Grain drying machine and internal thermal field balance control method thereof

Abstract

The invention is suitable for the technical field of grain drying, and particularly relates to a grain drying machine and an internal thermal field balance control method thereof. In response to the trigger signal, continuously acquiring first detection information and second detection information according to a preset interval time; wherein the trigger signal indicates that the grain just enters the first thermal field, the first detection information is used for reflecting exhaust humidity data of the first thermal field, and the second detection information is used for reflecting a surface image of the grain just entering the first thermal field; based on the preset interval time, the first drying parameter, the first detection information and the second detection information, a grain drying condition chain is determined; determining a thermal field adjustment strategy based on the target humidity, the grain drying condition chain and the first drying parameter; and adjusting the second thermal field based on the thermal field adjustment strategy. According to the internal thermal field balance control method for the grain drying machine, the drying standard reaching rate of the grain drying machine to grains can be increased.

Description

Technical Field

[0001] This application belongs to the field of grain drying technology, and in particular relates to a grain dryer and its internal thermal field equalization control method. Background Technology

[0002] Grain drying refers to the process of reducing the moisture content of grains inside a dryer by using dry, hot air to prevent the grains from molding or sprouting during storage. Grain drying is an essential operation for grain storage.

[0003] In related technologies, uniform drying parameters are generally set based on the initial moisture content of a single grain variety (e.g., 20%-25%). A heating device maintains a stable temperature in the drying zone, a fan provides a constant airflow, and the drying process is completed within a preset time. After drying, the grain moisture content is tested manually by sampling and weighing or by an offline moisture meter to confirm whether it meets the standards. However, during the drying process, the initial moisture content of the grain varies significantly due to factors such as climate differences during harvesting (e.g., higher moisture content on rainy days and lower moisture content on sunny days), different pre-drying levels before storage, uneven local stacking during spreading, and fluctuations in airflow distribution within the drying zone (the moisture content of the same batch of grain can fluctuate by 15%-30%). Furthermore, since grain drying is a continuous process—grain continuously enters and is dried in the dryer, and then the dried grain is output—the grain dryer may either fail to dry completely or over-dry, ultimately reducing the rate of grain meeting the drying standards. Summary of the Invention

[0004] This application provides a grain dryer and its internal thermal field equalization control method, which can improve the problem of declining grain drying compliance rate in grain dryers.

[0005] In a first aspect, embodiments of this application provide a method for equalizing the internal thermal field of a grain dryer, including: Obtain a first drying parameter and a target humidity; wherein, the first drying parameter is used to reflect the setting parameters for drying the grain entering the first hot zone of the grain dryer, and the target humidity is used to reflect the preset moisture content of the grain after drying in the first hot zone; In response to a trigger signal, first detection information and second detection information are continuously acquired at preset intervals; wherein, the trigger signal refers to the grain just entering the first hot field, the first detection information is used to reflect the exhaust humidity data of the first hot field, and the second detection information is used to reflect the surface image of the grain just entering the first hot field. Based on the preset interval time, the first drying parameters, the first detection information, and the second detection information, a grain drying status chain is determined; wherein, the grain drying status chain is used to reflect the drying status of grain in different areas of the first thermal field; Based on the target humidity, the grain drying condition chain, and the first drying parameter, a heat field adjustment strategy is determined; wherein, the heat field adjustment strategy is used to reflect the setting parameters and adjustment timing of the second heat field in the grain dryer, and the first heat field is located before the second heat field; The second thermal field is adjusted based on the aforementioned thermal field adjustment strategy.

[0006] The technical solutions described in this application embodiment have at least the following technical effects: The grain dryer internal thermal field equalization control method provided in this application first acquires a first drying parameter reflecting the setting parameters when grain enters the first thermal field of the grain dryer for drying, and a target humidity reflecting the moisture content of the grain when drying is completed. When a trigger signal reflecting that the grain has just entered the first thermal field is received, the method continuously acquires a first detection information reflecting the exhaust humidity data of the first thermal field and a second detection information reflecting the surface image of the grain just entering the first thermal field at preset intervals. Based on the preset interval, the first drying parameter, the first detection information and the second detection information, a grain drying status chain reflecting the drying status of the grain in different areas of the first thermal field is determined. Then, based on the target humidity, the grain drying status chain and the first drying parameter, a thermal field adjustment strategy reflecting the setting parameters of the second thermal field in the grain dryer and the timing of adjustment is determined. Finally, the second thermal field of the grain dryer is adjusted through the thermal field adjustment strategy.

[0007] This method can effectively capture humidity differences caused by initial moisture content fluctuations by real-time monitoring of exhaust humidity and grain surface images in the first hot zone. It transforms the adjustment of hot zone parameters from preset fixed values ​​to dynamic adaptive adjustment, enabling the drying process to accurately match the actual condition of the grain. By using exhaust humidity data and surface image information, the humidity distribution of the grain in each area of ​​the first hot zone is formed, which intuitively reflects the drying progress and uniformity. This transforms the traditional sampling inspection after drying into real-time monitoring, analysis and adjustment during the process, directly intervening in the drying status of the grain and ultimately improving the grain drying compliance rate.

[0008] In one possible implementation of the first aspect, determining the grain drying status chain based on the preset interval time, the first drying parameter, the first detection information, and the second detection information includes: Based on the first detection information, a first data segment and a second data segment are determined; wherein, the first data segment is used to reflect the exhaust humidity data when the grain has not yet completely covered the first hot field, and the second data segment is used to reflect the exhaust humidity data after the grain has completely covered the first hot field. Based on the preset interval time and the second detection information, a first humidity chain and a second humidity chain are determined; wherein, the first humidity chain is used to reflect the humidity data of the grain when the grain has not completely covered the first heat field, and the second humidity chain is used to reflect the humidity data of the grain after the grain has completely covered the first heat field. A first drying chain is determined based on the first humidity chain and the first data segment; wherein, the first drying chain is used to reflect the drying status data when the grain has not yet fully covered the first heat field; A second drying chain is determined based on the second humidity chain and the second data segment; wherein, the second drying chain is used to reflect the drying status data of the grain after it has been completely covered by the first heat field; The first drying chain and the second drying chain are collectively identified as the grain drying condition chain.

[0009] In one possible implementation of the first aspect, determining the first data segment and the second data segment based on the first detection information includes: Based on the first detection information, the humidity growth rate is determined; wherein, the humidity growth rate is used to reflect the growth rate of the exhaust humidity data of the first hot field during the process when the grain has not yet fully covered the first hot field. Based on the humidity growth rate, a stabilization time is determined; wherein, the stabilization time is used to reflect the point in time when the humidity growth rate becomes stable. Data in the first detection information that falls before the stabilization time is identified as the first data segment, and data in the first detection information that falls after the stabilization time is identified as the second data segment.

[0010] In one possible implementation of the first aspect, determining the first humidity chain and the second humidity chain based on the preset interval time and the second detection information includes: Sequential feature extraction is performed on the second detection information to determine the surface feature set corresponding to each surface image; wherein, the surface feature set is used to reflect the color and texture features of the grain surface; Based on feature mapping between the nth surface feature set and the (n+1)th surface feature set, multiple average grain moisture information is determined; wherein, the average grain moisture information is used to reflect the average value between the grain moisture corresponding to two adjacent surface feature sets; wherein, n is a positive integer greater than or equal to 1; Based on multiple average grain humidity information and the preset interval time, multiple grain humidity increments are determined; wherein, the grain humidity increment is used to reflect the increase in the total humidity of the grain in the first hot field during each preset interval time when the grain has not yet fully covered the first hot field; A grain humidity chain is constructed based on multiple grain humidity increments in a time sequence; wherein, the grain humidity chain refers to the increase in grain humidity in the first thermal field according to the preset interval time as the growth cycle; Based on the stabilization time, the grain humidity chain is decomposed into a first humidity chain and a second humidity chain.

[0011] In one possible implementation of the first aspect, determining the first drying chain based on the first humidity chain and the first data segment includes: The first data in the first data segment is compared with the first data in the first humidity chain to determine the first chain node of the first drying chain; Based on the data difference between the i-th data in the first data segment and the (i+1)-th data in the first data segment, and the data difference between the i-th data in the first humidity chain and the (i+1)-th data in the first humidity chain, the i-th chain node of the first drying chain is determined; where i is a positive integer greater than or equal to 1. Repeat the step of determining the i-th chain node of the first drying chain based on the data difference between the i-th data in the first data segment and the (i+1)-th data in the first data segment and the data difference between the i-th data in the first humidity chain and the (i+1)-th data in the first humidity chain, to obtain multiple chain nodes of the first drying chain and construct the first drying chain.

[0012] In one possible implementation of the first aspect, determining the second drying chain based on the second humidity chain and the second data segment includes: The data difference between the nth data in the second data segment and the nth data in the second humidity chain is identified as the nth chain node of the second drying chain; Repeat the step of identifying the data difference between the nth data in the second data segment and the nth data in the second humidity chain as the nth chain node of the second drying chain to obtain multiple chain nodes of the second drying chain and construct the second drying chain.

[0013] In one possible implementation of the first aspect, determining the thermal field adjustment strategy based on the target humidity, the grain drying condition chain, and the first drying parameter includes: When the grain drying condition chain is the first drying chain, a heat field adjustment strategy is determined based on the first drying chain and the first drying parameters. When the grain drying condition chain is the second drying chain, a heat field adjustment strategy is determined based on the second drying chain, the target humidity, and the first drying parameter.

[0014] In one possible implementation of the first aspect, determining the heat field adjustment strategy based on the first drying chain and the first drying parameters includes: The adjacent chain increment rate is determined based on the first drying chain; wherein, the adjacent chain increment rate is used to reflect the rate of change between two adjacent chain nodes in the first drying chain; A first time is determined based on the adjacent chain growth rate; wherein, the first time is used to reflect the point in time when the trend of the adjacent chain growth rate changes from decreasing to increasing; First drying data and second drying data are determined from the first drying chain based on the first time; wherein, the first drying data is used to reflect the data in the first drying chain that is at the first time, and the second drying data is used to reflect the data in the first drying chain that is not at the first time; Based on the first drying data, the second drying data, and the first drying parameters, the second drying parameters are determined. Grain characteristic regions are determined based on the first time point; The second drying parameter and the grain characteristic region are identified as the thermal field adjustment strategy.

[0015] In one possible implementation of the first aspect, determining the heat field adjustment strategy based on the second drying chain, the target humidity, and the first drying parameters includes: The target humidity is compared with the second drying chain to determine the comparison status; wherein the comparison status is used to reflect the comparison result between the chain nodes in the second drying chain and the target humidity; Based on the comparison, the first drying parameter and the second drying chain, the second drying parameter and the grain characteristic region are determined. The second drying parameter and the grain characteristic region are identified as the thermal field adjustment strategy.

[0016] In one possible implementation of the first aspect, determining the second drying parameter and the grain characteristic region based on the comparison conditions, the first drying parameter, and the second drying chain includes: A second time and a third time are determined based on the comparison status; wherein, the second time is used to reflect the time corresponding to when the comparison status is greater than 0, and the third time is used to reflect the time corresponding to when the comparison status is equal to 0; A third drying data is determined from the second drying chain based on the second time, and a fourth drying data is determined from the second drying chain based on the third time; wherein the third drying data is used to reflect the data in the second drying chain at the second time, and the fourth drying data is used to reflect the data in the second drying chain at the third time; Based on the third drying data, the fourth drying data, and the first drying parameter, the second drying parameter is determined; Grain characteristic regions are determined based on the second time.

[0017] Secondly, embodiments of this application provide an internal thermal field equalization control system for a grain dryer, comprising: The first acquisition unit is used to acquire the first drying parameters and the target humidity; wherein, the first drying parameters are used to reflect the setting parameters for drying the grain entering the first hot zone of the grain dryer, and the target humidity is used to reflect the preset moisture content of the grain after drying in the first hot zone; The second acquisition unit is used to continuously acquire first detection information and second detection information at a preset interval in response to a trigger signal; wherein, the trigger signal refers to the grain just entering the first hot field, the first detection information is used to reflect the exhaust humidity data of the first hot field, and the second detection information is used to reflect the surface image of the grain just entering the first hot field. The first analysis unit is used to determine a grain drying status chain based on the preset interval time, the first drying parameters, the first detection information, and the second detection information; wherein, the grain drying status chain is used to reflect the drying status of grain in different areas of the first thermal field. The second analysis unit is used to determine a thermal field adjustment strategy based on the target humidity, the grain drying condition chain, and the first drying parameters; wherein the thermal field adjustment strategy is used to reflect the setting parameters and adjustment timing of the second thermal field in the grain dryer, and the first thermal field is located before the second thermal field. An adjustment unit is used to adjust the second thermal field based on the thermal field adjustment strategy.

[0018] Thirdly, embodiments of this application provide a grain dryer, including a drying device and a control device. The drying device is electrically connected to the control device. The control device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, it implements the method described in any of the first aspects above.

[0019] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in any of the first aspects above.

[0020] Fifthly, embodiments of this application provide a computer program that, when run on a grain dryer, causes the grain dryer to perform the grain dryer internal thermal field equalization control method described in any of the first aspects above.

[0021] It is understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a schematic flowchart of a method for equalizing the internal thermal field of a grain dryer according to an embodiment of this application; Figure 2 This is a schematic diagram illustrating the implementation process of a grain dryer internal thermal field equalization control method provided in an embodiment of this application; Figure 3 This is a schematic diagram of the internal thermal field equalization control system of a grain dryer provided in an embodiment of this application; Figure 4 This is a schematic diagram of the control device for a grain dryer provided in one embodiment of this application. Detailed Implementation

[0024] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0025] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0026] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0027] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0028] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0029] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0030] In related technologies, uniform drying parameters are generally set based on the initial moisture content of a single grain variety (e.g., 20%-25%). A heating device maintains a stable temperature in the drying field, a fan provides a constant airflow, and the drying process is completed within a preset time. After drying, the grain moisture content is tested manually by sampling and weighing or by an offline moisture meter to confirm whether it meets the standards. However, during the drying process, the initial moisture content of the grain varies significantly due to factors such as climate differences during harvesting (e.g., higher moisture content on rainy days and lower moisture content on sunny days), different pre-drying levels before storage, uneven local stacking during spreading, and fluctuations in airflow distribution within the drying field. These factors can cause significant random variations (the moisture content of the same batch of grain can fluctuate by 15%-30%). Furthermore, since grain drying is a continuous process—grain continuously enters and is dried in the dryer before being output—the dryer may fail to dry the grain completely or may over-dry it, ultimately reducing the rate of grain meeting the drying standards.

[0031] To address the aforementioned problems, this application provides a grain dryer and its internal thermal field equalization control method. This method first acquires first drying parameters reflecting the setting parameters when grain enters the first thermal field of the grain dryer for drying, and a target humidity reflecting the moisture content of the grain upon completion of drying. When a trigger signal reflecting the grain just entering the first thermal field is received, first detection information reflecting the exhaust humidity data of the first thermal field and second detection information reflecting the surface image of the grain just entering the first thermal field are continuously acquired at preset intervals. Based on the preset interval, the first drying parameters, the first detection information, and the second detection information, a grain drying status chain reflecting the drying status of the grain in different areas of the first thermal field is determined. Then, based on the target humidity, the grain drying status chain, and the first drying parameters, a thermal field adjustment strategy reflecting the setting parameters and adjustment timing of the second thermal field in the grain dryer is determined. Finally, the second thermal field of the grain dryer is adjusted using the thermal field adjustment strategy.

[0032] This method can effectively capture humidity differences caused by initial moisture content fluctuations by real-time monitoring of exhaust humidity and grain surface images in the first hot zone. It transforms the adjustment of hot zone parameters from preset fixed values ​​to dynamic adaptive adjustment, enabling the drying process to accurately match the actual condition of the grain. By using exhaust humidity data and surface image information, the humidity distribution of the grain in each area of ​​the first hot zone is formed, which intuitively reflects the drying progress and uniformity. This transforms the traditional sampling inspection after drying into real-time monitoring, analysis and adjustment during the process, directly intervening in the drying status of the grain and ultimately improving the grain drying compliance rate.

[0033] The internal thermal field equalization control method for grain dryers provided in this application embodiment can be applied to grain dryers. In this case, the grain dryer is the subject of execution of the internal thermal field equalization control method for grain dryers provided in this application embodiment. This application embodiment does not impose any restrictions on the specific type of internal thermal field equalization control equipment for grain dryers.

[0034] A grain dryer includes drying equipment and a control device. The drying equipment and the control device are electrically connected. The drying equipment includes a drying mechanism, a conveying mechanism, and a detection mechanism. The drying mechanism includes a discharge port, a feed port, a vent, a drying heating zone, and a heat source device. The discharge port and feed port are located at opposite ends of the drying mechanism for continuous grain feeding. The vents are located at the top of the first and second heating zones. The drying heating zone includes a first heating zone and a second heating zone arranged sequentially along the grain conveying direction, used for segmented drying of the grain. The heat source device provides heat energy to each heating zone; for example, it can be a gas heater, an electric heater, or a biomass fuel hot air furnace. The conveying mechanism is located inside the drying mechanism and is used to convey the grain sequentially from the feed port through the first and second heating zones, and finally discharge it from the discharge port. For example, the conveying mechanism can be a belt conveyor, a chain conveyor, or a vibrating trough. The detection mechanism includes a humidity detection component and an image detection component. A humidity detection component is installed at the exhaust port of the first hot zone to monitor the exhaust humidity data in real time. This component can be a capacitive or resistive humidity sensor. An image detection component is installed at the feed inlet to capture surface images of the grain as it enters the first hot zone. This component can be an industrial camera or an infrared thermal imager module. A control device is used for real-time monitoring and automatic control of the grain drying process. This device can be a programmable logic controller (PLC), a distributed control system (DCS), or an industrial computer (IPC) equipped with a dedicated control program. The control device receives real-time data from the humidity and image detection components, analyzes the drying uniformity and moisture content trends of the grain in the current hot zone using built-in algorithms, and dynamically adjusts the power of the heat source device, the opening of the vents, and the speed of the conveyor mechanism accordingly. Specifically, the control device can determine the grain drying status based on the drying parameters of the first hot zone, the exhaust humidity, and the grain surface image information, and automatically generate adjustment strategies for the temperature, wind speed, and drying time of the second hot zone, thereby achieving balanced control between the hot zones and overall optimization of the drying process.

[0035] To better understand the method for equalizing the internal thermal field of a grain dryer provided in the embodiments of this application, the specific implementation process of the method for equalizing the internal thermal field of a grain dryer provided in the embodiments of this application will be described in the following exemplary manner.

[0036] Figure 1 and Figure 2 A schematic flowchart illustrating the internal thermal field equalization control method of a grain dryer provided in this application embodiment is shown. Please refer to [link / reference]. Figure 1 and Figure 2 Methods for equalizing the internal thermal field of a grain dryer include: S100, acquire the first drying parameters and the target humidity; wherein, the first drying parameters are used to reflect the setting parameters for drying the grain entering the first hot zone of the grain dryer, and the target humidity is used to reflect the preset moisture content of the grain after drying in the first hot zone.

[0037] It can be understood that the first drying parameter refers to the core preset settings for grain entering the first hot zone of the grain dryer to achieve the drying effect, including but not limited to hot air temperature, hot air flow rate, and conveyor belt speed. The target humidity refers to the preset moisture content standard that the grain in the first hot zone must reach after drying. Its value needs to be determined in conjunction with factors such as grain type (e.g., wheat, rice, corn), storage requirements, and subsequent processing uses (e.g., milling, brewing). The first hot zone is the drying area in the grain dryer used for the first drying of the grain. Both the first drying parameter and the target humidity can be obtained manually or directly from a drying database. The drying database contains different types of grains and their corresponding target humidity levels. This data can be obtained through laboratory experiments, on-site measurements and monitoring, and past experience. After acquisition, the collected data is organized, classified, and archived, useful information and patterns are extracted, and the relevant data is saved to the database to form a drying database.

[0038] S200, in response to a trigger signal, continuously acquires first detection information and second detection information at preset intervals; wherein, the trigger signal refers to the grain just entering the first hot field, the first detection information is used to reflect the exhaust humidity data of the first hot field, and the second detection information is used to reflect the surface image of the grain just entering the first hot field.

[0039] It can be understood that the trigger signal refers to the start signal generated when the grain has just entered the first hot zone after being conveyed from the dryer's feed inlet. The trigger condition is that the grain has just entered the first hot zone. The preset interval time refers to the time difference between two pre-set data collection points. The first detection information refers to the humidity data carried by the airflow exiting the chamber after the hot air exchanges heat with the grain in the first hot zone. The second detection information refers to the image data of the grain surface when the grain has just entered the first hot zone and has not yet undergone drying treatment.

[0040] For example, the trigger signal can be generated after real-time detection of the grain's position by an infrared sensor, weight sensor, or photoelectric switch installed at the inlet of the first hot zone. The first detection information can be obtained by a humidity sensor located at the exhaust port of the first hot zone. The second detection information can be obtained by an image detection device located at the inlet of the first hot zone.

[0041] S300, based on a preset interval time, a first drying parameter, a first detection information and a second detection information, determines a grain drying status chain; wherein, the grain drying status chain is used to reflect the drying status of grain in different areas of the first hot field.

[0042] For example, the first detection information can be used to determine the scheduling humidity data reflecting when the grain has not fully covered the first hot field and the grain humidity data reflecting when the grain has fully covered the first hot field. Then, based on a preset interval time and the second detection information, the grain humidity data reflecting when the grain has not fully covered the first hot field and the grain humidity data reflecting when the grain has fully covered the first hot field can be determined. Then, based on the scheduling humidity data reflecting when the grain has not fully covered the first hot field and the grain humidity data reflecting when the grain has not fully covered the first hot field, the drying status data reflecting when the grain has not fully covered the first hot field can be determined. And based on the grain humidity data reflecting when the grain has fully covered the first hot field and the grain humidity data reflecting when the grain has fully covered the first hot field, the drying status data reflecting when the grain has fully covered the first hot field can be determined. Finally, the two types of drying status data obtained from the analysis are jointly confirmed as the grain drying status chain. Alternatively, a preset interval time, a first drying parameter, a first detection information, and a second detection information can be input into the learning model. The learning model outputs a corresponding grain drying status chain. The training process of the learning model can use the data obtained after processing the preset interval time, the first drying parameter, the first detection information, the second detection information, and the corresponding grain drying status chain as the training dataset for the learning model. Then, the training dataset is input into the learning model for training, and finally, the learning model is obtained. And so on, but not limited to these.

[0043] In one possible implementation, step S300 involves determining a grain drying status chain based on a preset interval time, first drying parameters, first detection information, and second detection information, including: S310, based on the first detection information, determine the first data segment and the second data segment; wherein, the first data segment is used to reflect the exhaust humidity data when the grain has not yet completely covered the first hot field, and the second data segment is used to reflect the exhaust humidity data after the grain has completely covered the first hot field.

[0044] It is understandable that when drying grains, there are two states of change in the grains in the first hot zone. The first state is that the grains continuously enter the first hot zone, and the amount of grains in the first hot zone continuously accumulates. The second state is that the grains enter the first hot zone from the inlet at the conveying speed, and at the same time, the grains leave the first hot zone from the outlet at the same conveying speed. In this state, the amount of grains entering the first hot zone is the same as the amount of grains leaving the first hot zone.

[0045] For example, the growth rate of exhaust humidity data in the first hot field, reflecting the process before the grain fully covers the first hot field, can be determined using the first detection information. Then, the time point at which this growth rate stabilizes can be determined. Based on this time point, data in the first detection information prior to this time point can be used as the first data segment, and data in the first detection information after this time point can be used as the second data segment. Alternatively, the difference between the (i+1)th and ith data points in the first detection information and the difference between the ith and (i-1)th data points in the first detection information can be compared. When the ratio of these two differences is within a preset threshold range, this time point can be extracted, and data in the first detection information prior to this time point can be used as the first data segment, and data in the first detection information after this time point can be used as the second data segment.

[0046] In one possible implementation, step S310, determining the first data segment and the second data segment based on the first detection information, includes: S311, Based on the first detection information, determine the humidity growth rate; wherein, the humidity growth rate is used to reflect the growth rate of the exhaust humidity data of the first hot field before the grain has fully covered the first hot field.

[0047] It can be understood that the rate of increase in humidity refers to how quickly the exhaust humidity rises over time during the drying process of grain in the first heating zone. While the grain is not yet fully covering the first heating zone, the contact area between the grain and the hot air gradually expands as the grain accumulates, resulting in a continuous increase in exhaust humidity. However, once the grain fully covers the first heating zone, the exhaust volume is only related to the initial humidity of the grain entering the zone. In other words, when the grain transitions from a state of not fully covering the first heating zone to a state of fully covering it, the rate of increase in humidity tends to stabilize.

[0048] For example, the first detection information collected continuously at preset intervals is used to calculate the humidity difference between two adjacent detections, and then divided by the preset interval to obtain the humidity growth rate of a single detection.

[0049] S312, based on the humidity growth rate, determine the stabilization time; whereby the stabilization time is used to reflect the point in time when the humidity growth rate becomes stable.

[0050] It can be understood that a stable humidity growth rate means that the difference in humidity growth rate over multiple consecutive detection cycles is less than or equal to a preset stable threshold, or that the humidity growth rate is stable within the preset threshold range and no longer shows a significant upward or downward trend.

[0051] S313, the data in the first detection information that is before the stabilization time is identified as the first data segment, and the data in the first detection information that is after the stabilization time is identified as the second data segment.

[0052] It can be understood that the first data segment refers to the set of exhaust humidity data filtered from the first detection information within the time from the generation of the trigger signal to the stabilization time, and the second data segment refers to the set of exhaust humidity data filtered from the first detection information after the stabilization time. The second data segment accumulates continuously as the grain drying process proceeds.

[0053] With this setup, the humidity growth rate is used as the core indicator for segmentation. The growth rate of exhaust humidity directly reflects the intensity change of grain moisture evaporation (when the grain is not fully spread in the heat field, the amount of moisture evaporation gradually increases as the grain is filled, and the rate rises; after it is fully spread, the amount of evaporation stabilizes and the rate becomes stable). The stable time determined by the humidity growth rate is used as the segmentation node, transforming the abstract rate of stabilization into a specific time point, making the data segmentation change from a qualitative judgment to a quantitative judgment, and reducing human subjective error.

[0054] S320, based on a preset interval time and second detection information, determine a first humidity chain and a second humidity chain; wherein, the first humidity chain is used to reflect the humidity data of the grain when the grain has not yet fully covered the first hot field, and the second humidity chain is used to reflect the humidity data of the grain after the grain has fully covered the first hot field.

[0055] For example, the second detection information can be processed to extract the color and texture features of the surface image of each grain. Then, based on the color and texture features of two adjacent surface images, the average humidity between the grains in the two adjacent surface images can be determined. This average value, combined with a preset time interval, determines the increase in the total humidity of the grains in the first hot field within each preset time interval before the grains fully cover the first hot field. Multiple increases in total humidity are then used to construct a data chain showing the increase in grain humidity in the first hot field, with the preset time interval as the growth cycle. Finally, the data chain is linked to the first and second humidity chains by a settling time. Alternatively, the preset time interval and the second detection information can be input into a learning model, which outputs the corresponding first and second humidity chains, and so on, but are not limited to these methods.

[0056] In one possible implementation, step S320, determining the first humidity chain and the second humidity chain based on a preset interval time and second detection information, includes: S321, Sequential feature extraction is performed on the second detection information to determine the surface feature set corresponding to each surface image; wherein, the surface feature set is used to reflect the color and texture features of the grain surface.

[0057] It can be understood that sequential feature extraction refers to the process of extracting features from each image in the order of acquisition time of the surface images in the second detection information. The surface image refers to the visual data carrier focusing on the surface of the grain particles. A single image can cover multiple grain particles and includes visual features such as particle outline, surface gloss, moisture imprint (such as dark spots in humid areas), and texture details (such as grain skin texture and wrinkles). The surface feature set refers to the combination of features extracted from a single surface image that can quantitatively reflect the color and texture attributes of the grain surface. Among them, color features include quantifiable parameters such as RGB channel mean, gray value distribution, color saturation, and hue difference, while texture features include indicators such as texture density, roughness, texture direction, and particle edge contour complexity.

[0058] For example, features are extracted from the preprocessed images sequentially according to the acquisition order. When extracting color features, the mean of the RGB three channels, the peak value of the grayscale histogram, and the standard deviation of color saturation are calculated by traversing the pixels of a single image. When extracting texture features, the LBP algorithm is used to extract the local feature vector of the grain surface texture, and the texture entropy and contrast are calculated using the gray-level co-occurrence matrix. At the same time, the perimeter and area ratio of the particle edge contour are extracted using the edge detection algorithm to quantify the texture roughness. After the above processing, each surface image generates a multi-dimensional vector containing color feature parameters and texture feature parameters. This vector is the surface feature set corresponding to that image.

[0059] S322, based on the nth surface feature set and the (n+1)th surface feature set, perform feature mapping to determine multiple average grain moisture information; wherein, the average grain moisture information is used to reflect the average value between the grain moisture corresponding to two adjacent surface feature sets; wherein, n is a positive integer greater than or equal to 1.

[0060] Feature mapping can be understood as the process of converting color and texture quantization parameters in a surface feature set into corresponding grain humidity values ​​using a pre-defined feature-humidity association model. This model is trained and generated based on a large amount of sample data (the correspondence between grain surface features and actual humidity under different humidity levels), and its core function is to establish a mapping relationship between surface visual features and intrinsic humidity. Average grain humidity information refers to the result obtained by arithmetically averaging the humidity values ​​obtained from mapping the nth surface feature set and the (n+1)th surface feature set.

[0061] S323, based on multiple average grain humidity information and a preset interval time, determine multiple grain humidity increments; wherein, the grain humidity increment is used to reflect the increase in the total grain humidity in the first hot field within each preset interval time when the grain has not yet fully covered the first hot field.

[0062] It can be understood that the grain moisture increment refers to the change in the total moisture of all grains in the first hot field compared to the previous interval, after each preset interval, during the stage when the grains have not fully covered the first hot field. The value can be positive or negative.

[0063] For example, the grain moisture increment = average grain moisture information × preset interval time.

[0064] S324, based on multiple grain moisture increments constructed in a time sequence, wherein the grain moisture chain refers to the growth of grain moisture in the first thermal field according to a preset interval time as the growth cycle.

[0065] It can be understood that multiple grain moisture increments are used as core data, and preset intervals are used as node periods in the moisture chain structure. Each period corresponds to a moisture increment data, which ultimately forms the grain moisture chain.

[0066] S325 decomposes the grain moisture chain into a first moisture chain and a second moisture chain based on the stabilization time.

[0067] It can be understood that the first humidity chain refers to the grain humidity chain before the stabilization time, and the second humidity chain refers to the set of exhaust humidity data filtered from the first detection information after the grain humidity chain has stabilized.

[0068] This setup, through a progressive transformation path from surface feature set to average grain humidity, then to humidity increment, and finally to the humidity chain, leverages the strong correlation between the color and texture features of the grain surface image and the internal humidity of the equipment to convert visual data into humidity data. This overcomes the limitations of traditional contact-based humidity detection (such as grain damage and detection lag). By using a preset interval as the time scale for the humidity chain, the time dimension of the humidity chain is perfectly matched with the acquisition time of the first detection information (exhaust humidity), ensuring that the exhaust humidity data and grain humidity data correspond one-to-one in time when the first and second drying chains are constructed.

[0069] S330, a first drying chain is determined based on a first humidity chain and a first data segment; wherein, the first drying chain is used to reflect the drying status data when the grain has not yet fully covered the first heat field.

[0070] For example, the corresponding chain node in the first drying chain can be determined by the corresponding data between the first humidity chain and the first data segment, and finally the first drying chain can be constructed by multiple chain nodes. Alternatively, the first humidity chain and the first data segment can be input into the learning model, and the learning model can output the corresponding first drying chain, and so on, but not limited to these.

[0071] In one possible implementation, in step S330, determining the first drying chain based on the first humidity chain and the first data segment includes: S331, compare the first data in the first data segment with the first data in the first humidity chain to determine the first node of the first drying chain.

[0072] It can be understood that the data corresponding to the first chain node = the first data in the first humidity chain - the first data in the first data segment.

[0073] S332, based on the data difference between the i-th data in the first data segment and the (i+1)-th data in the first data segment and the data difference between the i-th data in the first humidity chain and the (i+1)-th data in the first humidity chain, determine the i-th chain node of the first drying chain; where i is a positive integer greater than or equal to 1.

[0074] It can be understood that the i-th node of the first drying chain = (the i-th data in the first humidity chain - the (i+1)-th data in the first humidity chain) - (the i-th data in the first data segment - the (i+1)-th data in the first data segment).

[0075] S333, repeatedly execute the step of determining the i-th chain node of the first drying chain based on the data difference between the i-th data in the first data segment and the (i+1)-th data in the first data segment and the data difference between the i-th data in the first humidity chain and the (i+1)-th data in the first humidity chain, to obtain multiple chain nodes of the first drying chain and construct the first drying chain.

[0076] It can be understood that this step is a loop execution of step S332. The core objective is to convert all data in the first data segment and the first humidity chain into the corresponding chain nodes of the first drying chain in chronological order, and finally form the grain drying status covering the entire drying process. This will not be elaborated further here.

[0077] With this setup, the first chain node uses direct data comparison, while subsequent chain nodes use adjacent data difference comparison. This ensures the baseline of the initial node and highlights the changing trend of the drying status (rather than static values) through adjacent data differences, which is more in line with the dynamic characteristics of the drying process. By repeatedly executing the chain node determination steps, a complete first drying chain can be automatically generated, which is suitable for the batch data processing needs of the automated control system and eliminates the need for manual calculation of each node.

[0078] S340, determine the second drying chain based on the second humidity chain and the second data segment; wherein, the second drying chain is used to reflect the drying status data of the grain after it has been completely covered by the first heat field.

[0079] For example, the corresponding chain nodes in the second drying chain can be determined by matching the data in the second data segment with the corresponding data in the second humidity chain. After repeating this process to obtain multiple chain nodes, these multiple chain nodes can be used to construct the second drying chain. Alternatively, the second humidity chain and the second data segment can be input into a learning model, and the learning model can output the corresponding second drying chain, and so on, but not limited to these methods.

[0080] In one possible implementation, in step S340, determining the second drying chain based on the second humidity chain and the second data segment includes: S341, the data difference between the nth data in the second data segment and the nth data in the second humidity chain is identified as the nth chain node of the second drying chain.

[0081] It can be understood that the nth chain node = the nth data in the second humidity chain - the nth data in the second data segment.

[0082] S342, Repeat the step of identifying the data difference between the nth data in the second data segment and the nth data in the second humidity chain as the nth chain node of the second drying chain, to obtain multiple chain nodes of the second drying chain and construct the second drying chain.

[0083] It can be understood that the process involves repeatedly calculating the data difference between the corresponding data in the second data segment and the second humidity chain, and using this difference as a chain node, until the value of n covers all the data in the second data segment and the second humidity chain. Multiple chain nodes are the sets of all data differences obtained after the iterative execution, arranged in order of the value of n. Constructing the second drying chain refers to organizing multiple chain nodes into a chain-like data structure according to time sequence, forming a complete second drying chain.

[0084] This setup uses direct data difference as the chain node. After the grain is spread throughout the hot field, the synchronicity between the changes in exhaust humidity and grain humidity is enhanced. Directly comparing the data difference between the two can accurately reflect the drying status without the need for complex calculations of adjacent data differences, thus reducing the computational power consumption of data processing. Furthermore, all chain nodes use the same data difference analysis steps, making the logic simple and easy to implement. This is suitable for large-scale batch data processing and improves the efficiency of drying chain generation.

[0085] S350 identifies the first drying chain and the second drying chain together as the grain drying condition chain.

[0086] It can be understood that the state of the grain entering the first hot zone of the dryer changes from not fully covering the first hot zone to fully covering the first hot zone. The grain drying status chain refers to the complete chain data formed by seamlessly splicing the first drying chain and the second drying chain in chronological order.

[0087] This setup, by distinguishing between the two key stages of grain covering the heat field, solves the problem of early (grain not fully covering the heat field) data interfering with the judgment of later (grain fully covering the heat field) data caused by the unified analysis throughout the entire drying process in traditional drying condition monitoring. It makes the feedback of drying condition more in line with the heat field characteristics of each stage. By adopting a segmented and chained structure (first data segment and second data segment, first humidity chain and second humidity chain, first drying chain and second drying chain), the drying process is divided into two stages: when the grain does not fully cover the first heat field and when the grain fully covers the first heat field. This fits the actual working process of the grain dryer (the process of grain gradually filling the heat field), making the monitoring and analysis of drying condition more in line with actual working conditions.

[0088] S400 determines a thermal field adjustment strategy based on the target humidity, the grain drying status chain, and the first drying parameters; wherein, the thermal field adjustment strategy is used to reflect the setting parameters and adjustment timing of the second thermal field in the grain dryer, and the first thermal field is located before the second thermal field.

[0089] It is understandable that the grain drying condition chain changes differently depending on the state of the grain as it enters the first hot zone of the dryer.

[0090] For example, when the grain drying chain is the first drying chain, meaning the grain has not yet fully covered the first heat field, the goal is to determine when the humidity of the grain entering the grain dryer differs from that of grain entering the dryer at other times, and to determine the setting parameters and adjustment timing of the second heat field based on the different humidity levels. When the grain drying chain is the second drying chain, meaning the grain fully covers the first heat field, it is only necessary to compare the target humidity with each node of the second drying chain, and determine the setting parameters and adjustment time of the second heat field based on the comparison.

[0091] In one possible implementation, in step S400, a heat field adjustment strategy is determined based on the target humidity, the grain drying condition chain, and the first drying parameters, including: S410, when the grain drying condition chain is the first drying chain, determine the heat field adjustment strategy based on the first drying chain and the first drying parameters.

[0092] For example, when the grain drying status chain is a first drying chain, the rate of change between two adjacent link points in the first drying chain can be determined based on the first drying chain. Then, based on the time point when the trend of the rate of change changes from decreasing to increasing, data at that time point and data not at that time point in the second drying chain can be determined. The second drying parameter is then determined by comparing the data at that time point with the data not at that time point in the second drying chain with the first drying parameter, and the adjustment timing is determined based on that time point. Alternatively, the data difference between two adjacent chain nodes can be determined through the first drying chain. Then, based on the time point corresponding to the change of the sign of the data difference from negative to positive, data at that time point and data not at that time point in the second drying chain can be determined. The second drying parameter is then determined by comparing the data at that time point with the data not at that time point in the second drying chain with the first drying parameter, and the adjustment timing is determined based on that time point.

[0093] In one possible implementation, in step S410, a heat field adjustment strategy is determined based on the first drying chain and the first drying parameters, including: S411, determine the adjacent chain growth rate based on the first drying chain; wherein, the adjacent chain growth rate is used to reflect the rate of change between two adjacent chain nodes in the first drying chain.

[0094] It can be understood that the adjacent chain growth rate = (i+1th chain node - ith chain node) ÷ ith chain node × 100%.

[0095] S412, determine the first time point based on the growth rate of adjacent chains; wherein, the first time point is used to reflect the time point when the trend of the growth rate of adjacent chains changes from decreasing to increasing.

[0096] It is understandable that when the grain has not yet fully covered the first heating zone, if the moisture content of the grain entering the first heating zone remains unchanged, the drying process of the grain will show an overall decreasing trend in internal moisture content as the time spent in the first heating zone increases. However, when the moisture content of the grain entering the first heating zone changes (referring specifically to an increase in moisture content; if the moisture content decreases, there will be no trend change), the increased grain content leads to a stronger drying process, resulting in more data collected from the first detection information. In other words, the trend in the first drying chain changes from a downward trend to an upward trend.

[0097] For example, by analyzing the time series of the growth rates of adjacent chains, the time interval corresponding to each growth rate is determined (for example, the kth growth rate in the growth rate sequence corresponds to the connection time between the kth and (k+1th)th nodes of the first drying chain, i.e., the end time of the (k+1th)th preset interval). Assuming the preset interval time of the first drying chain is 15s, and the growth rate sequence of adjacent chains is 14.2% (k=1, corresponding to 15s), -12.4% (k=2, corresponding to 30s), -8.1% (k=3, corresponding to 45s), 5.3% (k=4, corresponding to 60s), and 10.2% (k=5, corresponding to 75s), the inflection point of 45s needs to be found by analyzing the trend changes of the growth rate sequence, and so on.

[0098] S413, determining first drying data and second drying data from the first drying chain based on a first time; wherein, the first drying data is used to reflect data in the first drying chain that is at the first time, and the second drying data is used to reflect data in the first drying chain that is not at the first time.

[0099] It can be understood that the first drying data refers to the data in the first drying chain where the timestamp of the chain node is completely consistent with the first time, that is, the single chain node corresponding to the first time. The second drying data refers to any one of the remaining data in the first drying chain where the timestamp of the chain node is inconsistent with the first time, that is, the set of all chain nodes excluding the first drying data.

[0100] S414, based on the first drying data, the second drying data and the first drying parameters, determine the second drying parameters.

[0101] It can be understood that the first drying data refers to the drying status of grains with changes in humidity, while the second drying data refers to the drying status of grains without changes in humidity. In other words, enabling the second drying parameter means continuing to dry the grains in the second drying data until the drying status of the grains in the second drying data becomes the first drying data.

[0102] For example, the grains of the second drying data can be input into the drying condition-time curve to obtain the time difference between the grains of the second drying data becoming the grains of the first drying data. This time difference is the drying time of the second heat field. In addition, when determining the drying time of the second heat field as the second drying parameter based on the drying condition-time curve, the heat field temperature of the second heat field must be the same as the heat field temperature of the first heat field.

[0103] S415, based on the immediate determination of grain characteristic regions.

[0104] It can be understood that the first time corresponds to the time when the grain with increased moisture enters the first hot field. That is, when the grain does not completely cover the first hot field, the grain characteristic area is the area between the position of the grain at the first time and the position of the grain after a preset interval.

[0105] S416, the second drying parameter and the grain characteristic region are identified as the heat field adjustment strategy.

[0106] It is understandable that the thermal field adjustment strategy includes the grain characteristic areas that need to be dried a second time, as well as the second drying parameters for drying the grain characteristic areas a second time.

[0107] This setup, through trend analysis of adjacent chain growth rates, identifies inflection points in drying rate changes in advance, allowing for timely adjustments to thermal field parameters (such as lowering local temperatures to prevent excessively rapid drying). This prevents localized thermal field imbalances caused by uneven grain filling, enabling proactive thermal field adjustments during the incomplete spreading stage. By introducing the first moment as the adjustment node and using the turning point of the growth rate trend as the adjustment trigger, thermal field adjustments shift from passive response to proactive prediction, reducing the drying risks associated with waiting until drying conditions deteriorate before making adjustments.

[0108] S420, when the grain drying condition chain is the second drying chain, a heat field adjustment strategy is determined based on the second drying chain, the target humidity, and the first drying parameters.

[0109] For example, when the grain drying status chain is the second drying chain, the target humidity can be compared with the second drying chain. Based on the comparison results, the times when the comparison result is greater than 0 and the times when the comparison result is equal to 0 can be determined. Then, based on these two times, the data ratio determined from the second drying chain is analyzed with the first drying parameter to determine the second drying parameter and the grain characteristic region. Alternatively, the second drying chain, target humidity, and first drying parameter can be input into a learning model, and the learning model can output a corresponding thermal field adjustment strategy, etc., but not limited to these methods.

[0110] This setup, based on the drying chain type, clarifies the different strategy formulation logic for the first drying chain (incomplete spreading stage) and the second drying chain (complete spreading stage), aligning with the differences in thermal field characteristics between the two stages (the incomplete spreading stage requires attention to the matching of grain filling speed and drying rate, while the complete spreading stage requires attention to achieving the target humidity). Differentiated strategies are formulated for different drying stages, avoiding a one-size-fits-all approach (such as blindly pursuing the target humidity in the incomplete spreading stage, leading to uneven drying), and improving the targeted nature of thermal field parameter adjustments.

[0111] In one possible implementation, in step S420, a heat field adjustment strategy is determined based on the second drying chain, the target humidity, and the first drying parameters, including: S421, compare the target humidity with the second drying chain and determine the comparison status; wherein, the comparison status is used to reflect the comparison results between the chain nodes in the second drying chain and the target humidity.

[0112] It can be understood that the second drying condition chain is a data chain consisting of the difference between the total humidity of the grain entering the first hot zone and the total humidity of the exhaust gas from the first hot zone after the grain has completely covered the first hot zone.

[0113] For example, the comparison condition = chain node data of the second drying condition chain - target humidity.

[0114] S422, based on the comparison, the first drying parameters and the second drying chain, the second drying parameters and the grain characteristic region are determined.

[0115] For example, by comparing the conditions, two time points can be determined: one where the comparison condition is greater than 0 and the other where it is equal to 0. Then, based on these two time points, two corresponding drying data points can be determined from the second drying chain. Finally, the second drying parameter can be determined using these two drying data points and the first drying parameter, and the grain characteristic region can be determined based on the time point where the comparison condition is greater than 0. Alternatively, the comparison condition, the first drying parameter, and the second drying chain can be input into a learning model, and the learning model can output the corresponding second drying parameter and the grain characteristic region, etc., but not limited to these methods.

[0116] In one possible implementation, in step S422, based on the comparison conditions, the first drying parameter, and the second drying chain, the second drying parameter and the grain characteristic region are determined, including: S4221, Determine the second time and the third time based on the comparison status; wherein, the second time is used to reflect the time corresponding to when the comparison status is greater than 0, and the third time is used to reflect the time corresponding to when the comparison status is equal to 0.

[0117] It is understandable that the higher the humidity of the grain within the same temperature field, the faster its humidity changes. That is, when the humidity of the grain increases and it enters the first heating field, its humidity changes faster than that of the grain without increased humidity when it first enters the first heating field. In the second drying chain, this is manifested as the difference between the chain node of the second drying chain and the target humidity being greater than 0. The time corresponding to the comparison condition being greater than 0 can reflect the time point when the humidity of the grain increases and it enters the first heating field, while the time corresponding to the comparison condition being equal to 0 can reflect the time point when the humidity of the grain does not increase and it enters the first heating field.

[0118] S4222, determine third drying data from the second drying chain based on the second time, and determine fourth drying data from the second drying chain based on the third time; wherein, the third drying data is used to reflect the data in the second drying chain at the second time, and the fourth drying data is used to reflect the data in the second drying chain at the third time.

[0119] It is understandable that the third drying data refers to the drying status of grains with changes in humidity, while the fourth drying data refers to the drying status of grains without changes in humidity.

[0120] S4223, based on the third drying data, the fourth drying data and the first drying parameters, determine the second drying parameters.

[0121] It is understood that the second drying parameter can be obtained from the third drying data, the fourth drying data, and the first drying parameter by using the method in step S414 to determine the second drying parameter based on the first drying data, the second drying data, and the first drying parameter. This will not be elaborated further here.

[0122] S4224, based on the second time, the characteristic region of the grain is determined.

[0123] It can be understood that the second time corresponds to the time when the grain with increased moisture enters the first hot field. That is, after the grain completely covers the first hot field, the grain characteristic area is the area between the position of the grain at the second time and the position of the grain after a preset interval.

[0124] This setup divides time points based on comparison conditions. The determination of the second time (comparison condition > 0, insufficient drying) and the third time (comparison condition = 0, meeting the standard) accurately identifies the time intervals and achievement points that need adjustment. This allows strategy formulation to focus on the unmet standard stage. The second drying parameter is calculated using the third and fourth drying data, directly linking the adjustment range to the extent of insufficient drying (e.g., the larger the ratio, the larger the adjustment range). This reduces the problem of abnormal drying conditions caused by excessive parameter adjustment.

[0125] S423, the second drying parameter and the grain characteristic region are identified as the heat field adjustment strategy.

[0126] It is understandable that the thermal field adjustment strategy includes the grain characteristic areas that need to be dried a second time, as well as the second drying parameters for drying the grain characteristic areas a second time.

[0127] This setup, with the target humidity comparison as its core logic, directly compares the second drying chain with the target humidity to clearly identify the gap between the drying status and the final target. By comparing with the target humidity, it accurately judges whether the drying status meets the standard (e.g., if the chain node is higher than the target humidity, it indicates insufficient drying), so that the heat field adjustment is directly aimed at the target humidity, thereby improving the final drying quality.

[0128] S500 adjusts the second thermal field based on a thermal field adjustment strategy.

[0129] It is understandable that when grains with increased humidity enter the first hot zone, a hot zone adjustment strategy is implemented whereby the grains are dried a second time according to the second drying parameters after the characteristic areas of the grains enter the second hot zone.

[0130] This setup effectively captures humidity differences caused by initial moisture content fluctuations by real-time monitoring of exhaust humidity and grain surface images in the first hot zone. It transforms the adjustment of hot zone parameters from preset fixed values ​​to dynamic adaptive adjustment, ensuring that the drying process precisely matches the actual condition of the grain. By using exhaust humidity data and surface image information, it forms the humidity distribution of the grain in each area of ​​the first hot zone, intuitively reflecting the drying progress and uniformity. It transforms the traditional sampling inspection after drying into real-time monitoring, analysis, and adjustment during the process, directly intervening in the drying status of the grain during the drying process, ultimately improving the grain drying compliance rate.

[0131] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0132] Corresponding to the grain dryer internal thermal field equalization control method described in the above embodiments, this application embodiment also provides a grain dryer internal thermal field equalization control system. Each module of the grain dryer internal thermal field equalization control system can realize each step of the grain dryer internal thermal field equalization control method. Figure 3 A structural block diagram of the internal thermal field equalization control system of the grain dryer provided in the embodiment of this application is shown. For ease of explanation, only the parts related to the embodiment of this application are shown.

[0133] Reference Figure 3 The internal thermal field equalization control system of the grain dryer includes: The first acquisition unit is used to acquire the first drying parameters and the target humidity; wherein the first drying parameters are used to reflect the setting parameters for drying the grain entering the first hot zone of the grain dryer, and the target humidity is used to reflect the moisture content of the grain after drying.

[0134] The second acquisition unit is used to continuously acquire first detection information and second detection information at a preset interval in response to a trigger signal; wherein, the trigger signal refers to the grain just entering the first hot field, the first detection information is used to reflect the exhaust humidity data of the first hot field, and the second detection information is used to reflect the surface image of the grain just entering the first hot field.

[0135] The first analysis unit is used to determine the grain drying status chain based on a preset interval time, a first drying parameter, a first detection information, and a second detection information; wherein, the grain drying status chain is used to reflect the drying status of grain in different areas of the first hot field.

[0136] The second analysis unit is used to determine the thermal field adjustment strategy based on the target humidity, the grain drying status chain, and the first drying parameters. The thermal field adjustment strategy is used to reflect the setting parameters and adjustment timing of the second thermal field in the grain dryer, with the first thermal field preceding the second thermal field.

[0137] The adjustment unit is used to adjust the second thermal field based on the thermal field adjustment strategy.

[0138] It should be noted that the information interaction and execution process between the above systems / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0139] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0140] This application also provides a dryer, which includes a drying device and a control device, wherein the drying device and the control device are electrically connected. Figure 4 This is a schematic diagram of the structure of the control device 4 provided in one embodiment of this application. Figure 4 As shown, the control device 4 in this embodiment includes: at least one processor 40 ( Figure 4 Only one is shown in the image), at least one memory 41 ( Figure 4(Only one is shown in the image) and a computer program 42 stored in the at least one memory 41 and executable on the at least one processor 40, wherein when the processor 40 executes the computer program 42, it causes the control device 4 to implement the steps in any of the above embodiments of the grain dryer internal thermal field equalization control method, or causes the control device 4 to implement the functions of each module / unit in the above embodiments of the system.

[0141] For example, the computer program 42 may be divided into one or more modules / units, which are stored in the memory 41 and executed by the processor 40 to complete this application. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program 42 in the control device 4.

[0142] The control device 4 can be a computing device such as a desktop computer, laptop, handheld computer, or cloud server. The control device 4 may include, but is not limited to, a processor 40 and a memory 41. Those skilled in the art will understand that... Figure 4 This is merely an example of control device 4 and does not constitute a limitation on control device 4. It may include more or fewer components than shown, or combine certain components, or different components, such as input / output devices, network access devices, buses, etc.

[0143] The processor 40 can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0144] In some embodiments, the memory 41 may be an internal storage unit of the control device 4, such as a hard disk or memory of the control device 4. In other embodiments, the memory 41 may be an external storage device of the control device 4, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the control device 4. Furthermore, the memory 41 may include both internal storage units and external storage devices of the control device 4. The memory 41 is used to store operating systems, applications, bootloaders, data, and other programs, such as the program code of computer programs. The memory 41 can also be used to temporarily store data that has been output or will be output.

[0145] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in any of the above method embodiments.

[0146] This application provides a computer program product that, when run on a dryer, causes the dryer to perform the steps in any of the above method embodiments.

[0147] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to the dryer, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, such as a USB flash drive, a portable hard drive, a magnetic disk, or an optical disk.

[0148] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0149] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0150] In the embodiments provided in this application, it should be understood that the disclosed internal thermal field equalization control system for grain dryers can be implemented in other ways. For example, the embodiments of the internal thermal field equalization control system for grain dryers described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0151] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0152] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for equalizing the internal thermal field of a grain dryer, characterized in that, include: Obtain first drying parameters and target humidity; wherein, the first drying parameters are used to reflect the setting parameters for drying grains entering the first hot zone of the grain dryer, and the target humidity is used to reflect the preset moisture content of the grains after drying in the first hot zone; In response to a trigger signal, first detection information and second detection information are continuously acquired at preset intervals; wherein, the trigger signal refers to the grain just entering the first hot field, the first detection information is used to reflect the exhaust humidity data of the first hot field, and the second detection information is used to reflect the surface image of the grain just entering the first hot field. Based on the preset interval time, the first drying parameters, the first detection information, and the second detection information, a grain drying status chain is determined; wherein, the grain drying status chain is used to reflect the drying status of grain in different areas of the first thermal field; Based on the target humidity, the grain drying condition chain, and the first drying parameter, a heat field adjustment strategy is determined; wherein, the heat field adjustment strategy is used to reflect the setting parameters and adjustment timing of the second heat field in the grain dryer, and the first heat field is located before the second heat field; The second thermal field is adjusted based on the aforementioned thermal field adjustment strategy.

2. The method for equalizing the internal thermal field of a grain dryer as described in claim 1, characterized in that, The step of determining the grain drying status chain based on the preset interval time, the first drying parameter, the first detection information, and the second detection information includes: Based on the first detection information, a first data segment and a second data segment are determined; wherein, the first data segment is used to reflect the exhaust humidity data when the grain has not yet completely covered the first hot field, and the second data segment is used to reflect the exhaust humidity data after the grain has completely covered the first hot field. Based on the preset interval time and the second detection information, a first humidity chain and a second humidity chain are determined; wherein, the first humidity chain is used to reflect the humidity data of the grain when the grain has not completely covered the first heat field, and the second humidity chain is used to reflect the humidity data of the grain after the grain has completely covered the first heat field. A first drying chain is determined based on the first humidity chain and the first data segment; wherein, the first drying chain is used to reflect the drying status data when the grain has not yet fully covered the first heat field; A second drying chain is determined based on the second humidity chain and the second data segment; wherein, the second drying chain is used to reflect the drying status data of the grain after it has been completely covered by the first heat field; The first drying chain and the second drying chain are collectively identified as the grain drying condition chain.

3. The method for equalizing the internal thermal field of a grain dryer as described in claim 2, characterized in that, The step of determining the first data segment and the second data segment based on the first detection information includes: Based on the first detection information, the humidity growth rate is determined; wherein, the humidity growth rate is used to reflect the growth rate of the exhaust humidity data of the first hot field during the process when the grain has not yet fully covered the first hot field. Based on the humidity growth rate, a stabilization time is determined; wherein, the stabilization time is used to reflect the point in time when the humidity growth rate becomes stable. Data in the first detection information that falls before the stabilization time is identified as the first data segment, and data in the first detection information that falls after the stabilization time is identified as the second data segment.

4. The method for equalizing the internal thermal field of a grain dryer as described in claim 3, characterized in that, The step of determining the first humidity chain and the second humidity chain based on the preset interval time and the second detection information includes: Sequential feature extraction is performed on the second detection information to determine the surface feature set corresponding to each surface image; wherein, the surface feature set is used to reflect the color and texture features of the grain surface; Based on feature mapping between the nth surface feature set and the (n+1)th surface feature set, multiple average grain moisture information is determined; wherein, the average grain moisture information is used to reflect the average value between the grain moisture corresponding to two adjacent surface feature sets; wherein, n is a positive integer greater than or equal to 1; Based on multiple average grain humidity information and the preset interval time, multiple grain humidity increments are determined; wherein, the grain humidity increment is used to reflect the increase in the total humidity of the grain in the first hot field during each preset interval time when the grain has not yet fully covered the first hot field; A grain humidity chain is constructed based on multiple grain humidity increments in a time sequence; wherein, the grain humidity chain refers to the increase in grain humidity in the first thermal field according to the preset interval time as the growth cycle; Based on the stabilization time, the grain humidity chain is decomposed into a first humidity chain and a second humidity chain.

5. The method for equalizing the internal thermal field of a grain dryer as described in claim 2, characterized in that, Determining the first drying chain based on the first humidity chain and the first data segment includes: The first data in the first data segment is compared with the first data in the first humidity chain to determine the first chain node of the first drying chain; Based on the data difference between the i-th data in the first data segment and the (i+1)-th data in the first data segment, and the data difference between the i-th data in the first humidity chain and the (i+1)-th data in the first humidity chain, the i-th chain node of the first drying chain is determined; where i is a positive integer greater than or equal to 1. Repeat the step of determining the i-th node of the first drying chain based on the data difference between the i-th data in the first data segment and the (i+1)-th data in the first data segment and the data difference between the i-th data in the first humidity chain and the (i+1)-th data in the first humidity chain, to obtain multiple nodes of the first drying chain and construct the first drying chain.

6. The method for equalizing the internal thermal field of a grain dryer as described in claim 2, characterized in that, Determining the second drying chain based on the second humidity chain and the second data segment includes: The data difference between the nth data in the second data segment and the nth data in the second humidity chain is identified as the nth chain node of the second drying chain; Repeat the step of identifying the data difference between the nth data in the second data segment and the nth data in the second humidity chain as the nth chain node of the second drying chain to obtain multiple chain nodes of the second drying chain and construct the second drying chain.

7. The method for equalizing the internal thermal field of a grain dryer as described in claim 2, characterized in that, The step of determining a thermal field adjustment strategy based on the target humidity, the grain drying condition chain, and the first drying parameter includes: When the grain drying condition chain is the first drying chain, a heat field adjustment strategy is determined based on the first drying chain and the first drying parameters. When the grain drying condition chain is the second drying chain, a heat field adjustment strategy is determined based on the second drying chain, the target humidity, and the first drying parameter.

8. The method for equalizing the internal thermal field of a grain dryer as described in claim 7, characterized in that, The step of determining the heat field adjustment strategy based on the first drying chain and the first drying parameters includes: The adjacent chain increment rate is determined based on the first drying chain; wherein, the adjacent chain increment rate is used to reflect the rate of change between two adjacent chain nodes in the first drying chain; A first time is determined based on the adjacent chain growth rate; wherein, the first time is used to reflect the point in time when the trend of the adjacent chain growth rate changes from decreasing to increasing; First drying data and second drying data are determined from the first drying chain based on the first time; wherein, the first drying data is used to reflect the data in the first drying chain that is at the first time, and the second drying data is used to reflect the data in the first drying chain that is not at the first time; Based on the first drying data, the second drying data, and the first drying parameters, the second drying parameters are determined. Grain characteristic regions are determined based on the first time point; The second drying parameter and the grain characteristic region are identified as the thermal field adjustment strategy.

9. The method for equalizing the internal thermal field of a grain dryer as described in claim 7, characterized in that, The step of determining a heat field adjustment strategy based on the second drying chain, the target humidity, and the first drying parameters includes: The target humidity is compared with the second drying chain to determine the comparison status; wherein the comparison status is used to reflect the comparison result between the chain nodes in the second drying chain and the target humidity; Based on the comparison, the first drying parameter and the second drying chain, the second drying parameter and the grain characteristic region are determined. The second drying parameter and the grain characteristic region are identified as the thermal field adjustment strategy; And / or, determining the second drying parameter and grain characteristic region based on the comparison conditions, the first drying parameter, and the second drying chain includes: A second time and a third time are determined based on the comparison status; wherein, the second time is used to reflect the time corresponding to when the comparison status is greater than 0, and the third time is used to reflect the time corresponding to when the comparison status is equal to 0; A third drying data is determined from the second drying chain based on the second time, and a fourth drying data is determined from the second drying chain based on the third time; wherein the third drying data is used to reflect the data in the second drying chain at the second time, and the fourth drying data is used to reflect the data in the second drying chain at the third time; Based on the third drying data, the fourth drying data, and the first drying parameter, the second drying parameter is determined; Grain characteristic regions are determined based on the second time.

10. A grain dryer, characterized in that, The device includes a drying apparatus and a control device, the drying apparatus being electrically connected to the control device, the control device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method as described in any one of claims 1 to 9.

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