Drying detection method and system for agricultural product processing

Abstract

The invention discloses a drying detection method and system for agricultural product processing, and relates to the technical field of agricultural product drying detection. The drying detection method for agricultural product processing comprises the following steps: detecting a moisture distribution state; performing drying detection on the temperature control area; and air flow distribution area drying detection. According to the method, moisture distribution state detection and distribution uniformity quantification are carried out, then the influence degree of temperature differences of different temperature control areas in the drying box on agricultural product sample moisture distribution is quantified to judge whether temperature control parameter optimization is carried out or not, and finally residual drying time prediction is carried out to judge whether heat source boundary parameter optimization is carried out or not. The effect of improving the dynamic matching accuracy of the heat exchange characteristics in the corresponding drying detection stage in the agricultural product processing process is achieved, and the problem that in the prior art, the residual drying time prediction accuracy is not high due to the fact that dynamic matching of the heat exchange characteristics in the corresponding drying detection stage in the agricultural product processing process is unbalanced is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural product drying detection technology, and in particular to a drying detection method and system for agricultural product processing. Background Art

[0002] Agricultural product processing encompasses multiple stages, including raw material handling, primary processing, fine processing, and finished product packaging and storage. Within the entire agricultural product processing chain, drying is crucial for ensuring agricultural product quality, extending shelf life, and increasing added value. Therefore, drying, through precise control of heat and mass transfer, has become a core technology for ensuring quality, extending shelf life, and increasing added value. Especially in the processing of heat-resistant agricultural products such as grains and traditional Chinese medicines, drying testing requires differentiated monitoring of key areas such as temperature control zones, airflow distribution zones, and moisture migration zones to address industry pain points such as uneven drying and quality degradation.

[0003] In monitoring key areas of the agricultural product drying process (such as the interior of a drying oven), a terahertz pulse signal transmitter leverages the high penetration and non-destructive nature of high-frequency electromagnetic waves to scan the moisture distribution within the produce in real time. By analyzing the time-domain waveform and spectral characteristics of the reflected signal, it accurately quantifies the moisture migration state, addressing the limitation of traditional near-infrared spectroscopy, which can only detect surface moisture. Furthermore, an adjustable-angle deflector drive motor is deployed in the airflow distribution area. Designed with computational fluid dynamics simulation optimization, the motor dynamically adjusts the deflector opening and closing angle (adjustable from 0° to 60°) based on anemometer feedback, precisely controlling airflow direction and velocity, addressing the drying dead zones often created by traditional fixed deflectors. The synergistic effect: the terahertz device provides microscopic dynamic data on moisture distribution, while the deflector motor enables macroscopic airflow control. Combined with high-precision temperature sensors and variable-frequency heating elements, this creates a closed-loop control system for multiple parameters: temperature, airflow, and moisture. This improves drying uniformity by over 25% and effectively reduces the risk of loss of active ingredients during the drying process.

[0004] Existing technologies primarily conduct targeted drying tests in different inspection areas. For example, in the temperature-controlled area, the focus is on temperature stability and uniformity. This is because temperature directly affects the rate of water evaporation. Unstable temperatures can lead to uneven drying, affecting the quality of agricultural products. High-precision temperature sensors are required to monitor and provide feedback in real time, enabling timely adjustments to the heating equipment power. Furthermore, there is an airflow distribution area. Uniform airflow ensures consistent heating and moisture distribution across all parts of the agricultural products. Airflow speed and direction are monitored using equipment such as anemometers to ensure proper airflow distribution.

[0005] Drying testing is essential in this process. By monitoring moisture content in real time, it ensures that agricultural products are dried to the appropriate degree, avoiding nutrient loss and deterioration in color and taste due to overdrying, or the risk of spoilage due to insufficient drying. Drying testing involves a variety of equipment, including electronic balances for precise sample weighing, constant-temperature drying ovens that provide a stable drying environment to measure moisture loss, rapid moisture meters that use pyrolysis to quickly obtain moisture data, and near-infrared spectrometers. These advanced devices can non-destructively and rapidly analyze the internal moisture distribution and content of agricultural products, improving testing efficiency and accuracy.

[0006] For example, Chinese invention patent publication number CN115372373A discloses a method for rapidly determining the appearance morphology of fruits and vegetables during a drying process based on image analysis technology. The method comprises the following steps: washing, cutting, and drying the fruits and vegetables to obtain fruit and vegetable samples at different drying stages; laying the fruit and vegetable samples at different drying stages on an image acquisition platform to obtain three-channel color images of the fruit and vegetable samples; performing color analysis on the three-channel color images, dividing the fruit and vegetable sample regions, and extracting color values ​​of the portions containing the fruit and vegetable samples; analyzing the three-channel color images, performing grayscale stretching processing, thresholding processing, extracting the orthographic projection area of ​​the fruit and vegetable sample regions, and drawing an intuitive graph; and calculating the shrinkage rate of the fruit and vegetable samples at different drying stages based on changes in the orthographic projection area of ​​the fruit and vegetable sample regions.

[0007] Prior art methods, such as periodic sampling during drying to measure and record moisture content using methods such as a constant-temperature drying oven, employ terahertz pulse signal transmitters for moisture content detection. However, sampling itself disrupts the continuity and uniformity of the drying process, causing the initial parameters of the device to change. This sampling also alters the localized stacking state, temperature, and other conditions of the agricultural products, further affecting the drying progress of the surrounding agricultural products. Consequently, the measured moisture content does not fully reflect the actual moisture changes in the agricultural products throughout the drying process. Furthermore, local temperature imbalances in different temperature-controlled zones within the drying oven can easily occur (e.g., different temperatures at the top and bottom of a temperature-controlled zone), preventing a stable drying rhythm. Furthermore, the lag in temperature regulation leads to unnecessary extensions in drying time, further disrupting the equilibrium of heat exchange. Finally, when relevant data is input into a dynamic simulation model (such as a three-dimensional thermal-mass coupling model) to predict the remaining drying time, when drying is nearing the end and the heating intensity is adjusted, the model fails to update the key parameters related to heat exchange in a timely manner, and still calculates according to the initial state, which cannot truly reflect the actual heat exchange situation. There is a problem of low accuracy in the prediction of the remaining drying time due to the dynamic matching imbalance of the heat exchange characteristics corresponding to the drying detection stage in the agricultural product processing process. Summary of the Invention

[0008] In order to solve the technical problems in the prior art, the embodiment of the present invention provides a drying detection method and system for agricultural product processing. The technical solution is as follows: On the one hand, a drying detection method for agricultural product processing is provided, which includes: step 1, in the temperature control zone of the drying oven, using terahertz pulse signals to detect the moisture distribution status of the same batch of agricultural products inside the drying oven, and quantifying the distribution uniformity to screen agricultural product samples with qualified moisture distribution areas in the same batch of agricultural products; step 2, if the moisture distribution status detection is qualified, quantifying the degree of influence of the temperature difference in different temperature control zones inside the drying oven on the moisture distribution of the agricultural product samples, and at the same time determining whether to optimize the temperature control parameters to reduce the extension of the drying time caused by the temperature lag in different temperature control zones; step 3, in the airflow distribution zone of the drying oven, inputting the data of the agricultural product samples after qualified temperature control into a constructed dynamic simulation model of the agricultural product drying process to predict the remaining drying time, and at the same time determining whether to optimize the heat source boundary parameters to improve the heat exchange matching degree between the dynamic simulation model of the agricultural product drying process and the actual drying process of the agricultural product.

[0009] On the other hand, a drying detection system for agricultural product processing is provided. The system is applied to a drying detection method for agricultural product processing, and the system includes: a moisture distribution state detection module, a temperature control zone drying detection module, and an airflow distribution zone drying detection module; wherein the moisture distribution state detection module is used to detect the moisture distribution state of the same batch of agricultural products inside the temperature control zone of the drying oven through a terahertz pulse signal, and quantify the distribution uniformity to screen agricultural product samples with qualified moisture distribution areas in the same batch of agricultural products; the temperature control zone drying detection module is used to quantify the influence of the temperature difference between different temperature control zones inside the drying oven on the moisture distribution of the agricultural product samples if the moisture distribution state detection is qualified, and at the same time determine whether to optimize the temperature control parameters to reduce the extension of the drying time caused by the temperature lag in different temperature control zones; the airflow distribution zone drying detection module is used to input the data of the agricultural product samples after qualified temperature control into a constructed dynamic simulation model of the agricultural product drying process in the airflow distribution zone of the drying oven to predict the remaining drying time, and at the same time determine whether to optimize the heat source boundary parameters to improve the heat exchange matching degree between the dynamic simulation model of the agricultural product drying process and the actual drying process of the agricultural product.

[0010] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least: 1. First, in the temperature-controlled zone of the drying oven, terahertz pulse signals are used to detect the moisture distribution of the same batch of agricultural products and screen qualified samples. Next, if qualified, the impact of temperature differences between different temperature-controlled zones on the sample moisture distribution is quantified to determine whether to optimize the temperature control parameters. Finally, in the airflow distribution zone, the qualified sample data is input into a dynamic simulation model to predict the remaining drying time and determine whether to optimize the heat source boundary parameters. Compared to existing technologies, this method: Detecting moisture distribution accurately screens qualified samples, avoiding uneven drying and improving product quality; Quantifying the impact of temperature differences and optimizing temperature control parameters can reduce the extended drying time caused by temperature lag and improve drying efficiency; Using a dynamic simulation model to predict the remaining drying time allows for timely parameter adjustments to better match the model with actual heat exchange, making the drying process more controllable and reducing energy waste.

[0011] 2. By quantifying and determining signal stability before using terahertz pulse signals to detect moisture distribution in agricultural products, pulse signal problems can be detected in advance, avoiding moisture distribution detection errors caused by signal instability and ensuring reliable test results. Dynamically adjusting the transmission frequency and power based on the standard deviation reduces interference from terahertz wave reflections on the surface and internal scattering of agricultural products, enhancing signal stability. Optimizing the retesting and verification mechanism further ensures signal quality, laying the foundation for subsequent accurate quantification of moisture distribution uniformity in agricultural products. This helps improve the drying quality of agricultural products and reduce product losses caused by uneven drying.

[0012] 3. By calculating the root mean square error (RMS) of moisture content at the end of moisture distribution testing for the same batch of agricultural products, the passing or failing drying test is determined. If not, the drying oven parameters are optimized, adjusting the temperature and wind speed, followed by verification. If verified, subsequent testing proceeds; if not, an alert is issued. Compared to existing technologies, this method dynamically adjusts the drying oven temperature and wind speed based on error deviations, effectively accelerating the evaporation of moisture within the agricultural products and enhancing air flow, improving the uniformity of moisture distribution. The drying oven parameter verification step further ensures the effectiveness of the optimization measures. After optimization is completed, subsequent drying testing proceeds to ensure the orderly progress of the entire drying process. Alerts of abnormal moisture content can promptly alert operators to address issues, preventing large-scale uneven drying and helping to improve the drying quality of agricultural products. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0014] Figure 1 A flow chart of a drying detection method for agricultural product processing provided by an embodiment of the present invention; Figure 2 A flowchart of quantification and optimization of terahertz pulse signal stability provided by an embodiment of the present invention; Figure 3 A flow chart of quantifying and optimizing the uniformity of moisture distribution provided by an embodiment of the present invention; Figure 4 A flow chart for quantifying and optimizing the impact of temperature differences in temperature-controlled areas provided by an embodiment of the present invention; Figure 5 A flowchart of the optimization determination of the switching response in the drying stage provided by an embodiment of the present invention; Figure 6 A flowchart of the remaining drying time prediction and heat source optimization determination provided by an embodiment of the present invention; Figure 7 A schematic structural diagram of a drying detection system for agricultural product processing provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0015] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0016] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as an "exemplary" in the present invention should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of the word "exemplary" is intended to present concepts in a concrete manner. Furthermore, in the embodiments of the present invention, "and / or" can mean both or either of the two.

[0017] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0018] The embodiment of the present invention provides a drying detection method for agricultural product processing, such as Figure 1 The flowchart of a drying detection method for agricultural product processing is shown. The processing flow of the method may include the following steps: Step 1: quantify the pulse signal stability: First, in the pre-detection stage, count the status monitoring duration of the terahertz pulse signal corresponding to the same batch of agricultural products inside the drying box in each pre-detection period to obtain the average status monitoring duration. At the same time, count the difference between the status monitoring duration corresponding to each pre-detection period and the average status monitoring duration, and perform standard deviation processing to obtain the standard deviation of the status monitoring duration.

[0019] Then, the pulse signal parameters are optimized and judged: if the standard deviation of the acquired state monitoring time is not greater than the standard deviation of the state monitoring time set in the database, it indicates that the stability of the terahertz pulse signal meets the requirements of moisture distribution state detection, and the distribution uniformity is quantified; otherwise, it indicates that the stability of the terahertz pulse signal does not meet the requirements of moisture distribution state detection, and the pulse signal parameters are optimized.

[0020] Finally, in the temperature control area of ​​the drying oven, by counting the root mean square error of the moisture content of the same batch of agricultural products at the end of the moisture distribution status detection period, the discrete degree of the moisture distribution of the agricultural products can be accurately reflected, providing a quantitative basis for judging whether the drying test is qualified; comparison is made according to the standards set by the database, and if qualified, the agricultural product samples with qualified moisture distribution areas in the same batch of agricultural products are obtained, and if unqualified, the drying oven parameters are optimized; drying oven parameter optimization: by inputting the root mean square error deviation of the moisture content into the database, the temperature and wind speed adjustment values ​​are accurately obtained according to the mapping relationship, and the initial parameters of the drying oven can be quickly and dynamically adjusted, effectively accelerating the evaporation of moisture inside the agricultural products and enhancing air flow, thereby improving drying uniformity. At the same time, the drying oven parameter verification link is entered to compare the re-obtained error with the set value. If qualified, the optimization is completed and the next stage of detection is entered. If unqualified, a timely warning is issued.

[0021] In step two, if the moisture distribution status test passes, the impact of temperature differences between different temperature-controlled zones within the drying oven on the moisture distribution of the agricultural product samples is quantified. At the same time, a determination is made as to whether temperature control parameters should be optimized to reduce the extended drying time caused by temperature lags between different temperature-controlled zones. Each temperature-controlled zone is equipped with the same number of temperature sensors and variable-frequency heating tubes in the same locations, and all use adjustable-angle guide plates. Computational fluid dynamics (CFD) simulations are used to optimize air velocity distribution.

[0022] Step three: In the air flow distribution area of ​​the drying box, the agricultural product sample data that has passed temperature control is input into the constructed dynamic simulation model of the agricultural product drying process to predict the remaining drying time. At the same time, it is determined whether to optimize the heat source boundary parameters to improve the heat exchange matching between the dynamic simulation model of the agricultural product drying process and the actual drying process of the agricultural product.

[0023] like Figure 2The figure shows a flowchart for quantifying and optimizing the stability of terahertz pulse signals, as provided in an embodiment of the present invention. The specific design logic is as follows: First, the standard deviation of the pre-detection period is calculated. After calculating the standard deviation, the signal stability is determined. If it is qualified, the distribution uniformity is quantified. If it is not qualified, the transmission frequency and power are adjusted, and the standard deviation is determined again. If the standard deviation is qualified again, the distribution uniformity is quantified again. If it is still not qualified, the calibration device (i.e., the terahertz pulse signal transmitter) is prompted. The entire process revolves around signal stability and standard deviation, ensuring signal quality through continuous adjustment and judgment.

[0024] Among them, the pre-detection stage represents the stability quantification process of terahertz pulse signal monitoring before the distribution uniformity is quantified. Each pre-detection period has the same time length. The state monitoring duration represents the time length during which the terahertz pulse signal reflects the water characteristic information of agricultural products in each pre-detection period. The standard deviation of the state monitoring duration is used to reflect the degree of dispersion of the state monitoring duration of the terahertz pulse signal corresponding to each pre-detection period relative to the average state monitoring duration. It represents the result of squaring the difference between the state monitoring duration corresponding to each pre-detection period and the average state monitoring duration, averaging the difference, and then performing square root processing. The smaller the standard deviation of the state monitoring duration, the more concentrated the state monitoring duration and the better the stability of the terahertz signal. Conversely, the larger the standard deviation of the state monitoring duration, the more dispersed the state monitoring duration and the worse the stability of the terahertz signal. The state monitoring duration is recorded by a timing device.

[0025] It should be understood that the input of the constructed dynamic simulation model of the agricultural product drying process is the sample data of agricultural products after temperature control, including but not limited to the initial moisture content, size specifications, stacking method, and the current temperature, humidity, and air flow velocity in the drying oven. The output is the predicted remaining drying time. The specific construction process is: based on Fourier's law in heat transfer and Fick's law in mass transfer, considering the internal moisture diffusion of agricultural products, surface moisture evaporation, and heat and moisture exchange with the surrounding air, a basic mathematical model of the agricultural product drying process is established to clarify the quantitative relationship between various physical quantities; then, a large number of drying experiments are carried out to record the data of the agricultural product drying process under different conditions, so as to determine the key parameters in the model, such as the heat transfer coefficient, mass transfer coefficient, and boundary conditions, such as the initial moisture content, ambient temperature and humidity; then the numerical calculation method is used to solve the model. After repeated debugging of parameters and comparison of simulation and experimental results, the model can accurately simulate the actual drying and realize the accurate prediction of the remaining drying time based on the input data.

[0026] In this embodiment, this method ensures the stability of the terahertz pulse signal by quantifying pulse signal stability and optimizing parameters to accurately detect the moisture distribution of agricultural products and screen qualified samples. The method also quantifies the impact of temperature differences between different temperature control zones, optimizes temperature control parameters, and reduces drying time. A dynamic simulation model is constructed, inputting qualified sample data to accurately predict the remaining drying time. The method also determines whether to optimize heat source boundary parameters to improve the heat exchange matching between the model and the actual drying process. These steps effectively address the existing problem of inaccurate remaining drying time predictions due to unbalanced dynamic matching of heat exchange characteristics, thereby improving the efficiency and quality of agricultural drying.

[0027] Furthermore, the specific process of pulse signal parameter optimization is as follows: the obtained state monitoring time standard deviation is input into the database, and the actual transmission frequency adjustment value and the actual transmission power adjustment value are obtained based on the mapping relationship between the state monitoring time standard deviation stored in the database and the transmission frequency adjustment value and the transmission power adjustment value, and the transmission frequency and transmission power of the terahertz pulse signal transmitting device are dynamically adjusted to reduce the reflection and internal scattering of terahertz waves on the surface of agricultural products, thereby reducing the impact of these interferences on the stability of the terahertz pulse signal. The state monitoring time standard deviation represents the difference between the obtained state monitoring time standard deviation and the set state monitoring time standard deviation. The set state monitoring The standard deviation of the duration is expressed by the sum and average of the standard deviations of the historical state monitoring duration in the historical pre-detection stage in the database. The transmission power and transmission frequency in the dynamic adjustment process are fed back by the terahertz pulse signal transmitting device itself; after a pulse signal parameter optimization, the stability of the terahertz pulse signal monitoring is re-quantified. If the regained state monitoring duration standard deviation is greater than the state monitoring duration standard deviation set in the database, the preset personnel is prompted to calibrate the terahertz pulse signal transmitting device, otherwise it indicates that the pulse signal parameter optimization is effective in correcting the stability of the terahertz pulse signal, and the distribution uniformity is quantified. A pulse signal parameter optimization includes a transmission frequency optimization and a transmission power optimization.

[0028] In this embodiment, in the pulse signal parameter optimization, the dynamic adjustment of the transmission frequency and transmission power of the terahertz pulse signal transmitting device can be implemented in combination with the proportional-integral-derivative (PID) feedback algorithm. Specifically, after the standard deviation of the state monitoring time is calculated, it is used as the input error signal of the PID algorithm. Since the obtained standard deviation of the state monitoring time is greater than the set standard deviation of the state monitoring time, it means that the current signal stability is poor. At this time, the PID algorithm increases the transmission frequency and transmission power based on the input error signal, enhances the signal energy, and reduces the impact of surface reflection and internal scattering of agricultural products on the signal, until the obtained standard deviation of the state monitoring time is no greater than the standard deviation of the state monitoring time set in the database.

[0029] This example accurately calculates the standard deviation of the condition monitoring duration and dynamically adjusts the transmission frequency and power of the terahertz pulse signal transmitter based on database mappings. This effectively reduces terahertz wave reflection on the surface of agricultural products and internal scattering, reduces interference, and significantly improves signal stability. Re-quantifying stability after an optimization step allows for timely verification of the optimization results. If the results fall short, a device calibration prompt is prompted to ensure signal quality. This optimization process provides reliable assurance for subsequent accurate quantification of moisture uniformity in agricultural products, helping to improve the accuracy and efficiency of agricultural product drying testing.

[0030] Furthermore, the distribution uniformity is quantified, specifically including: statistically analyzing the root mean square error of the moisture content of the same batch of agricultural products inside the drying box at the end of the moisture distribution status detection period. The root mean square error of the moisture content is used to reflect the degree of dispersion of the moisture content of each agricultural product in the same batch of agricultural products relative to the average moisture content (the average moisture content of each agricultural product in the same batch of agricultural products at the end of the moisture distribution status detection period). The larger the root mean square error of the moisture content, the more uneven the moisture content distribution of the agricultural products in the drying box. Conversely, the smaller the root mean square error of the moisture content, the more uniform the moisture content distribution of the agricultural products in the drying box. The moisture content is monitored by the moisture sensor; if the root mean square error of the moisture content obtained is not greater than the root mean square error of the moisture content set in the database, it indicates that The drying test during the moisture distribution state detection period is qualified and the corresponding agricultural product samples are obtained. The set moisture content root mean square error is represented by the sum and average of the historical moisture content root mean square errors of the same batch of agricultural products at the end of the historical moisture distribution state detection period in the database; if the obtained moisture content root mean square error is greater than the moisture content root mean square error set in the database, it indicates that the drying test during the moisture distribution state detection period is unqualified and the drying oven parameters are optimized; the drying oven parameter optimization includes drying oven parameter adjustment and drying oven parameter verification. The drying oven parameter adjustment means adjusting the drying oven temperature and the drying oven wind speed to improve the uniformity of moisture divergence in various parts of the agricultural products. The drying oven parameter verification is used to verify the effectiveness of the drying oven parameter optimization in improving the uniformity of the moisture distribution state of the agricultural products.

[0031] In this embodiment, the above process realizes the scientific evaluation and precise control of the uniformity of moisture distribution of agricultural products, which helps to improve the drying quality of agricultural products, reduce quality problems caused by uneven moisture distribution, and ensure the stability and reliability of agricultural product processing.

[0032] like Figure 3 The figure shows a flowchart for quantifying and optimizing the uniformity of moisture distribution provided by an embodiment of the present invention. The specific design logic is as follows: First, the root mean square error (RMS) of the moisture content is calculated. Then, a determination is made as to whether it is less than or equal to a set value. If so, a qualified sample is directly obtained. If not, the drying oven parameters are optimized, i.e., the temperature and wind speed are adjusted. Afterwards, data is reacquired and the RMS error is calculated. A determination is again made as to whether the set value is reached. If so, a qualified sample is obtained. If not, a moisture anomaly warning is triggered. This entire process ensures that moisture content meets requirements and guarantees sample quality through continuous parameter adjustment and repeated testing.

[0033] What needs to be further understood is that the drying oven parameter adjustment is specifically as follows: the obtained moisture content root mean square error deviation is input into the database, and the actual drying oven temperature adjustment value and the actual drying oven wind speed adjustment value are obtained based on the mapping relationship between the moisture content root mean square error deviation stored in the database and the drying oven temperature adjustment value and the drying oven wind speed adjustment value, and the initial temperature and initial wind speed of the drying oven are dynamically adjusted to accelerate the evaporation rate of water inside the agricultural products while enhancing the air flow in the drying oven. The moisture content root mean square error deviation represents the difference between the obtained moisture content root mean square error and the set moisture content root mean square error. The set moisture content root mean square error is represented by the sum and average of the historical moisture content root mean square errors at the end of the historical moisture distribution state detection period in the database. The drying oven temperature during the adjustment process is monitored by the temperature sensor, and the drying oven wind speed is monitored by the wind speed sensor.

[0034] In addition, the drying oven parameters are verified as follows: if the root mean square error of the moisture content obtained after the drying oven parameters are adjusted is not greater than the set root mean square error of the moisture content, the drying oven parameters are optimized and the drying test of the drying temperature control period is entered at the same time, otherwise an abnormal moisture content warning is issued.

[0035] In this example, since the RMS error of the moisture content obtained is greater than the set RMS error, indicating that the moisture distribution is extremely uneven, the PID control algorithm uses the RMS error of the moisture content as an input variable. Combined with the database rule combination of "increasing initial temperature + increasing initial wind speed," the algorithm increases the initial temperature of the drying oven to accelerate the evaporation of moisture from the agricultural product. Simultaneously, the initial wind speed is increased to enhance air flow and remove more moisture. This process achieves precise control of the drying process, helping to improve the drying quality of agricultural products, reduce quality issues caused by uneven drying, and ensure production efficiency and product qualification rates.

[0036] Furthermore, the degree of influence of the temperature difference in different temperature control zones inside the drying oven on the moisture distribution of agricultural product samples is quantified, specifically including: counting the top-bottom temperature deviation and the top-bottom average temperature deviation corresponding to each temperature control zone inside the drying oven at the end of the drying temperature control period (the average value of the top-bottom temperature deviation of each temperature control zone inside the drying oven at the end of the drying temperature control period), and obtaining the top-bottom temperature root mean square error, where the top-bottom temperature is monitored by a temperature sensor; the top-bottom temperature deviation is used to reflect the degree of difference between the top temperature and the bottom temperature inside the drying oven at the end of the drying temperature control period, and the top-bottom temperature root mean square error is used to reflect the degree of discreteness between the top-bottom temperature deviation corresponding to each temperature control zone inside the drying oven at the end of the drying temperature control period relative to the top-bottom average temperature deviation.

[0037] In this embodiment, the top and bottom temperatures inside the drying oven differ due to the flow characteristics of hot air within the drying oven. Hot air, with its low density, rises, while cold air, with its high density, sinks. This results in uneven vertical heat distribution, causing different heating levels at the top and bottom, which in turn creates temperature differences and affects the uniformity of agricultural product drying. To quantify the impact of temperature differences between different temperature-controlled zones within the drying oven on the moisture distribution of agricultural product samples, the top-bottom temperature deviation and the average top-bottom temperature deviation of each temperature-controlled zone at the end of the drying temperature control period are calculated, and then the top-bottom temperature root mean square error (RMS) is calculated. The top-bottom temperature deviation intuitively represents the temperature difference between the top and bottom of the drying oven at the end of drying, while the top-bottom temperature root mean square error (RMS) reflects the degree of dispersion of the top-bottom temperature deviations of each temperature-controlled zone relative to the average deviation. A greater degree of dispersion indicates a more complex temperature difference between different zones, and a more uneven impact on the moisture distribution of agricultural products.

[0038] like Figure 4 As shown, it is a flow chart for quantifying and optimizing the influence degree of temperature difference in the temperature control zone provided by an embodiment of the present invention. Its specific design logic is: first monitor the temperature control status, and then determine whether the temperature error is qualified. If qualified, directly enter the drying stage switching determination; if unqualified, first adjust the guide plate angle. Then determine whether the heating tube temperature exceeds the limit. If it exceeds the limit, calculate the power density correction value, and then re-obtain the temperature error, and determine whether the error is still out of limit. If it is still out of limit, trigger the temperature control zone warning. If it is not out of limit, enter the drying stage switching determination; if the heating tube temperature is not out of limit, return to the guide plate angle adjustment step and continue to adjust until the temperature error is qualified or the warning is triggered.

[0039] What needs to be further understood is that the determination of whether to optimize the temperature control parameters specifically includes: if the obtained top-bottom temperature root mean square error is not greater than the top-bottom temperature root mean square error set in the database, it indicates that the drying test of the drying temperature control period is qualified and the drying stage switching response judgment is performed. The drying stage switching response judgment is used to determine whether the response time between the temperature control zone switching from the constant speed drying stage to the speed reduction drying stage meets the expected response requirements; if the obtained top-bottom temperature root mean square error is greater than the top-bottom temperature root mean square error set in the database, it indicates that the drying test of the drying temperature control period is unqualified and the temperature control parameters are optimized.

[0040] Among them, the temperature control parameter optimization is specifically as follows: based on the obtained top-bottom temperature root mean square error deviation, the guide plate angle correction value is mapped in the database to correct the wind speed difference between the top and bottom corresponding to different temperature control zones inside the drying oven. The top-bottom temperature root mean square error deviation represents the difference between the obtained top-bottom temperature root mean square error and the set top-bottom temperature root mean square error. The set top-bottom temperature root mean square error is represented by the sum and average of the historical top-bottom temperature root mean square errors at the end of the historical drying temperature control period in the database; during the guide plate angle correction process, monitor whether the surface temperature of the heating tube inside the drying oven is greater than the surface temperature of the heating tube set in the database. If so, then Based on the obtained heating tube surface temperature deviation, a heating tube power density correction value is mapped in the database to reduce the probability of local overheating of the heating tube surface temperature. Otherwise, the guide plate angle correction is continued. The heating tube surface temperature is monitored by the temperature sensor. The heating tube surface temperature deviation represents the difference between the obtained heating tube surface temperature and the set heating tube surface temperature. If, after the guide plate angle is corrected, the re-acquired top-bottom temperature root mean square error is not greater than the top-bottom temperature root mean square error set in the database, the temperature control parameter optimization is completed and the drying stage switching response judgment is performed. Otherwise, a temperature control zone warning is issued. The guide plate angle is monitored by the angle sensor, and the heating tube power density is monitored by the power sensor.

[0041] In this embodiment, deflector angle correction is implemented based on a fuzzy control algorithm. The root mean square error (RMS) of the top-to-bottom temperature difference is first calculated and mapped to a database to obtain an initial correction value for the deflector angle. The fuzzy control algorithm then fuzzifies this error and the change in the deflector angle. Inference is performed based on preset fuzzy rules to obtain a more accurate deflector angle correction. The deflector drive motor, using the built-in fuzzy control algorithm, increases the initial correction value of the deflector angle based on the obtained deflector angle correction. Correcting the deflector angle allows for wind speed differential adjustment because the deflector can alter the direction and velocity distribution of the airflow within the drying oven. Changing the deflector angle also alters the vertical distribution of the airflow, thereby adjusting the top and bottom wind speeds to a balance and reducing the temperature deviation caused by the wind speed difference. Ultimately, this corrects the top-to-bottom wind speed difference, resulting in a more uniform temperature distribution within the drying oven and improved agricultural product drying quality.

[0042] This example sets the top-bottom temperature root mean square error threshold to accurately determine whether the drying test is qualified, and promptly decides whether to optimize the temperature control parameters to ensure drying quality. Mapping the guide plate angle correction value with the error deviation effectively adjusts the wind speed difference between different areas in the drying oven, making the temperature distribution more uniform. At the same time, monitoring the surface temperature of the heating tube and adjusting the power density appropriately can reduce the risk of local overheating and extend equipment life. If the optimization meets the standard, the drying stage is switched; if it does not, an early warning is issued, forming a complete closed-loop control, improving the stability and reliability of the agricultural product drying process.

[0043] like Figure 5 The figure shows a flow chart for optimizing the switching response during the drying phase according to an embodiment of the present invention. The specific design logic is as follows: first, the switching response time is calculated, and then its qualification is determined. If the response time is qualified, the airflow distribution zone is directly entered; if not, the heating tube power is adjusted, the initial power is updated, and the process returns to the step of determining the response time again. This process forms a cyclic adjustment process until the response time is qualified, and the subsequent airflow distribution zone operation is entered.

[0044] It is further necessary to understand that the determination of the drying stage switching response specifically includes: based on the total drying stage switching response time obtained at the end of the drying temperature control period and the total drying stage switching response time set in the database, a determination is made on the heating tube power adjustment: if the obtained total drying stage switching response time is greater than the set total drying stage switching response time, it indicates that the drying stage switching response is unqualified and the heating tube power adjustment is performed; otherwise, it indicates that the drying stage switching response is qualified and a drying detection of the air flow distribution area is performed. The set total drying stage switching response time is represented by the sum and average of the historical drying stage switching total response times at the end of the historical drying temperature control periods in the database; the heating tube power adjustment indicates that the heating tube power preheating correction value is obtained by mapping the obtained drying stage switching total response time deviation in the database as the initial power of the heating tube at the beginning of the next temperature difference control period. The drying stage switching total response time deviation represents the difference between the obtained drying stage switching total response time and the set drying stage switching total response time.

[0045] In this embodiment, at the beginning of the next temperature difference control period, the system adjusts the initial power of the heater tube according to a preset database rule (i.e., the mapping relationship between the historical drying phase switching total response time deviation and the heater tube power preheat correction value in the database). The heater tube power during the adjustment process is monitored by the power sensor. If the database is set to use an additive method, the current heater tube initial power is directly added to the heater tube power preheat correction value obtained by mapping the drying phase switching total response time deviation. For example, if the current initial power is 500W and the correction value is 100W, the new initial power is 600W. If the database is set to merge according to a specific ratio, assuming a ratio of 7:3, the current initial power accounts for 70% and the correction value accounts for 30%. If the current initial power is 500W and the correction value is 150W, the new initial power is 395W (i.e., 500W×0.7+150W×0.3=395W).

[0046] If the switching response during the drying phase is unsatisfactory, it indicates a slow switching speed and an inefficient drying process. The heating tubes require time to heat up. Increasing the initial power at the start of the next temperature difference control period allows the heating tubes to receive more energy during the startup phase and reach the required temperature more quickly. This allows for faster heat supply during the subsequent drying process, accelerating the evaporation of moisture from the agricultural products and enabling more timely transitions between drying phases. This shortens the switching response time throughout the drying phase, making the drying process more efficient and stable, and ensuring the consistency and controllability of the agricultural product drying quality.

[0047] like Figure 6 As shown in the figure, it is a flowchart of the remaining drying time prediction and heat source optimization determination provided by an embodiment of the present invention. Its specific design logic is: first, the sample data is input into the dynamic simulation model of the agricultural product drying process to predict the remaining drying time; then it is judged whether the remaining drying time is qualified. If it is qualified (that is, the obtained remaining drying time is less than the remaining drying time set in the database), the heat source boundary parameters are updated; if it is unqualified, monitoring is continued.

[0048] It is further understood that determining whether to optimize the heat source boundary parameters specifically includes: at the end of the drying period in the airflow distribution area, obtaining the remaining drying time predicted by the constructed dynamic simulation model of the agricultural product drying process; if the obtained remaining drying time is less than the remaining drying time set in the database, prompting the preset personnel to reduce the heating tube voltage based on the obtained remaining drying time deviation to achieve the effect of reducing the heating tube power inside the drying box, and sending a heat source boundary parameter update instruction; otherwise, continue to monitor the prediction process of the constructed dynamic simulation model of the agricultural product drying process, and the remaining drying time deviation represents the difference between the set remaining drying time and the obtained remaining drying time; the heat source boundary parameter update instruction is used to prompt the preset personnel to input the heat source boundary parameter update value obtained by mapping the obtained remaining drying time deviation in the database into the constructed dynamic simulation model of the agricultural product drying process to update the heat source boundary parameters, so as to improve the heat exchange matching degree between the constructed dynamic simulation model of the agricultural product drying process and the actual drying process, and at the same time send a drying detection completion instruction, and the heat source boundary parameter update value includes the surface heat transfer coefficient update value and the convective heat transfer coefficient update value.

[0049] It is important to understand that if the remaining drying time obtained is less than the remaining drying time set in the database (usually set to 10 minutes), it indicates that the agricultural products may be close to or have reached the expected degree of dryness. Continuing to maintain the original power will cause the agricultural products to over-dry, affecting their quality, such as loss of nutrients, deterioration of color, and a hard taste. Reducing the power can avoid this. The heat source boundary parameter update instruction is sent for update because the current model prediction deviates from the actual situation. The remaining drying time is less than the set value, indicating that the model's simulation of the drying process is not accurate enough. At this time, by mapping the remaining drying time deviation in the database to obtain the updated values ​​of the heat source boundary parameters such as the updated value of the surface heat transfer coefficient and the updated value of the convective heat transfer coefficient, and inputting the model update parameters, the model simulation of the heat exchange process can be adjusted.

[0050] During the actual drying process, heat exchange conditions are constantly changing due to various factors. If the heat source boundary parameters are not updated, the heat exchange match between the dynamic simulation model of the agricultural product drying process and the actual drying process will decrease, resulting in inaccurate prediction results. By inputting the updated values ​​of the surface heat transfer coefficient and the convective heat transfer coefficient, obtained based on the remaining drying time deviation mapping, into the simulation model, the model can be made more consistent with the actual heat exchange conditions. Among them, the surface heat transfer coefficient reflects the heat exchange capacity between the surface of the heating tube and the surrounding environment, and the convective heat transfer coefficient reflects the heat transfer efficiency between the airflow and the agricultural products. Updating these two parameters can improve the accuracy of the model prediction, provide a reliable basis for the precise control of the subsequent drying process, and ensure the drying quality of agricultural products.

[0051] The embodiment of the present invention provides a drying detection system for agricultural product processing, such as Figure 7 The structure of a drying detection system for agricultural product processing is shown in FIG. The system includes the following modules: a moisture distribution state detection module, a temperature control zone drying detection module, and an airflow distribution zone drying detection module. The moisture distribution state detection module is used to detect the moisture distribution state of the same batch of agricultural products inside the temperature control zone of the drying oven using a terahertz pulse signal, and quantify the distribution uniformity to screen agricultural product samples with qualified moisture distribution areas in the same batch of agricultural products. The temperature control zone drying detection module is used to quantify the impact of the temperature difference between different temperature control zones inside the drying oven on the moisture distribution of the agricultural product samples if the moisture distribution state detection is qualified, and at the same time determine whether to optimize the temperature control parameters to reduce the extension of the drying time caused by the temperature lag between different temperature control zones. The airflow distribution zone drying detection module is used to input the data of the agricultural product samples after qualified temperature control into the constructed dynamic simulation model of the agricultural product drying process in the airflow distribution zone of the drying oven to predict the remaining drying time and determine whether to optimize the heat source boundary parameters to improve the heat exchange matching between the dynamic simulation model of the agricultural product drying process and the actual drying process of the agricultural product.

[0052] In this embodiment, the moisture distribution detection module uses terahertz pulse signals to meticulously analyze the moisture distribution of agricultural products, accurately screening qualified samples and laying a solid foundation for drying. The temperature-controlled zone drying detection module quantifies the impact of temperature differences between different temperature-controlled zones on sample moisture, enabling timely optimization of temperature control parameters to avoid temperature lags that extend drying time. The airflow distribution zone drying detection module inputs qualified sample data into a dynamic simulation model to accurately predict remaining drying time and optimize heat source boundary parameters, improving the match between the model and actual heat exchange. These multiple modules work closely together to precisely control different aspects of the drying process, ensuring efficient and high-quality agricultural product drying, significantly improving drying efficiency and product quality.

[0053] It should be noted that the present invention selects the temperature control zone and the airflow distribution zone to analyze the drying effect because they have a key impact on the drying of agricultural products and have different mechanisms of action. The temperature control zone focuses on controlling the temperature. The temperature difference between different areas affects the evaporation rate of water. By quantifying the degree of temperature difference and optimizing the temperature control parameters, the extension of drying time caused by temperature lag can be reduced and the uniformity of drying can be ensured. The airflow distribution zone focuses on the promotion of drying by airflow. By inputting qualified sample data into the dynamic simulation model to predict the remaining drying time and optimizing the heat source boundary parameters, the matching degree between the model and the actual heat exchange can be improved, and the drying process can be accurately controlled.

[0054] By setting up various time periods, including pre-test, moisture distribution status detection, drying temperature control, and airflow distribution zone drying, the drying process can be comprehensively monitored. Pre-test ensures the stability of the terahertz pulse signal, laying the foundation for accurate moisture distribution detection. The moisture distribution status detection period screens qualified samples; the drying temperature control period optimizes temperature control; and the airflow distribution zone drying period predicts remaining time and optimizes parameters. Each time period is progressively improved, achieving high-precision detection and optimization of drying results.

[0055] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0056] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0057] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0058] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0059] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0060] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A drying detection method for agricultural product processing, characterized in that: The method comprises: Step 1: In the temperature-controlled area of ​​the drying oven, the moisture distribution of the same batch of agricultural products inside the drying oven is detected using terahertz pulse signals, and the distribution uniformity is quantified to screen agricultural product samples with qualified moisture distribution areas within the same batch of agricultural products; Step 2: If the moisture distribution status test is qualified, the degree of influence of the temperature difference between different temperature control zones inside the drying oven on the moisture distribution of the agricultural product samples is quantified, and at the same time, it is determined whether to optimize the temperature control parameters to reduce the extension of the drying time caused by the temperature lag between different temperature control zones; Step three: In the air flow distribution area of ​​the drying box, the agricultural product sample data that has passed temperature control is input into the constructed dynamic simulation model of the agricultural product drying process to predict the remaining drying time. At the same time, it is determined whether to optimize the heat source boundary parameters to improve the heat exchange matching between the dynamic simulation model of the agricultural product drying process and the actual drying process of the agricultural product.

2. A drying detection method for agricultural product processing according to claim 1, characterized in that: The moisture distribution state of the same batch of agricultural products in the drying box is detected by using terahertz pulse signals, which also includes quantification and determination of the stability of the terahertz pulse signals, specifically: The first step is to calculate the state monitoring duration of the terahertz pulse signal for the same batch of agricultural products in the drying oven during each pre-detection period to obtain the average state monitoring duration. At the same time, the difference between the state monitoring duration corresponding to each pre-detection period and the average state monitoring duration is calculated and processed with the standard deviation to obtain the standard deviation of the state monitoring duration. In the second step, if the obtained standard deviation of the state monitoring time is not greater than the standard deviation of the state monitoring time set in the database, the distribution uniformity is quantified, otherwise the pulse signal parameters are optimized.

3. A drying detection method for agricultural product processing according to claim 2, characterized in that: The specific process of the pulse signal parameter optimization is as follows: The obtained standard deviation of the state monitoring time is input into the database. Based on the mapping relationship between the standard deviation of the state monitoring time and the transmission frequency adjustment value and the transmission power adjustment value stored in the database, the actual transmission frequency adjustment value and the actual transmission power adjustment value are obtained, and the transmission frequency and transmission power of the terahertz pulse signal transmitting device are dynamically adjusted to reduce the influence of the reflection and internal scattering of the terahertz wave on the surface of the agricultural product on the stability of the terahertz pulse signal; After a pulse signal parameter optimization, if the re-acquired state monitoring time standard deviation is greater than the state monitoring time standard deviation set in the database, the preset personnel will be prompted to calibrate the terahertz pulse signal transmitting device, otherwise the distribution uniformity will be quantified.

4. A drying detection method for agricultural product processing according to claim 1, characterized in that: The quantification of distribution uniformity specifically includes: Calculating the root mean square error of the moisture content of the same batch of agricultural products in the drying oven at the end of the moisture distribution state detection period, wherein the root mean square error of the moisture content is used to reflect the degree of dispersion of the moisture content of each agricultural product in the same batch of agricultural products relative to the average moisture content; If the root mean square error of the moisture content obtained is not greater than the root mean square error of the moisture content set in the database, it indicates that the drying test during the moisture distribution state detection period is qualified and the corresponding agricultural product sample is obtained; If the root mean square error of the moisture content obtained is greater than the root mean square error of the moisture content set in the database, it indicates that the drying test during the moisture distribution state detection period is unqualified and the drying oven parameters are optimized; The drying oven parameter optimization includes drying oven parameter adjustment and drying oven parameter verification. The drying oven parameter adjustment means adjusting the drying oven temperature and drying oven wind speed to improve the uniformity of moisture distribution in various parts of agricultural products. The drying oven parameter verification is used to verify the effectiveness of the drying oven parameter optimization in improving the uniformity of moisture distribution in agricultural products.

5. A drying detection method for agricultural product processing according to claim 4, characterized in that: The drying oven parameter adjustment is specifically as follows: The obtained moisture content root mean square error deviation is input into the database. Based on the mapping relationship between the moisture content root mean square error deviation and the drying box temperature adjustment value and the drying box wind speed adjustment value stored in the database, the actual drying box temperature adjustment value and the actual drying box wind speed adjustment value are obtained, and the initial temperature and initial wind speed of the drying box are dynamically adjusted. The drying oven parameter verification is specifically as follows: if the root mean square error of the moisture content obtained after the drying oven parameters are adjusted is not greater than the set root mean square error of the moisture content, the drying oven parameter optimization is completed and the drying test in the drying temperature control period is entered at the same time; otherwise, an abnormal moisture content warning is issued.

6. A drying detection method for agricultural product processing according to claim 1, characterized in that: The degree of influence of the temperature difference between different temperature control zones within the quantified drying oven on the moisture distribution of the agricultural product samples specifically includes: Statistically calculate the top-bottom temperature deviation and top-bottom average temperature deviation of each temperature control zone inside the drying oven at the end of the drying temperature control period to obtain the top-bottom temperature root mean square error; The top-bottom temperature deviation is used to reflect the degree of difference between the top temperature and the bottom temperature inside the drying oven at the end of the drying temperature control period. The top-bottom temperature root mean square error is used to reflect the degree of dispersion between the top-bottom temperature deviations corresponding to each temperature control zone inside the drying oven at the end of the drying temperature control period relative to the top-bottom average temperature deviation.

7. A drying detection method for agricultural product processing according to claim 6, characterized in that: The determining whether to perform temperature control parameter optimization specifically includes: If the obtained top-bottom temperature root mean square error is not greater than the top-bottom temperature root mean square error set in the database, it indicates that the drying test of the drying temperature control period has passed and a drying stage switching response determination is performed. The drying stage switching response determination is used to determine whether the response time between the temperature control zone switching from the constant speed drying stage to the reduced speed drying stage meets the expected response requirements; If the obtained top-bottom temperature root mean square error is greater than the top-bottom temperature root mean square error set in the database, it indicates that the drying test in the drying temperature control period fails and the temperature control parameters are optimized; The temperature control parameter optimization specifically includes: mapping the obtained top-bottom temperature root mean square error deviation in the database to obtain a guide plate angle correction value to correct the wind speed difference between the top and bottom corresponding to different temperature control zones inside the drying oven; During the guide plate angle correction process, monitor whether the surface temperature of the heating tube inside the drying oven is greater than the heating tube surface temperature set in the database. If so, a correction value for the heating tube power density is obtained based on the obtained heating tube surface temperature deviation and mapped in the database to reduce the probability of local overheating of the heating tube surface temperature. Otherwise, continue to correct the guide plate angle. If the root mean square error of the top-bottom temperature obtained after the guide plate angle is corrected is not greater than the root mean square error of the top-bottom temperature set in the database, the temperature control parameter optimization is completed and the drying stage switching response judgment is made; otherwise, a temperature control zone warning is issued.

8. A drying detection method for agricultural product processing according to claim 7, characterized in that: The drying stage switching response determination specifically includes: If the total response time of the drying stage switching is longer than the set total response time of the drying stage switching, it indicates that the drying stage switching response is unqualified and the heating tube power is adjusted. Otherwise, it indicates that the drying stage switching response is qualified and the drying test of the airflow distribution area is carried out. The heating tube power adjustment means mapping the heating tube power preheating correction value in the database based on the obtained drying stage switching total response time deviation to serve as the heating tube initial power at the beginning of the next temperature difference control period.

9. A drying detection method for agricultural product processing according to claim 1, characterized in that: The determination of whether to perform heat source boundary parameter optimization specifically includes: At the end of the drying period in the airflow distribution area, the remaining drying time predicted by the constructed dynamic simulation model of the agricultural product drying process is obtained. If the obtained remaining drying time is less than the remaining drying time set in the database, the preset personnel are prompted to reduce the power of the heating tube inside the drying box based on the obtained remaining drying time deviation and send a heat source boundary parameter update instruction. Otherwise, the prediction process of the constructed dynamic simulation model of the agricultural product drying process is continued to be monitored; The heat source boundary parameter update instruction is used to prompt the preset personnel to input the heat source boundary parameter update value obtained by mapping the remaining drying time deviation in the database into the constructed dynamic simulation model of the agricultural product drying process to update the heat source boundary parameter, and at the same time send a drying detection completion instruction. The heat source boundary parameter update value includes the surface heat transfer coefficient update value and the convective heat transfer coefficient update value.

10. A drying detection system for agricultural product processing, using the drying detection method for agricultural product processing according to any one of claims 1 to 9, characterized in that: include: Moisture distribution state detection module, temperature control area drying detection module and air flow distribution area drying detection module; The moisture distribution state detection module is used to detect the moisture distribution state of the same batch of agricultural products inside the drying oven in the temperature control area of ​​the drying oven through terahertz pulse signals, and quantify the distribution uniformity to screen agricultural product samples with qualified moisture distribution areas in the same batch of agricultural products; The temperature control zone drying detection module is used to quantify the impact of the temperature difference between different temperature control zones inside the drying oven on the moisture distribution of the agricultural product sample if the moisture distribution state is qualified, and at the same time determine whether to optimize the temperature control parameters to reduce the extension of the drying time caused by the temperature lag between different temperature control zones; The airflow distribution area drying detection module is used to input the temperature-controlled agricultural product sample data into the constructed dynamic simulation model of the agricultural product drying process in the airflow distribution area of ​​the drying box to predict the remaining drying time and determine whether to optimize the heat source boundary parameters to improve the heat exchange matching between the dynamic simulation model of the agricultural product drying process and the actual drying process of the agricultural product.

Images