Intelligent packaging method and system for instant noodle production line

Abstract

The invention discloses an intelligent packaging method and system for an instant noodle production line, and relates to the technical field of packaging.The method comprises the steps that a first film wrapping data set is extracted, and the first film wrapping data set comprises a first film wrapping characteristic parameter set and a first film wrapping heat sealing parameter set; performing supervised learning on the first coating characteristic parameter set and a first coating heat sealing index obtained by analyzing the first coating heat sealing parameter set to obtain a coating heat sealing prediction model; performing correlation analysis to determine a target envelope characteristic index; analyzing the collected target characteristic parameters through a coating heat-sealing prediction model to obtain a target coating heat-sealing index; judging whether the first heat sealing decision meets a target film wrapping heat sealing index or not; if yes, first fitness is obtained in combination with a preset heat sealing fitness function; and when the first fitness meets a preset fitness threshold value, the first heat sealing decision is used as a target heat sealing decision. And therefore, the technical effects of improving the packaging yield and the packaging quality and ensuring the packaging efficiency are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of packaging, and in particular to an intelligent packaging method and system for an instant noodle production line. Background Art

[0002] As an important convenience food, instant noodles are easy to store, transport and eat. The efficiency of the production line and the packaging method are very important to manufacturers.

[0003] Most existing instant noodle production lines have introduced automated equipment, such as automatic packaging machines, conveyor belts, etc. These devices can improve production efficiency, but there are still some problems. Packaging production lines based on predetermined packaging parameters are often difficult to adapt to diverse packaging designs and packaging needs. There are technical problems such as unstable packaging quality and affecting packaging efficiency. Summary of the invention

[0004] The present invention provides an intelligent packaging method and system for an instant noodle production line, so as to solve the technical problems in the prior art of unstable packaging quality and affecting packaging efficiency, and achieve the technical effect of improving packaging yield and packaging quality and ensuring packaging efficiency.

[0005] In a first aspect, the present invention provides an intelligent packaging method for an instant noodle production line, wherein the method comprises:

[0006] Extracting a first film envelope data set from a film envelope database, wherein the first film envelope database includes a first film envelope characteristic parameter set and a first film envelope heat sealing parameter set;

[0007] Performing supervised learning on the first film characteristic parameter set and the first film heat sealing index obtained by analyzing the first film heat sealing parameter set to obtain a film heat sealing prediction model;

[0008] Determine a target film characteristic index according to a result obtained by performing a correlation analysis on the first film characteristic parameter set and the first film heat sealing index;

[0009] Analyzing the target characteristic parameters of the target instant bread film collected based on the target film characteristic index by the film heat sealing prediction model to obtain a target film heat sealing index;

[0010] Determining whether a first heat sealing decision extracted from a predetermined heat sealing optimization space satisfies the target film heat sealing index;

[0011] If the conditions are met, a first fitness of the first heat sealing decision is obtained by combining a predetermined heat sealing fitness function;

[0012] When the first fitness meets a predetermined fitness threshold, the first heat-sealing decision is used as a target heat-sealing decision, and the target heat-sealing decision is used to perform heat-sealing packaging on the target instant bread film.

[0013] In this method, the characteristic parameters of the film, the heat sealing parameters and the heat sealing index are integrated, and the intelligent optimization of the heat sealing process is realized through machine learning modeling and fitness evaluation. The efficiency and accuracy of the heat sealing process are improved by using supervised learning, correlation analysis and fitness function. Through these methods, the heat sealing process of the target film can be precisely controlled, thereby improving packaging quality and production efficiency.

[0014] In a feasible implementation, the process of obtaining the heat sealing index of the first film includes:

[0015] The first film heat-sealing parameter set includes a first film heat-sealing temperature, a first film heat-sealing time, and a first film heat-sealing pressure;

[0016] The first film heat sealing index is obtained by performing weighted calculation on the normalized first film heat sealing temperature, the first film heat sealing time and the first film heat sealing pressure using the coefficient of variation principle.

[0017] The method in the above implementation mode, through the above process, comprehensively considers parameters such as heat sealing temperature, time and pressure, and obtains an index reflecting the heat sealing effect, providing a reference for the optimization of the film coating heat sealing process.

[0018] In a feasible implementation, the process of determining the target envelope characteristic index includes:

[0019] Taking the first film envelope characteristic parameter extracted from the first film envelope characteristic parameter set as an independent variable, and taking the first film envelope heat sealing index as a dependent variable;

[0020] Draw a scatter plot according to the mapping relationship between the independent variable and the dependent variable;

[0021] Performing gridding processing on the scatter plot based on a predetermined gridding scheme to obtain a scatter plot grid result;

[0022] Obtaining a first maximum mutual information value of a first grid result in the scatter plot grid result;

[0023] When the target maximum mutual information value obtained by analyzing the first maximum mutual information value reaches a predetermined mutual information value threshold, the first envelope feature corresponding to the first envelope feature parameter is added to the target envelope feature index.

[0024] The method in the above implementation mode extracts the first film characteristic parameter from the first film characteristic parameter set as the independent variable, takes the first film heat sealing index as the dependent variable, draws a scatter plot based on the mapping relationship between the two, performs correlation analysis through gridding, mutual information value analysis and threshold discrimination, determines the target film characteristic index, and provides a basis for the analysis and optimization of the film characteristics.

[0025] Among them, the target film characteristic index refers to the film characteristic parameter that has a significant impact or effect on the film heat sealing index. The film characteristic parameters are screened through correlation analysis to obtain the target film characteristic index, which reduces the number of characteristic indicators and avoids irrelevant interference and overfitting in the subsequent learning process. This helps to improve the accuracy of the film heat sealing index prediction.

[0026] In a feasible implementation, the process of determining the predetermined heat-sealing optimal space includes:

[0027] Obtaining a predetermined temperature threshold of a heat sealing control temperature;

[0028] obtaining a predetermined pressure threshold of heat sealing control pressure;

[0029] Obtaining a predetermined time threshold of heat sealing control time;

[0030] The predetermined heat sealing optimization space is constructed based on the predetermined temperature threshold, the predetermined pressure threshold and the predetermined time threshold.

[0031] That is to say, by setting the ranges of the three dimensions of heat sealing control temperature, heat sealing control pressure, and heat sealing control time respectively, a predetermined heat sealing optimization space is formed, wherein the predetermined heat sealing optimization space is a multidimensional parameter space, which determines the generation range of the heat sealing decision.

[0032] In a feasible implementation, the expression of the predetermined heat sealing fitness function is as follows:

[0033]

[0034] Among them, f(x) represents the first fitness of the first heat sealing decision x, t(x), p(x) and t′(x) respectively represent the first temperature, the first pressure and the first time in the first heat sealing decision x, a represents the temperature energy consumption adjustment coefficient, b represents the pressure energy consumption adjustment coefficient, a+b=1, α represents the energy consumption weight, β represents the efficiency weight, α+β=1.

[0035] In a feasible implementation, the method further includes:

[0036] The target thickness parameter is obtained by detecting with a thickness sensor probe, wherein the thickness sensor probe is arranged at a target sealing position of the target instant bread film;

[0037] Extracting a first thickness of a first sealing point in the target thickness parameter, where the first sealing point is within the range of the target sealing position;

[0038] When the first thickness is at a predetermined thickness threshold, issuing a heat-sealing packaging instruction;

[0039] The target heat sealing decision is called based on the heat sealing packaging instruction to perform heat sealing packaging on the target instant bread film.

[0040] In a feasible implementation, after extracting the first thickness of the first sealing point in the target thickness parameter, the method further includes:

[0041] Extracting a second thickness of a second sealing point in the target thickness parameter, where the second sealing point is a point different from the first sealing point within the range of the target sealing position;

[0042] The first support of the second thickness to the first thickness is obtained by combining a predetermined support function, and the expression of the predetermined support function is as follows:

[0043] sup(thick1,thick2)=k[1+(thick1-thick2) e ] -1 ;

[0044] Wherein, sup(thick1,thick2) represents the first support of the second thickness thickness2 to the first thickness thickness1, k represents the amplitude of the predetermined support function, k∈[0,1], e represents the attenuation factor, e≥0;

[0045] When the first support does not meet a predetermined support threshold, a secondary thickness detection is performed on the first sealing point by using the thickness sensor detection head.

[0046] In a second aspect, the present invention further provides an intelligent packaging system for an instant noodle production line, wherein the system comprises:

[0047] A parameter acquisition module, the parameter acquisition module is used to extract a first film envelope data set from a film envelope database, the first film envelope database includes a first film envelope characteristic parameter set and a first film envelope heat sealing parameter set;

[0048] A prediction mapping path module, wherein the prediction mapping path is used to perform supervised learning on the first film characteristic parameter set and the first film heat sealing index obtained by analyzing the first film heat sealing parameter set to obtain a film heat sealing prediction model;

[0049] A relevant index selection module, wherein the relevant index selection module is used to determine a target film envelope characteristic index according to a result obtained by performing a correlation analysis on the first film envelope characteristic parameter set and the first film envelope heat sealing index;

[0050] A prediction and analysis module, the prediction and analysis module is used to analyze the target characteristic parameters of the target instant bread film collected based on the target film characteristic index through the film heat sealing prediction model to obtain a target film heat sealing index;

[0051] A decision screening module, the decision screening module is used to determine whether the first heat sealing decision extracted from the predetermined heat sealing optimization space meets the target film heat sealing index;

[0052] An adaptability analysis module, wherein if the conditions are met, the adaptability analysis module is used to obtain a first fitness of the first heat sealing decision by combining a predetermined heat sealing fitness function;

[0053] A decision output module is used to use the first heat sealing decision as a target heat sealing decision when the first fitness meets a predetermined fitness threshold, and the target heat sealing decision is used to perform heat sealing packaging on the target instant bread film.

[0054] The present invention discloses an intelligent packaging method and system for an instant noodle production line, comprising: extracting a first film envelope data set from a film envelope database, wherein the first film envelope database comprises a first film envelope characteristic parameter set and a first film envelope heat sealing parameter set; performing supervised learning on the first film envelope characteristic parameter set and a first film envelope heat sealing index obtained by analyzing the first film envelope heat sealing parameter set to obtain a film envelope heat sealing prediction model; determining a target film envelope characteristic index according to a result obtained by performing correlation analysis on the first film envelope characteristic parameter set and the first film envelope heat sealing index; analyzing target characteristic parameters of a target instant noodle film collected based on the target film envelope characteristic index by using the film envelope heat sealing prediction model to obtain a target film envelope heat sealing index; judging whether a first heat sealing decision extracted from a predetermined heat sealing optimization space satisfies the target film envelope heat sealing index; if so, obtaining a first fitness of the first heat sealing decision in combination with a predetermined heat sealing fitness function; when the first fitness meets a predetermined fitness threshold, taking the first heat sealing decision as a target heat sealing decision, and the target heat sealing decision is used to perform heat sealing packaging on the target instant noodle film. The invention discloses an intelligent packaging method and system for an instant noodle production line, which solves the technical problem of unstable packaging quality and affecting packaging efficiency, and achieves the technical effect of improving packaging yield and packaging quality and ensuring packaging efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 A schematic diagram of a process of an intelligent packaging method for an instant noodle production line of the present invention;

[0056] Figure 2The figure is a schematic diagram of the structure of an intelligent packaging system for an instant noodle production line according to the present invention.

[0057] Explanation of reference numerals: parameter acquisition module 11, prediction mapping path module 12, relevant indicator selection module 13, prediction analysis module 14, decision screening module 15, adaptability analysis module 16, decision output module 17. DETAILED DESCRIPTION

[0058] The technical solution provided in the embodiments of the present invention is to solve the technical problems of unstable packaging quality and affecting packaging efficiency in the prior art. The overall idea adopted is as follows:

[0059] First, extract the first film data set in the film database, which includes the first film characteristic parameter set and the first film heat sealing parameter set. Then, analyze the first film characteristic parameter set and the first film heat sealing parameter set to obtain the first film heat sealing index, and conduct supervised learning to establish a film heat sealing prediction model. Then, according to the result of the correlation analysis between the first film characteristic parameter set and the first film heat sealing index, determine the target film characteristic index. Next, analyze the target characteristic parameters of the target instant film collected based on the target film characteristic index through the film heat sealing prediction model to obtain the target film heat sealing index. Subsequently, determine whether the first heat sealing decision extracted from the predetermined heat sealing optimization space meets the target film heat sealing index. If the condition is met, combine the predetermined heat sealing fitness function to obtain the first fitness of the first heat sealing decision. Finally, when the first fitness meets the predetermined fitness threshold, the first heat sealing decision is used as the target heat sealing decision for heat sealing packaging of the target instant film.

[0060] The above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods of the specification to better understand the above technical solution. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments of the present invention. It should be understood that the present invention is not limited to the example embodiments used only to explain the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. In addition, it should be noted that, for the convenience of description, only the parts related to the present invention are shown in the drawings, rather than all of them.

[0061] Embodiment 1

[0062] Figure 1 The present invention is a schematic flow diagram of an intelligent packaging method for an instant noodle production line; wherein, the method comprises:

[0063] Extracting a first film envelope data set from a film envelope database, wherein the first film envelope database includes a first film envelope characteristic parameter set and a first film envelope heat sealing parameter set;

[0064] Optionally, extract the first film envelope data set from the film envelope database, wherein the first film envelope database includes a first film envelope characteristic parameter set and a first film envelope heat sealing parameter set. The first film envelope characteristic parameter set includes information about film envelope material characteristics, size, shape, quality and other characteristics. Exemplarily, it includes film envelope type, film envelope melting point, film envelope thickness, film envelope strength, film envelope density and the like. The first film envelope heat sealing parameter set includes information about film envelope heat sealing temperature, time, pressure and other parameters. The above-mentioned first data set provides a data basis for subsequent intelligent analysis and optimization of film envelope process.

[0065] Performing supervised learning on the first film characteristic parameter set and the first film heat sealing index obtained by analyzing the first film heat sealing parameter set to obtain a film heat sealing prediction model;

[0066] Among them, the film heat sealing model is a machine learning-based prediction model obtained through supervised learning. The film heat sealing prediction model establishes a complex mapping relationship between the first film characteristic parameter set and the first film heat sealing index through analysis and learning of the two. Specifically, the first film characteristic parameter set is the model input index data, and the first film heat sealing index is the model output response data.

[0067] Optionally, the first film heat sealing index is obtained based on the analysis of the first film heat sealing parameters, and the first film heat sealing index quantitatively evaluates the heat sealing packaging effects of multiple groups of film heat sealing parameters in the first film heat sealing parameter set as a sample. Preferably, the analysis of the first film heat sealing parameters includes the analysis and evaluation of multiple packaging product indicators such as heat sealing quality and heat sealing efficiency, thereby realizing a quantitative reflection of the execution effect of the first film heat sealing parameters. Exemplarily, a fusion calculation is performed on multiple packaging product indicators according to different weight coefficients and normalization methods, wherein the product indicators focused on by the target packaging scenario have a higher weight coefficient, so as to achieve a targeted evaluation that meets the packaging optimization needs of the target scenario.

[0068] By obtaining the film heat sealing prediction model, it is helpful to predict the influence of different film characteristic parameters and heat sealing parameters on the film heat sealing quality, help optimize the heat sealing process, and improve the quality and efficiency of film heat sealing.

[0069] Determine a target film characteristic index according to a result obtained by performing a correlation analysis on the first film characteristic parameter set and the first film heat sealing index;

[0070] Among them, preferably, the first film characteristic parameter set also includes moisture resistance, oil permeability resistance, puncture resistance, number of surface pinholes, toughness, strength and other indicator parameters to meet the target instant noodle packaging scenario for sealing, moisture resistance, leakage resistance, packaging firmness and other packaging requirements.

[0071] Optionally, the correlation between the first film characteristic parameter set and the first film heat sealing index is analyzed based on statistical methods or machine learning techniques, thereby determining which film characteristic parameters have a significant impact on the heat sealing effect, and providing important input parameters for the subsequent heat sealing prediction model.

[0072] Analyzing the target characteristic parameters of the target instant bread film collected based on the target film characteristic index by the film heat sealing prediction model to obtain a target film heat sealing index;

[0073] Optionally, target characteristic parameters of the target instant bread film are collected according to the target film characteristic indicators, including interacting with the target instant bread film production source, obtaining performance characteristic data for analysis and extraction, and obtaining based on experiments.

[0074] Optionally, the collected target characteristic parameters are input as independent variable data into the film heat sealing prediction model, and the film heat sealing prediction model analyzes the rules and correlations obtained by previous training, and outputs a target film heat sealing index corresponding to the target characteristic parameters of the target instant film. The target film heat sealing index reflects the expected heat sealing effect of heat sealing the target film under ideal conditions according to the target characteristic parameters, and provides a quantitative evaluation result.

[0075] Determining whether a first heat sealing decision extracted from a predetermined heat sealing optimization space satisfies the target film heat sealing index;

[0076] Optionally, the first heat sealing decision is a heat sealing decision obtained by randomly selecting in a predetermined heat sealing optimization space, and the first heat sealing decision includes the film heat sealing temperature, the film heat sealing time and the film heat sealing pressure.

[0077] Optionally, the first heat sealing decision is a heat sealing decision obtained by performing random fluctuations on the current heat sealing decision in a predetermined heat sealing optimization space, wherein the random fluctuations include constructing a random number generator, presetting the fluctuation granularity, and activating the random number generator to determine the correction direction of the fluctuation granularity, thereby obtaining the first heat sealing decision.

[0078] Optionally, the first heat sealing decision is a homologous heat sealing decision of the current heat sealing decision in the predetermined heat sealing optimization space. The homologous heat sealing decision refers to a historical heat sealing decision with a high similarity to the target instant noodle packaging scene or packaging task. Exemplarily, based on the instant noodle packaging scene environmental index or task requirement index, a suitable similarity calculation method (such as cosine similarity, Euclidean distance, etc.) is used to calculate the similarity between the target instant noodle packaging scene or packaging task and the instant noodle packaging scene or packaging task in the historical packaging record. Then, a similarity threshold is set, and the historical heat sealing decision whose similarity exceeds the threshold is used as a homologous heat sealing decision.

[0079] If the conditions are met, a first fitness of the first heat sealing decision is obtained by combining a predetermined heat sealing fitness function;

[0080] The first fitness of the first heat sealing decision is used to reflect the energy consumption ratio of the first heat sealing decision, including temperature energy consumption and pressure energy consumption. Specifically, the fitness value of the heat sealing decision is obtained by substituting the relevant parameters of the first heat sealing decision into a predetermined heat sealing fitness function for calculation.

[0081] When the first fitness meets a predetermined fitness threshold, the first heat-sealing decision is used as a target heat-sealing decision, and the target heat-sealing decision is used to perform heat-sealing packaging on the target instant bread film.

[0082] In some embodiments, the process of obtaining the heat sealing index of the first film includes:

[0083] The first film heat-sealing parameter set includes a first film heat-sealing temperature, a first film heat-sealing time, and a first film heat-sealing pressure;

[0084] The first film heat sealing index is obtained by performing weighted calculation on the normalized first film heat sealing temperature, the first film heat sealing time and the first film heat sealing pressure using the coefficient of variation principle.

[0085] Exemplarily, first, the first film heat sealing temperature, the first film heat sealing time and the first film heat sealing pressure in the first film heat sealing parameter set are normalized and converted into numerical values ​​between 0 and 1 for weighted calculation.

[0086] Optionally, the normalized first film heat sealing temperature, first film heat sealing time and first film heat sealing pressure are weightedly calculated using the coefficient of variation principle. The coefficient of variation is the ratio of the standard deviation to the mean value, which is used to measure the degree of variation of the data. The larger the coefficient of variation, the more dispersed the data.

[0087] Exemplarily, the coefficient of variation of the first film heat-sealing temperature, the first film heat-sealing time and the first film heat-sealing pressure after normalization is calculated respectively. Assume that the first film heat-sealing temperature, the first film heat-sealing time and the first film heat-sealing pressure after normalization are x1, x2 and x3 respectively, and their means are μ1, μ2 and μ3 respectively, and their standard deviations are σ1, σ2 and σ3 respectively. Then, the coefficient of variation CV of the first film heat-sealing temperature, the first film heat-sealing time and the first film heat-sealing pressure is calculated as follows:

[0088] CV i =μ i σ i ×100%;

[0089] Wherein, i=1, 2, 3 correspond to the first film heat-sealing temperature, the first film heat-sealing time, and the first film heat-sealing pressure, respectively.

[0090] Furthermore, the three coefficients of variation are used as weight coefficients to perform weighted calculation of the normalized first film heat-sealing temperature, the first film heat-sealing time and the first film heat-sealing pressure, thereby obtaining the first film heat-sealing index.

[0091] In some embodiments, the process of determining the target capsule characteristic index includes:

[0092] Taking the first film envelope characteristic parameter extracted from the first film envelope characteristic parameter set as an independent variable, and taking the first film envelope heat sealing index as a dependent variable;

[0093] Draw a scatter plot according to the mapping relationship between the independent variable and the dependent variable;

[0094] Performing gridding processing on the scatter plot based on a predetermined gridding scheme to obtain a scatter plot grid result;

[0095] Obtaining a first maximum mutual information value of a first grid result in the scatter plot grid result;

[0096] When the target maximum mutual information value obtained by analyzing the first maximum mutual information value reaches a predetermined mutual information value threshold, the first envelope feature corresponding to the first envelope feature parameter is added to the target envelope feature index.

[0097] Optionally, the first film characteristic parameter is extracted from the first film characteristic parameter set as the independent variable, and the first film heat sealing index is used as the dependent variable. A scatter plot is drawn to show the relationship between the dependent variable and the corresponding relationship of the dependent variable. Then, according to a predetermined gridding scheme, the scatter plot is gridded and divided into a plurality of grids. Exemplarily, the predetermined gridding scheme includes a preset grid spacing, a grid shape, and a grid starting position. After the gridding process, each grid may contain a plurality of scattered points, forming a scatter plot grid result.

[0098] Optionally, for the selected grid, calculate the mutual information value between all the scattered points in it. Mutual information is a measure used to measure the correlation and information between two variables. Specifically, first, by counting the frequency of each scattered point in the grid, the probability distribution of each scattered point in the grid is calculated for each scattered point in the grid; then, for each pair of scattered points, by counting the number of scattered point pairs that appear in the same grid at the same time, calculate the probability that they appear in the same grid at the same time; then, use the joint probability distribution and the respective probability distribution to calculate the mutual information until the mutual information of all scattered point pairs is calculated; finally, for each grid result, select the maximum value of the mutual information values ​​of all scattered point pairs as the maximum mutual information value of the grid result, which reflects the degree of association between the independent variable and the dependent variable.

[0099] Exemplarily, the mutual information is calculated based on the following formula:

[0100]

[0101] Where I(X;Y) is the mutual information between variables X and Y; p(x,y) is the probability that X and Y take the values ​​x and y at the same time, and p(x) and p(y) are the marginal probabilities that X and Y take the values ​​x and y respectively.

[0102] Optionally, when the maximum mutual information value obtained by analysis reaches a predetermined mutual information value threshold, the corresponding first envelope characteristic parameter is added to the target envelope characteristic index. These characteristic parameters are considered to have a high correlation with the heat sealing index and are stored as the target envelope characteristic index.

[0103] In some embodiments, the process of determining the predetermined heat-sealing optimal space includes:

[0104] Obtaining a predetermined temperature threshold of a heat sealing control temperature;

[0105] obtaining a predetermined pressure threshold of heat sealing control pressure;

[0106] Obtaining a predetermined time threshold of heat sealing control time;

[0107] The predetermined heat sealing optimization space is constructed based on the predetermined temperature threshold, the predetermined pressure threshold and the predetermined time threshold.

[0108] The predetermined heat sealing optimization space is a multi-dimensional parameter space, which contains all possible heat sealing parameter combinations. Specifically, it includes the heat sealing control temperature dimension, the heat sealing control pressure dimension, and the heat sealing control time dimension. By combining the predetermined temperature, pressure, and time range into a three-dimensional space, it is used to find the optimal heat sealing parameter configuration.

[0109] Among them, the predetermined temperature threshold specifies the optimal control temperature range during the heat sealing process, which is determined based on process requirements and material properties. The predetermined pressure threshold specifies the optimal control pressure range during the heat sealing process, which is determined based on process requirements and equipment performance. The predetermined time threshold specifies the optimal control time range during the heat sealing process, which is determined based on process requirements and equipment performance.

[0110] In some embodiments, the expression of the predetermined heat sealing fitness function is as follows:

[0111]

[0112] Among them, f(x) represents the first fitness of the first heat sealing decision x, t(x), p(x) and t′(x) respectively represent the first temperature, the first pressure and the first time in the first heat sealing decision x, a represents the temperature energy consumption adjustment coefficient, b represents the pressure energy consumption adjustment coefficient, a+b=1, α represents the energy consumption weight, β represents the efficiency weight, α+β=1.

[0113] Specifically, the predetermined heat sealing fitness function takes into account the influence of temperature energy consumption, pressure energy consumption and time on the heat sealing decision, and weighs the temperature energy consumption, pressure energy consumption and efficiency through energy consumption weight and efficiency weight, so as to find the optimal heat sealing decision and achieve the best cost performance.

[0114] In some embodiments, the method further comprises:

[0115] The target thickness parameter is obtained by detecting with a thickness sensor probe, wherein the thickness sensor probe is arranged at a target sealing position of the target instant bread film;

[0116] Extracting a first thickness of a first sealing point in the target thickness parameter, where the first sealing point is within the range of the target sealing position;

[0117] When the first thickness is at a predetermined thickness threshold, issuing a heat-sealing packaging instruction;

[0118] The target heat sealing decision is called based on the heat sealing packaging instruction to perform heat sealing packaging on the target instant bread film.

[0119] Optionally, a thickness sensor refers to a sensor for measuring the thickness of an object, including a detection head that can measure the distance from the surface of the object to the other side in contact or non-contact, thereby determining the thickness of the object, and by arranging the thickness sensor detection head at the target sealing position of the target convenience film, it is ensured that the thickness at the sealing position can be accurately detected.

[0120] Specifically, the sensor detection head obtains thickness parameters of multiple positions of the target instant bread film sealing position through detection, and extracts the thickness of the first sealing point in the parameters, and the first sealing point is any point within the range of the target sealing position.

[0121] Optionally, the predetermined thickness threshold includes an upper threshold limit and a lower threshold limit. When it is detected that the thickness of the first sealing point is at the predetermined thickness threshold, it indicates that the target sealing position of the target instant film does not have an abnormal situation of being too thick or too thin.

[0122] By randomly selecting multiple points at the sealing position for thickness detection, abnormal thickness caused by wrinkles in the film material can be avoided. When no abnormal thickness is detected, a heat-sealing packaging instruction is issued. This improves the quality of the sealing and ensures the integrity and aesthetics of the product packaging.

[0123] In some implementations, after extracting the first thickness of the first sealing point in the target thickness parameter, the method further includes:

[0124] Extracting a second thickness of a second sealing point in the target thickness parameter, where the second sealing point is a point different from the first sealing point within the range of the target sealing position;

[0125] The first support of the second thickness to the first thickness is obtained by combining a predetermined support function, and the expression of the predetermined support function is as follows:

[0126] sup(thick1,thick2)=k[1+(thicl1-thicl2) e ] -1 ;

[0127] Wherein, sup(thick1,thick2) represents the first support of the second thickness thickness2 to the first thickness thickness1, k represents the amplitude of the predetermined support function, k∈[0,1], e represents the attenuation factor, e≥0;

[0128] When the first support does not meet a predetermined support threshold, a secondary thickness detection is performed on the first sealing point by using the thickness sensor detection head.

[0129] Optionally, multiple points at the sealing position are randomly measured to detect the thickness of different areas of the sealing position respectively to avoid wrinkles or poor quality of the packaging material, which may affect the packaging quality.

[0130] Exemplarily, based on the first sealing point, a position different from the first sealing point is selected as the second sealing point, and then the support of the second thickness to the first thickness is calculated in combination with a predetermined support function. If the support does not meet the predetermined threshold, a second thickness test is performed on the first sealing point to ensure the sealing quality.

[0131] Among them, k is used to characterize the amplitude of the support function. In other words, k controls the steepness of the support function, that is, the sensitivity of the support to thickness differences.

[0132] In summary, the intelligent packaging method for an instant noodle production line provided by the present invention has the following technical effects:

[0133] Extract the first film data set in the film database, the first film database includes the first film characteristic parameter set and the first film heat sealing parameter set; perform supervised learning on the first film characteristic parameter set and the first film heat sealing index obtained by analyzing the first film heat sealing parameter set to obtain a film heat sealing prediction model; determine the target film characteristic index according to the result obtained by performing correlation analysis on the first film characteristic parameter set and the first film heat sealing index; analyze the target characteristic parameters of the target instant film collected based on the target film characteristic index through the film heat sealing prediction model to obtain the target film heat sealing index; determine whether the first heat sealing decision extracted from the predetermined heat sealing optimization space meets the target film heat sealing index; if it meets, obtain the first fitness of the first heat sealing decision in combination with the predetermined heat sealing fitness function; when the first fitness meets the predetermined fitness threshold, use the first heat sealing decision as the target heat sealing decision, and the target heat sealing decision is used to heat seal the target instant film. The intelligent packaging method and system of the instant noodle production line disclosed by the present invention solves the technical problems of unstable packaging quality and affecting packaging efficiency, and achieves the technical effect of improving packaging yield and packaging quality and ensuring packaging efficiency.

[0134] Embodiment 2

[0135] Figure 2 Schematic diagram of the structure of an intelligent packaging system for an instant noodle production line of the present invention. Figure 1 The schematic flow diagram of the intelligent packaging method of an instant noodle production line of the present invention can be shown as follows: Figure 2 The structure shown is implemented.

[0136] Based on the same concept as the intelligent packaging method of an instant noodle production line in the embodiment, the present invention also provides an intelligent packaging system for an instant noodle production line, including:

[0137] A parameter acquisition module 11 is used to extract a first film envelope data set from a film envelope database, wherein the first film envelope database includes a first film envelope characteristic parameter set and a first film envelope heat sealing parameter set;

[0138] A prediction mapping path module 12, configured to perform supervised learning on the first film characteristic parameter set and the first film heat sealing index obtained by analyzing the first film heat sealing parameter set to obtain a film heat sealing prediction model;

[0139] A related index selection module 13, used to determine a target film envelope characteristic index according to a result obtained by performing a correlation analysis on the first film envelope characteristic parameter set and the first film envelope heat sealing index;

[0140] The prediction and analysis module 14 is used to analyze the target characteristic parameters of the target instant bread film collected based on the target film characteristic index through the film heat sealing prediction model to obtain a target film heat sealing index;

[0141] A decision screening module 15, used to determine whether the first heat sealing decision extracted from the predetermined heat sealing optimization space meets the target film heat sealing index;

[0142] An adaptability analysis module 16, configured to obtain a first fitness of the first heat sealing decision by combining a predetermined heat sealing fitness function if the conditions are met;

[0143] The decision output module 17 is used to use the first heat sealing decision as a target heat sealing decision when the first fitness meets a predetermined fitness threshold, and the target heat sealing decision is used to perform heat sealing packaging on the target instant bread film.

[0144] The prediction mapping path module 12 includes a normalization analysis unit:

[0145] The first film heat-sealing parameter set includes a first film heat-sealing temperature, a first film heat-sealing time, and a first film heat-sealing pressure;

[0146] The normalization analysis unit is used to perform weighted calculation on the first film heat sealing temperature, the first film heat sealing time and the first film heat sealing pressure after normalization by using the coefficient of variation principle to obtain the first film heat sealing index.

[0147] Among them, the relevant indicator selection module 13 also includes:

[0148] A variable configuration unit, used for taking the first film envelope characteristic parameter extracted from the first film envelope characteristic parameter set as an independent variable, and taking the first film envelope heat sealing index as a dependent variable;

[0149] A mapping drawing unit, used for drawing a scatter plot according to the mapping relationship between the independent variable and the dependent variable;

[0150] A gridding unit, used for performing gridding processing on the scatter plot based on a predetermined gridding scheme to obtain a scatter plot grid result;

[0151] A mutual information value unit, used to obtain a first maximum mutual information value of a first grid result in the scatter plot grid result;

[0152] A feature adding unit is used to add the first envelope feature corresponding to the first envelope feature parameter to the target envelope feature index when a target maximum mutual information value obtained by analyzing the first maximum mutual information value reaches a predetermined mutual information value threshold.

[0153] The decision screening module 15 further includes:

[0154] A heat sealing control temperature unit, used to obtain a predetermined temperature threshold of the heat sealing control temperature;

[0155] A heat sealing control pressure unit, used to obtain a predetermined pressure threshold of the heat sealing control pressure;

[0156] A heat sealing control time unit, used to obtain a predetermined time threshold of the heat sealing control time;

[0157] The optimization space construction unit is used to construct the predetermined heat sealing optimization space based on the predetermined temperature threshold, the predetermined pressure threshold and the predetermined time threshold.

[0158] The adaptability analysis module 16 further includes:

[0159] The predetermined heat sealing fitness function unit includes the predetermined heat sealing fitness function, and the expression is as follows:

[0160]

[0161] Among them, f(x) represents the first fitness of the first heat sealing decision x, t(x), p(x) and t′(x) respectively represent the first temperature, the first pressure and the first time in the first heat sealing decision x, a represents the temperature energy consumption adjustment coefficient, b represents the pressure energy consumption adjustment coefficient, a+b=1, α represents the energy consumption weight, β represents the efficiency weight, α+β=1.

[0162] Furthermore, the system also includes:

[0163] A thickness parameter detection unit, used for detecting and obtaining a target thickness parameter through a thickness sensor detection head, wherein the thickness sensor detection head is arranged at a target sealing position of the target instant bread film;

[0164] a thickness extraction unit, used for extracting a first thickness of a first sealing point in the target thickness parameter, wherein the first sealing point is within the range of the target sealing position;

[0165] a thickness determination unit, configured to issue a heat-sealing packaging instruction when the first thickness is within a predetermined thickness threshold;

[0166] The heat-sealing packaging unit is used to call the target heat-sealing decision based on the heat-sealing packaging instruction to perform heat-sealing packaging on the target instant bread film.

[0167] Furthermore, the system also includes:

[0168] A second thickness acquisition unit, used for extracting a second thickness of a second sealing point in the target thickness parameter, where the second sealing point is a point different from the first sealing point within the range of the target sealing position;

[0169] A support unit is used to obtain a first support of the second thickness to the first thickness by combining a predetermined support function, wherein the expression of the predetermined support function is as follows:

[0170] sup(thick1,thick2)=k[1+(thick1-thick2) e ] -1 ;

[0171] Wherein, sup(thick1,thick2) represents the first support of the second thickness thickness2 to the first thickness thickness1, k represents the amplitude of the predetermined support function, k∈[0,1], e represents the attenuation factor, e≥0;

[0172] The secondary thickness detection unit is used to perform secondary thickness detection on the first sealing point through the thickness sensor detection head when the first support does not meet the predetermined support threshold.

[0173] It should be understood that the embodiments mentioned in this specification focus on their differences from other embodiments. The specific embodiments in the aforementioned embodiment one are also applicable to the intelligent packaging system for an instant noodle production line described in embodiment two. For the sake of brevity of the specification, they will not be further elaborated here.

[0174] It should be understood that the embodiments disclosed in the present invention and the above description can enable those skilled in the art to use the present invention to implement the present invention. At the same time, the present invention is not limited to the above-mentioned embodiments. It should be understood that those skilled in the art can still modify the technical solutions recorded in the above-mentioned embodiments, or replace some of the technical features therein by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. An intelligent packaging method for an instant noodle production line, characterized in that: include: Extracting a first film envelope data set from a film envelope database, wherein the first film envelope database includes a first film envelope characteristic parameter set and a first film envelope heat sealing parameter set; Performing supervised learning on the first film characteristic parameter set and the first film heat sealing index obtained by analyzing the first film heat sealing parameter set to obtain a film heat sealing prediction model; Determine a target film characteristic index according to a result obtained by performing a correlation analysis on the first film characteristic parameter set and the first film heat sealing index; Analyzing the target characteristic parameters of the target instant bread film collected based on the target film characteristic index by the film heat sealing prediction model to obtain a target film heat sealing index; Determining whether a first heat sealing decision extracted from a predetermined heat sealing optimization space satisfies the target film heat sealing index; If the conditions are met, a first fitness of the first heat sealing decision is obtained by combining a predetermined heat sealing fitness function; When the first fitness meets a predetermined fitness threshold, the first heat-sealing decision is used as a target heat-sealing decision, and the target heat-sealing decision is used to perform heat-sealing packaging on the target instant bread film.

2. According to claim 1, the intelligent packaging method for an instant noodle production line is characterized in that: The process of obtaining the first film heat sealing index includes: The first film heat-sealing parameter set includes a first film heat-sealing temperature, a first film heat-sealing time, and a first film heat-sealing pressure; The first film heat sealing index is obtained by performing weighted calculation on the normalized first film heat sealing temperature, the first film heat sealing time and the first film heat sealing pressure using the coefficient of variation principle.

3. According to claim 1, the intelligent packaging method for an instant noodle production line is characterized in that: The process of determining the target envelope characteristic index includes: Taking the first film envelope characteristic parameter extracted from the first film envelope characteristic parameter set as an independent variable, and taking the first film envelope heat sealing index as a dependent variable; Draw a scatter plot according to the mapping relationship between the independent variable and the dependent variable; Performing gridding processing on the scatter plot based on a predetermined gridding scheme to obtain a scatter plot grid result; Obtaining a first maximum mutual information value of a first grid result in the scatter plot grid result; When the target maximum mutual information value obtained by analyzing the first maximum mutual information value reaches a predetermined mutual information value threshold, the first envelope feature corresponding to the first envelope feature parameter is added to the target envelope feature index.

4. The intelligent packaging method for instant noodle production line according to claim 1, characterized in that: The process of determining the predetermined heat-sealing optimal space includes: Obtaining a predetermined temperature threshold of a heat sealing control temperature; obtaining a predetermined pressure threshold of heat sealing control pressure; Obtaining a predetermined time threshold of heat sealing control time; The predetermined heat sealing optimization space is constructed based on the predetermined temperature threshold, the predetermined pressure threshold and the predetermined time threshold.

5. The intelligent packaging method for instant noodle production line according to claim 1, characterized in that: The expression of the predetermined heat sealing fitness function is as follows: Among them, f(x) represents the first fitness of the first heat sealing decision x, t(x), p(x) and t′(x) respectively represent the first temperature, the first pressure and the first time in the first heat sealing decision x, a represents the temperature energy consumption adjustment coefficient, b represents the pressure energy consumption adjustment coefficient, a+b=1, α represents the energy consumption weight, β represents the efficiency weight, α+β=1.

6. The intelligent packaging method for instant noodle production line according to claim 1, characterized in that: The method also includes: The target thickness parameter is obtained by detecting with a thickness sensor probe, wherein the thickness sensor probe is arranged at a target sealing position of the target instant bread film; Extracting a first thickness of a first sealing point in the target thickness parameter, where the first sealing point is within the range of the target sealing position; When the first thickness is at a predetermined thickness threshold, issuing a heat-sealing packaging instruction; The target heat sealing decision is called based on the heat sealing packaging instruction to perform heat sealing packaging on the target instant bread film.

7. The intelligent packaging method for an instant noodle production line according to claim 6, characterized in that: After extracting the first thickness of the first sealing point in the target thickness parameter, the method further includes: Extracting a second thickness of a second sealing point in the target thickness parameter, where the second sealing point is a point different from the first sealing point within the range of the target sealing position; The first support of the second thickness to the first thickness is obtained by combining a predetermined support function, and the expression of the predetermined support function is as follows: sup(thick1,thick2)=k[1+(thick1-thick2) e ] -1 ; Wherein, sup(thick1,thick2) represents the first support of the second thickness thickness2 to the first thickness thickness1, k represents the amplitude of the predetermined support function, k∈[0,1], e represents the attenuation factor, e≥0; When the first support does not meet a predetermined support threshold, a secondary thickness detection is performed on the first sealing point by using the thickness sensor detection head.

8. An intelligent packaging system for an instant noodle production line, characterized in that: include: A parameter acquisition module, the parameter acquisition module is used to extract a first film envelope data set from a film envelope database, the first film envelope database includes a first film envelope characteristic parameter set and a first film envelope heat sealing parameter set; A prediction mapping path module, wherein the prediction mapping path is used to perform supervised learning on the first film characteristic parameter set and the first film heat sealing index obtained by analyzing the first film heat sealing parameter set to obtain a film heat sealing prediction model; A relevant index selection module, wherein the relevant index selection module is used to determine a target film envelope characteristic index according to a result obtained by performing a correlation analysis on the first film envelope characteristic parameter set and the first film envelope heat sealing index; A prediction and analysis module, the prediction and analysis module is used to analyze the target characteristic parameters of the target instant bread film collected based on the target film characteristic index through the film heat sealing prediction model to obtain a target film heat sealing index; A decision screening module, the decision screening module is used to determine whether the first heat sealing decision extracted from the predetermined heat sealing optimization space meets the target film heat sealing index; An adaptability analysis module, wherein if the conditions are met, the adaptability analysis module is used to obtain a first fitness of the first heat sealing decision by combining a predetermined heat sealing fitness function; A decision output module is used to use the first heat sealing decision as a target heat sealing decision when the first fitness meets a predetermined fitness threshold, and the target heat sealing decision is used to perform heat sealing packaging on the target instant bread film.

Images