Drying optimization method and related equipment

Abstract

The invention relates to the technical field of drying system control, and discloses a drying optimization method and related equipment, and the method comprises the steps: obtaining the operation information of a drying system in real time; inputting the operation information into a trained target regression model to obtain a corresponding predicted discharge water content; the target discharging water content is obtained; and controlling the drying system based on the predicted water content of the discharged material and the target water content of the discharged material so as to control the temperature of the drying system. According to the method, the actual water content of discharged materials can be adjusted in time, the water content of the discharged materials in batches is uniform, and quality control in the production process is facilitated.

Description

Technical Field

[0001] The present application relates to the technical field of drying system control, and in particular to a drying optimization method and related equipment. Background Art

[0002] The drying system is a system equipment for dehydrating and drying slurries. For example, the drying system can be used to dehydrate and dry slurries such as polyvinyl chloride (PVC), engineering plastics (such as ABS resin, MBS resin), cellulose, ammonium sulfate, ammonium chloride, and sodium sulfate. During the drying operation, the moisture content of the dried product (also known as the output) will vary due to the influence of various factors. Therefore, when the drying system performs the drying operation, it is necessary to detect the moisture content of the output (hereinafter referred to as the output moisture content), and control the drying system based on the test results so that the output moisture content meets the standard. However, this method of regulating the drying system based on the detection of the output moisture content and feedback makes the adjustment of the output moisture content hysteresis, which leads to uneven moisture content of batch output, which is not conducive to the quality control of the production process.

[0003] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present application, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0004] In order to provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. The summary is not an extensive review, nor is it intended to identify key / critical components or delineate the scope of protection of these embodiments, but rather serves as a prelude to the detailed description that follows.

[0005] In order to overcome the problem that the adjustment of moisture content of the discharge material has a lag, which may easily cause uneven moisture content in batch discharge material and be detrimental to quality control of the production process, the embodiments of the present disclosure provide a drying optimization method and related equipment.

[0006] In some embodiments, a drying optimization method includes: acquiring operating information of a drying system in real time; inputting the operating information into a trained target regression model to obtain a corresponding predicted discharge moisture content; obtaining a target discharge moisture content; and controlling the drying system based on the predicted discharge moisture content and the target discharge moisture content to control the temperature of the drying system.

[0007] In some embodiments, a drying optimization device includes: a first acquisition module, used to acquire the operation information of a drying system in real time; a prediction module, used to input the operation information into a trained target regression model to obtain the corresponding predicted discharge moisture content; a second acquisition module, used to obtain the target discharge moisture content; and a control module, used to control the drying system based on the predicted discharge moisture content and the target discharge moisture content to control the temperature of the drying system.

[0008] In some embodiments, a drying optimization device includes a processor and a memory storing program instructions, and the processor is configured to execute a drying optimization method as described above when running the program instructions.

[0009] In some embodiments, a computer-readable storage medium is characterized in that instructions are stored in the computer-readable storage medium, and when the instructions are executed on a terminal device, the terminal device executes a drying optimization method as described above.

[0010] The drying optimization method and related equipment provided by the embodiments of the present disclosure can achieve the following technical effects:

[0011] By acquiring the operation information of the drying system in real time and inputting it into the trained target regression model, the corresponding predicted discharge moisture content is obtained so as to determine the discharge moisture content in time. In this way, by predicting the discharge moisture content and controlling the drying system based on the predicted discharge moisture content and the target discharge moisture content to control the temperature of the drying system, the actual discharge moisture content can be adjusted in time, so that the moisture content of the batch discharge is uniform, which is beneficial to the quality control of the production process.

[0012] The above general description and the following description are exemplary and explanatory only and are not intended to limit the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] One or more embodiments are exemplarily described by corresponding drawings, which do not limit the embodiments. Elements with the same reference numerals in the drawings are shown as similar elements, and the drawings do not constitute a scale limitation, and wherein:

[0014] Figure 1 is a schematic flow chart of a drying optimization method provided by an embodiment of the present disclosure;

[0015] Figure 2 is a flow chart of another drying optimization method according to an embodiment of the present disclosure;

[0016] Figure 3 is a structural schematic diagram of a drying optimization device provided by an embodiment of the present disclosure;

[0017] Figure 4 It is a structural schematic diagram of another drying optimization device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0018] In order to be able to understand the features and technical contents of the embodiments of the present disclosure in more detail, the implementation of the embodiments of the present disclosure is described in detail below in conjunction with the accompanying drawings. The attached drawings are for reference only and are not used to limit the embodiments of the present disclosure. In the following technical description, for the convenience of explanation, a full understanding of the disclosed embodiments is provided through multiple details. However, one or more embodiments can still be implemented without these details. In other cases, to simplify the drawings, well-known structures and devices can be simplified for display.

[0019] The terms "first", "second", etc. in the specification and claims of the embodiments of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the terms used in this way can be interchanged where appropriate, so as to describe the embodiments of the embodiments of the present disclosure described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions.

[0020] Unless otherwise stated, the term "plurality" means two or more.

[0021] In the embodiment of the present disclosure, the character " / " indicates that the preceding and following objects are in an "or" relationship. For example, A / B indicates: A or B.

[0022] The term "and / or" is a description of the association relationship between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or, A and B.

[0023] The term "correspondence" may refer to an association relationship or a binding relationship. The correspondence between A and B means that there is an association relationship or a binding relationship between A and B.

[0024] A drying optimization method and related equipment provided in the embodiments of the present disclosure are applied to a drying system. The drying system includes a feeding device, a fluidized bed air chamber (in which a fluidized bed is arranged), a steam device, an airflow pipe and other equipment. Specifically, the feeding device feeds the slurry into the fluidized bed of the fluidized bed air chamber, the steam device transmits steam to the fluidized bed air chamber, and the airflow pipe transmits hot air to the fluidized bed air chamber, so that the slurry is dehydrated and dried to obtain a corresponding output material, and the output material is the slurry after dehydration and drying by the drying system. In this embodiment, a drying optimization method and related equipment include a drying optimization method, a device and a computer storage medium.

[0025] Combination Figure 1As shown, the embodiment of the present disclosure provides a drying optimization method, comprising:

[0026] S101, obtaining operation information of the drying system in real time.

[0027] In this embodiment, the operation information includes at least two operation parameters. The operation parameters are bed temperature, steam pressure, air flow pipe bottom temperature, hot air temperature, and feed flow rate. The bed temperature is the actual temperature of the fluidized bed. The feed flow rate is the product of the feed rate and the slurry concentration.

[0028] In addition, in this embodiment, the steam pressure can be obtained by an observer. For example, the observer is a disturbance observer. In this way, the steam pressure can be estimated by the observer, so that the steam pressure can be obtained more quickly.

[0029] S102, inputting the operation information into the trained target regression model to obtain the corresponding predicted discharge moisture content.

[0030] S103, obtaining the target moisture content of the discharge material.

[0031] Among them, the target discharge moisture content represents the optimal discharge moisture content, that is, the discharge moisture content that meets the standard.

[0032] S104, controlling the drying system based on the predicted discharge moisture content and the target discharge moisture content to control the temperature of the drying system.

[0033] In this embodiment, controlling the temperature of the drying system includes increasing or decreasing the temperature of the drying system. The temperature of the hot air transmitted to the fluidized bed air chamber is increased (or decreased) by adjusting the opening of the air temperature valve, and / or the temperature of the drying system is controlled by increasing (or decreasing) the bed temperature, that is, increasing (or decreasing) the temperature of the drying system. It can be understood that the air temperature valve represents a valve that transmits hot air to the fluidized bed air chamber.

[0034] A drying optimization method provided by an embodiment of the present disclosure is adopted, by obtaining the operation information of the drying system in real time and inputting it into the trained target regression model, the corresponding predicted discharge moisture content is obtained, so as to timely determine the discharge moisture content. In this way, by predicting the discharge moisture content, and controlling the drying system based on the predicted discharge moisture content and the target discharge moisture content to control the temperature of the drying system, the actual discharge moisture content can be adjusted in time, the hysteresis of the drying system is reduced, and the batch discharge moisture is uniform, which is beneficial to the quality control of the production process.

[0035] Optionally, the target regression model is obtained by training in the following manner: obtaining multiple historical operation information of the drying system and the first historical discharge moisture content corresponding to each historical operation information; using the multiple historical operation information and the multiple first historical discharge moisture contents as training sets to train the original regression model to obtain the target regression model. Wherein, the original regression model and the target regression model are both linear regression models.

[0036] In this way, the original regression model is trained using the historical operation information of the drying system and the corresponding first historical discharge moisture content to obtain a target regression model, so that the target regression model can predict the discharge moisture content based on the operation information of the drying system.

[0037] Specifically, multiple historical operation information and multiple first historical discharge moisture contents are used as training sets to train the original regression model, including: constructing a first matrix based on multiple historical operation information, and constructing a second matrix based on multiple first historical discharge moisture contents; using the first matrix and the second matrix as training sets to train the original regression model to obtain a target regression model.

[0038] Furthermore, constructing a first matrix based on multiple historical operation information includes: for each historical operation information, determining a first row vector corresponding to each historical operation information based on each operation parameter corresponding to each historical operation information; and determining a first matrix based on each first row vector.

[0039] For example, the operation information includes five operation parameters: bed temperature, steam pressure, airflow tube bottom temperature, hot air temperature, and feed flow rate, and the bed temperature is a, the steam pressure is b, the airflow tube bottom temperature is c, the hot air temperature is d, and the feed flow rate is e. Then m i =[a i b i c i d i e i ] is the first row vector corresponding to the i-th historical operation information, then the first matrix m is In this embodiment, there are k historical operation information in total.

[0040] Furthermore, constructing a second matrix based on a plurality of first historical discharge moisture contents includes: determining a second row vector corresponding to each first historical discharge rate, and determining the second matrix based on each second row vector.

[0041] For example, i is the second row vector corresponding to the i-th first historical discharge moisture content, then the second matrix n is Among them, in this embodiment, there are k first historical discharge moisture contents.

[0042] In some embodiments, the target regression model for in, is a constant term, is the coefficient of a, is the coefficient of b, is the coefficient of c, is the coefficient of d, is the coefficient of e.

[0043] Optionally, multiple historical operation information and multiple first historical discharge moisture contents are used as training sets to train the original regression model, including: preprocessing the multiple historical operation information and multiple first historical discharge moisture contents; using the preprocessed multiple historical operation information and the preprocessed multiple first historical discharge moisture contents as training sets to train the original regression model.

[0044] In this way, by preprocessing the historical operation information and the first historical discharge moisture content, abnormal data can be removed and the accuracy of the data can be improved.

[0045] Specifically, the preprocessing is performed on the plurality of historical operation information, including: for each piece of historical operation information, normalizing each operation parameter in each piece of historical operation information.

[0046] Since different parameters have different dimensions, normalization is performed on each operating parameter to facilitate the fusion of different operating parameters of historical operating information, so as to improve the prediction accuracy of the target regression model.

[0047] Specifically, the plurality of historical operation information and the plurality of first historical discharge moisture contents are preprocessed, including: removing the historical operation information with abnormal data and the corresponding first historical discharge moisture contents, and removing the first discharge moisture contents with abnormal data and the corresponding historical operation information. The presence of abnormal data in the historical operation information indicates that any one of the operation parameters in the historical operation information is empty. The presence of abnormal data in the first historical discharge moisture content indicates that the first historical discharge moisture content is empty.

[0048] Since one historical operation information corresponds to one historical discharge moisture content, when there is abnormal data in the historical abnormal information, the corresponding first historical discharge moisture content is also likely to be abnormal; similarly, when there is abnormal data in the first historical discharge moisture content, the corresponding historical operation information is also likely to be abnormal. Therefore, by removing the historical operation information and the first historical discharge moisture content corresponding to the abnormal data, the accuracy of the training set can be improved, so as to improve the prediction accuracy of the target regression model.

[0049] Optionally, controlling the drying system based on the predicted discharge moisture content and the target discharge moisture content includes: determining a first moisture content deviation value based on the predicted discharge moisture content and the target discharge moisture content; and controlling the drying system based on the first moisture content deviation value.

[0050] In this way, the first moisture content deviation value can reflect the difference between the moisture content of the discharge obtained after the drying system dries the slurry and the target moisture content of the discharge, thereby facilitating the control of the temperature of the hot air transmitted to the fluidized bed air chamber, so as to adjust the moisture content of the discharge by controlling the temperature of the drying system.

[0051] Specifically, the first moisture content deviation value = predicted discharge moisture content - target discharge moisture content. If the first moisture content deviation value is a positive number, it means that the predicted discharge moisture content is greater than the target discharge moisture content, and the actual discharge moisture content needs to be lowered, and the temperature of the drying system needs to be increased. If the first moisture content deviation value is a negative number, it means that the predicted discharge moisture content is less than the target discharge moisture content, and the actual discharge moisture content needs to be increased, and the temperature of the drying system needs to be lowered.

[0052] Further, based on the first moisture content deviation value, the drying system is controlled, including: using a fuzzy control algorithm to control the drying system based on the first moisture content deviation value. The fuzzy control algorithm has a small amount of calculation and high real-time performance. Therefore, using the fuzzy control algorithm to control the drying system can control the drying system faster.

[0053] Specifically, the drying system is controlled based on the first moisture content deviation value by using the fuzzy control algorithm, including: fuzzifying the first moisture content deviation value to obtain a first fuzzification result; performing fuzzy reasoning on the first fuzzification result to obtain a first reasoning result; performing fuzzy reasoning on the first reasoning result to obtain a control variable, and controlling the drying system based on the control variable. The first moisture content deviation value is the input of the fuzzy control algorithm, also known as the observed quantity. The control variable is the output of the fuzzy control algorithm, also known as the control variable.

[0054] In some embodiments, the control variable is a bed temperature variable or an opening variable of an air temperature valve. The bed temperature variable represents the temperature value for adjusting the bed temperature. The opening variable of the air temperature valve represents the opening value for adjusting the opening of the air temperature valve. For example, when the control variable is that the current bed temperature is q and the bed temperature variable is 1, the bed temperature is increased by 1 degree to control the drying system. It is understandable that different control variables correspond to different fuzzy control algorithms, such as different membership functions and different fuzzy inference rules when fuzzifying the first moisture content deviation value.

[0055] Optionally, the drying system is controlled based on the predicted discharge moisture content and the target discharge moisture content, including: determining the moisture content change trend based on multiple predicted discharge moisture contents; determining a second moisture content deviation value based on the predicted discharge moisture content at the latest moment corresponding to the moisture content change trend and the target moisture content; and controlling the drying system based on the second moisture content deviation value and the moisture content change trend.

[0056] The moisture content change trends corresponding to the multiple predicted discharge moisture contents can intuitively reflect the change of the discharge moisture content. In this way, the drying system is controlled based on the second moisture content deviation value and the moisture content change trend, so that the actual discharge moisture content can be adjusted more accurately.

[0057] It is understandable that, since the drying system is constantly drying, the multiple predicted discharge moisture contents corresponding to the moisture content change trend should change in real time. Therefore, the moisture content change trend in this embodiment is a real-time change trend. The predicted discharge moisture content at the latest moment corresponding to the moisture content change trend is the predicted discharge moisture content corresponding to the latest moment in the real-time change trend. For example, a predicted discharge moisture content is determined at 10:01, 10:02, and 10:03 respectively, and a moisture content change trend is determined to correspond to these three predicted discharge moisture contents, then the latest moment corresponding to the moisture content change trend is 10:03.

[0058] Further, based on the second moisture content deviation value and the moisture content variation trend, the drying system is controlled, including: using a fuzzy control algorithm to control the drying system based on the second moisture content deviation value and the moisture content variation trend. The fuzzy control algorithm has a small amount of calculation and high real-time performance. Therefore, the fuzzy control algorithm can be used to control the drying system quickly and accurately.

[0059] Specifically, the drying system is controlled by using a fuzzy control algorithm based on the second moisture content deviation value and the moisture content change trend, including: fuzzifying the second moisture content deviation value to obtain a second fuzzified result, fuzzifying the moisture content change trend to obtain a third fuzzified result; performing fuzzy reasoning based on the second fuzzified result and the third fuzzified result to obtain a second reasoning result; defuzzifying the second reasoning result to obtain a control variable, and controlling the drying system based on the control variable. Among them, the second moisture content deviation value and the moisture content change trend are both input quantities of the fuzzy control algorithm, also known as observed quantities. The control variable is the output quantity of the fuzzy control algorithm, also known as the control quantity.

[0060] Optionally, a drying optimization method further includes: obtaining a moisture content threshold condition; and issuing an early warning if the predicted moisture content of the discharge does not meet the moisture content threshold condition. The moisture content threshold condition indicates that the moisture content of the discharge is abnormal.

[0061] When the drying system is drying, the moisture content of the discharge material will be affected by factors such as feed flow rate and steam fluctuations. However, in industrial production, this difference is usually a reasonable data. Therefore, when the predicted moisture content of the discharge material does not meet the moisture content threshold condition, that is, when the moisture content of the discharge material is abnormal, it means that the value of the predicted moisture content of the discharge material is unreasonable. For example, the drying system may be abnormal or the target regression model may be abnormal. At this time, an early warning prompt is issued to remind the staff so that the staff can be aware of the abnormal situation in time and deal with the abnormal situation in time, thereby avoiding the aggravation of the situation and reducing losses.

[0062] In some embodiments, issuing a pre-alert means issuing a warning message to a preset terminal, or controlling a preset voice device to issue a voice warning. The terminal may be a mobile phone, tablet, etc. The voice device may be a speaker, radio, etc.

[0063] Optionally, obtaining the moisture content threshold condition includes: obtaining multiple second historical discharge moisture contents of the drying system; determining an upper moisture content limit and a lower moisture content limit based on the multiple second historical discharge moisture contents; and determining the moisture content threshold condition based on the upper moisture content limit and the lower moisture content limit.

[0064] The multiple second historical discharge moisture contents can reflect the regularity of historical discharge moisture contents, so that the upper and lower limits of moisture contents can be reasonably determined in combination with actual data, thereby obtaining accurate moisture content threshold conditions.

[0065] In this embodiment, the first historical discharge moisture content may be the same as or different from the first historical discharge moisture content.

[0066] Furthermore, the upper and lower limits of moisture content are determined based on multiple second historical discharge moisture contents, including: determining the variance of the population using t distribution based on the acquired multiple second historical discharge moisture contents; and determining the upper and lower limits of moisture content using a preset confidence level and the variance of the population.

[0067] It can be understood that according to the central limit theorem, the sample mean of any population is usually normally distributed around the actual mean of the population, so a reasonable threshold condition can be determined based on the confidence interval.

[0068] Specifically, based on the obtained multiple second historical discharge moisture contents, the variance of the population is determined using t distribution, including: calculating the sample mean of all second historical discharge moisture contents And calculate the corresponding sample standard deviation as Based on the corresponding sample standard deviation, the variance of the population described by the t distribution is in, is the average moisture content of all the second historical discharges, X iis the i-th second historical discharge moisture content, n is the number of second historical discharge moisture contents, S is the sample standard deviation, t is the variance of the population, and μ is the mean of the population.

[0069] Specifically, the upper and lower limits of the moisture content are determined using the preset confidence level and the variance of the population, including: using the preset confidence level to construct a confidence interval of the population mean, using the lower confidence limit of the confidence interval as the lower limit of the moisture content, and using the upper confidence limit of the confidence interval as the upper limit of the moisture content. The calculation formula is as follows:

[0070]

[0071] Among them, α is the significance level, and (1-α) is the preset confidence level. Represents the right tail area of ​​a t distribution with n-1 degrees of freedom The corresponding t value is, Represents the confidence width, which describes the distribution interval of the acceptance domain. is the lower confidence limit, is the upper confidence limit.

[0072] Furthermore, the moisture content threshold condition is determined based on the moisture content upper limit and the moisture content lower limit, including: using the moisture content upper limit as the upper threshold of the moisture content threshold condition, and using the moisture content lower limit as the lower threshold of the moisture content threshold condition.

[0073] In this embodiment, when the predicted moisture content of the discharge material is greater than or equal to and less than or equal to When the drying system is not abnormal, there is no need to issue an early warning.

[0074] The moisture content of the material is less than Or, greater than When the drying system is abnormal, an early warning prompt needs to be issued.

[0075] Combination Figure 2 As shown, the embodiment of the present disclosure provides another drying optimization method, comprising:

[0076] S201, obtaining operation information of the drying system in real time.

[0077] S202, inputting the operation information into the trained target regression model to obtain the corresponding predicted discharge moisture content.

[0078] S203, obtaining the target moisture content of the discharge material.

[0079] S204, controlling the drying system based on the predicted discharge moisture content and the target discharge moisture content to control the temperature of the drying system.

[0080] S205, obtaining multiple second historical discharge moisture contents of the drying system.

[0081] S206, determining an upper limit and a lower limit of moisture content based on a plurality of second historical discharge moisture contents.

[0082] S207: Determine a moisture content threshold condition based on the moisture content upper limit and the moisture content lower limit.

[0083] S208: If the predicted moisture content of the discharged material does not meet the moisture content threshold condition, an early warning prompt is issued.

[0084] Combination Figure 3 As shown, the embodiment of the present disclosure provides a drying optimization device, including a first acquisition module, a prediction module, a second acquisition module and a control module. Among them, the first acquisition module is used to obtain the operation information of the drying system in real time. The prediction module is used to input the operation information into the trained target regression model to obtain the corresponding predicted discharge moisture content. The second acquisition module is used to obtain the target discharge moisture content. The control module is used to control the drying system based on the predicted discharge moisture content and the target discharge moisture content to control the temperature of the drying system.

[0085] A drying optimization device provided by an embodiment of the present disclosure is used to obtain the operation information of the drying system in real time and input it into the trained target regression model to obtain the corresponding predicted discharge moisture content so as to determine the discharge moisture content in time. In this way, by predicting the discharge moisture content and controlling the drying system based on the predicted discharge moisture content and the target discharge moisture content to control the temperature of the drying system, the actual discharge moisture content can be adjusted in time, the hysteresis of the drying system can be reduced, and the moisture content of the batch discharge can be uniform, which is beneficial to the quality control of the production process.

[0086] Preferably, the prediction module is specifically used to train and obtain a target regression model in the following manner: obtain multiple historical operation information of the drying system and the first historical discharge moisture content corresponding to each historical operation information; use the multiple historical operation information and the multiple first historical discharge moisture contents as training sets to train the original regression model to obtain the target regression model.

[0087] Preferably, the prediction module is specifically used to preprocess multiple historical operation information and multiple first historical discharge moisture contents; and use the preprocessed multiple historical operation information and the preprocessed multiple first historical discharge moisture contents as training sets to train the original regression model.

[0088] Preferably, the control module is specifically used to determine a first moisture content deviation value based on the predicted discharge moisture content and the target discharge moisture content; and control the drying system based on the first moisture content deviation value.

[0089] Preferably, the control module is specifically used to determine the moisture content change trend based on multiple predicted discharge moisture contents; determine the second moisture content deviation value based on the predicted discharge moisture content at the latest moment corresponding to the moisture content change trend and the target moisture content; and control the drying system based on the second moisture content deviation value and the moisture content change trend.

[0090] Preferably, a drying optimization device further includes a prompt module. The prompt module is used to obtain a moisture content threshold condition; if the predicted moisture content of the discharge material does not meet the moisture content threshold condition, an early warning prompt is issued.

[0091] Preferably, the prompt module is specifically used to obtain multiple second historical discharge moisture contents of the drying system; determine the moisture content upper limit and the moisture content lower limit based on the multiple second historical discharge moisture contents; and determine the moisture content threshold condition based on the moisture content upper limit and the moisture content lower limit.

[0092] Combination Figure 4 As shown, the embodiment of the present disclosure provides another drying optimization device, including a processor and a memory. Optionally, the device may also include a communication interface and a bus. The processor, the communication interface, and the memory may communicate with each other through the bus. The communication interface may be used for information transmission. The processor may call the logic instructions in the memory to execute a drying optimization method of the above embodiment.

[0093] In addition, the logic instructions in the above-mentioned memory can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product.

[0094] The memory, as a computer-readable storage medium, can be used to store software programs and computer executable programs, such as program instructions / modules corresponding to the method in the embodiment of the present disclosure. The processor executes the function application and data processing by running the program instructions / modules stored in the memory, that is, realizing a drying optimization method in the above embodiment.

[0095] The memory may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required for at least one function; the data storage area may store data created according to the use of the terminal device, etc. In addition, the memory may include a high-speed random access memory and may also include a non-volatile memory.

[0096] In an embodiment of the present invention, a computer-readable storage medium is provided, in which instructions are stored. When the instructions are executed, the above drying optimization method is executed.

[0097] The computer-readable storage medium may be a transient computer-readable storage medium or a non-transitory computer-readable storage medium.

[0098] The technical solution of the embodiment of the present disclosure can be embodied in the form of a software product, which is stored in a storage medium and includes one or more instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method of the embodiment of the present disclosure. The aforementioned storage medium may be a non-transient storage medium, including: a USB flash drive, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a disk or an optical disk, and other media that can store program codes, or a transient storage medium.

[0099] The above description and accompanying drawings fully illustrate the embodiments of the present disclosure so that those skilled in the art can practice them. Other embodiments may include structural, logical, electrical, process and other changes. The embodiments represent possible changes only. Unless explicitly required, separate components and functions are optional, and the order of operation may vary. The parts and features of some embodiments may be included in or replace the parts and features of other embodiments. Moreover, the words used in this application are only used to describe the embodiments and are not used to limit the claims. As used in the description of the embodiments and claims, unless the context clearly indicates, the singular forms of "a", "an" and "the" are intended to include plural forms as well. Similarly, the term "and / or" as used in this application refers to any and all possible combinations of listings containing one or more associated ones. In addition, when used in the present application, the term "comprise" and its variants "comprises" and / or comprising refer to the presence of stated features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or groups thereof. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method or device including the elements. In this article, each embodiment may focus on the differences from other embodiments, and the same and similar parts between the various embodiments may refer to each other. For the methods, products, etc. disclosed in the embodiments, if they correspond to the method part disclosed in the embodiments, then the relevant parts can refer to the description of the method part.

[0100] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software may depend on the specific application and design constraints of the technical solution. Technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the embodiments of the present disclosure. Technicians can clearly understand that for the convenience and simplicity of description, the specific working process of the above-described system, device and unit can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0101] In the embodiments disclosed herein, the disclosed methods and products (including but not limited to devices, equipment, etc.) can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units can be only a logical function division, and there can 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. In addition, the coupling or direct coupling or communication connection between each other shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms. The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units can be selected according to actual needs to implement this embodiment. In addition, each functional unit in the embodiment of the present disclosure can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0102] The flowchart and block diagram in the accompanying drawings show the possible architecture, functions and operations of the system, method and computer program product according to the embodiment of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and a part of the module, program segment or code contains one or more executable instructions for realizing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. In the description corresponding to the flowchart and block diagram in the accompanying drawings, the operations or steps corresponding to different boxes can also occur in a different order from the order disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. Each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a dedicated hardware-based system that performs the specified functions or actions, or may be implemented by a combination of dedicated hardware and computer instructions.

Claims

1. A drying optimization method, characterized in that: include: Obtain the operation information of the drying system in real time; Inputting the operation information into the trained target regression model to obtain the corresponding predicted discharge moisture content; Obtain target discharge moisture content; The drying system is controlled based on the predicted discharge moisture content and the target discharge moisture content to control the temperature of the drying system.

2. The method according to claim 1, characterized in that The target regression model is trained in the following way: Acquire multiple historical operation information of the drying system and the first historical discharge moisture content corresponding to each historical operation information; The original regression model is trained using the plurality of the historical operation information and the plurality of the first historical discharge moisture contents as training sets to obtain a target regression model.

3. The method according to claim 2, characterized in that The training of the original regression model using the plurality of the historical operation information and the plurality of the first historical discharge moisture contents as training sets includes: Preprocessing the plurality of the historical operation information and the plurality of the first historical discharge moisture contents; The preprocessed multiple historical operation information and the preprocessed multiple first historical discharge moisture contents are used as training sets to train the original regression model.

4. The method according to any one of claims 1 to 3, characterized in that: The controlling the drying system based on the predicted discharge moisture content and the target discharge moisture content includes: Determining a first moisture content deviation value based on the predicted discharge moisture content and the target discharge moisture content; The drying system is controlled based on the first moisture content deviation value.

5. The method according to any one of claims 1 to 3, characterized in that: The controlling the drying system based on the predicted discharge moisture content and the target discharge moisture content includes: Determine the moisture content change trend based on multiple predicted discharge moisture contents; Determining a second moisture content deviation value based on the predicted discharge moisture content at the latest moment corresponding to the moisture content change trend and the target moisture content; The drying system is controlled based on the second moisture content deviation value and the moisture content variation trend.

6. The method according to any one of claims 1 to 3, characterized in that: Also includes: Get the moisture content threshold condition; If the predicted discharge moisture content does not meet the moisture content threshold condition, an early warning prompt is issued.

7. The method according to claim 6, characterized in that The obtaining of the moisture content threshold condition includes: Obtaining a plurality of second historical discharge moisture contents of the drying system; Determine an upper limit and a lower limit of moisture content based on a plurality of the second historical discharge moisture contents; The moisture content threshold condition is determined based on the moisture content upper limit and the moisture content lower limit.

8. A drying optimization device, characterized in that: include: A first acquisition module is used to acquire the operation information of the drying system in real time; A prediction module, used for inputting the operation information into the trained target regression model to obtain the corresponding predicted discharge moisture content; The second acquisition module is used to obtain the target discharge moisture content; A control module is used to control the drying system based on the predicted discharge moisture content and the target discharge moisture content to control the temperature of the drying system.

9. A drying optimization device, comprising a processor and a memory storing program instructions, characterized in that: The processor is configured to execute a drying optimization method according to any one of claims 1 to 7 when running the program instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed on a terminal device, the terminal device executes a drying optimization method according to any one of claims 1 to 7.

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