Outlet moisture and temperature control method and device for thin-plate cut-tobacco drying machine for cut tobacco production line

Abstract

The invention belongs to the technical field of cut tobacco production, and discloses an outlet moisture and temperature control method and device of a cut tobacco production sheet cut tobacco dryer. Obtaining first moisture temperature data according to the first cut tobacco drying data, the first steam environment data and a preset machine learning model, and predicting outlet moisture temperature data at a specified moment by using an intelligent algorithm; then, when the difference value between the first moisture temperature data and the standard moisture temperature data exceeds a preset difference threshold value, optimizing the first moisture temperature data to obtain second cut tobacco drying data, and automatically regulating and controlling the outlet moisture temperature according to the second cut tobacco drying data; the stability of the cut tobacco drying process is ensured in a mode of adjusting key variable data, the outlet moisture temperature of the cut tobacco production line thin plate cut tobacco dryer can meet the normal requirement, and the consistency of product quality and production efficiency can be improved.

Description

Technical Field

[0001] The present application belongs to the technical field of silk thread production, and in particular, relates to a method and device for controlling the outlet moisture temperature of a silk thread thin plate drying machine. Background Art

[0002] In the tobacco shred workshop, the thin-plate shred dryer is a crucial equipment in the tobacco shred processing process. Its outlet moisture and outlet temperature are key process indicators that affect the quality of shredded tobacco. As the tobacco industry continues to increase its requirements for product quality, how to effectively control the outlet moisture and temperature of the shred dryer has become a long-term exploration direction for the industry.

[0003] In the traditional tobacco drying machine control method, the one-stage thin-plate tobacco drying machine for tobacco drying has a relatively simple structure and only involves a single steam heating area. The temperature regulation of tobacco during the drying process is relatively direct, and the operator only needs to adjust a single internal energy to achieve stable control of the outlet temperature. For the two-stage thin-plate tobacco drying machine for tobacco drying, since the thin-plate tobacco drying machine is equipped with two independent steam cylinder wall heating areas, the tobacco needs to go through two heating stages, which significantly increases the complexity of temperature control.

[0004] In the actual production process, the two-stage thin-plate tobacco drying machine has better temperature uniformity than the one-stage thin-plate tobacco drying machine, and can effectively reduce the impact of fluctuations. However, since there are many variables affecting the outlet moisture and temperature, and there is a significant coupling effect between different variables, the traditional moisture and temperature control method of the two-stage thin-plate tobacco drying machine is difficult to cope with complex control requirements, which not only affects the control stability of the moisture and temperature of the tobacco, but also easily reduces the consistency of product quality and production efficiency. Summary of the invention

[0005] In order to solve the technical defects that the traditional moisture temperature control method of the two-stage thin-plate drying machine mentioned above is difficult to cope with complex control requirements, not only affects the control stability of the moisture temperature of the tobacco, but also easily reduces the consistency of product quality and production efficiency, this application proposes a method and device for controlling the outlet moisture temperature of the thin-plate drying machine for making silk thread, and its technical solution is as follows: In a first aspect, an embodiment of the present application provides a method for controlling the outlet moisture temperature of a silk thread thin plate drying machine, comprising: Obtaining first wire drying data and first steam environment data of a wire-making thin plate wire drying machine at a specified time, and obtaining first moisture temperature data according to the first wire drying data, the first steam environment data and a preset machine learning model; When the difference between the first moisture temperature data and the standard moisture temperature data exceeds a preset difference threshold, the first moisture temperature data is optimized to obtain second wire drying data; The outlet moisture temperature of the silk thread thin plate drying machine is controlled and processed according to the second silk drying data.

[0006] In an optional solution of the first aspect, obtaining first moisture temperature data according to the first tow-baked data, the first steam environment data, and a preset machine learning model includes: Determine the historical moment based on the preset sliding period and the designated moment, and obtain the historical wire drying data and historical steam environment data of the wire thin plate drying machine from the historical moment to the designated moment; Calculating third wire-baking data according to the first wire-baking data and the historical wire-baking data; Calculating second steam environment data according to the first steam environment data and historical steam environment data; The third silk drying data and the second steam environment data are input into a preset machine learning model to obtain the first moisture temperature data.

[0007] In yet another optional solution of the first aspect, the second steam environment data includes at least one steam data and at least one environment data; Before inputting the third wire-baking data and the second steam environment data into a preset machine learning model to obtain the first moisture temperature data, the method further includes: Divide the historical steam data corresponding to each type of steam data from the historical steam environment data, and divide the historical environment data corresponding to each type of environment data; Perform correlation analysis on the historical steam data corresponding to any two steam data to obtain the corresponding steam correlation results; Perform correlation analysis on the historical environmental data corresponding to any two environmental data to obtain corresponding environmental correlation results; Obtaining a steam environment data set according to all steam correlation results and all environment correlation results, and screening and processing the second steam environment data based on the steam environment data set; The third tow-baked data and the second steam environment data are input into a preset machine learning model to obtain the first moisture temperature data, including: The third silk drying data and the processed second steam environment data are input into a preset machine learning model to obtain the first moisture temperature data.

[0008] In another optional solution of the first aspect, a steam environment data set is obtained according to all steam correlation results and all environment correlation results, including: When it is detected that at least one steam correlation result among all steam correlation results is in a preset correlation interval, the total number of steam correlation results in the preset correlation interval is counted; When the total number of steam correlation results exceeds a preset number threshold, determining the steam data type corresponding to at least one steam correlation result in a preset correlation interval; When it is detected that at least one environment correlation result among all environment correlation results is in a preset correlation interval, the total number of environment correlation results in the preset correlation interval is counted; When the total number of environmental correlation results exceeds a preset number threshold, at least one environmental correlation result in a preset correlation interval and the corresponding environmental data type are determined, and all steam data types and all environmental data types are taken as a steam environment data set.

[0009] In another optional solution of the first aspect, the first moisture temperature data is optimized to obtain the second wire drying data, including: The first moisture temperature data is processed based on a preset multi-objective optimization algorithm to obtain at least two groups of candidate tow-baked data; Based on the preset hierarchical analysis algorithm, each group of candidate dried silk data is weighted and processed to obtain the corresponding weight results; All candidate dried tofu data and all weight results are processed based on a preset ideal solution sorting algorithm, and the second dried tofu data is determined from all candidate dried tofu data according to the processing results.

[0010] In yet another alternative of the first aspect, the method further comprises: Obtaining second moisture temperature data according to the second tow-baked data, the first steam environment data, and a preset machine learning model; When the difference between the second moisture temperature data and the standard moisture temperature data exceeds a preset difference threshold, the second moisture temperature data is optimized to obtain fourth wire drying data; The outlet moisture temperature of the silk thread thin plate drying machine is controlled and processed according to the fourth silk drying data.

[0011] In another optional scheme of the first aspect, the first tobacco drying data includes hot air speed and dehumidification damper opening, and the first steam environment data includes at least one inlet tobacco data, at least one environment data, at least one steam data and preset water removal flow data.

[0012] In a second aspect, the embodiment of the present application provides an outlet moisture temperature control device of a silk thread thin plate drying machine, comprising: The first processing module is used to obtain the first wire drying data and the first steam environment data of the wire thin plate drying machine at a specified time, and obtain the first moisture temperature data according to the first wire drying data, the first steam environment data and a preset machine learning model; A second processing module is used to optimize the first moisture temperature data to obtain second dried silk data when the difference between the first moisture temperature data and the standard moisture temperature data exceeds a preset difference threshold; The third processing module is used to control the outlet moisture temperature of the silk line thin plate drying machine according to the second silk drying data.

[0013] In a third aspect, the embodiment of the present application further provides an outlet moisture temperature control device of a silk thread thin plate drying machine, comprising a processor and a memory; The processor is connected to the memory; A memory for storing executable program codes; The processor runs the program corresponding to the executable program code by reading the executable program code stored in the memory, so as to implement the outlet moisture temperature control method of the silk wire thin plate drying machine provided by the first aspect of the embodiment of the present application or any one of the implementation methods of the first aspect.

[0014] In a fourth aspect, an embodiment of the present application provides a computer storage medium, which stores a computer program. The computer program includes program instructions. When the program instructions are executed by a processor, the outlet moisture temperature control method of the silk wire thin plate drying machine provided by the first aspect of the embodiment of the present application or any one of the implementation methods of the first aspect can be implemented.

[0015] Beneficial effects of this application: When controlling the outlet moisture temperature of a two-stage thin-plate wire drying machine, the first wire drying data (including key variable data affecting the outlet moisture temperature) and the first steam environment data of the thin-plate wire drying machine for silk making at a specified time are obtained, and the first moisture temperature data is obtained based on the first wire drying data, the first steam environment data and a preset machine learning model, so as to accurately predict the outlet moisture temperature data of the thin-plate wire drying machine for silk making at a specified time by using an intelligent learning algorithm; then, when the difference between the first moisture temperature data and the standard moisture temperature data exceeds a preset difference threshold, the first moisture temperature data is optimized to obtain the second wire drying data, and the outlet moisture temperature of the thin-plate wire drying machine for silk making is automatically controlled according to the second wire drying data, so as to ensure the stability of the wire drying process by adjusting the key variable data, so that not only the outlet moisture temperature of the thin-plate wire drying machine for silk making meets normal requirements, but also the consistency of product quality and production efficiency can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 An overall flow chart of a method for controlling the outlet moisture temperature of a silk thread thin plate drying machine provided in an embodiment of the present application; Figure 2 A schematic diagram of the structure of an outlet moisture temperature control device of a thin plate drying machine for silk thread production provided in an embodiment of the present application; Figure 3 A schematic structural diagram of an outlet moisture temperature control device for a silk thread thin plate drying machine provided in an embodiment of the present application. DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0019] In the following introduction, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance. The following introduction provides multiple embodiments of the present application, and different embodiments can be replaced or combined, so the present application can also be considered to include all possible combinations of the same and / or different embodiments recorded. Therefore, if one embodiment includes features A, B, and C, and another embodiment includes features B and D, then the present application should also be considered to include embodiments containing one or more of all other possible combinations of A, B, C, and D, although the embodiment may not be clearly recorded in the following text.

[0020] The following description provides examples and does not limit the scope, applicability or examples set forth in the claims. Changes may be made to the functions and arrangements of the elements described without departing from the scope of the present application. Various processes or components may be appropriately omitted, substituted or added to each example. For example, the described method may be performed in an order different from the order described, and various steps may be added, omitted or combined. In addition, features described in some examples may be combined in other examples.

[0021] It should be emphasized that most of the current one-stage thin-plate drying machines are difficult to take into account the specific requirements of temperature and moisture in different drying stages, which makes it difficult to accurately control the moisture and temperature of the tobacco outlet. Especially when the initial moisture distribution of different batches of tobacco is uneven, it is more likely to cause fluctuations in the outlet moisture, resulting in unstable product quality. In addition, due to the morphological differences (such as thickness, fiber distribution, etc.) of tobacco during the drying process, the migration and evaporation rates of moisture are different. The one-stage thin-plate drying machine lacks the ability to adjust in detail, which is prone to uneven moisture distribution, causing some tobacco to be over-dried while others are still wet; secondly, the one-stage thin-plate drying machine usually uses a higher hot air temperature and wind speed. This high-intensity drying method is prone to "surface hardening", that is, the surface moisture evaporates quickly, while the internal moisture fails to migrate in time, thereby affecting the drying uniformity, and may also cause the destruction of fiber tissue and the loss of aroma components, thereby affecting the flavor and smoking quality of the tobacco. In summary, the current one-stage thin-plate tobacco drying machine is difficult to correct quickly by relying solely on simple hot air adjustments, has poor adjustment flexibility, and is prone to causing large fluctuations in batch tobacco quality.

[0022] Based on the shortcomings of the above-mentioned one-stage thin-plate drying machine, there is currently a two-stage thin-plate drying machine, which significantly improves the drying efficiency and quality by dividing the drying process into the initial drying stage and the stable drying stage. In the initial drying stage, high-temperature hot air is used to quickly evaporate the surface moisture of the tobacco, shorten the drying time and reduce the load of the subsequent stage; in the stable drying stage, by reducing the hot air temperature and wind speed, the moisture migration rate is carefully adjusted to ensure that the internal moisture is fully migrated and evaporated evenly, effectively improving the problem of uneven moisture distribution. However, in the design of the two-stage thin-plate drying machine, due to the use of two barrel wall structures, the complexity and difficulty of the adjustment process are increased to a certain extent. For example, the heat exchange and mutual influence between the two barrel walls make the temperature of one barrel wall adjusted, and the temperature of the other barrel wall may also be affected. This mutually coupled relationship makes temperature control more complicated, and the operator needs to pay attention to the temperature changes of the two barrel walls at the same time, which increases the difficulty of control. In addition, operators may cause overshoot due to inaccurate judgment of temperature changes. For example, when the temperature of a barrel wall needs to rise, excessive adjustment may cause the temperature to rise rapidly, exceeding the preset range, thereby affecting the drying effect of tobacco and even damaging the equipment; secondly, since the temperature changes of the two barrel walls take a certain amount of time to be detected and fed back to the control system, this feedback delay may cause a lag in real-time regulation, increasing the difficulty of regulation. In the actual production environment, the traditional moisture temperature control method of the two-stage thin plate drying machine requires the use of complex control strategies and algorithms to comprehensively consider multiple factors, increasing the design and control requirements of the overall control method; on the other hand, it also needs to be adjusted by operators with high professional knowledge and experience, which is easy to cause errors in manual judgment during peak production periods, thereby affecting the stable control of tobacco moisture temperature, and also easily reducing product quality consistency and production efficiency.

[0023] Based on this, the present application will explain the outlet moisture temperature control method of the silk thread thin plate drying machine in combination with one or more embodiments shown below, so as to solve the technical defects of the above-mentioned two-stage thin plate drying machine.

[0024] See next Figure 1 , Figure 1 An overall flow chart of an outlet moisture temperature control method of a silk thread thin plate drying machine provided in an embodiment of the present application is shown.

[0025] like Figure 1 As shown, the outlet moisture temperature control method of the silk thread thin plate drying machine can at least include the following steps: Step 102: obtain the first wire drying data and the first steam environment data of the wire thin plate drying machine at a specified time, and obtain the first moisture temperature data according to the first wire drying data, the first steam environment data and a preset machine learning model.

[0026] In an embodiment of the present application, the outlet moisture temperature control method of the silk line thin plate wire drying machine can be but is not limited to being applied to a control terminal, which can be connected to a two-stage thin plate wire drying machine to obtain data collected during the operation of the two-stage thin plate wire drying machine, and automatically control the outlet moisture temperature of the two-stage thin plate wire drying machine based on the data, so as to effectively optimize the drying effect of the two-stage thin plate wire drying machine on tobacco, thereby ensuring that the production quality meets the standards.

[0027] Here, the data collected by the two-stage thin-plate tobacco drying machine during operation may include but are not limited to inlet tobacco humidity, inlet tobacco flow, workshop ambient temperature, workshop ambient humidity, HT inlet tobacco flow (HT can be understood as a tunnel-type rehumidification machine), HT steam pressure, HT outlet temperature, hot air temperature, hot air speed, dehumidification damper opening, area 1 steam mass flow (i.e., the steam mass flow in the first cylinder wall structure), area 1 cylinder temperature (i.e., the temperature in the first cylinder wall structure), area 2 steam mass flow (i.e., the steam mass flow in the second cylinder wall structure) and area 2 cylinder temperature (i.e., the temperature in the second cylinder wall structure), etc. Each type of data can be collected by sensors or control devices arranged at corresponding positions and fed back to the control terminal in real time.

[0028] It can be understood that the control terminal can obtain the first wire drying data (including key variable data affecting the outlet moisture temperature) and the first steam environment data of the silk wire thin plate wire drying machine at a specified time, and obtain the first moisture temperature data based on the first wire drying data, the first steam environment data and the preset machine learning model, so as to use the intelligent learning algorithm to accurately predict the outlet moisture temperature data of the silk wire thin plate wire drying machine at the specified time; then, when the difference between the first moisture temperature data and the standard moisture temperature data exceeds the preset difference threshold, the first moisture temperature data is optimized to obtain the second wire drying data, and the outlet moisture temperature of the silk wire thin plate wire drying machine is automatically controlled according to the second wire drying data, so as to ensure the stability of the wire drying process by adjusting the key variable data, which not only makes the outlet moisture temperature of the silk wire thin plate wire drying machine meet normal requirements, but also improves the consistency of product quality and production efficiency.

[0029] Specifically, when controlling the outlet moisture temperature of the two-stage thin-plate wire drying machine, the control terminal can, but is not limited to, combine the historical data of the two-stage thin-plate wire drying machine and the corresponding outlet moisture temperature data to analyze multiple types of data with the strongest correlation with the outlet moisture temperature, and use the multiple types of data as key variable data affecting the outlet moisture temperature, so that when the outlet moisture temperature is subsequently controlled, the key variable data affecting the outlet moisture temperature is adjusted, and other variable data are kept unchanged (that is, no adjustment is performed), which not only effectively reduces external interference, but also ensures the stability of the wire drying process. Here, the key variable data affecting the outlet moisture temperature may be but are not limited to the hot air speed and dehumidification damper opening collected during the operation of the two-stage thin-plate wire drying machine, that is, by adjusting the hot air speed and dehumidification damper opening of the two-stage thin-plate wire drying machine, the outlet moisture temperature can be effectively controlled; other variable data can be understood as various types of data associated with the outlet moisture temperature, which may be but are not limited to the inlet tobacco humidity, inlet tobacco flow, workshop ambient temperature, workshop ambient humidity, HT inlet tobacco flow, HT steam pressure, HT outlet temperature, hot air temperature, zone 1 steam mass flow, zone 1 drum temperature, zone 2 steam mass flow and zone 2 drum temperature collected during the operation of the two-stage thin-plate wire drying machine, and are not limited to these.

[0030] It can be understood that when analyzing the multiple types of data with the strongest correlation with the outlet moisture temperature, the control terminal can, but is not limited to, perform a correlation analysis on each type of data in the historical data and the corresponding outlet temperature data, so as to use the type of data with the highest correlation result as the key variable data affecting the outlet temperature; and can also perform a correlation analysis on each type of data in the historical data and the corresponding outlet moisture data, so as to use the type of data with the highest correlation result as the key variable data affecting the outlet moisture, and use the key variable data affecting the outlet temperature and the key variable data affecting the outlet moisture as the multiple types of data with the strongest correlation with the outlet moisture temperature, and is not limited to this.

[0031] It should be noted that other variable data can also be determined through the correlation results mentioned above, for example but not limited to taking all types of data corresponding to all correlation results in a preset interval as other variable data, so as to effectively avoid the other variable data containing one or more types of data that have no correlation with the outlet moisture temperature, and is not limited to this.

[0032] Furthermore, after determining the key variable data and other variable data that affect the outlet moisture temperature, the control terminal can monitor the working status of the two-stage thin-plate wire drying machine in real time, so as to obtain the first wire drying data and the first steam environment data of the two-stage thin-plate wire drying machine at a specified time after the two-stage thin-plate wire drying machine is in operation. Here, the specified time can be but is not limited to the current time or any subsequent time. The first tobacco drying data can be understood as all types of data at the specified time included in the key variable data affecting the outlet moisture temperature mentioned above, such as the hot air speed and the dehumidification damper opening at the specified time; the first steam environment data can be understood as all types of data at the specified time included in the other variable data mentioned above, which may specifically include at least one inlet tobacco data, at least one environmental data, at least one steam data and preset water removal flow data at the specified time. The at least one inlet tobacco data can be but is not limited to including at least one of the inlet tobacco humidity, the inlet tobacco flow and the HT inlet tobacco flow. The at least one environmental data can be but is not limited to including at least one of the workshop ambient temperature and the workshop ambient humidity. The at least one steam data can be but is not limited to including at least one of the HT steam pressure, the HT outlet temperature, the hot air temperature, the steam mass flow of area 1, the barrel temperature of area 1, the steam mass flow of area 2 and the barrel temperature of area 2.

[0033] Furthermore, after determining the first wire drying data and the first steam environment data, the control terminal can, but is not limited to, pre-process the first wire drying data and the first steam environment data, respectively, such as performing outlier cleaning processing and normalization processing in sequence, and inputting the processed first wire drying data and the first steam environment data into a preset machine learning model to predict the outlet moisture temperature data of the two-stage thin-plate wire drying machine at a specified time through an intelligent learning algorithm, that is, the first moisture temperature data.

[0034] It can be understood that the preset machine learning model can be but is not limited to a neural network structure well known in the art, which is trained by at least two sets of historical data of the two-stage thin-plate silk drying machine, the outlet moisture temperature data corresponding to each set of historical data, and the moisture temperature standard data, and the at least two sets of historical data of the two-stage thin-plate silk drying machine can also be updated at preset time intervals, that is, the preset machine learning model can be updated in real time to ensure the accuracy and effectiveness of the output results of the preset machine learning model.

[0035] As an option of the embodiment of the present application, the first moisture temperature data is obtained according to the first tow-baked data, the first steam environment data and a preset machine learning model, including: Determine the historical moment based on the preset sliding period and the designated moment, and obtain the historical wire drying data and historical steam environment data of the wire thin plate drying machine from the historical moment to the designated moment; Calculating third wire-baking data according to the first wire-baking data and the historical wire-baking data; Calculating second steam environment data according to the first steam environment data and historical steam environment data; The third silk drying data and the second steam environment data are input into a preset machine learning model to obtain the first moisture temperature data.

[0036] Specifically, in order to ensure the consistency of the wire drying data and the steam environment data in the time dimension, so as to avoid the lag of real-time control and thus improve the accuracy of subsequent data processing, the control terminal can also determine the historical moment based on the preset sliding period and the designated moment, for example, but not limited to taking the designated moment as A hour and B minute, and the preset sliding period as C minute as an example, the difference result A hour (BC) minute between the designated moment and the preset sliding period can be taken as the historical moment, and the historical wire drying data and historical steam environment data collected from the historical moment to the designated moment when the two-stage thin plate wire drying machine is in operation can be obtained. Here, the historical wire drying data can be understood as the data of all types from the historical moment to the designated moment included in the key variable data affecting the outlet moisture temperature mentioned above, and the historical steam environment data can be understood as the data of all types from the historical moment to the designated moment included in the other variable data mentioned above, and the historical wire drying data can include the wire drying data corresponding to at least two moments from the historical moment to the designated moment, and the historical steam environment data can include the steam environment data corresponding to at least two moments from the historical moment to the designated moment.

[0037] Next, the control terminal may substitute each type of data in the first wire-roasting data and all data of the corresponding type in the historical wire-roasting data into a preset sliding average formula for calculation, and use the calculation results of all types as the third wire-roasting data. Here, the preset sliding average formula may be, but is not limited to, as follows: In the above formula, It can be the calculation result of any type of data in the first silk-roasting data at time t (that is, the specified time), and n can be a preset sliding period. It can be the data of the corresponding type in the historical silk-bread baking data at time ti (i can be any value from 0 to n, when i=0, it can be understood as the data of the corresponding type in the first silk-bread baking data, when i=n-1, it can be understood as the data of the corresponding type in the historical silk-bread baking data corresponding to the historical moment).

[0038] It can be understood that the control terminal can also substitute each type of data in the first steam environment data and all data of the corresponding type in the historical steam environment data into the above-mentioned preset sliding average formula, and use the calculation results of all types as the second steam environment data.

[0039] Next, after determining the third wire drying data and the second steam environment data, the control terminal can, but is not limited to, pre-process the third wire drying data and the second steam environment data, respectively, such as performing outlier cleaning processing and normalization processing in sequence, and inputting the processed third wire drying data and the second steam environment data into a preset machine learning model to predict the outlet moisture temperature data of the two-stage thin plate wire drying machine at a specified time through an intelligent learning algorithm, that is, the first moisture temperature data.

[0040] As another option of the embodiment of the present application, the second steam environment data includes at least one steam data and at least one environment data; Before inputting the third wire-baking data and the second steam environment data into a preset machine learning model to obtain the first moisture temperature data, the method further includes: Divide the historical steam data corresponding to each type of steam data from the historical steam environment data, and divide the historical environment data corresponding to each type of environment data; Perform correlation analysis on the historical steam data corresponding to any two steam data to obtain the corresponding steam correlation results; Perform correlation analysis on the historical environmental data corresponding to any two environmental data to obtain corresponding environmental correlation results; Obtaining a steam environment data set according to all steam correlation results and all environment correlation results, and screening and processing the second steam environment data based on the steam environment data set; The third tow-baked data and the second steam environment data are input into a preset machine learning model to obtain the first moisture temperature data, including: The third silk drying data and the processed second steam environment data are input into a preset machine learning model to obtain the first moisture temperature data.

[0041] Specifically, in order to further improve the prediction accuracy and efficiency of the preset machine learning model, the control terminal can also divide all the historical steam data corresponding to each type of steam data from the historical steam environment data mentioned above, and can perform correlation analysis on all the historical steam data corresponding to any two types of steam data, for example but not limited to standardizing all the historical steam data corresponding to any two types of steam data, and substituting all the historical steam data corresponding to the processed two types of steam data into the preset Pearson correlation coefficient calculation formula to obtain the steam correlation results corresponding to the two types of steam data. Here, the range of the steam correlation result can be but is not limited to between -1 and 1. The closer the steam correlation result is to 1 or -1, the stronger the correlation between the two types of steam data; the closer the steam correlation result is to 0, the weaker the correlation between the two types of steam data.

[0042] It can be understood that the control terminal can also divide all historical environmental data corresponding to each type of environmental data from the historical steam environment data mentioned above, and can perform correlation analysis on all historical environmental data corresponding to any two types of environmental data, such as but not limited to standardizing all historical environmental data corresponding to any two types of environmental data, and substituting all historical environmental data corresponding to the two types of processed environmental data into the preset Pearson correlation coefficient calculation formula to obtain the environmental correlation results corresponding to the two types of environmental data.

[0043] Next, after obtaining all steam correlation results and all environment correlation results, the control terminal can also determine a steam environment data set, which includes at least one steam data type corresponding to a higher steam correlation result and at least one environment data type corresponding to a higher environment correlation result, and can screen the second steam environment data based on the steam environment set to eliminate all steam data corresponding to all steam data types in the steam environment set and eliminate all environment data corresponding to all environment data types in the steam environment set, thereby achieving the purpose of reducing the data amount of the second steam environment data.

[0044] Next, after screening and processing the second steam environment data, the control terminal can also pre-process the third wire drying data and the processed second steam environment data, such as performing outlier cleaning and normalization processing in sequence, and inputting the processed third wire drying data and the second steam environment data into the preset machine learning model to predict the outlet moisture temperature data of the two-stage thin plate wire drying machine at a specified time through an intelligent learning algorithm, that is, the first moisture temperature data.

[0045] As another option of the embodiment of the present application, a steam environment data set is obtained according to all steam correlation results and all environment correlation results, including: When it is detected that at least one steam correlation result among all steam correlation results is in a preset correlation interval, the total number of steam correlation results in the preset correlation interval is counted; When the total number of steam correlation results exceeds a preset number threshold, determining the steam data type corresponding to at least one steam correlation result in a preset correlation interval; When it is detected that at least one environment correlation result among all environment correlation results is in a preset correlation interval, the total number of environment correlation results in the preset correlation interval is counted; When the total number of environmental correlation results exceeds a preset number threshold, at least one environmental correlation result in a preset correlation interval and the corresponding environmental data type are determined, and all steam data types and all environmental data types are taken as a steam environment data set.

[0046] Specifically, when determining the steam environment data set, the control terminal can also determine whether there is at least one steam correlation result among all steam correlation results that exceeds a preset correlation interval, so as to determine whether there are two types of data with high correlation in the steam environment data, and when it is detected that there is a steam correlation result that exceeds a preset correlation interval, the total number of steam correlation results in the preset correlation interval is counted. It can be understood that when the total number of steam correlation results exceeds the preset number threshold, it indicates that there are at least two types of data with high correlation in the steam environment data, and the two steam data corresponding to each steam correlation result in the preset correlation interval can be screened out, so as to calculate the proportion of the number of screening times of each steam data in all the screened steam data, and take the steam data type with the highest proportion of screening times as the steam data type corresponding to at least one steam correlation result in the preset correlation interval. Of course, in the embodiment of the present application, multiple steam data types with a high proportion of screening times can also be taken as the steam data type corresponding to at least one steam correlation result in the preset correlation interval, and are not limited thereto.

[0047] In addition, the control terminal can also determine at least one environmental correlation result in a preset correlation interval. The corresponding environmental data type and its processing method can be referred to the above description, which will not be elaborated here. All steam data types and all environmental data types can be regarded as a steam environment data set.

[0048] It should be noted that if any steam data type and any environmental data type are not determined, it may indicate that there are not at least two steam data with high correlation and at least two environmental data with high correlation in the steam environment data, and thus there is no need to screen the second steam environment data.

[0049] Step 104: When the difference between the first moisture temperature data and the standard moisture temperature data exceeds a preset difference threshold, the first moisture temperature data is optimized to obtain second tow-roasting data.

[0050] Specifically, after predicting the first moisture temperature data, the control terminal can, but is not limited to, perform a difference calculation between the first moisture temperature data and the standard moisture temperature data. For example, a difference calculation can be performed between the temperature data in the first moisture temperature data and the standard temperature data, and a difference calculation can be performed between the moisture data in the first moisture temperature data and the standard moisture data. When it is detected that any difference result exceeds a preset difference threshold, it indicates that there is an abnormality in the current outlet moisture temperature of the two-stage thin-plate wire drying machine. Then, the first moisture temperature data can be processed based on a preset multi-objective optimization algorithm to obtain at least two sets of alternative wire drying data (which can also be understood as the optimal solution Pareto solution set, and each set of alternative wire drying data may include alternative hot air speeds and alternative dehumidification air door openings). Here, the preset multi-objective optimization algorithm may include but is not limited to a preset evolutionary algorithm, multiple preset objective functions, constraints, and parameters such as the number of iterations. Each preset objective function may be understood as a function expression (not limited to this) containing a moisture temperature data type and a dried silk data type. The constraints may be understood as the data interval corresponding to the moisture temperature data type and the data interval corresponding to the dried silk data type. The preset multi-objective optimization algorithm may also refer to the technical means well known in the art, which will not be elaborated here.

[0051] Furthermore, after obtaining multiple groups of candidate dried tofu data, the control terminal can also perform weight distribution processing on each group of candidate dried tofu data based on a preset hierarchical analysis algorithm to obtain corresponding weight results. Here, the preset hierarchical analysis algorithm can construct a judgment matrix corresponding to each group of candidate dried tofu data to calculate the eigenvalues ​​and eigenvectors of each judgment matrix to obtain corresponding weight results, and the preset hierarchical analysis algorithm can also refer to the technical means known in the art, but will not be described in detail here.

[0052] Further, after obtaining the weight result of each group of candidate dried silk data, the control terminal can also process all candidate dried silk data and all weight results based on the preset ideal solution sorting algorithm to obtain the sorting result of all candidate dried silk data, and use the best candidate dried silk data in the sorting result as the second dried silk data. Here, the preset ideal solution sorting algorithm can be but not limited to constructing a decision matrix according to each group of candidate dried silk data, and then normalizing each decision matrix, so as to determine the ideal solution and the negative ideal solution in each product result by multiplying each processed decision matrix with the corresponding weight result; then the distance between each group of candidate dried silk data and the ideal solution and the negative ideal solution can be calculated, so as to determine the corresponding relative proximity according to the two distances, and then all candidate dried silk data can be sorted in the order of the highest relative proximity to the lowest, at which time the candidate dried silk data at the front of the sorting can be used as the second dried silk data, and the preset ideal solution sorting algorithm can also refer to the technical means known in the art, but it is not repeated here.

[0053] It should be noted that the second torrefied drying data obtained through optimization can effectively balance the unstable factors of the two-stage model, avoid frequent overshoot, and further make the two-stage model suitable for the formal production environment, ultimately ensuring the stability and efficiency of the torrefied drying production process, and providing a reliable guarantee for improving production quality and efficiency.

[0054] Step 106: Control the outlet moisture temperature of the silk thread thin plate drying machine according to the second silk drying data.

[0055] Specifically, after obtaining the second wire drying data, the control terminal can, but is not limited to, adjust the two-stage thin-plate wire drying machine according to the second wire drying data. For example, taking the second wire drying data including the target hot air speed and the target dehumidification air door opening as an example, the hot air equipment of the two-stage thin-plate wire drying machine can be adjusted to adjust the current hot air speed to be consistent with the target hot air speed, thereby achieving control of the outlet moisture; at the same time, the dehumidification pipe solenoid valve of the two-stage thin-plate wire drying machine can also be adjusted to adjust the current dehumidification air door opening to be consistent with the target dehumidification air door opening, thereby achieving control of the outlet temperature.

[0056] As another option of the embodiment of the present application, the method further includes: Obtaining second moisture temperature data according to the second wire-baking data, the first steam environment data, and a preset machine learning model; When the difference between the second moisture temperature data and the standard moisture temperature data exceeds the preset difference threshold, the second moisture temperature data is optimized to obtain fourth wire drying data; The outlet moisture temperature of the silk thread thin plate drying machine is controlled and processed according to the fourth silk drying data.

[0057] Specifically, in order to ensure the continuous stability of the outlet moisture temperature, after the control terminal controls the outlet moisture temperature of the silk line thin plate silk drying machine according to the second silk drying data, it can also, but is not limited to, pre-process the second silk drying data and the first steam environment data separately, such as performing outlier cleaning and normalization processing in sequence, and inputting the processed second silk drying data and the first steam environment data into a preset machine learning model, so as to predict the outlet moisture temperature data of the two-stage thin plate silk drying machine after control processing, that is, the second moisture temperature data, through an intelligent learning algorithm.

[0058] It can be understood that when the difference between the second moisture temperature data and the standard moisture temperature data does not exceed the preset difference threshold, the real-time wire drying data of the two-stage thin-plate wire drying machine can be kept consistent with the second wire drying data to ensure the continuous stability of the outlet moisture temperature; when the difference exceeds the preset difference threshold, it indicates that the two-stage thin-plate wire drying machine still has abnormal outlet moisture temperature after control processing, and then refer to one or more of the above-mentioned embodiments to optimize the second moisture temperature data to obtain the fourth wire drying data, and control the outlet moisture temperature of the wire thin-plate wire drying machine again according to the fourth wire drying data, but no further details are given here until the outlet moisture temperature of the two-stage thin-plate wire drying machine is within the normal range after control processing.

[0059] See next Figure 2 , Figure 2 A schematic structural diagram of an outlet moisture temperature control device of a silk thread thin plate drying machine provided in an embodiment of the present application is shown.

[0060] like Figure 2 As shown, the outlet moisture temperature control device of the silk thread thin plate drying machine may include at least a first processing module 201, a second processing module 202 and a third processing module 203, wherein: The first processing module 201 is used to obtain the first wire drying data and the first steam environment data of the wire thin plate drying machine at a specified time, and obtain the first moisture temperature data according to the first wire drying data, the first steam environment data and a preset machine learning model; The second processing module 202 is used to optimize the first moisture temperature data to obtain second dried silk data when the difference between the first moisture temperature data and the standard moisture temperature data exceeds a preset difference threshold; The third processing module 203 is used to control the outlet moisture temperature of the silk thread thin plate drying machine according to the second silk drying data.

[0061] In some possible embodiments, obtaining the first moisture temperature data according to the first wire-baking data, the first steam environment data, and a preset machine learning model includes: Determine the historical moment based on the preset sliding period and the designated moment, and obtain the historical wire drying data and historical steam environment data of the wire thin plate drying machine from the historical moment to the designated moment; Calculating third wire-baking data according to the first wire-baking data and the historical wire-baking data; Calculating second steam environment data according to the first steam environment data and historical steam environment data; The third silk drying data and the second steam environment data are input into a preset machine learning model to obtain the first moisture temperature data.

[0062] In some possible embodiments, the second steam environment data includes at least one steam data and at least one environment data; Before inputting the third wire-baking data and the second steam environment data into a preset machine learning model to obtain the first moisture temperature data, the method further includes: Divide the historical steam data corresponding to each type of steam data from the historical steam environment data, and divide the historical environment data corresponding to each type of environment data; Perform correlation analysis on the historical steam data corresponding to any two steam data to obtain the corresponding steam correlation results; Perform correlation analysis on the historical environmental data corresponding to any two environmental data to obtain corresponding environmental correlation results; Obtaining a steam environment data set according to all steam correlation results and all environment correlation results, and screening and processing the second steam environment data based on the steam environment data set; The third tow-baked data and the second steam environment data are input into a preset machine learning model to obtain the first moisture temperature data, including: The third silk drying data and the processed second steam environment data are input into a preset machine learning model to obtain the first moisture temperature data.

[0063] In some possible embodiments, a steam environment data set is obtained according to all steam correlation results and all environment correlation results, including: When it is detected that at least one steam correlation result among all steam correlation results is in a preset correlation interval, the total number of steam correlation results in the preset correlation interval is counted; When the total number of steam correlation results exceeds a preset number threshold, determining the steam data type corresponding to at least one steam correlation result in a preset correlation interval; When it is detected that at least one environment correlation result among all environment correlation results is in a preset correlation interval, the total number of environment correlation results in the preset correlation interval is counted; When the total number of environmental correlation results exceeds a preset number threshold, at least one environmental correlation result in a preset correlation interval and the corresponding environmental data type are determined, and all steam data types and all environmental data types are taken as a steam environment data set.

[0064] In some possible embodiments, the first moisture temperature data is optimized to obtain the second wire drying data, including: The first moisture temperature data is processed based on a preset multi-objective optimization algorithm to obtain at least two groups of candidate tow-baked data; Based on the preset hierarchical analysis algorithm, each group of candidate dried silk data is weighted and processed to obtain the corresponding weight results; All candidate dried tofu data and all weight results are processed based on a preset ideal solution sorting algorithm, and the second dried tofu data is determined from all candidate dried tofu data according to the processing results.

[0065] In some possible embodiments, the device further includes: Obtaining second moisture temperature data according to the second tow-baked data, the first steam environment data, and a preset machine learning model; When the difference between the second moisture temperature data and the standard moisture temperature data exceeds a preset difference threshold, the second moisture temperature data is optimized to obtain fourth wire drying data; The outlet moisture temperature of the silk thread thin plate drying machine is controlled and processed according to the fourth silk drying data.

[0066] In some possible embodiments, the first tobacco drying data includes hot air velocity and dehumidification damper opening, and the first steam environment data includes at least one inlet tobacco data, at least one environment data, at least one steam data and preset dehydration flow data.

[0067] Those skilled in the art can clearly understand that the technical solutions of the embodiments of the present application can be implemented with the help of software and / or hardware. The "unit" and "module" in this specification refer to software and / or hardware that can independently complete or cooperate with other components to complete specific functions, where the hardware can be, for example, a field programmable gate array (FPGA), an integrated circuit (IC), etc.

[0068] See also Figure 3 , Figure 3A structural schematic diagram of an outlet moisture temperature control device of another silk thread thin plate drying machine provided in an embodiment of the present application is shown.

[0069] like Figure 3 As shown, the outlet moisture temperature control device 300 of the wire thin plate drying machine may include at least one processor 301 , at least one network interface 304 , a user interface 303 , a memory 305 and at least one communication bus 302 .

[0070] The communication bus 302 may be used to realize the connection and communication among the above-mentioned components.

[0071] The user interface 303 may include buttons, and the optional user interface may also include a standard wired interface or a wireless interface.

[0072] The network interface 304 may include, but is not limited to, a Bluetooth module, an NFC module, a Wi-Fi module, etc.

[0073] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect various parts in the outlet moisture temperature control device 300 of the silk wire thin plate drying machine, and executes various functions and processes data of the outlet moisture temperature control device 300 of the routing silk wire thin plate drying machine by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can be implemented in at least one hardware form of DSP, FPGA, and PLA. The processor 301 can integrate one or a combination of CPU, GPU, modem, etc. Among them, the CPU mainly processes the operating system, user interface, and application program; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communication. It can be understood that the above-mentioned modem may not be integrated into the processor 301, and it can be implemented by a single chip.

[0074] The memory 305 may include a RAM or a ROM. Optionally, the memory 305 includes a non-transitory computer-readable medium. The memory 305 may be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may optionally be at least one storage device located away from the aforementioned processor 301. As Figure 3As shown, the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an outlet moisture temperature control application for the silk thread thin plate drying machine.

[0075] Specifically, the processor 301 can be used to call the outlet moisture temperature control application of the silk thread thin plate drying machine stored in the memory 305, and specifically perform the following operations: Obtaining first wire drying data and first steam environment data of a wire-making thin plate wire drying machine at a specified time, and obtaining first moisture temperature data according to the first wire drying data, the first steam environment data and a preset machine learning model; When the difference between the first moisture temperature data and the standard moisture temperature data exceeds a preset difference threshold, the first moisture temperature data is optimized to obtain second wire drying data; The outlet moisture temperature of the silk thread thin plate drying machine is controlled and processed according to the second silk drying data.

[0076] In some possible embodiments, obtaining the first moisture temperature data according to the first wire-baking data, the first steam environment data, and a preset machine learning model includes: Determine the historical moment based on the preset sliding period and the designated moment, and obtain the historical wire drying data and historical steam environment data of the wire thin plate drying machine from the historical moment to the designated moment; Calculating third wire-baking data according to the first wire-baking data and the historical wire-baking data; Calculating second steam environment data according to the first steam environment data and historical steam environment data; The third silk drying data and the second steam environment data are input into a preset machine learning model to obtain the first moisture temperature data.

[0077] In some possible embodiments, the second steam environment data includes at least one steam data and at least one environment data; Before inputting the third wire-baking data and the second steam environment data into a preset machine learning model to obtain the first moisture temperature data, the method further includes: Divide the historical steam data corresponding to each type of steam data from the historical steam environment data, and divide the historical environment data corresponding to each type of environment data; Perform correlation analysis on the historical steam data corresponding to any two steam data to obtain the corresponding steam correlation results; Perform correlation analysis on the historical environmental data corresponding to any two environmental data to obtain corresponding environmental correlation results; Obtaining a steam environment data set according to all steam correlation results and all environment correlation results, and screening and processing the second steam environment data based on the steam environment data set; The third tow-baked data and the second steam environment data are input into a preset machine learning model to obtain the first moisture temperature data, including: The third silk drying data and the processed second steam environment data are input into a preset machine learning model to obtain the first moisture temperature data.

[0078] In some possible embodiments, a steam environment data set is obtained according to all steam correlation results and all environment correlation results, including: When it is detected that at least one steam correlation result among all steam correlation results is in a preset correlation interval, the total number of steam correlation results in the preset correlation interval is counted; When the total number of steam correlation results exceeds a preset number threshold, determining the steam data type corresponding to at least one steam correlation result in a preset correlation interval; When it is detected that at least one environment correlation result among all environment correlation results is in a preset correlation interval, the total number of environment correlation results in the preset correlation interval is counted; When the total number of environmental correlation results exceeds a preset number threshold, at least one environmental correlation result in a preset correlation interval and the corresponding environmental data type are determined, and all steam data types and all environmental data types are taken as a steam environment data set.

[0079] In some possible embodiments, the first moisture temperature data is optimized to obtain the second wire drying data, including: The first moisture temperature data is processed based on a preset multi-objective optimization algorithm to obtain at least two groups of candidate tow-baked data; Based on the preset hierarchical analysis algorithm, each group of candidate dried silk data is weighted and processed to obtain the corresponding weight results; All candidate dried tofu data and all weight results are processed based on a preset ideal solution sorting algorithm, and the second dried tofu data is determined from all candidate dried tofu data according to the processing results.

[0080] In some possible embodiments, the processor 301 is further configured to execute: Obtaining second moisture temperature data according to the second tow-baked data, the first steam environment data, and a preset machine learning model; When the difference between the second moisture temperature data and the standard moisture temperature data exceeds a preset difference threshold, the second moisture temperature data is optimized to obtain fourth wire drying data; The outlet moisture temperature of the silk thread thin plate drying machine is controlled and processed according to the fourth silk drying data.

[0081] In some possible embodiments, the first tobacco drying data includes hot air velocity and dehumidification damper opening, and the first steam environment data includes at least one inlet tobacco data, at least one environment data, at least one steam data and preset dehydration flow data.

[0082] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0083] In the several embodiments provided in the present application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0084] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0085] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0086] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a memory, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: U disk, read-only memory (ROM), random access memory (RAM), mobile hard disk, disk or optical disk and other media that can store program codes.

Claims

1. A method for controlling the outlet moisture temperature of a silk thread thin plate drying machine, characterized in that: include: Acquire first wire drying data and first steam environment data of a wire-making thin plate wire drying machine at a specified time, and obtain first moisture temperature data according to the first wire drying data, the first steam environment data and a preset machine learning model; When the difference between the first moisture temperature data and the standard moisture temperature data exceeds a preset difference threshold, the first moisture temperature data is optimized to obtain second wire drying data; The outlet moisture temperature of the silk thread thin plate drying machine is controlled and processed according to the second silk drying data.

2. The method according to claim 1, characterized in that The step of obtaining the first moisture temperature data according to the first wire-baking data, the first steam environment data and a preset machine learning model includes: Determine a historical moment based on a preset sliding period and the designated moment, and obtain historical wire drying data and historical steam environment data of the wire thin plate drying machine from the historical moment to the designated moment; Calculating third wire-baking data according to the first wire-baking data and the historical wire-baking data; Calculating second steam environment data according to the first steam environment data and the historical steam environment data; The third wire drying data and the second steam environment data are input into a preset machine learning model to obtain the first moisture temperature data.

3. The method according to claim 2, characterized in that The second steam environment data includes at least one steam data and at least one environment data; Before inputting the third wire-baking data and the second steam environment data into a preset machine learning model to obtain the first moisture temperature data, the method further includes: Dividing the historical steam environment data into historical steam data corresponding to each type of the steam data, and dividing the historical environment data corresponding to each type of the environment data; Performing correlation analysis on the historical steam data respectively corresponding to any two of the steam data to obtain corresponding steam correlation results; Performing correlation analysis on the historical environmental data respectively corresponding to any two of the environmental data to obtain corresponding environmental correlation results; Obtaining a steam environment data set according to all of the steam correlation results and all of the environment correlation results, and screening and processing the second steam environment data based on the steam environment data set; The step of inputting the third wire-baking data and the second steam environment data into a preset machine learning model to obtain the first moisture temperature data includes: The third wire drying data and the processed second steam environment data are input into a preset machine learning model to obtain the first moisture temperature data.

4. The method according to claim 3, characterized in that The step of obtaining a steam environment data set according to all the steam correlation results and all the environment correlation results includes: When it is detected that at least one of all the steam correlation results is in a preset correlation interval, the total number of the steam correlation results in the preset correlation interval is counted; When the total number of the steam correlation results exceeds a preset number threshold, determining the steam data type corresponding to at least one of the steam correlation results in the preset correlation interval; When it is detected that at least one of all the environment correlation results is in the preset correlation interval, the total number of the environment correlation results in the preset correlation interval is counted; When the total number of the environmental correlation results exceeds the preset number threshold, determine at least one of the environmental correlation results in the preset correlation interval and the corresponding environmental data type, and take all the steam data types and all the environmental data types as a steam environment data set.

5. The method according to claim 1, characterized in that The step of optimizing the first moisture temperature data to obtain second wire drying data includes: Processing the first moisture and temperature data based on a preset multi-objective optimization algorithm to obtain at least two groups of candidate dried tofu data; Based on a preset hierarchical analysis algorithm, weight distribution processing is performed on each group of candidate dried tofu data to obtain corresponding weight results; All the candidate dried tofu data and all the weight results are processed based on a preset ideal solution sorting algorithm, and the second dried tofu data is determined from all the candidate dried tofu data according to the processing results.

6. The method according to any one of claims 1 to 5, characterized in that: The method further comprises: Obtaining second moisture temperature data according to the second wire-baking data, the first steam environment data, and a preset machine learning model; When the difference between the second moisture temperature data and the standard moisture temperature data exceeds the preset difference threshold, the second moisture temperature data is optimized to obtain fourth wire drying data; The outlet moisture temperature of the silk thread thin plate drying machine is controlled and processed according to the fourth silk drying data.

7. The method according to claim 1, characterized in that The first tobacco-baking data includes hot air velocity and dehumidification damper opening, and the first steam environment data includes at least one inlet tobacco data, at least one environment data, at least one steam data and preset dehydration flow data.

8. An outlet moisture temperature control device for a silk thread thin plate drying machine, characterized in that: include: A first processing module is used to obtain first wire drying data and first steam environment data of the wire thin plate wire drying machine at a specified time, and obtain first moisture temperature data according to the first wire drying data, the first steam environment data and a preset machine learning model; A second processing module is used for optimizing the first moisture temperature data to obtain second dried silk data when the difference between the first moisture temperature data and the standard moisture temperature data exceeds a preset difference threshold; The third processing module is used to control the outlet moisture temperature of the silk line thin plate drying machine according to the second silk drying data.

9. An outlet moisture temperature control device for a silk thread thin plate drying machine, characterized in that: including a processor and a memory; The processor is connected to the memory; The memory is used to store executable program code; The processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to execute the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed on a computer or a processor, the computer or the processor executes the steps of the method according to any one of claims 1 to 7.

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