Heat pump drying system and drying control method

Abstract

The invention relates to a heat pump drying system and a drying control method. The method comprises the steps that real-time operation data in the grain drying process is obtained; performing coupling calculation on the real-time operation data based on a pre-trained grain drying heat pump kinetic model to obtain a drying prediction factor; and according to a comparison result between the drying prediction factor and the real-time operation data, the output power of the heat pump set is dynamically adjusted. Performing coupling calculation on real-time operation data in a grain drying process through the grain drying heat pump kinetic model to obtain a drying prediction factor, and dynamically adjusting the output power of the heat pump group according to a comparison result between the drying prediction factor and the real-time operation data to obtain a drying prediction result. Therefore, accurate identification and accurate judgment of the drying process, the drying degree and the drying effect of the grains are realized, further accurate control of the drying operation of the grains is realized, and the method has wide applicability.

Description

Technical Field

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

[0002] When drying grains, existing grain drying devices find it difficult to accurately identify and judge the drying process, drying degree and drying effect of the grains, which makes it difficult to achieve timely control of the grain drying operation and cannot ensure long-term stable storage of grains.

[0003] It should be noted that the above introduction to the technical background is only for the convenience of providing a clear and complete description of the technical solutions of the present application and for the convenience of understanding by those skilled in the art. It cannot be considered that the above technical solutions are well known to those skilled in the art simply because they are described in the background technology section of the present application. Summary of the invention

[0004] The purpose of the present application is to provide a heat pump drying system and a drying control method to solve the problem in the prior art that it is difficult to accurately identify and judge the drying process, drying degree and drying effect of grains, resulting in difficulty in timely control of the drying operation of grains and inability to ensure long-term stable storage of grains.

[0005] In order to solve the above problems, a heat pump drying system involved in this application adopts the following technical solutions:

[0006] It includes: a heat pump group, a drying chamber, a grain feeding device, a grain discharging device, an induced draft fan and a temperature and humidity detection module, wherein the heat pump group adjusts the air supply temperature and the heating capacity by a start-stop combination; the drying chamber is connected to the heat pump group to dry the grains; the grain feeding device is connected to the drying chamber; the grain discharging device is connected to the drying chamber; the induced draft fan is connected to the drying chamber; the temperature and humidity detection module is connected to the drying chamber to detect the temperature and humidity status of the grains.

[0007] In order to solve the above problems, a drying control method involved in the present application is applicable to the heat pump drying system involved in the present application. The drying control method adopts the following technical solutions:

[0008] Get real-time operating data during grain drying;

[0009] Based on a pre-trained grain drying heat pump dynamics model, coupling calculation is performed on the real-time operation data to obtain a drying prediction factor;

[0010] According to the comparison result between the drying prediction factor and the real-time operation data, the output power of the heat pump group is dynamically adjusted.

[0011] In order to solve the above problems, a drying control device involved in the present application is applicable to the heat pump drying system involved in the present application. The drying control device adopts the following technical solutions:

[0012] A first acquisition module, the first acquisition module is used to acquire real-time operation data during the grain drying process;

[0013] a second acquisition module, the second acquisition module being used to perform coupling calculation on the real-time operation data based on a pre-trained grain drying heat pump dynamics model to obtain a drying prediction factor;

[0014] The regulating module is used to dynamically regulate the output power of the heat pump group according to the comparison result between the drying prediction factor and the real-time operation data.

[0015] In order to solve the above problems, an electronic device involved in the present application includes: a processor; a memory for storing instructions executable by the processor; wherein the processor is configured to execute the instructions to implement the drying control method involved in the present application.

[0016] In order to solve the above problems, the present application relates to a non-temporary computer-readable storage medium. When the instructions in the storage medium are executed by a processor of an electronic device, the electronic device can execute the drying control method involved in the present application.

[0017] In order to solve the above-mentioned problem, a computer program product involved in the present application includes a computer program, and the computer program implements the drying control method involved in the present application when executed by a processor in a communication device.

[0018] The beneficial effects of this application are as follows:

[0019] The grain drying heat pump dynamics model is used to couple the real-time operating data of the grain drying process to obtain the drying prediction factor, and the output power of the heat pump group is dynamically adjusted according to the comparison results between the drying prediction factor and the real-time operating data, thereby realizing accurate identification and judgment of the drying process, drying degree and drying effect of the grain, and then accurately controlling the grain drying operation, which has wide applicability. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solution of the embodiment of the present application, the following is a brief introduction to the drawings required for use in the embodiment:

[0021] Figure 1 A schematic diagram of the structure of a heat pump drying system provided in an embodiment of the present application;

[0022] Figure 2 A schematic flow chart of a drying control method provided in an embodiment of the present application;

[0023] Figure 3 A schematic flow chart of another drying control method provided in an embodiment of the present application;

[0024] Figure 4 A structural schematic diagram of a drying control device provided in an embodiment of the present application;

[0025] Figure 5 A schematic diagram of the structure of an electronic device provided according to an embodiment of the present application;

[0026] Figure 6 It is a schematic diagram of the structure of another electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0027] In order to make the technical purpose, technical solution and beneficial effects of the present application clearer, the technical solution of the present application is further described below in conjunction with the drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application, that is, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations.

[0028] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the embodiments of the present application. The singular forms of "a" and "the" used in the embodiments of the present application and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0029] It should be understood that, although the terms first, second, third, etc. may be used to describe various information in the embodiments of the present application, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the embodiments of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein may be interpreted as "at..." or "when..." or "in response to determination".

[0030] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0031] It should be noted that the heat pump drying system provided in any embodiment of the present application can be executed alone, or can be executed in combination with possible implementation methods in other embodiments, or can be executed in combination with any technical solution in the related technology.

[0032] The heat pump drying system and drying control method thereof according to an embodiment of the present application will be described below with reference to the accompanying drawings.

[0033] Figure 1 A schematic diagram of the structure of a heat pump drying system provided in an embodiment of the present application.

[0034] like Figure 1 The heat pump drying system includes but is not limited to: a heat pump group, a drying chamber, a grain feeding device, a grain discharging device, an induced draft fan and a temperature and humidity detection module, wherein the heat pump group adjusts the air supply temperature and the heating capacity by a start-stop combination; the drying chamber is connected to the heat pump group to dry the grains; the grain feeding device is connected to the drying chamber, and the grain discharging device is connected to the drying chamber; the induced draft fan is connected to the drying chamber; the temperature and humidity detection module is connected to the drying chamber to detect the temperature and humidity status of the grains.

[0035] It should be noted that drying is an indispensable step in the production and processing of grain agricultural products. Drying is an extremely important condition to ensure its long-term safe storage, and drying is a nonlinear, strongly coupled, complex heat and mass transfer process. Compared with traditional coal-fired steam drying, hot air drying, and infrared drying, heat pump drying has the advantages of low energy consumption, excellent drying effect and a wide range of applications.

[0036] The drying chamber uses the reverse Carnot principle to absorb heat from the surrounding environment through a heat pump group, thereby increasing the indoor temperature and achieving grain drying. The drying chamber can be divided into four sections, namely the slow revival section, the drying section, the cooling section and the grain discharge section. The grain enters the top of the drying chamber through the grain feeding device, and then the grain is thrown off as much as possible in the slow revival section through the throwing plate, and then the grain is transferred from the slow revival section to the drying section for heat pump drying. When the temperature and humidity detection module detects that the moisture content of the grain reaches the target, the drying is stopped; the grain is transferred from the drying section to the cooling section for cooling, and then transferred to the grain discharge section; the grain discharge device is connected to the grain discharge section to take the dried grain out of the drying chamber. It should be added that, usually, the drying chamber also includes a circulating working medium for realizing the transfer and utilization of heat energy.

[0037] In the heat pump drying system, the operating parameters of each component are often set by the operator based on experience, which may make it difficult to accurately control the degree of drying. In addition, different types of grains and different uses require different degrees of drying. If the operator performs drying based on experience, it may lead to problems such as uneven quality after drying and low drying efficiency. On the other hand, although heat pump drying has the advantages of high efficiency and energy saving, its drying process is affected by many factors, such as grain type, initial moisture content, drying temperature, wind speed, etc. The interactions between these factors are complex and difficult to accurately describe and control using traditional methods. Since drying control is a closed-loop control with feedback function, it is necessary to accurately control the drying process of the heat pump drying system based on the on-site temperature and humidity data values ​​detected by the humidity detection module.

[0038] In order to solve the above problems, an embodiment of the present application provides a drying control method.

[0039] Figure 2 A schematic flow chart of a drying control method provided in an embodiment of the present application.

[0040] like Figure 2 As shown, the drying control method is applicable to the heat pump drying system provided in the embodiment of the present application, and the drying control method includes but is not limited to the following steps:

[0041] S201, obtaining real-time operation data during the grain drying process.

[0042] In a feasible implementation, during the grain heat pump drying process, obtaining real-time operating data is crucial for monitoring drying efficiency, ensuring grain quality and optimizing energy use. Real-time operating data generally includes ambient temperature and humidity data, hot air temperature and humidity data, grain temperature data, real-time grain moisture content data, power loss data, etc. Among them, ambient temperature and humidity data reflect the external environmental conditions of the drying operation, and have a direct impact on the operating efficiency and energy consumption of the heat pump drying system. A high temperature and low humidity environment is conducive to accelerating the drying speed, while a low temperature and high humidity environment will increase the drying difficulty and energy consumption. Hot air is the main heat source and drying medium for grain drying, and its temperature and humidity data directly determines the evaporation rate and drying effect of the internal moisture of the grain. A suitable combination of hot air temperature and humidity data It can significantly improve the drying efficiency and quality; the grain temperature data reflects the temperature changes of the grain during the drying process, which is an important basis for judging the degree of drying and avoiding overheating damage. By real-time monitoring of the grain temperature, the drying conditions can be adjusted in time to ensure that the grain maintains an appropriate temperature range during the drying process; the real-time moisture content data of the grain is a key indicator for judging the drying effect. By real-time monitoring of the moisture content of the grain, the drying process can be accurately controlled to avoid over-drying or under-drying, thereby ensuring the quality and nutritional value of the grain; the power loss data reflects the energy consumption of the heat pump drying system, which is an important indicator for evaluating the economy and environmental protection of the heat pump drying system. By optimizing the drying parameters and improving the efficiency of the equipment, the power loss can be reduced and the goal of energy conservation and emission reduction can be achieved.

[0043] Furthermore, the real-time operation data are interrelated and mutually influential in the grain drying process, and together constitute the real-time operation monitoring system of the heat pump drying system. The collected real-time operation data is cleaned to remove outliers and noise to ensure the accuracy and reliability of the data; then the real-time operation data is converted into the input format required by the pre-trained grain drying heat pump dynamics model for subsequent processing. By real-time monitoring and regulation of real-time operation data, precise control of the grain drying process can be achieved, drying efficiency and quality can be improved, and energy consumption and costs can be reduced.

[0044] S202, performing coupling calculation on real-time operation data based on a pre-trained grain drying heat pump dynamics model to obtain a drying prediction factor.

[0045] In a feasible implementation, according to the characteristics of grain drying and the working principle of the heat pump drying system, a suitable grain drying heat pump dynamics model is selected or constructed. The model should be able to describe the energy conversion and transfer process of the heat pump during the grain drying process, as well as the evaporation and diffusion process of water inside the grain. Specifically, firstly, the physical and chemical properties of the grain are obtained, such as water content, density, thermal conductivity, specific heat capacity, etc.; then, the working principle, performance parameters, selection of refrigerants, etc. of the heat pump drying system are obtained; then, according to the physical and chemical properties of the grain and the mutual correlation and mutual influence between the heat pump drying system, the heat and mass transfer mechanism in the grain drying process, such as heat conduction, convection and radiation, etc., is determined; at the same time, the influence of external conditions such as the drying chamber ambient temperature, humidity, wind speed, etc. on the drying process is considered.

[0046] As an example, mathematical equations describing the operation of grain drying and heat pump drying systems can be established based on the principles of thermodynamics and mass transfer. The grain drying process can be regarded as a complex process involving momentum, mass and heat transfer. It can usually be described by drying characteristic curves, including the curve of grain moisture changing with drying time M = f (t), the curve of grain temperature changing with time T = f (T) and the curve of grain drying speed changing with drying time. When modeling, it is necessary to consider the moisture gradient and temperature gradient inside the grain and the conditions of the external environment (such as temperature, humidity, wind speed, etc.). In order to mathematize this process, it may be necessary to introduce some drying models, such as distributed models or lumped parameter models. The distributed model considers the spatial distribution of temperature and humidity inside the grain and how these distributions change with time. In the grain drying process, various variables (such as temperature and humidity) change continuously with the change of spatial position. This change can be described by the distributed parameter process method. The lumped parameter model ignores the spatial distribution of temperature and humidity inside the grain, assuming that the temperature and humidity of the entire grain material are uniform. This model regards the grain as a whole and uses a single parameter (such as average temperature and average humidity) to describe its state. It is usually derived based on the principles of conservation of mass and conservation of energy to obtain a simplified equation describing the grain drying process.

[0047] Taking the distributed model as an example, assuming that the grain drying equation is expressed as:

[0048]

[0049] Among them, MR is the moisture ratio; M is the moisture content of the grain at time t; M C is the moisture content of grains at equilibrium; M 0 is the moisture content of grain at the initial moment; k is the drying rate constant; n is the drying rate index.

[0050] The heat pump drying system equation involves the thermodynamic process in the refrigeration cycle, including compression, condensation, expansion and evaporation. Each stage has a corresponding thermodynamic equation to describe the energy conversion and transfer of the system. For example, the compression stage can be described by the compressor power, compression ratio and refrigerant properties; the condensation stage involves the heat exchange efficiency and temperature distribution of the condenser.

[0051] As an example, the heat pump drying system follows the principle of energy conservation during operation. The system absorbs energy from the low-temperature heat source and transfers the heat to the high-temperature heat source through the refrigerant cycle, while consuming a certain amount of electrical energy. Therefore, the energy conservation equation of the heat pump drying system can be described as:

[0052] Input electrical energy + heat absorbed by low-temperature heat source = heat released by high-temperature heat source + heat loss of the system

[0053] In the heat pump drying system, heat transfer is an important process. The refrigerant absorbs energy in the evaporator and releases energy in the condenser. The heat transfer equation can be described as:

[0054] Q=A*U*△t

[0055] Among them, Q is the transferred heat, A is the heat exchange area, U is the convection heat transfer coefficient, and △t is the average heat transfer temperature difference.

[0056] The refrigerant cycle in the heat pump drying system involves multiple links such as compression, condensation, expansion and evaporation. The refrigerant cycle equation can be described as:

[0057] The mass conservation equation, that is, the mass of the refrigerant remains constant during the cycle;

[0058] The energy conservation equation means that the energy of the refrigerant is conserved during the cycle, that is, the heat absorbed is equal to the heat released plus the change in energy within the system;

[0059] The equation of state is the equation that describes the state of the refrigerant at different pressures and temperatures, such as saturated vapor pressure, temperature, density, etc.

[0060] Furthermore, after obtaining the grain drying equation and the heat pump drying system equation, it is clarified that the output temperature and flow rate of the heat pump drying system, and the moisture content and temperature during the grain drying process are the key parameters for the interaction between the grain drying process and the heat pump drying system; then based on the above key parameters, a coupling equation describing the interaction between the grain drying process and the heat pump drying system is established, wherein the coupling equation involves multiple aspects, including mass balance, heat balance and the interaction between grain and the heat pump drying system.

[0061] As an example, the mass balance equation describes the reduction of moisture in the grain drying process. It is based on the law of conservation of mass, that is, the amount of moisture entering the heat pump drying system is equal to the amount of moisture leaving the heat pump drying system plus the change in moisture in the system. For the grain drying process, it can be expressed as:

[0062]

[0063] Where M is the moisture content of grain at time t; M C is the moisture content of grains at equilibrium; k d is the drying rate constant, which is related to factors such as grain type, temperature, and humidity.

[0064] The heat balance equation describes the heat transfer during the grain drying process. It is based on the law of heat conduction and the principle of heat balance, linking the temperature change of grains with heat transfer. The heat balance equation can be expressed as:

[0065] Qin=Qout+Qloss+ΔQ

[0066] Among them, Qin is the heat provided by the heat pump drying system; Qout is the heat when the grain leaves the heat pump drying system, including the heat taken away by the grain and the heat of the exhaust air; Qloss is the heat lost by the heat pump drying system, such as heat radiation and heat conduction to the surrounding environment; ΔQ is the heat consumed by the evaporation of water in the grain.

[0067] The equation between grain and heat pump drying system includes the performance and working state of heat pump drying system, as well as refrigerant cycle, compressor power, heat exchange between condensing device and evaporating device, etc. The equation between grain and heat pump drying system can be simplified as follows:

[0068]

[0069] Wherein, COP is the heating efficiency of the heat pump drying system; Q h is the heat provided by the heat pump drying system, i.e. Qin; W is the electrical energy consumed by the heat pump drying system.

[0070] Then the above equations are coupled, the heat provided by the heat pump drying system is used as the heat source term in the grain drying process, and the moisture and heat released during the grain drying process are used as the load terms in the heat pump drying system equation. Then based on finite element analysis, the coupled equations are characterized as a grain drying heat pump dynamics model.

[0071] Furthermore, the real-time operating data is substituted into the grain drying heat pump dynamics model. The grain drying heat pump dynamics model predicts the grain drying process based on the ambient temperature and humidity data, hot air temperature and humidity data, grain temperature data, real-time grain moisture content data, power loss data, etc., and obtains the drying prediction factor, wherein the drying prediction factor involves the elements of drying speed, energy consumption, and drying time.

[0072] S203: dynamically adjust the output power of the heat pump group according to the comparison result between the drying prediction factor and the real-time operation data.

[0073] In a feasible implementation, the drying prediction factor includes key indicators such as drying time prediction, drying efficiency prediction, and energy consumption prediction obtained by coupling. The drying prediction factor is compared and analyzed with the real-time operation data to evaluate whether the output power of the heat pump drying system (i.e., the output power of the heat pump group) meets the drying requirements. If the drying prediction factor shows that the drying efficiency is lower than expected, it may be necessary to increase the output power of the heat pump group to improve the drying efficiency; if the drying prediction factor shows that the energy consumption is higher than expected, it may be necessary to reduce the output power of the heat pump group to reduce energy consumption; if the drying prediction factor is basically consistent with the results shown by the actual operation data, the output power of the heat pump group remains unchanged.

[0074] The operator can manually adjust the power setting of the heat pump group according to the comparative analysis results; or the operator can automatically adjust the output power of the heat pump group according to the preset rules or algorithms through the built-in intelligent program or external intelligent controller. During the adjustment process, the operating status and drying effect of the heat pump group are monitored in real time, and the adjustment strategy is adjusted according to the feedback results.

[0075] In summary, the real-time operation data in the grain drying process is coupled and calculated through the grain drying heat pump dynamics model to obtain the drying prediction factor. According to the comparison results between the drying prediction factor and the real-time operation data, the output power of the heat pump group is dynamically adjusted, thereby realizing accurate identification and accurate judgment of the drying process, drying degree and drying effect of the grain, and then accurately controlling the grain drying operation, which has wide applicability.

[0076] Figure 3 A schematic flow chart of another drying control method provided in an embodiment of the present application.

[0077] like Figure 3 As shown, the drying control method is applicable to the heat pump drying system provided in the embodiment of the present application, and the drying control method includes but is not limited to the following steps:

[0078] S301, based on preprocessing of environmental temperature and humidity data, hot air temperature and humidity data, grain temperature data, real-time grain moisture content data and power loss data, real-time operation data of the grain drying process is obtained.

[0079] In a feasible implementation, target moisture content data of the grains to be dried is determined according to the type of the grains to be dried, wherein identification information such as the type and particle diameter of the grains to be dried can be identified through the image of the grains to be dried, and target moisture content data of the grains that does not generate heat, germinate, or become moldy during storage is determined according to the type and particle diameter of the grains. On the other hand, the target moisture content data of the grains may vary depending on factors such as the variety, origin, climatic conditions, and storage conditions of the grains. As an example, if the grain to be dried is corn, its target moisture content is generally 14.5%; if it is rice, its target moisture content is between 14.5% and 15%; if it is wheat, its target moisture content is between 12.5% ​​and 13%; if it is soybeans, its target moisture content is between 13% and 14%. The drying temperature range data that the grains to be dried can withstand should be determined according to the specific scenario.

[0080] Next, according to the spreading thickness data of the grains to be dried, the drying temperature range data that the grains to be dried can withstand is determined. Among them, a thinner spreading thickness is conducive to the full contact between hot air and grains, improves the heat exchange efficiency, and speeds up the drying speed. A higher drying temperature can be appropriately selected, but generally does not exceed the upper limit of the safe drying temperature of grains. For example, the upper limit of the safe drying temperature of corn is usually around 100°C; a thicker spreading thickness may weaken the penetration of hot air in the grain layer, resulting in uneven drying and prolonged drying time. To avoid overheating and uneven drying of grains, a lower drying temperature should be selected and the drying time should be appropriately extended. On the other hand, when setting the drying temperature range data, the growth temperature of grains must also be considered. Taking rice as an example, the growth temperature of rice can reach 60 degrees Celsius, but too high or too fast drying can easily lead to quality degradation. Therefore, a lower drying temperature is usually selected and an appropriate drying time is maintained. The spreading thickness data of the grains to be dried should be determined according to the specific scenario, and based on the spreading thickness data, the drying temperature range data that the grains to be dried can withstand should be determined.

[0081] Next, the initial output power of the heat pump group is determined according to the target moisture content data, the drying temperature interval data, and the moisture content data of the grains to be dried before drying. First, the moisture to be removed is calculated according to the moisture content data of the grains before drying and the target moisture content data. The drying energy consumption is related to the moisture to be removed, the drying temperature interval data, and the thermal characteristics of the grains. Generally, the higher the drying temperature, the greater the energy consumption. The more moisture to be removed, the greater the energy consumption. The energy consumption value between the moisture to be removed and the moisture to be removed can be determined by consulting the relevant drying energy consumption data. Then the initial output power of the heat pump group can be calculated, and the moisture to be removed can be converted into heat (obtained through the vaporization heat of water and the specific heat capacity of grains). Then the required initial output power can be determined according to the energy efficiency ratio and heating capacity of the heat pump group.

[0082] Then, according to the initial output power, the grains to be dried are dried, and the ambient temperature and humidity data, hot air temperature and humidity data, grain temperature data, real-time grain moisture content data and power loss data during the grain drying process are obtained.

[0083] Next, based on data cleaning and data integration of environmental temperature and humidity data, hot air temperature and humidity data, grain temperature data, real-time grain moisture content data and power loss data, a data set is obtained; based on data transformation and data induction of the data set, real-time operation data of the grain drying process is obtained.

[0084] For further details of step S301, please refer to the relevant contents in the above embodiment, which will not be repeated here.

[0085] S302, performing coupling calculation on real-time operation data based on a pre-trained grain drying heat pump dynamics model to obtain a drying prediction factor.

[0086] In a feasible implementation, a grain drying heat pump dynamics model is first constructed, wherein a grain drying equation describing the change of grain moisture content over time can be established based on the physicochemical properties of the grain and the heat transfer mechanism in the grain drying process, and a heat pump drying system equation describing the refrigerant cycle, heat exchange process and energy conversion efficiency of the heat pump drying system is established; based on finite element analysis, the grain drying equation and the heat pump drying system equation are coupled to establish a coupling equation describing the interaction between the grain drying process and the heat pump system; the coupling equation is trained based on the historical operation data of grain drying to obtain a grain drying heat pump dynamics model.

[0087] After obtaining the grain drying heat pump dynamics model, the real-time operation data is transmitted to the grain drying heat pump dynamics model to obtain the moisture migration curve and temperature change curve of the grain during the heat pump drying process; based on the time series coupling of the moisture migration curve and the temperature change curve, the drying prediction factors related to the drying time, drying efficiency and energy consumption are obtained. Specifically, based on the feature extraction of the moisture migration curve and the temperature change curve, multiple features related to the grain moisture drop rate, temperature rise rate and temperature stability range are obtained; according to machine learning, the importance of multiple features is analyzed to determine the drying prediction factors that affect the grain drying time, drying efficiency and energy consumption, wherein the random forest and gradient tree in machine learning can be used to calculate the number of times each feature is used or the number of times it is used for decomposition to measure the importance of the feature. Taking random forest as an example, the average number of selections of each feature in all decision trees or the average information gain can be calculated to determine the importance of the feature. The importance of features can also be measured by linear regression by calculating the coefficient of each feature. In grain drying prediction, drying time, drying efficiency and energy consumption can be used as target variables, and various drying parameters (such as temperature, humidity, wind speed, grain type, etc.) can be used as feature variables to construct a linear regression model and analyze the coefficient of each feature.

[0088] Then, based on the results of importance analysis, the drying prediction factors affecting grain drying time, drying efficiency and energy consumption were determined.

[0089] For further details of step S302, please refer to the relevant contents in the above embodiment, which will not be repeated here.

[0090] S303: dynamically adjust the output power of the heat pump group according to the comparison result between the drying prediction factor and the real-time operation data.

[0091] For further details of step S303, please refer to the relevant contents in the above embodiment, which will not be repeated here.

[0092] In summary, the real-time operation data in the grain drying process is coupled and calculated through the grain drying heat pump dynamics model to obtain the drying prediction factor. According to the comparison results between the drying prediction factor and the real-time operation data, the output power of the heat pump group is dynamically adjusted, thereby realizing accurate identification and accurate judgment of the drying process, drying degree and drying effect of the grain, and then accurately controlling the grain drying operation, which has wide applicability.

[0093] Figure 4 A schematic diagram of the structure of a drying control device provided in an embodiment of the present application.

[0094] like Figure 4As shown, the drying control device 400 includes:

[0095] A first acquisition module 401, the first acquisition module 401 is used to acquire real-time operation data during the grain drying process;

[0096] A second acquisition module 402, the second acquisition module 402 is used to perform coupling calculation on real-time operation data based on a pre-trained grain drying heat pump dynamics model to obtain a drying prediction factor;

[0097] The adjustment module 403 is used to dynamically adjust the output power of the heat pump group according to the comparison result between the drying prediction factor and the real-time operation data.

[0098] Figure 5 The present invention is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present application. Figure 5 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0099] like Figure 5 As shown, the electronic device 500 includes a processor 501, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 502 or a program loaded from a memory 506 to a random access memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the electronic device 700 are also stored. The processor 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0100] The following components are connected to the I / O interface 505: a memory 506 including a hard disk, etc.; and a communication part 507 including a network interface card such as a LAN (Local Area Network) card, a modem, etc., and the communication part 507 performs communication processing via a network such as the Internet; a drive 508 is also connected to the I / O interface 505 as needed.

[0101] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 507. When the computer program is executed by the processor 501, the above-mentioned functions defined in the method of the present application are executed.

[0102] In an exemplary embodiment, a storage medium including instructions is also provided, such as a memory including instructions, and the instructions can be executed by the processor 501 of the electronic device 500 to complete the above method. Optionally, the storage medium can be a non-transitory computer-readable storage medium, for example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0103] In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, apparatus, or device. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0104] Figure 6 It is a schematic diagram of the structure of another electronic device provided according to an embodiment of the present application. Figure 6 The electronic device shown is only an example and should not limit the functions and scope of use of the embodiments of the present application. Figure 6 As shown, the electronic device 600 includes a processor 601 and a memory 602. The memory 602 is used to store program codes, and the processor 601 is connected to the memory 602 and is used to read the program codes from the memory 602 to implement the drying control method in the above embodiment.

[0105] Optionally, the number of processors 601 may be one or more.

[0106] Optionally, the electronic device may further include an interface 603, and the number of the interface 603 may be multiple. The interface 603 may be connected to an application program and may receive data from an external device such as a sensor.

[0107] Finally, it should be noted that the above embodiments are only used to illustrate but not limit the technical solutions of the present application. Any equivalent substitutions of the present application and any modifications or partial substitutions that do not depart from the spirit and scope of the present application shall be included in the scope of protection of the claims of the present application.

Claims

1. A heat pump drying system, characterized in that: include: A heat pump group, a drying chamber, a grain feeding device, a grain discharging device, an induced draft fan and a temperature and humidity detection module, wherein the heat pump group adjusts the air supply temperature and the heating capacity by a start-stop combination; the drying chamber is connected to the heat pump group to dry the grains; the grain feeding device is connected to the drying chamber; the grain discharging device is connected to the drying chamber; the induced draft fan is connected to the drying chamber; the temperature and humidity detection module is connected to the drying chamber to detect the temperature and humidity status of the grains.

2. A drying control method, characterized in that: Applicable to the heat pump drying system according to claim 1, the drying control method comprises: Get real-time operating data during grain drying; Based on a pre-trained grain drying heat pump dynamics model, coupling calculation is performed on the real-time operation data to obtain a drying prediction factor; According to the comparison result between the drying prediction factor and the real-time operation data, the output power of the heat pump group is dynamically adjusted.

3. The method according to claim 2, characterized in that The real-time operation data of the grain drying process is obtained, including: Determining target moisture content data of the grains to be dried according to the types of the grains to be dried; Determining the drying temperature range data that the grains to be dried can withstand according to the spreading thickness data of the grains to be dried; Determining the initial output power of the heat pump group according to the target moisture content data, the drying temperature interval data and the moisture content data of the grains to be dried before drying; Dry the grains to be dried according to the initial output power, and obtain environmental temperature and humidity data, hot air temperature and humidity data, grain temperature data, real-time grain moisture content data, and power loss data during the grain drying process; Based on preprocessing the ambient temperature and humidity data, the hot air temperature and humidity data, the grain temperature data, the real-time grain moisture content data and the power loss data, real-time operation data in the grain drying process is obtained.

4. The method according to claim 3, characterized in that The method of obtaining real-time operation data in the grain drying process based on preprocessing the ambient temperature and humidity data, the hot air temperature and humidity data, the grain temperature data, the real-time grain moisture content data and the power loss data includes: Acquire a data set based on data cleaning and data integration of the ambient temperature and humidity data, the hot air temperature and humidity data, the grain temperature data, the real-time grain moisture content data, and the power loss data; Based on data transformation and data induction of the data set, real-time operation data in the grain drying process is obtained.

5. The method according to claim 2, characterized in that: The pre-trained grain drying heat pump dynamics model is used to couple the real-time operation data to obtain a drying prediction factor, including: Construct a dynamic model of a grain drying heat pump; The real-time operation data is transmitted to the grain drying heat pump dynamics model to obtain the moisture migration curve and temperature change curve of the grain during the heat pump drying process; Based on time series coupling of the moisture migration curve and the temperature change curve, drying prediction factors related to drying time, drying efficiency and energy consumption are obtained.

6. The method according to claim 5, characterized in that The grain drying heat pump dynamics model is constructed, including: Based on the physicochemical properties of grains and the heat transfer mechanism during grain drying, a grain drying equation is established to describe the change of grain moisture content over time. Establish the heat pump drying system equations to describe the refrigerant cycle, heat exchange process and energy conversion efficiency of the heat pump drying system; Based on finite element analysis, the grain drying equation and the heat pump drying system equation are coupled to establish a coupling equation describing the interaction between the grain drying process and the heat pump system; The coupling equation is trained based on the historical operation data of grain drying to obtain the grain drying heat pump dynamics model.

7. The method according to claim 5, characterized in that The method of obtaining drying prediction factors related to drying time, drying efficiency and energy consumption based on time series coupling of the moisture migration curve and the temperature change curve includes: Based on feature extraction of the moisture migration curve and the temperature change curve, a plurality of features related to the grain moisture decrease rate, the temperature increase rate and the temperature stability range are obtained; According to machine learning, importance analysis is performed on a plurality of the features to determine drying prediction factors that affect grain drying time, drying efficiency and energy consumption.

8. A drying control device, applicable to the heat pump drying system according to claim 1, characterized in that: include: A first acquisition module, the first acquisition module is used to acquire real-time operation data during the grain drying process; a second acquisition module, the second acquisition module being used to perform coupling calculation on the real-time operation data based on a pre-trained grain drying heat pump dynamics model to obtain a drying prediction factor; The regulating module is used to dynamically regulate the output power of the heat pump group according to the comparison result between the drying prediction factor and the real-time operation data.

9. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement the method as claimed in any one of claims 2 to 7.

10. A non-transitory computer-readable storage medium, characterized in that: When the instructions in the storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the method as claimed in any one of claims 2 to 7.

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