A moisture regain water adding proportion prediction method and system, electronic equipment and storage medium

Abstract

This invention discloses a method, system, electronic device, and storage medium for predicting the rehydration ratio. The method includes: collecting process parameters and actual values ​​of the rehydration ratio during the vacuum rehydration process; selecting input variables for a multiple linear regression prediction model of the rehydration ratio based on the process parameters; establishing the multiple linear regression prediction model of the rehydration ratio based on the input variables; fitting the multiple linear regression prediction model with historical data of the input variables and the actual values ​​of the rehydration ratio; collecting real-time values ​​of the input variables and inputting them into the fitted multiple linear regression prediction model to predict the rehydration ratio. This invention has clear quantitative relationships, higher model interpretability, is more intuitive and closer to the principle of rehydration, and, given a fixed leaf blend formula and tobacco pack grade, already possesses the necessary data for calculation, allowing for the calculation of the water addition ratio for different grades of tobacco packs before production.

Description

Technical Field

[0001] This invention relates to the field of tobacco production technology, and more specifically, to a method, system, electronic device, and storage medium for predicting the rehydration ratio. Background Technology

[0002] Currently, the rehydration process fully moistens and loosens the tobacco leaves, bringing their moisture content to between 17-18%. After rehydration, the tobacco leaves are packed and then vacuum-sealed. The tobacco bales become fluffy after rehydration, allowing for precise control of the vacuuming intensity. If the moisture content is too low, vacuuming cannot completely remove the raw, unpleasant odors; if the moisture content is too high, vacuuming will cause excessive aroma loss.

[0003] Existing technical solutions:

[0004] By retrieving historical data, the average value is obtained as the water addition ratio, and then the value is manually adjusted by human experience.

[0005] Disadvantages of existing technology:

[0006] The existing technology for determining fire-induced faulty circuits has the following two shortcomings:

[0007] (1) The control of the moisture content of the sheet tobacco after vacuum rehydration is greatly affected by the incoming tobacco leaves. Using the average value as the water addition ratio results in large deviations in some raw materials.

[0008] (2) Manual adjustment of the water ratio has a certain lag and can only be adjusted in a small amount.

[0009] Therefore, how to provide a method, system, electronic device and storage medium for predicting the re-moistening water ratio has become a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0010] The purpose of this invention is to provide a method, system, electronic device, and storage medium for predicting the proportion of re-moistening and water addition.

[0011] The first aspect of this invention discloses a method for predicting the proportion of reabsorption water, the method comprising:

[0012] Step S1: Collect the actual values ​​of the production process parameters and the proportion of water added during the rehydration process before vacuum rehydration;

[0013] Step S2: Based on the parameters of the pre-vacuum rehydration and water addition production process, select the input variables for the multiple linear regression prediction model of the rehydration and water addition ratio;

[0014] Step S3: Based on the input variables, establish a multiple linear regression prediction model for the re-wetting and water addition ratio;

[0015] Step S4: Apply the historical data of the input variables and the actual value of the reabsorption and water addition ratio to the data fitting of the multiple linear regression prediction model to obtain the regression coefficients;

[0016] Step S5: Collect the real-time values ​​of the input variables and input them into the fitted multiple linear regression prediction model to predict the reabsorption water ratio.

[0017] According to the method of the first aspect of the present invention, in step S1, the parameters of the pre-vacuum rehydration and water addition production process include: instantaneous flow rate of the rehydration inlet electronic scale, cumulative amount of the rehydration inlet electronic scale, moisture content of the rehydration inlet, moisture content of the rehydration outlet, actual value of production water flow rate, and cumulative amount of production water.

[0018] According to the method of the first aspect of the present invention, in step S2, the method for selecting the input variables of the multiple linear regression prediction model for the rehydration ratio based on the parameters of the pre-vacuum rehydration production process includes:

[0019] The input variables were obtained by performing correlation analysis using multiple regression analysis.

[0020] According to the method of the first aspect of the present invention, in step S2, the input variables include: moisture content at the re-moistening inlet, moisture content at the re-moistening outlet, cumulative amount of production water, and cumulative amount at the re-moistening inlet electronic scale.

[0021] According to the method of the first aspect of the present invention, in step S3, the method of establishing a multiple linear regression prediction model for the reabsorption water ratio based on the input variables includes:

[0022]

[0023]

[0024] in, The water ratio for the previous re-wetting process; The target value for the moisture content of the rehydrated material; The cumulative amount of the electronic scale at the entrance for re-humidification; This represents the average moisture content at the re-humidification inlet. This represents the average moisture content of the rehydrated material. denoted as the cumulative amount of water used in production; k is the regression coefficient.

[0025] According to the method of the first aspect of the present invention, in step S4, the multiple linear regression prediction model is fitted with data to obtain a regression coefficient of 1.14.

[0026] A second aspect of this invention discloses a system for predicting the proportion of reabsorption water, the system comprising:

[0027] The first processing module is configured to collect the actual values ​​of the parameters of the rehydration and water addition process before vacuum rehydration and the rehydration and water addition ratio.

[0028] The second processing module is configured to select the input variables of a multiple linear regression prediction model for the rehydration ratio based on the parameters of the pre-vacuum rehydration and water addition production process.

[0029] The third processing module is configured to establish a multiple linear regression prediction model for the re-wetting and water addition ratio based on the input variables.

[0030] The fourth processing module is configured to apply the historical data of the input variables and the actual value of the reabsorption and water addition ratio to perform data fitting on the multiple linear regression prediction model to obtain regression coefficients.

[0031] The fifth processing module is configured to collect the real-time values ​​of the input variables and input them into the fitted multiple linear regression prediction model to predict the reabsorption water ratio.

[0032] A third aspect of this invention discloses an electronic device. The electronic device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the steps of a method for predicting the reabsorption water ratio according to any one of the first aspects of this disclosure.

[0033] A fourth aspect of this invention discloses a computer-readable storage medium. The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a method for predicting the reabsorption / water ratio according to any one of the first aspects of this disclosure.

[0034] According to the technical content disclosed in this invention, the following beneficial effects are achieved: it has a clear quantitative relationship, the model is more interpretable, more intuitive and closer to the principle of rehydration and water addition, and when the leaf group formula and tobacco pack grade are determined, the data required for calculation are already available, and the water addition ratio of different grades of tobacco packs can be calculated before production.

[0035] Other features and advantages of the invention will become clear from the following detailed description of exemplary embodiments of the invention with reference to the accompanying drawings. Attached Figure Description

[0036] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments of the invention and, together with their description, serve to explain the principles of the invention.

[0037] Figure 1 This is a flowchart of a method for predicting the re-moistening water ratio according to an embodiment.

[0038] Figure 2This is a structural diagram of a re-moistening water addition ratio prediction system according to an embodiment of the present invention.

[0039] Figure 3 This is a structural diagram of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0040] Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of the invention.

[0041] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the invention or its application or use.

[0042] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and equipment should be considered part of the specification.

[0043] In all the examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values.

[0044] It should be noted that similar labels and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be discussed further in subsequent figures.

[0045] Example 1:

[0046] This invention discloses a method for predicting the proportion of water added during re-wetting. Figure 1 A flowchart of a method for predicting the re-humidification water ratio according to an embodiment of the present invention is shown below. Figure 1 As shown, the method includes:

[0047] Step S1: Collect the actual values ​​of the production process parameters and the proportion of water added during the rehydration process before vacuum rehydration;

[0048] Step S2: Based on the parameters of the pre-vacuum rehydration and water addition production process, select the input variables for the multiple linear regression prediction model of the rehydration and water addition ratio;

[0049] Step S3: Based on the input variables, establish a multiple linear regression prediction model for the re-wetting and water addition ratio;

[0050] Step S4: Apply the historical data of the input variables and the actual value of the reabsorption and water addition ratio to the data fitting of the multiple linear regression prediction model to obtain the regression coefficients;

[0051] Step S5: Collect the real-time values ​​of the input variables and input them into the fitted multiple linear regression prediction model to predict the reabsorption water ratio.

[0052] In step S1, the actual values ​​of the production process parameters and the proportion of water added during the rehydration process before vacuum rehydration are collected.

[0053] In some embodiments, in step S1, the parameters of the pre-vacuum rehydration and water addition production process include: instantaneous flow rate of the rehydration inlet electronic scale, cumulative amount of the rehydration inlet electronic scale, moisture content of the rehydration inlet, moisture content of the rehydration outlet, actual value of production water flow rate, and cumulative amount of production water.

[0054] Specifically, data from December 2021 to June 2022 was collected and processed in 51 batches, with each batch being collected every 2 seconds, totaling approximately 3,600 data points. Parameters included instantaneous flow rate of the rehydration inlet electronic scale, cumulative volume of the rehydration inlet electronic scale, moisture content of the rehydration inlet, moisture content of the rehydration outlet, actual value of production water flow rate, and cumulative production water volume, all of which were dimensionless.

[0055] In step S2, the input variables of the multiple linear regression prediction model for the rehydration ratio are selected based on the parameters of the pre-vacuum rehydration and water addition production process.

[0056] In some embodiments, in step S2, the method for selecting the input variables of the multiple linear regression prediction model for the rehydration water addition ratio based on the parameters of the pre-vacuum rehydration water addition production process includes: performing correlation analysis using a multiple regression analysis method to obtain the input variables. The input variables include: rehydration inlet moisture content, rehydration outlet moisture content, cumulative production water consumption, and cumulative volume of the electronic scale at the rehydration inlet.

[0057] Specifically, since many factors influence the rehydration water ratio, it is difficult to accurately determine whether each factor significantly affects the water ratio. To establish the optimal multiple linear regression prediction model, it is necessary to select parameters with a high correlation to the water ratio and remove those with a low correlation. Correlation analysis was performed on the collected data using a Python multiple regression analysis tool. The results are shown in Table 1. Four factors—rehydration inlet moisture content, rehydration outlet moisture content, cumulative production water consumption, and cumulative rehydration inlet electronic scale consumption—showed a strong correlation with the water ratio. In subsequent modeling, the multiple linear regression prediction model was determined through variable relationships. The instantaneous flow rate of the rehydration inlet electronic scale and the actual value of the production water flow rate showed a weak correlation and could be removed.

[0058] Table 1

[0059]

[0060] In step S3, a multiple linear regression prediction model for the re-wetting and water addition ratio is established based on the input variables.

[0061] In some embodiments, in step S3, the method for establishing a multiple linear regression prediction model for the reabsorption water ratio based on the input variables includes:

[0062]

[0063]

[0064] in, The water ratio for the previous re-wetting process; The target value for the moisture content of the rehydrated material; The cumulative amount of the electronic scale at the entrance for re-humidification; This represents the average moisture content at the re-humidification inlet. This represents the average moisture content of the rehydrated material. denoted as the cumulative amount of water used in production; k is the regression coefficient.

[0065] Specifically, before the actual production of the vacuum rehydration process for a target batch of a certain brand, process data from the most recent historical vacuum rehydration batch of the same brand are selected. The data sampling frequency is once every 2 seconds, with approximately 3600 data entries per batch. After data alignment according to batch time-series data alignment rules, the batch process data is processed using a data slicing method. The number of cigarette packs added in one batch is N=40. The water ratio for the previous re-wetting process; The target value for the moisture content of the rehydrated material; The cumulative amount of the electronic scale at the entrance for re-humidification; This represents the average moisture content at the re-humidification inlet. This represents the average moisture content of the rehydrated material. denoted as cumulative water consumption for production; k is the regression coefficient; i = 1, 2, 3, ..., 40.

[0066] Based on the mechanism of water addition control, the predicted value of the rehydration water addition ratio for each cigarette pack in the target batch is as follows:

[0067]

[0068]

[0069] In step S4, the historical data of the input variables and the actual value of the reabsorption and water addition ratio are used to fit the data of the multiple linear regression prediction model to obtain the regression coefficients.

[0070] In some embodiments, in step S4, the multiple linear regression prediction model is fitted with data to obtain a regression coefficient of 1.14.

[0071] Specifically, the fitted multiple linear regression prediction model is as follows:

[0072]

[0073]

[0074] Multiple linear regression is a statistical analysis method that uses linear equations to express the quantitative relationship between a dependent variable and multiple independent variables. Let Y be the dependent variable, and X be the independent variable. i If (i = 1, 2, ..., m) is the i-th independent variable, then:

[0075] Y = β0 + β1X1 + β2X2 + ... + β m X m +ε; (1)

[0076] Where β0 is a constant term, also known as the intercept; β0, β2, β2, ..., β m The regression coefficient represents the coefficient of X when all other independent variables remain constant. i (i = 1, 2, ..., m) represents the average change in the dependent variable Y when it increases or decreases by one unit; ε is the random error after removing the influence of the m independent variables on Y. The following basic assumptions are met in the multiple linear regression model:

[0077] ① The explanatory variables are deterministic variables, not random variables, and there is no correlation between the explanatory variables;

[0078] ②The random error term has zero mean, homoscedasticity, and is independent of each other at different sample points;

[0079] ③ The explanatory variables are not correlated with the random error term;

[0080] ④ The random error term follows a normal distribution;

[0081] ⑤ The regression model is correctly configured.

[0082] The least squares method is a commonly used estimation method in regression equations. It minimizes the sum of squares of the residuals (i.e., estimates of random error) between the observed and estimated values ​​of the dependent variable, thereby obtaining the mean squares. and The method.

[0083] In summary, the multiple regression-based rehydration ratio calculation model is derived from the mechanism model of moisture addition to tobacco leaf materials. Specifically, it classifies tobacco bales according to the accumulated material volume on the electronic scale, analyzes the hygroscopic characteristics of different grades of tobacco leaves, and calculates the dry matter to moisture ratio of the output material. Therefore, this model has a clear quantitative relationship, higher interpretability, and is more intuitive and closely reflects the rehydration principle. Furthermore, given a fixed leaf blend formula and tobacco bale grade, the necessary data is already available, allowing for the calculation of the moisture addition ratio for different grades of tobacco bales before production. In actual production, this model is suitable for calculating the theoretical moisture addition ratio before production begins, providing a preset reference value for the rehydration equipment. Finally, we use the multiple regression-based rehydration ratio calculation model to calculate the theoretical moisture addition ratio. Taking a batch of 8000kg as an example, with one tobacco bale weighing approximately 200kg and a batch containing 40 bales, the multiple regression model can be used to calculate the theoretical moisture addition ratio for each bale, providing the theoretical rehydration moisture addition ratio and adjustment points before production begins.

[0084] The target value of the moisture content of the rehydrated material was determined. The theoretical water addition ratio and water addition ratio adjustment point for batch QYA1 were calculated and used as the theoretical water addition ratio for the production of batch QYA1. The calculation results are shown in Table 2.

[0085]

[0086] Example 2:

[0087] This invention discloses a system for predicting the proportion of re-moistening water addition. Figure 2 This is a structural diagram of a moisture regain and water addition ratio prediction system according to an embodiment of the present invention; as shown. Figure 2 As shown, the system 100 includes:

[0088] The first processing module 101 is configured to collect the actual values ​​of the parameters of the pre-vacuum rehydration and water addition process and the rehydration and water addition ratio.

[0089] The second processing module 102 is configured to select the input variables of the multiple linear regression prediction model for the rehydration ratio based on the parameters of the pre-vacuum rehydration and water addition production process.

[0090] The third processing module 103 is configured to establish a multiple linear regression prediction model for the re-wetting and water addition ratio based on the input variables.

[0091] The fourth processing module 104 is configured to apply the historical data of the input variables and the actual value of the reabsorption and water addition ratio to perform data fitting on the multiple linear regression prediction model to obtain regression coefficients.

[0092] The fifth processing module 105 is configured to collect the real-time values ​​of the input variables and input the fitted multiple linear regression prediction model to predict the re-wetting and water addition ratio.

[0093] According to the system of the second aspect of the present invention, the first processing module 101 is specifically configured such that the parameters of the pre-vacuum rehydration and water addition production process include: instantaneous flow rate of the rehydration inlet electronic scale, cumulative amount of the rehydration inlet electronic scale, moisture content of the rehydration inlet, moisture content of the rehydration discharge, actual value of production water flow rate, and cumulative amount of production water.

[0094] Specifically, data from December 2021 to June 2022 was collected and processed in 51 batches, with each batch being collected every 2 seconds, totaling approximately 3,600 data points. Parameters included instantaneous flow rate of the rehydration inlet electronic scale, cumulative volume of the rehydration inlet electronic scale, moisture content of the rehydration inlet, moisture content of the rehydration outlet, actual value of production water flow rate, and cumulative production water volume, all of which were dimensionless.

[0095] According to the system of the second aspect of the present invention, the second processing module 102 is specifically configured to select the input variables of the multiple linear regression prediction model for the rehydration ratio based on the parameters of the pre-vacuum rehydration water addition production process. The method includes: performing correlation analysis using a multiple regression analysis method to obtain the input variables. The input variables include: rehydration inlet moisture content, rehydration outlet moisture content, cumulative production water consumption, and cumulative volume of the electronic scale at the rehydration inlet.

[0096] Specifically, since many factors influence the rehydration water ratio, it is difficult to accurately determine whether each factor significantly affects the water ratio. To establish the optimal multiple linear regression prediction model, it is necessary to select parameters with a high correlation to the water ratio and remove those with a low correlation. Correlation analysis was performed on the collected data using a Python multiple regression analysis tool. The results are shown in Table 1. Four factors—rehydration inlet moisture content, rehydration outlet moisture content, cumulative production water consumption, and cumulative rehydration inlet electronic scale consumption—showed a strong correlation with the water ratio. In subsequent modeling, the multiple linear regression prediction model was determined through variable relationships. The instantaneous flow rate of the rehydration inlet electronic scale and the actual value of the production water flow rate showed a weak correlation and could be removed.

[0097] Table 1

[0098]

[0099] According to the system of the second aspect of the present invention, the third processing module 103 is specifically configured such that the method for establishing a multiple linear regression prediction model for the reabsorption water ratio based on the input variables includes:

[0100]

[0101]

[0102] in, The water ratio for the previous re-wetting process; The target value for the moisture content of the rehydrated material; The cumulative amount of the electronic scale at the entrance for re-humidification; This represents the average moisture content at the re-humidification inlet. This represents the average moisture content of the rehydrated material. denoted as the cumulative amount of water used in production; k is the regression coefficient.

[0103] Specifically, before the actual production of the vacuum rehydration process for a target batch of a certain brand, process data from the most recent historical vacuum rehydration batch of the same brand are selected. The data sampling frequency is once every 2 seconds, with approximately 3600 data entries per batch. After data alignment according to batch time-series data alignment rules, the batch process data is processed using a data slicing method. The number of cigarette packs added in one batch is N=40. The water ratio for the previous re-wetting process; The target value for the moisture content of the rehydrated material; The cumulative amount of the electronic scale at the entrance for re-humidification; This represents the average moisture content at the re-humidification inlet. This represents the average moisture content of the rehydrated material. denoted as cumulative water consumption for production; k is the regression coefficient; i = 1, 2, 3, ..., 40.

[0104] Based on the mechanism of water addition control, the predicted value of the rehydration water addition ratio for each cigarette pack in the target batch is as follows:

[0105]

[0106]

[0107] According to the system of the second aspect of the present invention, the fourth processing module 104 is specifically configured to perform data fitting on the multiple linear regression prediction model to obtain a regression coefficient of 1.14.

[0108] Specifically, the fitted multiple linear regression prediction model is as follows:

[0109]

[0110]

[0111] Multiple linear regression is a statistical analysis method that uses linear equations to express the quantitative relationship between a dependent variable and multiple independent variables. Let Y be the dependent variable, and X be the independent variable. i If (i = 1, 2, ..., m) is the i-th independent variable, then:

[0112] Y = β0 + β1X1 + β2X2 + ... + β m X m +ε; (1)

[0113] Where β0 is a constant term, also known as the intercept; β0, β2, β2, ..., β m The regression coefficient represents the coefficient of X when all other independent variables remain constant. i (i = 1, 2, ..., m) represents the average change in the dependent variable Y when it increases or decreases by one unit; ε is the random error after removing the influence of the m independent variables on Y. The following basic assumptions are met in the multiple linear regression model:

[0114] ① The explanatory variables are deterministic variables, not random variables, and there is no correlation between the explanatory variables;

[0115] ②The random error term has zero mean, homoscedasticity, and is independent of each other at different sample points;

[0116] ③ The explanatory variables are not correlated with the random error term;

[0117] ④ The random error term follows a normal distribution;

[0118] ⑤ The regression model is correctly configured.

[0119] The least squares method is a commonly used estimation method in regression equations. It minimizes the sum of squares of the residuals (i.e., estimates of random error) between the observed and estimated values ​​of the dependent variable, thereby obtaining the mean squares. and The method.

[0120] Example 3:

[0121] This invention discloses an electronic device. The electronic device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the steps in the method for predicting the reabsorption water ratio according to any one of the embodiments of this invention.

[0122] Figure 3 This is a structural diagram of an electronic device according to an embodiment of the present invention, such as... Figure 3As shown, the electronic device includes a processor, memory, communication interface, display screen, and input device connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, Near Field Communication (NFC), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input device can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the device's casing, or an external keyboard, touchpad, or mouse.

[0123] Those skilled in the art will understand that Figure 3 The structure shown is merely a structural diagram of the part related to the technical solution of this disclosure and does not constitute a limitation on the electronic device to which the solution of this application is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0124] Example 4:

[0125] This invention discloses a computer-readable storage medium. The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a method for predicting the reabsorption / water ratio according to any one of Embodiment 1 of this invention.

[0126] Please note that the technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments have been described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification. The above embodiments only illustrate several implementation methods of this application, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the invention patent. It should be pointed out that for those skilled in the art, several modifications and improvements can be made without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

[0127] The embodiments of the subject matter and functional operation described in this specification can be implemented in the following ways: digital electronic circuits, tangibly embodied computer software or firmware, computer hardware including the structures disclosed in this specification and their structural equivalents, or combinations thereof. Embodiments of the subject matter described in this specification can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions encoded on a tangible, non-transitory program carrier for execution by a data processing apparatus or for controlling the operation of a data processing apparatus. Alternatively or additionally, the program instructions may be encoded on artificially generated propagation signals, such as machine-generated electrical, optical, or electromagnetic signals, which are generated to encode information and transmit it to a suitable receiving device for execution by the data processing apparatus. The computer storage medium may be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or combinations thereof.

[0128] The processing and logic flow described in this specification can be executed by one or more programmable computers that execute one or more computer programs to perform corresponding functions by operating on input data and generating output. The processing and logic flow can also be executed by dedicated logic circuitry—such as FPGAs (Field-Programmable Gate Arrays) or ASICs (Application-Specific Integrated Circuits), and the device can also be implemented as dedicated logic circuitry.

[0129] Suitable computers for executing computer programs include, for example, general-purpose and / or special-purpose microprocessors, or any other type of central processing unit. Typically, the central processing unit receives instructions and data from read-only memory and / or random access memory. The basic components of a computer include a central processing unit for implementing or executing instructions and one or more memory devices for storing instructions and data. Typically, a computer will also include one or more mass storage devices for storing data, such as disks, magneto-optical disks, or optical disks, or the computer will be operatively coupled to such mass storage devices to receive data from or transfer data to them, or both. However, a computer is not required to have such devices. Furthermore, a computer can be embedded in another device, such as a mobile phone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a global positioning system (GPS) receiver, or a portable storage device such as a universal serial bus (USB) flash drive, to name a few.

[0130] Computer-readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media, and memory devices, such as semiconductor memory devices (e.g., EPROM, EEPROM, and flash memory devices), magnetic disks (e.g., internal hard disks or removable disks), magneto-optical disks, and CD-ROM and DVD-ROM disks. Processors and memory may be supplemented by or incorporated into dedicated logic circuitry.

[0131] While this specification contains numerous specific implementation details, these should not be construed as limiting the scope of any invention or the scope of the claims, but rather are primarily intended to describe features of specific embodiments of a particular invention. Certain features described in the various embodiments herein may also be implemented in combination in a single embodiment. Conversely, various features described in a single embodiment may also be implemented separately in various embodiments or in any suitable sub-combination. Furthermore, while features may function in certain combinations as described above and even initially claimed in this way, one or more features from a claimed combination may be removed from that combination in some cases, and a claimed combination may refer to a sub-combination or a variation thereof.

[0132] Similarly, although the operations are depicted in a specific order in the accompanying drawings, this should not be construed as requiring these operations to be performed in the specific order shown or sequentially, or requiring all illustrated operations to be performed to achieve the desired result. In some cases, multitasking and parallel processing may be advantageous. Furthermore, the separation of various system modules and components in the above embodiments should not be construed as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.

[0133] Thus, specific embodiments of the subject matter have been described. Other embodiments are within the scope of the appended claims. In some cases, the actions recited in the claims may be performed in a different order and still achieve the desired result. Furthermore, the processes depicted in the drawings are not necessarily shown in a specific order or sequence to achieve the desired result. In some implementations, multitasking and parallel processing may be advantageous.

[0134] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0135] While specific embodiments of the invention have been described in detail by way of examples, those skilled in the art should understand that the examples are for illustrative purposes only and not intended to limit the scope of the invention. Those skilled in the art should understand that modifications can be made to the above embodiments without departing from the scope and spirit of the invention. The scope of the invention is defined by the appended claims.

Claims

1. A moisture regain water addition ratio prediction method characterized by, The method includes: Step S1: Collect the actual values ​​of the production process parameters and the proportion of water added during the rehydration process before vacuum rehydration; Step S2: Based on the parameters of the pre-vacuum rehydration and water addition production process, select the input variables for the multiple linear regression prediction model of the rehydration and water addition ratio; Step S3: Based on the input variables, establish a multiple linear regression prediction model for the re-wetting and water addition ratio; Step S4: Apply the historical data of the input variables and the actual value of the reabsorption and water addition ratio to the data fitting of the multiple linear regression prediction model to obtain the regression coefficients; Step S5: Collect the real-time values ​​of the input variables and input them into the fitted multiple linear regression prediction model to predict the reabsorption water ratio. In step S1, the parameters of the pre-vacuum rehydration and water addition production process include: instantaneous flow rate of the rehydration inlet electronic scale, cumulative amount of the rehydration inlet electronic scale, moisture content of the rehydration inlet, moisture content of the rehydration outlet, actual value of production water flow rate, and cumulative amount of production water. In step S2, the method for selecting the input variables of the multiple linear regression prediction model for the rehydration ratio based on the parameters of the pre-vacuum rehydration production process includes: The input variables were obtained by performing correlation analysis using multiple regression analysis. In step S2, the input variables include: moisture content at the re-moistening inlet, moisture content at the re-moistening outlet, cumulative production water consumption, and cumulative amount at the re-moistening inlet electronic scale. In step S3, the method for establishing a multiple linear regression prediction model for the reabsorption water ratio based on the input variables includes: ； ； wherein, is the moisture regain water addition ratio of the last time; is the moisture regain discharge moisture content target value; is the moisture regain inlet electronic scale cumulative amount; is the moisture regain inlet moisture content average value; is the moisture regain discharge moisture content average value; is the production water cumulative amount; k is the regression coefficient.

2. The method of claim 1, wherein In step S4, the regression coefficient is 1.

14.

3. A moisture regain water addition ratio prediction system characterized by, The system includes: The first processing module is configured to collect the actual values ​​of the parameters of the rehydration and water addition process before vacuum rehydration and the rehydration and water addition ratio. The second processing module is configured to select the input variables of a multiple linear regression prediction model for the rehydration ratio based on the parameters of the pre-vacuum rehydration and water addition production process. The third processing module is configured to establish a multiple linear regression prediction model for the re-wetting and water addition ratio based on the input variables. The fourth processing module is configured to apply the historical data of the input variables and the actual value of the reabsorption and water addition ratio to perform data fitting on the multiple linear regression prediction model to obtain regression coefficients. The fifth processing module is configured to collect the real-time values ​​of the input variables and input them into the fitted multiple linear regression prediction model to predict the reabsorption water ratio.

4. An electronic device, comprising: The electronic device includes a memory and a processor. The memory stores a computer program. When the processor executes the computer program, it implements the steps in the method for predicting the proportion of re-moistening water added according to any one of claims 1 to 2.

5. A computer readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the method for predicting the re-moistening water ratio according to any one of claims 1 to 2.

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