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CN-122016548-A - Method and system for rapidly detecting water holding characteristic of tobacco leaves

CN122016548ACN 122016548 ACN122016548 ACN 122016548ACN-122016548-A

Abstract

The present disclosure relates to a method and system for rapidly detecting water retention characteristics of tobacco leaves. The method comprises the steps of obtaining weight change data of at least two groups of tobacco samples, obtaining fitting parameters based on the weight change data and a preset model function, determining at least two groups of time vectors based on all fitting parameters and a derivative function corresponding to the preset model function, determining correlation coefficients based on all time vectors and all fitting parameters, determining a target time vector set based on all correlation coefficients and a preset correlation coefficient threshold value, and obtaining a detection duration interval based on the target time vector set. The method can accurately obtain the correlation coefficient representing the correlation between the short-time detection time and the water retention characteristic parameter by using the statistical analysis and the dynamic model, and obtain the optimal detection duration interval, so that the detection duration and the labor cost are greatly shortened, the instrument loss and the fault risk caused by long-time operation can be reduced, and further, the quick quality evaluation and the process guidance of the production site are realized.

Inventors

  • WANG GE
  • XU WEIJIE
  • YANG KAI
  • ZHANG XIN

Assignees

  • 上海烟草集团有限责任公司

Dates

Publication Date
20260512
Application Date
20260211

Claims (10)

  1. 1. A method for rapidly detecting the water holding characteristics of tobacco leaves, comprising: acquiring weight change data of at least two groups of tobacco leaf samples, and acquiring fitting parameters based on the weight change data and a preset model function; Determining at least two sets of time vectors based on all the fitting parameters and derivative functions corresponding to the preset model functions, and determining correlation coefficients based on each of the time vectors and all the fitting parameters, and And determining a target time vector set based on all the correlation coefficients and a preset correlation coefficient threshold value, and obtaining a detection duration interval based on the target time vector set.
  2. 2. The method of claim 1, wherein the obtaining fitting parameters based on each of the weight change data and a predetermined model function comprises: determining a first equilibrium moisture content weight of the tobacco sample at an initial relative humidity, a second equilibrium moisture content weight at a target relative humidity, and a moisture content weight for at least two durations based on each of the weight change data, the initial relative humidity being less than the target relative humidity; determining a moisture ratio based on the water cut weight, the first equilibrium water cut weight, and the second equilibrium water cut weight for each of the time periods, and And fitting a preset model function based on all the time periods and the moisture ratio corresponding to each time period to obtain fitting parameters.
  3. 3. The method of claim 1, wherein determining at least two sets of time vectors based on all of the fitting parameters and a derivative function corresponding to the predetermined model function comprises: Selecting at least two constants in a preset constant range; substituting each fitting parameter into a derivative function corresponding to the preset model function, and respectively solving the derivative function substituted with each fitting parameter based on each constant to obtain a solving time length, and And integrating all the solving time lengths corresponding to the constants according to the numbering sequence of all the tobacco samples to obtain a time vector.
  4. 4. A method according to claim 3, wherein the fitting parameters include a water retention characteristic parameter and a shape parameter; Said determining a correlation coefficient based on each of said time vectors and all of said fitting parameters, comprising: Extracting the water holding characteristic parameters from the fitting parameters, integrating all the water holding characteristic parameters according to the serial numbers of all the tobacco samples to obtain a water holding characteristic parameter set, and Substituting each time vector and the water holding characteristic parameter set into a preset correlation coefficient formula to obtain a correlation coefficient.
  5. 5. The method of claim 4, wherein determining the set of target time vectors based on all of the correlation coefficients and a preset correlation coefficient threshold comprises: screening all the correlation coefficients larger than a preset correlation coefficient threshold value from all the correlation coefficients; Determining a target constant interval based on all the correlation coefficients greater than the preset correlation coefficient threshold value, and determining a target constant set from the target constant interval according to a preset step length; Based on each target constant in the target constant set, respectively solving the derivative function substituted into each fitting parameter to obtain target duration, and And integrating all the target time durations corresponding to each target constant according to the numbering sequence of all the tobacco samples to obtain target time vectors, and integrating all the target time vectors into a target time vector set.
  6. 6. The method of claim 5, wherein the deriving a detection duration interval based on the set of target time vectors comprises: identifying a minimum target time length and a maximum target time length from the target time vector set, and And obtaining a detection duration interval based on the minimum target duration and the maximum target duration.
  7. 7. The method according to claim 1, wherein the method further comprises: Constructing a long-duration moisture ratio change curve based on each weight change data, and determining the similarity between each long-duration moisture ratio change curve and all other long-duration moisture ratio change curves; Determining whether each of the similarities exceeds a preset similarity threshold, and And determining that the weight change data corresponding to the similarity is abnormal in response to determining that any one of the similarities does not exceed the preset similarity threshold.
  8. 8. A system for rapidly detecting the water holding characteristics of tobacco leaves, comprising: the fitting parameter determining module is configured to acquire weight change data of at least two groups of tobacco leaf samples, and obtain fitting parameters based on the weight change data and a preset model function; A correlation coefficient determining module configured to determine at least two sets of time vectors based on all the fitting parameters and the derivative functions corresponding to the preset model functions, and determine a correlation coefficient based on each of the time vectors and all the fitting parameters, and And the duration interval determining module is configured to determine a target time vector set based on all the correlation coefficients and a preset correlation coefficient threshold value, and obtain a detection duration interval based on the target time vector set.
  9. 9. A computer readable storage medium having stored thereon a computer program having instructions stored therein, which when run on a computer or processor, cause the computer or processor to perform the steps of the method according to any of claims 1-7.
  10. 10. An electronic device, comprising: one or more processors, and A memory associated with the one or more processors for storing program instructions that, when read for execution by the one or more processors, perform the steps of the method of any of claims 1-7.

Description

Method and system for rapidly detecting water holding characteristic of tobacco leaves Technical Field The embodiment of the specification belongs to the technical field of tobacco leaf water holding characteristic detection, and particularly relates to a method and a system for rapidly detecting tobacco leaf water holding characteristics. Background The tobacco leaf moisture is one of the most central indexes in the whole cigarette production process, and is an object of important attention in the threshing and redrying homogenizing processing control and the cigarette production and manufacturing process. In the working procedures of tobacco leaf threshing, redrying and resurgence, leaf wetting, redrying working procedures, tobacco processing and cut tobacco making and the like, the moisture absorption and release processes of frequent moisture exchange between tobacco leaves and the environment are often carried out, so that the moisture absorption and release characteristics of the tobacco leaves and the tobacco leaves in the processing process are explored, namely the water holding performance of the tobacco leaves, and the method plays an important role in guiding the parameterization processing of the tobacco leaves and improving the quality of the tobacco leaves. At present, the research on the water holding performance of tobacco leaves mainly depends on a multi-station gravimetric gas Vapor adsorption (DVS) for detection, and the detection principle is to observe the weight change rule of the tobacco leaves under different environmental temperature and humidity conditions so as to measure the water holding characteristics of the tobacco leaves. However, in actual production and application, the water holding characteristic of the tobacco leaves is detected for too long, so that the problems of influencing the production progress, increasing the consumption, detecting abnormality of equipment and the like are easily caused. Disclosure of Invention The embodiment of the disclosure provides a method and a system for rapidly detecting the water holding characteristic of tobacco leaves. In a first aspect of the present disclosure, a method for rapidly detecting a water retention characteristic of tobacco leaves is provided. The method comprises the steps of obtaining weight change data of at least two groups of tobacco leaf samples, and obtaining fitting parameters based on the weight change data and a preset model function. The method further includes determining at least two sets of time vectors based on all fitting parameters and a derivative function corresponding to a predetermined model function, and determining a correlation coefficient based on each time vector and all fitting parameters. In addition, the method further comprises the steps of determining a target time vector set based on all the correlation coefficients and a preset correlation coefficient threshold value, and obtaining a detection duration interval based on the target time vector set. In a second aspect of the present disclosure, a system for rapidly detecting water retention characteristics of tobacco leaves is provided. The system comprises a fitting parameter determining module, a fitting parameter determining module and a fitting parameter determining module, wherein the fitting parameter determining module is configured to acquire weight change data of at least two groups of tobacco leaf samples, and obtain fitting parameters based on the weight change data and a preset model function. The system further comprises a correlation coefficient determination module configured to determine at least two sets of time vectors based on all fitting parameters and a derivative function corresponding to a preset model function, and determine a correlation coefficient based on each time vector and all fitting parameters. In addition, the system further comprises a duration interval determining module, which is configured to determine a target time vector set based on all the correlation coefficients and a preset correlation coefficient threshold value, and obtain a detection duration interval based on the target time vector set. In a third aspect of the present disclosure, there is provided a computer program product comprising a computer program for execution by a processor to implement the method according to the first aspect. In a fourth aspect of the present disclosure, a machine-readable storage medium is provided. The machine-readable storage medium has stored thereon machine-executable instructions that are executed by a processor to implement the method provided according to the first aspect of the present disclosure. It should be understood that what is described in this summary is not intended to limit the critical or essential features of the embodiments of the disclosure nor to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following description. Drawings The above