method as claimed in claim 1, wherein the building the density value prediction model according to the microwave signal value and the density value after data correction comprises:
dividing the microwave signal value after data correction and data corresponding to the density value into a correction set and a verification set;
and establishing the density value prediction model through the data of the correction set, and verifying the prediction accuracy of the density value prediction model through the data of the verification set.
3. The method of claim 1, wherein before the data correction of microwave signal and density values of different types of sample cartons by microwave signal and density values of the sample cartons, the method further comprises:
and repeatedly detecting the sample tobacco flake smoke box with the preset weight gradient by the microwave method to obtain the variation coefficient of the sample tobacco flake smoke box.
4. The method for building the in-box tobacco lamina density prediction model based on the nine-hole sampling method of claim 1, wherein the detecting the density value of the sample tobacco lamina box by the nine-point detection method comprises:
punching the surface of the sample tobacco slice box to obtain a test hole of the sample tobacco slice box;
selecting a middle hole in the test holes, and detecting the density value of the middle hole;
and calculating the average value of the density values of the middle holes to obtain the density value of the sample tobacco flake smoke box.
5. The method of claim 1, wherein the data correction of microwave signal values and density values of different types of sample lamina tobacco bins by microwave signal values and density values of the sample lamina tobacco bins comprises:
dividing the sample tobacco flake smoke boxes into different types of sample tobacco flake smoke boxes according to production places;
and performing data correction on the microwave signal values and the density values of different types of sample tobacco flake smoke boxes through the microwave signal values and the density values of the sample tobacco flake smoke boxes.
6. The method for building the in-box lamina density prediction model based on the nine-hole sampling method according to claim 1, wherein the method further comprises:
the sample tobacco flake box with the preset weight gradient comprises finished tobacco flake boxes with 9 weight gradients.
7. An in-box lamina density prediction model building device based on nine-hole sampling method is characterized by comprising the following steps:
the acquisition module is used for acquiring a sample tobacco flake smoke box with a preset weight gradient and acquiring a microwave signal value of the sample tobacco flake smoke box by a microwave method;
the detection module is used for detecting the density value of the sample tobacco flake smoke box by a nine-point detection method;
the comparison module is used for comparing microwave signal values of the sample tobacco flake boxes with different weights in the preset weight gradient with the density value, and obtaining the correlation between the microwave signal values and the density value according to the comparison result;
the data correction module is used for performing data correction on microwave signal values and density values of different types of sample tobacco flake smoke boxes through the microwave signal values and density values of the sample tobacco flake smoke boxes when the correlation is larger than a preset threshold value;
and the establishing module is used for establishing a density value prediction model according to the microwave signal value and the density value after data correction.
8. The in-box lamina density prediction model creation device based on nine-hole sampling method according to claim 7, characterized in that the device further comprises:
the dividing module is used for dividing the microwave signal value after data correction and the data corresponding to the density value into a correction set and a verification set;
and the verification module is used for establishing the density value prediction model through the data of the correction set and verifying the prediction accuracy of the density value prediction model through the data of the verification set.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the steps of the nine-hole sampling method-based in-box lamina density prediction model building method according to any one of claims 1 to 6.
10. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the steps of the in-box lamina density prediction model building method based on nine-hole sampling method according to any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110087764.9A CN112730148A (en) | 2021-01-22 | 2021-01-22 | In-box lamina density prediction model establishing method and device based on nine-hole sampling method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110087764.9A CN112730148A (en) | 2021-01-22 | 2021-01-22 | In-box lamina density prediction model establishing method and device based on nine-hole sampling method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112730148A true CN112730148A (en) | 2021-04-30 |
Family
ID=75595181
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110087764.9A Pending CN112730148A (en) | 2021-01-22 | 2021-01-22 | In-box lamina density prediction model establishing method and device based on nine-hole sampling method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112730148A (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102095658A (en) * | 2010-06-24 | 2011-06-15 | 龙岩烟草工业有限责任公司 | Method and system for detecting quality of tobacco slice |
| CN203011792U (en) * | 2012-11-12 | 2013-06-19 | 云南烟叶复烤有限责任公司保山复烤厂 | Pre-compaction device capable of detecting tobacco strip packing density |
| CN103900932A (en) * | 2014-04-10 | 2014-07-02 | 上海创和亿电子科技发展有限公司 | System and method for detecting density deviation of flue-cured tobacco lamina box |
| CN106323808A (en) * | 2016-08-31 | 2017-01-11 | 上海创和亿电子科技发展有限公司 | System and method for detecting density in due time on basis of microwave moisture meter |
-
2021
- 2021-01-22 CN CN202110087764.9A patent/CN112730148A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102095658A (en) * | 2010-06-24 | 2011-06-15 | 龙岩烟草工业有限责任公司 | Method and system for detecting quality of tobacco slice |
| CN203011792U (en) * | 2012-11-12 | 2013-06-19 | 云南烟叶复烤有限责任公司保山复烤厂 | Pre-compaction device capable of detecting tobacco strip packing density |
| CN103900932A (en) * | 2014-04-10 | 2014-07-02 | 上海创和亿电子科技发展有限公司 | System and method for detecting density deviation of flue-cured tobacco lamina box |
| CN106323808A (en) * | 2016-08-31 | 2017-01-11 | 上海创和亿电子科技发展有限公司 | System and method for detecting density in due time on basis of microwave moisture meter |
Non-Patent Citations (1)
| Title |
|---|
| 马松: "片烟密度偏差率检测仪在打叶复烤企业的应用", 《企业技术开发》 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10324989B2 (en) | Microblog-based event context acquiring method and system | |
| JP2019148583A5 (en) | ||
| CN110763601B (en) | Tobacco shred screening instrument and tobacco shred structure distribution measuring method | |
| CN111177655B (en) | Data processing method and device and electronic equipment | |
| CN113360983A (en) | Slope reliability analysis and risk assessment method | |
| CN116337783A (en) | Multi-point calibration method and system for gas analyzer | |
| CN112683730A (en) | Method and device for establishing in-box tobacco lamina density prediction model based on theoretical density value | |
| CN110797046B (en) | Method and device for establishing prediction model of voice quality MOS value | |
| CN114461637A (en) | Product defect analysis method and device based on data modeling and storage medium | |
| CN112730148A (en) | In-box lamina density prediction model establishing method and device based on nine-hole sampling method | |
| CN110876481A (en) | Control method and device for tobacco shred drying parameters | |
| CN117330850B (en) | Radiation detection method, system, equipment and medium for intelligent mobile terminal | |
| CN113128797A (en) | Method and device for monitoring abnormal business indexes | |
| CN112782036A (en) | Characterization method and device for density deviation rate of tobacco lamina in gridding box | |
| CN108982766B (en) | Method for determining dense end index of cigarette and application | |
| CN116660176A (en) | Fourier spectrum automatic baseline correction method, device and storage medium | |
| CN115952584A (en) | Mechanical test and simulation method for thin-wall steel structure based on digital twinning | |
| CN107239594B (en) | Analog circuit dispersibility optimization method based on PSPICE | |
| CN112730149A (en) | In-box tobacco lamina density prediction model establishing method and device based on ionizing radiation method | |
| CN111865453B (en) | Polarization multiplexing phase damage assessment method and device | |
| CN112683927A (en) | Method and device for representing density deviation rate of tobacco flakes in zoning box | |
| CN110108239B (en) | Pole piece quality information acquisition method, system and equipment | |
| CN109325287B (en) | Method for estimating reliability parameters of mechanical part | |
| CN110555011B (en) | Application audit failure identification method, device and system and readable storage medium | |
| CN117378801A (en) | Tobacco leaf loosening, conditioning and water adding amount control method and system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20210430 |