Cloud-based Drying Process Optimization System

Abstract

This invention discloses a cloud computing-based method for optimizing the drying process. The method includes the following steps: Step 1: Acquire temperature and humidity change data of the grain entering the drying environment, and establish quantitative and functional relationships between various parameters during the grain drying process; Step 2: Set the model parameters of an exponential model, determine the grain density, thermal conductivity, and effective moisture diffusion coefficient, and obtain the influence and degree of influence of different parameters on its drying quality; Step 3: Construct parameter optimization targets for the grain drying process based on the reflected original evaluation indicators; Step 4: With the output safe moisture content as the control target, further optimize the system control during the grain drying process to improve drying quality. In other words, this application achieves precise and efficient planning of the grain drying process and improves grain drying quality through parameter optimization and system control optimization for the grain.

Description

Technical Field

[0001] This invention relates to the field of cloud computing technology, specifically to a cloud-based drying process optimization system. Background Technology

[0002] Grain drying is a complex heat and mass transfer process, involving the exchange of moisture and energy between the grain and the drying medium, along with heat and mass transfer. Grain itself is a complex chemical substance, and its drying process is influenced by various internal and external factors and parameters. It's not simply about removing internal moisture; it's also about maintaining the quality of the dried grain. This includes both the drying process and the material parameters after drying. Requirements include appropriate moisture content to minimize chemical decomposition reactions, maintain product structure and composition, achieve the desired color, and control product density and porosity. Drying process control, based on optimized drying technology, aims to achieve the required moisture content with low energy consumption and high efficiency while ensuring grain quality. This is a nonlinear, long-time-delay, unsteady, and strongly coupled multivariate complex system. Current grain dryer process control only monitors and controls outlet moisture and air temperature, lacking analysis and prediction of the relationships between various factors and parameters during the drying process. This leads to unreasonable control parameter settings, affecting the overall quality of the dried grain. Therefore, designing a cloud-based drying process optimization system that optimizes parameter prediction and improves grain drying quality is essential. Summary of the Invention

[0003] The purpose of this invention is to provide a cloud computing-based drying process optimization system to solve the problems mentioned in the background art.

[0004] To address the aforementioned technical problems, this invention provides the following technical solution: a cloud computing-based drying process optimization method, comprising the following steps:

[0005] Step 1: Obtain temperature and humidity change data when the dried grain enters the drying environment, and establish quantitative and functional relationships between various parameters during the grain drying process;

[0006] Step 2: Set the model parameters of the exponential model, determine the grain density, thermal conductivity and effective moisture diffusion coefficient of the grain, and obtain the influence relationship and degree of influence of different parameters on its drying quality;

[0007] Step 3: Construct parameter optimization objectives for the grain drying process based on the original evaluation indicators.

[0008] Step 4: While keeping the output safe moisture content as the control target, further optimize the system control during the grain drying process to improve the drying quality.

[0009] According to the above technical solution, the step of establishing the quantitative relationship and functional change relationship between various parameters in the grain drying process based on empirical data includes:

[0010] Step 11: Use the accumulated temperature for grain drying as the output index of the drying experiment, establish a regression equation, and use the accumulated temperature as an indicator to judge the progress of the grain drying stage.

[0011] Step 12: By dividing the various drying stages in the drying process into different intervals, and dividing the different drying sections into equal parts, record the surface moisture data and total moisture content of the grain before entering the drying environment, and use the grain drying mathematical model to model and analyze the parameters of the grain at different stages in the drying process.

[0012] Step 13: Analyze and calculate the drying rate of grains in the first stage of drying by using the evaporation area of ​​the grain surface and the characteristic parameters of the drying medium. For grains that have entered the second drying stage, the surface moisture has reached the equilibrium moisture content state corresponding to the drying medium. At this time, the dehydration of the grains shows the phenomenon of the drying surface retreating. The drying surface gradually approaches the center from the outside, and the moisture in the center continuously migrates outward. Analyze the drying rate of grains in the second stage by using the rate parameter of the migration of moisture from the grain body to the outer surface.

[0013] Step 14: Calculate the heat required for evaporation of moisture during the drying process based on the state parameters of the grain, ambient temperature, humidity and other parameters throughout the drying process. Then, further analyze the relationship between grain moisture content and equilibrium temperature under different environments and relative humidity by using different gradient moisture contents of the dried grain and ambient relative humidity, and establish a quantitative index model corresponding to the grain drying characteristics.

[0014] According to the above technical solution, the method steps for setting the model parameters of the exponential model include:

[0015] Step 21: When the grain undergoes heat and mass exchange with the hot air medium, heat is transferred from the drying medium to the grain through the exchange and migration of moisture and energy. During this process, the density of grains with different moisture contents is determined by the displacement method to obtain the fitting curve between the dry basis moisture content and density of the grain.

[0016] Step 22: Set the parameters of different hot air temperature, initial moisture content and hot air velocity in the exponential model, and conduct multiple single-factor experiments in multiple groups. When the grain is dried to the set safe moisture content, the second group of grain drying begins. The average value of all groups is taken according to the total number of drying experiments. Record the drying characteristic curve of grain moisture changing with time and the drying rate changing with its moisture content to analyze the drying characteristics of the grain.

[0017] Step 23: After the moisture contained in the grain evaporates due to heat and is carried out of the drying equipment by the drying medium, the grain density, thermal conductivity and effective moisture diffusion coefficient of the grain are used as analytical variables by principal component analysis. By reducing the dimensionality, the parameter variables are recombined to form a comprehensive index that is independent of each other, which indirectly reflects the original evaluation index.

[0018] According to the above technical solution, the step of constructing the parameter optimization target in the grain drying process includes:

[0019] Step 31: Obtain the parameters corresponding to the current state during the grain drying process by sampling, and divide the current state of the entire grain drying process into the initial current state and the drying current state, and obtain the parameter sequence of the grain drying process according to the different current states;

[0020] Step 32: Based on the obtained parameter sequence, optimize and control the parameters of the grain drying process, and use the grain drying process model for predictive control. In the initial optimization stage, collect the current real-time moisture content of the grain, which is the initial moisture content of the current state. Set the drying process parameters of the grain in the current state and calculate the grain drying quality prediction evaluation value of the current state based on these parameters.

[0021] Step 33: Determine the range of moisture content of the grain in real time. Since the moisture lost from the grain through evaporation is equal to the moisture absorbed by the drying hot air, the mass balance equation is used to calculate the change in the moisture content of the drying hot air and obtain the relative humidity value after the drying hot air and the grain exchange moisture and heat.

[0022] Step 34: When the current real-time moisture content of the grain is lower than the set moisture content threshold range, the optimal drying parameters for the grain are determined by comparing and analyzing the expected drying effects of different predictive controls, so as to achieve precise and efficient planning of the grain drying process and enable the grain moisture content to reach the specified target efficiently and quickly.

[0023] According to the above technical solution, the steps for optimizing system control during the grain drying process include:

[0024] Step 41: The grain drying process is affected not only by external conditions and internal factors, but also by system factors. Since the removal of moisture from the grain during the drying process requires the assistance of a medium, and the heat required for moisture evaporation comes from the medium selected by the system, the heat and mass exchange between the grain and the medium in the drying layer is directional. In order to obtain an accurate online moisture content value during the grain drying process, the drying efficiency of the system is analyzed based on the process structure characteristics of the system.

[0025] Step 42: In the grain drying system, the discharge speed, hot air temperature and hot air volume are selected as control variables, and the final moisture content and drying quality of the grain are selected as controlled variables. During the drying process, the grain flows from top to bottom by its own weight. The flow speed is controlled by the discharge wheel installed at the bottom of the dryer. The faster the discharge wheel speed, the shorter the drying time of the grain in the dryer and the higher the outlet moisture content.

[0026] Step 43: Using the initial moisture content of the grain to be dried, hot air temperature, wind speed, and drying time as inputs, and the outlet moisture content after one cycle as output, establish an input-output process parameter expert system using the least squares vector machine algorithm prediction model;

[0027] Step 44: The drying control system obtains the predicted moisture content through the moisture content prediction model, and the actual moisture content at the outlet is measured by the moisture detector. The difference between the predicted value and the actual value of the moisture content is calculated. Through fuzzy PID calculation, the rotation speed of the grain discharge wheel in the system is output to control the drying time in the dryer, thereby controlling the change of grain moisture content.

[0028] Step 45: Under the condition of ensuring equal air volume to grain ratio, the reasonable air volume to grain ratio for deep drying is the maximum average water removal rate within a specific drying volume. When the air volume to grain ratio calculated based on the set grain air volume is less than this value, the ventilation energy consumption per unit water removal increases. When it is greater than this value, the exhaust heat loss of the medium increases, and the thermal efficiency of the system decreases. By detecting the actual moisture content of the grain at the outlet, the grain discharge wheel motor is controlled to make it close to the predicted moisture content, thereby achieving control over drying time and drying quality, resulting in more precise grain drying and higher quality.

[0029] According to the above technical solution, the system includes:

[0030] The module for determining the relationship between drying parameters is used to establish quantitative and functional relationships between various parameters during the grain drying process.

[0031] The parameter optimization target module is used to construct parameter optimization targets in the grain drying process based on the original evaluation indicators reflected.

[0032] The drying process control optimization module is used to optimize the system control during the grain drying process.

[0033] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: The present invention optimizes the parameters of the grain and the system control during the grain drying process. By detecting the actual moisture content of the grain, the grain discharge wheel motor is controlled to make it close to the predicted moisture content, thereby achieving control over drying time and drying quality. This results in more precise grain drying, higher quality, and overall improved drying quality. The invention also achieves precise and efficient planning of the grain drying process, thereby improving the quality of grain drying. Attached Figure Description

[0034] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0035] Figure 1 A flowchart of a cloud computing-based drying process optimization method provided in Embodiment 1 of the present invention;

[0036] Figure 2 This is a schematic diagram of the module composition of the cloud computing-based drying process optimization system provided in Embodiment 2 of the present invention. Detailed Implementation

[0037] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0038] Example 1: Figure 1 This is a flowchart of a cloud-based drying process optimization method provided in Embodiment 1 of the present invention. This embodiment can be applied to grain drying scenarios. The method can be executed by the cloud-based drying process optimization system provided in this embodiment. Figure 1 As shown, the method specifically includes the following steps:

[0039] Step 1: Obtain temperature and humidity change data when the dried grain enters the drying environment, and establish quantitative and functional relationships between various parameters during the grain drying process;

[0040] In this embodiment of the invention, the steps of establishing quantitative relationships and functional changes among various parameters during grain drying based on empirical data include:

[0041] Step 11: Use the accumulated temperature for grain drying as the output index of the drying experiment, establish a regression equation, and use the accumulated temperature as an indicator to judge the progress of the grain drying stage.

[0042] Step 12: By dividing the various drying stages in the drying process into different intervals, and dividing the different drying sections into equal parts, record the surface moisture data and total moisture content of the grain before entering the drying environment, and use the grain drying mathematical model to model and analyze the parameters of the grain at different stages in the drying process.

[0043] Step 13: Analyze and calculate the drying rate of grains in the first stage of drying by using the evaporation area of ​​the grain surface and the characteristic parameters of the drying medium. For grains that have entered the second drying stage, the surface moisture has reached the equilibrium moisture content state corresponding to the drying medium. At this time, the dehydration of the grains shows the phenomenon of the drying surface retreating. The drying surface gradually approaches the center from the outside, and the moisture in the center continuously migrates outward. Analyze the drying rate of grains in the second stage by using the rate parameter of the migration of moisture from the grain body to the outer surface.

[0044] Step 14: Calculate the heat required for evaporation of moisture during the drying process based on the state parameters of the grain, ambient temperature, humidity and other parameters throughout the drying process. Then, further analyze the relationship between grain moisture content and equilibrium temperature under different environments and relative humidity by using different gradient moisture contents of the dried grain and ambient relative humidity, and establish a quantitative index model corresponding to the grain drying characteristics.

[0045] Step 2: Set the model parameters of the exponential model, determine the grain density, thermal conductivity and effective moisture diffusion coefficient of the grain, and obtain the influence relationship and degree of influence of different parameters on its drying quality;

[0046] In this embodiment of the invention, the method for setting the model parameters of the exponential model includes the following steps:

[0047] Step 21: When the grain undergoes heat and mass exchange with the hot air medium, heat is transferred from the drying medium to the grain through the exchange and migration of moisture and energy. During this process, the density of grains with different moisture contents is determined by the displacement method to obtain the fitting curve between the dry basis moisture content and density of the grain.

[0048] Step 22: Set the parameters of different hot air temperature, initial moisture content and hot air velocity in the exponential model, and conduct multiple single-factor experiments in multiple groups. When the grain is dried to the set safe moisture content, the second group of grain drying begins. The average value of all groups is taken according to the total number of drying experiments. Record the drying characteristic curve of grain moisture changing with time and the drying rate changing with its moisture content to analyze the drying characteristics of the grain.

[0049] Step 23: After the moisture contained in the grain evaporates due to heat and is carried out of the drying equipment by the drying medium, the grain density, thermal conductivity and effective moisture diffusion coefficient of the grain are used as analytical variables by principal component analysis. By reducing the dimensionality, the parameter variables are recombined to form a comprehensive index that is independent of each other, which indirectly reflects the original evaluation index.

[0050] Step 3: Construct parameter optimization objectives for the grain drying process based on the original evaluation indicators.

[0051] In this embodiment of the invention, the step of constructing the parameter optimization target in the grain drying process is as follows:

[0052] Step 31: Obtain the parameters corresponding to the current state during the grain drying process by sampling, and divide the current state of the entire grain drying process into the initial current state and the drying current state, and obtain the parameter sequence of the grain drying process according to the different current states;

[0053] Step 32: Based on the obtained parameter sequence, optimize and control the parameters of the grain drying process, and use the grain drying process model for predictive control. In the initial optimization stage, collect the current real-time moisture content of the grain, which is the initial moisture content of the current state. Set the drying process parameters of the grain in the current state and calculate the grain drying quality prediction evaluation value of the current state based on these parameters.

[0054] Step 33: Determine the range of moisture content of the grain in real time. Since the moisture lost from the grain through evaporation is equal to the moisture absorbed by the drying hot air, the mass balance equation is used to calculate the change in the moisture content of the drying hot air and obtain the relative humidity value after the drying hot air and the grain exchange moisture and heat.

[0055] Step 34: When the current real-time moisture content of the grain is lower than the set moisture content threshold range, the optimal drying parameters for the grain are determined by comparing and analyzing the expected drying effects of different predictive controls, so as to achieve precise and efficient planning of the grain drying process and enable the grain moisture content to reach the specified target efficiently and quickly.

[0056] Step 4: While maintaining the output safe moisture content as the control target, further optimize the system control during the grain drying process to improve the drying quality;

[0057] In this embodiment of the invention, the steps for optimizing system control during the grain drying process include:

[0058] Step 41: The grain drying process is affected not only by external conditions and internal factors, but also by system factors. Since the removal of moisture from the grain during the drying process requires the assistance of a medium, and the heat required for moisture evaporation comes from the medium selected by the system, the heat and mass exchange between the grain and the medium in the drying layer is directional. In order to obtain an accurate online moisture content value during the grain drying process, the drying efficiency of the system is analyzed based on the process structure characteristics of the system.

[0059] Step 42: In the grain drying system, the discharge speed, hot air temperature and hot air volume are selected as control variables, and the final moisture content and drying quality of the grain are selected as controlled variables. During the drying process, the grain flows from top to bottom by its own weight. The flow speed is controlled by the discharge wheel installed at the bottom of the dryer. The faster the discharge wheel speed, the shorter the drying time of the grain in the dryer and the higher the outlet moisture content.

[0060] Step 43: Using the initial moisture content of the grain to be dried, hot air temperature, wind speed, and drying time as inputs, and the outlet moisture content after one cycle as output, establish an input-output process parameter expert system using the least squares vector machine algorithm prediction model;

[0061] Step 44: The drying control system obtains the predicted moisture content through the moisture content prediction model, and the actual moisture content at the outlet is measured by the moisture detector. The difference between the predicted value and the actual value of the moisture content is calculated. Through fuzzy PID calculation, the rotation speed of the grain discharge wheel in the system is output to control the drying time in the dryer, thereby controlling the change of grain moisture content.

[0062] Step 45: Under the condition of ensuring equal air volume to grain ratio, the reasonable air volume to grain ratio for deep drying is the maximum average water removal rate within a specific drying volume. When the air volume to grain ratio calculated based on the set grain air volume is less than this value, the ventilation energy consumption per unit water removal increases. When it is greater than this value, the exhaust heat loss of the medium increases, and the thermal efficiency of the system decreases. By detecting the actual moisture content of the grain at the outlet, the grain discharge wheel motor is controlled to make it close to the predicted moisture content, thereby achieving control over drying time and drying quality, resulting in more precise grain drying and higher quality.

[0063] Example 2: Example 2 of the present invention provides a cloud computing-based drying process optimization system. Figure 2 This is a schematic diagram of the module composition of the cloud computing-based drying process optimization system provided in Embodiment 2 of the present invention, as shown below. Figure 2 As shown, the system includes:

[0064] The module for determining the relationship between drying parameters is used to establish quantitative and functional relationships between various parameters during the grain drying process.

[0065] The parameter optimization target module is used to construct parameter optimization targets in the grain drying process based on the original evaluation indicators reflected.

[0066] The drying process control optimization module is used to optimize the system control during the grain drying process.

[0067] In some embodiments of the present invention, the drying parameter variable relationship module includes:

[0068] The regression equation establishment module is used to establish a regression equation by taking the accumulated temperature of grain drying as the output index of the drying experiment.

[0069] The drying stage progress judgment module is used to use accumulated temperature as an indicator to judge the progress of grain drying stage.

[0070] The quantitative index model building module is used to build quantitative index models corresponding to the drying characteristics of grains.

[0071] In some embodiments of the present invention, the parameter optimization target module includes:

[0072] The model parameter setting module is used to set the model parameters of the exponential model;

[0073] The drying characteristics analysis module is used to analyze the drying characteristics of grains based on the drying characteristic curve of grain moisture changing over time and the drying rate changing with its moisture content.

[0074] The parameter variable analysis module is used to analyze the grain density, thermal conductivity, and effective moisture diffusion coefficient of grains using principal component analysis.

[0075] The parameter sequence calculation and acquisition module is used to calculate and acquire the parameter sequence during the grain drying process based on different states.

[0076] In some embodiments of the present invention, the drying process control optimization module includes:

[0077] The parameter optimization and control module is used to optimize and control the parameters of the grain drying process based on the acquired parameter sequence.

[0078] The system control optimization module is used to optimize the system control during the grain drying process when the output safe moisture content is the control target.

[0079] The grain moisture content control module is used to control the drying time in the dryer by using fuzzy PID calculations and outputting the rotational speed of the grain discharge wheel in the system, thereby controlling the change in grain moisture content.

[0080] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0081] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A cloud computing-based drying process optimization method, applied to a cloud computing-based drying process optimization system, characterized in that: The method includes the following steps: Step 1: Obtain temperature and humidity change data when the dried grain enters the drying environment, and establish quantitative and functional relationships between various parameters during the grain drying process; Step 2: Set the model parameters of the exponential model, determine the grain density, thermal conductivity and effective moisture diffusion coefficient of the grain, and obtain the influence relationship and degree of influence of different parameters on its drying quality; Step 3: Construct parameter optimization objectives for the grain drying process based on the original evaluation indicators. Step 4: While maintaining a safe output moisture content as the control target, further optimize the system control during the grain drying process to improve drying quality, including: Step 41: The grain drying process is affected not only by external conditions and internal factors, but also by system factors. Since the removal of moisture from the grain during the drying process requires the assistance of a medium, and the heat required for moisture evaporation comes from the medium selected by the system, the heat and mass exchange between the grain and the medium in the drying layer is directional. In order to obtain an accurate online moisture content value during the grain drying process, the drying efficiency of the system is analyzed based on the process structure characteristics of the system. Step 42: In the grain drying system, the discharge speed, hot air temperature and hot air volume are selected as control variables, and the final moisture content and drying quality of the grain are selected as controlled variables. During the drying process, the grain flows from top to bottom by its own weight. The flow speed is controlled by the discharge wheel installed at the bottom of the dryer. The faster the discharge wheel speed, the shorter the drying time of the grain in the dryer and the higher the outlet moisture content. Step 43: Using the initial moisture content of the grain to be dried, hot air temperature, wind speed, and drying time as inputs, and the outlet moisture content after one cycle as output, establish an input-output process parameter expert system using the least squares vector machine algorithm prediction model; Step 44: The drying control system obtains the predicted moisture content through the moisture content prediction model, and the actual moisture content at the outlet is measured by the moisture detector. The difference between the predicted value and the actual value of the moisture content is calculated. Through fuzzy PID calculation, the rotation speed of the grain discharge wheel in the system is output to control the drying time in the dryer, thereby controlling the change of grain moisture content. Step 45: Under the condition of ensuring equal air volume to grain ratio, the reasonable air volume to grain ratio for deep drying is the maximum average water removal rate within a specific drying volume. When the air volume to grain ratio calculated based on the set grain air volume is less than this value, the ventilation energy consumption per unit water removal increases. When it is greater than this value, the exhaust heat loss of the medium increases, and the thermal efficiency of the system decreases. By detecting the actual moisture content of the grain at the outlet, the grain discharge wheel motor is controlled to make it close to the predicted moisture content.

2. The cloud computing-based drying process optimization method according to claim 1, characterized in that: The steps for establishing quantitative relationships and functional changes among various parameters during grain drying based on empirical data include: Step 11: Use the accumulated temperature for grain drying as the output index of the drying experiment, establish a regression equation, and use the accumulated temperature as an indicator to judge the progress of the grain drying stage. Step 12: By dividing the various drying stages in the drying process into different intervals, and dividing the different drying sections into equal parts, record the surface moisture data and total moisture content of the grain before entering the drying environment, and use the grain drying mathematical model to model and analyze the parameters of the grain at different stages in the drying process. Step 13: Analyze and calculate the drying rate of grains in the first stage of drying by using the evaporation area of ​​the grain surface and the characteristic parameters of the drying medium. For grains that have entered the second drying stage, the surface moisture has reached the equilibrium moisture content state corresponding to the drying medium. At this time, the dehydration of the grains shows the phenomenon of the drying surface retreating. The drying surface gradually approaches the center from the outside, and the moisture in the center continuously migrates outward. Analyze the drying rate of grains in the second stage by using the rate parameter of the migration of moisture from the grain body to the outer surface. Step 14: Based on the state parameters of the grain, ambient temperature, and humidity parameters during the entire grain drying process, calculate the heat required for evaporation of moisture during the drying process, i.e., the drying load. Then, by analyzing the relationship between grain moisture content and equilibrium temperature under different environmental and relative humidity conditions and different gradient moisture contents of the dried grain, establish a quantitative index model corresponding to the grain drying characteristics.

3. The cloud computing-based drying process optimization method according to claim 2, characterized in that: The method for setting the model parameters of the exponential model includes the following steps: Step 21: When the grain undergoes heat and mass exchange with the hot air medium, heat is transferred from the drying medium to the grain through the exchange and migration of moisture and energy. During this process, the density of grains with different moisture contents is determined by the displacement method to obtain the fitting curve between the dry basis moisture content and density of the grain. Step 22: Set the parameters of different hot air temperature, initial moisture content and hot air velocity in the exponential model, and conduct multiple single-factor experiments in multiple groups. When the grain is dried to the set safe moisture content, the second group of grain drying begins. The average value of all groups is taken according to the total number of drying experiments. Record the drying characteristic curve of grain moisture changing with time and the drying rate changing with its moisture content to analyze the drying characteristics of the grain. Step 23: After the moisture contained in the grain evaporates due to heat and is carried out of the drying equipment by the drying medium, the grain density, thermal conductivity and effective moisture diffusion coefficient of the grain are used as analytical variables by principal component analysis. By reducing the dimensionality, the parameter variables are recombined to form a comprehensive index that is independent of each other, which indirectly reflects the original evaluation index.

4. The cloud computing-based drying process optimization method according to claim 3, characterized in that: The steps for optimizing parameters in the grain drying process include: Step 31: Obtain the parameters corresponding to the current state during the grain drying process by sampling, and divide the current state of the entire grain drying process into the initial current state and the drying current state, and obtain the parameter sequence of the grain drying process according to the different current states; Step 32: Based on the obtained parameter sequence, optimize and control the parameters of the grain drying process, and use the grain drying process model for predictive control. In the initial optimization stage, collect the current real-time moisture content of the grain, which is the initial moisture content of the current state. Set the drying process parameters of the grain in the current state and calculate the grain drying quality prediction evaluation value of the current state based on these parameters. Step 33: Determine the range of moisture content of the grain in real time. Since the moisture lost from the grain through evaporation is equal to the moisture absorbed by the drying hot air, the mass balance equation is used to calculate the change in the moisture content of the drying hot air and obtain the relative humidity value after the drying hot air and the grain exchange moisture and heat. Step 34: When the current real-time moisture content of the grain is lower than the set moisture content threshold range, the optimal drying parameters for the grain are determined by comparing and analyzing the expected drying effects of different predictive controls, so as to achieve precise and efficient planning of the grain drying process and enable the grain moisture content to reach the specified target efficiently and quickly.

5. A cloud-based drying process optimization system for implementing the cloud-based drying process optimization method as described in claim 1, characterized in that: The system includes: The module for determining the relationship between drying parameters is used to establish quantitative and functional relationships between various parameters during the grain drying process. The parameter optimization target module is used to construct parameter optimization targets in the grain drying process based on the original evaluation indicators reflected. The drying process control optimization module is used to optimize the system control during the grain drying process.

6. The cloud computing-based drying process optimization system according to claim 5, characterized in that: The drying parameter variable relationship module includes: The regression equation establishment module is used to establish a regression equation by taking the accumulated temperature of grain drying as the output index of the drying experiment. The drying stage progress judgment module is used to use accumulated temperature as an indicator to judge the progress of grain drying stage. The quantitative index model building module is used to build quantitative index models corresponding to the drying characteristics of grains.

7. The cloud computing-based drying process optimization system according to claim 6, characterized in that: The parameter optimization objective module includes: The model parameter setting module is used to set the model parameters of the exponential model; The drying characteristics analysis module is used to analyze the drying characteristics of grains based on the drying characteristic curve of grain moisture changing over time and the drying rate changing with its moisture content. The parameter variable analysis module is used to analyze the grain density, thermal conductivity, and effective moisture diffusion coefficient of grains using principal component analysis. The parameter sequence calculation and acquisition module is used to calculate and acquire the parameter sequence during the grain drying process based on different states.

8. The cloud computing-based drying process optimization system according to claim 7, characterized in that: The drying process control and optimization module includes: The parameter optimization and control module is used to optimize and control the parameters of the grain drying process based on the acquired parameter sequence. The system control optimization module is used to optimize the system control during the grain drying process when the output safe moisture content is the control target. The grain moisture content control module is used to control the drying time in the dryer by using fuzzy PID calculations and outputting the rotational speed of the grain discharge wheel in the system, thereby controlling the change in grain moisture content.

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