A method for degrading vomitoxin in the deep processing of corn

Abstract

The present invention discloses a method for degrading vomitoxin in a corn deep processing process, relates to the technical field of corn deep processing, and specifically comprises the following steps: obtaining the temperature dynamic distribution information of corn slurry in each region, judging whether the corn slurry in each region has the phenomenon of over-rapid heating and over-slow heating, and dividing each region into a temperature increasing region, a temperature balancing region, and a temperature hysteresis region according to the judgment result; based on the division result of each region, constructing an adjustment mechanism of heating power and time, and performing different temperature adjustments on the temperature increasing region, the temperature balancing region, and the temperature hysteresis region respectively; in the process of the adjustment mechanism adjusting the temperature of each region, evaluating whether the temperature adjustment effect of the adjustment mechanism in each region reaches the expectation, and optimizing the adjustment mechanism according to the evaluation result. The present invention solves the problem of uneven temperature during the heating process of corn slurry, and achieves the effect of uniformly degrading vomitoxin.

Description

Technical Field

[0001] The invention relates to the technical field of corn deep processing, and in particular to a method for degrading vomitoxin in the process of corn deep processing. Background Art

[0002] Corn deep processing refers to the process of converting corn into a variety of high-value-added products, such as starch, syrup, alcohol, feed and biofuel, through a series of physical, chemical or biological treatments. This process can make full use of corn resources and enhance its economic value and product diversity. However, corn may be contaminated by fungi during growth and storage, producing vomitoxin, which is harmful to humans and animals and has serious toxicity and carcinogenicity. Therefore, during the deep processing of corn, vomitoxin must be degraded or removed through specific treatment methods to ensure the safety of the final product, meet the hygiene standards of food and feed, protect consumer health, and avoid negative impacts on product quality and market reputation.

[0003] The existing technology for degrading vomitoxin during the deep processing of corn is mainly achieved through a series of refined processing steps. First, the surface impurities and contaminants are removed as much as possible during the screening and pretreatment of the corn raw materials. Then, specific degradation enzymes, such as vomitoxin degrading enzymes, are used to enzymatically hydrolyze the corn under precisely controlled temperature, pH and time conditions to decompose or convert vomitoxin. Then, by adding specific fermentation strains, such as lactic acid bacteria or yeast, the metabolic activity of microorganisms is used to further degrade the residual vomitoxin during the fermentation process. In order to ensure the degradation effect and product safety, heat treatment, such as pasteurization or high temperature and short-time treatment, is usually carried out to further reduce the content of vomitoxin and inactivate harmful microorganisms that may exist. Finally, the product is purified and tested by means of solid-liquid separation, filtration and centrifugation to ensure that the residual amount of vomitoxin meets the food safety standards, thereby ensuring the safety and quality of the product.

[0004] The prior art has the following deficiencies:

[0005] During the deep processing of corn, when the corn slurry is treated for degrading vomitoxin using segmented heating, especially in the initial stage of the heating process, the temperature of local areas in the slurry may rise rapidly due to the uneven temperature rise rate when the heat source is started, while the temperature rise in other areas is relatively slow. This uneven temperature distribution is caused by the lag effect of the temperature control system during the startup or adjustment of the equipment, especially in large heating equipment or long-term continuous production. Due to these temperature differences, some areas in the slurry may have excessive temperatures, resulting in excessive decomposition of vomitoxin and even the generation of new harmful byproducts, while areas with lower temperatures may not reach the temperature threshold for effective degradation of vomitoxin, resulting in insufficient degradation. It is difficult for existing technologies to monitor and accurately adjust the temperature of each area in real time under such a complex environment to ensure uniform temperature distribution of the entire slurry, thereby achieving uniform degradation of vomitoxin. This problem will lead to inconsistent residual amounts of vomitoxin in the final product, and some products may not meet safety standards due to excessive toxin residues, or cause new safety risks due to the generation of by-products. In addition, the unevenness of the heating process may also lead to frequent parameter adjustments and additional energy consumption, increase production costs, and affect the operating efficiency of the production line and the stability of product quality.

[0006] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0007] The purpose of the present invention is to provide a method for degrading vomitoxin during the deep processing of corn to solve the problems in the above-mentioned background technology.

[0008] In order to achieve the above object, the present invention provides the following technical solution: a method for degrading vomitoxin in the deep processing of corn, specifically comprising the following steps:

[0009] Preliminarily treating the corn slurry for degrading vomitoxin, dividing it into a plurality of regions evenly, and evenly distributing the corn slurry for degrading vomitoxin in each region;

[0010] In the initial stage of the segmented heating process, the temperature information of the corn slurry in each area is obtained in real time, and analyzed after the acquisition to detect whether there is a phenomenon of uneven temperature distribution, and if the phenomenon is detected, the following steps are continued;

[0011] The temperature dynamic distribution information of the corn slurry in each area is obtained, and analyzed after the acquisition to determine whether the corn slurry in each area has a phenomenon of over-rapid temperature rise or over-slow temperature rise, and each area is divided into a temperature increasing area, a temperature equilibrium area and a temperature hysteresis area according to the judgment result;

[0012] Based on the division results of each area, a heating power and time adjustment mechanism is constructed to make different temperature adjustments for the temperature acceleration area, temperature balance area and temperature hysteresis area respectively;

[0013] When the adjustment mechanism adjusts the temperature of each area, the temperature response information of each area is obtained in real time, and analyzed after acquisition to evaluate whether the temperature adjustment effect of the adjustment mechanism in each area meets expectations, and the adjustment mechanism is optimized according to the evaluation results;

[0014] The temperature data, adjustment records and vomitoxin degradation effects during the entire heating process are recorded and analyzed in real time, and the temperature adjustment strategy is continuously improved and the adjustment mechanism is improved in real time.

[0015] Preferably, in the initial stage of the segmented heating process, the temperature information of the corn slurry in each area is obtained in real time, and analyzed after acquisition to detect whether there is a phenomenon of uneven temperature distribution, which specifically includes the following steps:

[0016] In the initial stage of the segmented heating process, the temperature information of the corn slurry in each area is obtained in real time and pre-processed after being obtained;

[0017] The actual temperature of corn slurry in each area at different times within a period of time is extracted from the preprocessed temperature information and calibrated as , Indicates Corn slurry in each region over a period of time The actual temperature at the moment, , , and are all positive integers;

[0018] Calculate the average actual temperature of corn slurry in each area over a period of time according to the formula: , Indicates The average actual temperature of corn slurry in a certain area over a period of time;

[0019] Calculate the standard deviation of the actual temperature of corn slurry in all areas over a period of time. The specific calculation formula is as follows: In the formula, is the standard deviation of the actual temperature of corn slurry in all regions over a period of time;

[0020] Determine the preset temperature standard deviation threshold and calibrate it as ;

[0021] The standard deviation of the actual temperature of corn slurry in all areas over a period of time The temperature standard deviation threshold is set For comparison, if , then there is no uneven temperature distribution between the corn slurry in each area. , there is an uneven temperature distribution in the corn slurry in each area.

[0022] Preferably, the temperature dynamic distribution information of the corn slurry in each area is obtained, and after the acquisition, it is analyzed to determine whether the corn slurry in each area has a phenomenon of too fast heating and too slow heating, and each area is divided into a temperature acceleration area, a temperature balance area and a temperature hysteresis area according to the judgment result, which specifically includes the following steps:

[0023] Obtaining the temperature dynamic distribution information of corn slurry in each area and performing preprocessing after obtaining;

[0024] Extracting the environmental temperature response information and heat conduction effect information of each region from the temperature dynamic distribution information of the corn slurry in each region that has been pretreated, and analyzing them after extraction to generate the temperature response coefficient and thermal effect index of each region respectively;

[0025] A temperature rise assessment model is constructed for the temperature response coefficient and thermal effect index generated for each area, and the temperature rise coefficient of each area is generated. The generated temperature rise coefficient of each area is compared with the pre-set temperature rise coefficient threshold range, and the comparison results are used to determine whether the corn slurry in each area has a phenomenon of heating up too fast or heating up too slow. According to the judgment results, each area is divided into a temperature acceleration area, a temperature balance area and a temperature hysteresis area.

[0026] Preferably, the logic for obtaining the temperature response coefficient and thermal effect index of each region is as follows:

[0027] The temperature dynamic distribution information of the corn slurry in each area after pretreatment is extracted, including the average temperature, average humidity and average heat capacity of the corn slurry in each area at different times within a period of time, and calibrated as , and , Indicates Corn slurry in each region over a period of time The average temperature at the time, Indicates Corn slurry in each region over a period of time The average humidity at the time, Indicates Corn slurry in each region over a period of time The average heat capacity at time, , , and are all positive integers;

[0028] Calculate the temperature response coefficient of each area. The specific calculation formula is as follows: In the formula, For the The temperature response coefficient of each region;

[0029] The temperature dynamic distribution information of the corn slurry in each area after pretreatment is extracted, including the average density and heat transfer coefficient of the corn slurry in each area at different times within a period of time, and calibrated as and , Indicates Corn slurry in each region over a period of time The average density at the moment, Indicates Corn slurry in each region over a period of time The heat transfer coefficient at the moment;

[0030] Calculate the thermal effect index of each area. The specific calculation formula is as follows: In the formula, For the Thermal effect index of a region.

[0031] Preferably, the temperature response coefficients of the generated regions are Thermal effect index Construct a temperature rise assessment model and generate the temperature rise coefficient of each region through weighted summation , the temperature rise coefficients of each region generated The pre-set temperature rise coefficient threshold range Compare and judge whether the corn slurry in each area has the phenomenon of too fast or too slow temperature rise according to the comparison results, and divide each area into temperature increase area, temperature balance area and temperature hysteresis area according to the judgment results. The specific comparison analysis and division process is as follows:

[0032] like , the corn slurry in this area has the phenomenon of slow temperature rise, so this area is divided into the temperature hysteresis area;

[0033] like , the corn slurry in this area does not have the phenomenon of too fast or too slow temperature rise, and is within the normal range, so this area is divided into a temperature balance area;

[0034] like , the corn slurry in this area has the phenomenon of heating up too fast, so this area is divided into the temperature increasing area.

[0035] Preferably, based on the division results of each area, an adjustment mechanism for heating power and time is constructed, specifically: according to the division results of the temperature increase area, the temperature balance area and the temperature hysteresis area, different heating power and heating time parameters are set respectively to form an adjustment mechanism; the adjustment mechanism is based on the temperature rise coefficient and the current temperature state of each area, and automatically determines the adjustment range and method of the heating power and heating time through pre-set rules;

[0036] Different temperature adjustments are made to the temperature acceleration area, temperature balance area and temperature hysteresis area respectively, specifically including: in the temperature acceleration area, the low-power heating parameters in the adjustment mechanism are used to reduce the heating time and reduce the heating power; in the temperature balance area, the standard heating parameters in the adjustment mechanism are maintained without changing the current heating power and time; in the temperature hysteresis area, the high-power heating parameters in the adjustment mechanism are used to increase the heating time and increase the heating power.

[0037] Preferably, in the process of adjusting the temperature of each area by the adjustment mechanism, the temperature response information of each area is obtained in real time, and analyzed after acquisition to evaluate whether the temperature adjustment effect of the adjustment mechanism in each area meets expectations, and optimize the adjustment mechanism according to the evaluation results, which specifically includes the following steps:

[0038] In the process of adjusting the temperature of each area by the adjustment mechanism, the temperature response information of each area is obtained in real time and preprocessed after being obtained;

[0039] Extracting the temperature change response information and temperature control feedback information of each region from the preprocessed temperature response information of each region, and analyzing them after extraction to generate a real-time temperature change response coefficient and a temperature control response index for each region respectively;

[0040] The real-time temperature change response coefficient and temperature control response index of each area are used to construct a temperature adjustment response evaluation model, and the temperature adjustment response index of each area is generated. The generated temperature adjustment response index of each area is compared with the pre-set temperature adjustment response index threshold to evaluate whether the temperature adjustment effect of the adjustment mechanism in each area meets the expectation, and optimize the adjustment mechanism according to the evaluation results.

[0041] Preferably, the acquisition logic of the real-time temperature change response coefficient and the temperature control response index of each area is as follows:

[0042] The temperature change response information of each region in the preprocessed temperature response information of each region is extracted, specifically including the temperature change rate, thermal conductivity and heating power of the corn slurry in each region at different times within a period of time, and calibrated as , and , Indicates Corn slurry in each region over a period of time The temperature change rate at time, Indicates Corn slurry in each region over a period of time Thermal conductivity at time, Indicates Corn slurry in each region over a period of time Heating power at the moment, , , and are all positive integers;

[0043] Calculate the real-time temperature change response coefficient of each area. The specific calculation formula is as follows: In the formula, For the Real-time temperature change response coefficient of each area;

[0044] The temperature control feedback information of each area in the pre-processed temperature response information of each area is extracted, including the temperature deviation, specific heat capacity and temperature target value of the corn slurry in each area at different times within a period of time, and calibrated as , and , Indicates Corn slurry in each region over a period of time Temperature deviation at any time, Indicates Corn slurry in each region over a period of time The specific heat capacity at time, Indicates Corn slurry in each region over a period of time The temperature target value at the moment;

[0045] Calculate the temperature control response index of each area. The specific calculation formula is as follows: In the formula, For the The temperature control response index of each area.

[0046] Preferably, the real-time temperature change response coefficients of each region are generated and temperature regulation response index Construct a temperature adjustment response evaluation model and generate the temperature adjustment response index of each area through weighted summation , and the temperature adjustment response index of each region is generated Adjustable response index with pre-set temperature thresholds Compare and evaluate whether the temperature adjustment effect of the adjustment mechanism in each area meets expectations, and optimize the adjustment mechanism based on the evaluation results. The specific comparison analysis is as follows:

[0047] like , the temperature adjustment effect of the adjustment mechanism in this area reaches the expected level, and the adjustment mechanism does not need to be optimized;

[0048] like The temperature adjustment effect of the adjustment mechanism in this area did not meet expectations, and the adjustment mechanism needs to be optimized, including: for this area, reducing the heating power and shortening the heating time to prevent the temperature from being too high; increasing the heating power and extending the heating time to accelerate the temperature rise.

[0049] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0050] 1. The present invention significantly improves the temperature uniformity during the heating process by real-time monitoring and dynamic adjustment of the temperature of the corn slurry during the heating process. By introducing key parameters such as the temperature response coefficient, the thermal effect index, and the temperature control response index, the system can accurately identify the temperature changes in each area and dynamically adjust the heating power and time according to real-time data. This refined temperature control effectively avoids the situation where the local temperature is too high or too low, ensures the uniformity of the temperature distribution of the corn slurry during the entire heating process, thereby stabilizing the entire process and reducing the quality problems that may be caused by uneven temperature.

[0051] 2. The present invention can ensure that corn slurry degrades vomitoxin under optimal temperature conditions through segmented heating and real-time control mechanisms. The introduction of the temperature control response index enables the system to evaluate and optimize the effect of temperature control in real time, ensuring that the temperature in each area can reach the threshold for effectively degrading vomitoxin. This precise temperature control not only improves the degradation rate of vomitoxin and avoids the formation of harmful by-products, but also greatly reduces the risk of excessive toxin residues, thereby improving the safety and quality consistency of the final product.

[0052] 3. The present invention establishes a real-time optimized feedback mechanism by continuously monitoring and analyzing the temperature data, adjustment records and degradation effects of each area. This mechanism can not only adjust the heating power and time in time to prevent overheating or insufficient heating, but also continuously improve the temperature control strategy to make the heating process more efficient and energy-saving. This dynamic adjustment method helps to reduce unnecessary energy consumption, reduce the operating costs of the production line, and improve production efficiency. In addition, by preventing overheating and temperature fluctuations, the need for equipment maintenance is also reduced, further optimizing production costs and improving the economy and sustainability of the process. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0054] Figure 1 The present invention is a schematic flow diagram of a method for degrading vomitoxin in a corn deep processing process. DETAILED DESCRIPTION

[0055] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art.

[0056] The present invention provides Figure 1 The method for degrading vomitoxin in a corn deep processing process shown in the figure specifically comprises the following steps:

[0057] Preliminarily treating the corn slurry for degrading vomitoxin, dividing it into a plurality of regions evenly, and evenly distributing the corn slurry for degrading vomitoxin in each region;

[0058] When the corn slurry to be degraded for deoxynivalenol is subjected to preliminary treatment, a series of steps can be taken to ensure the uniformity and adaptability of the slurry. First, the slurry is uniformly mixed using a stirring device to ensure that the solid particles and liquid components are evenly distributed. The stirring process can be controlled by an automated system, and the stirring speed and time can be adjusted in real time according to the viscosity, concentration and other physical properties of the slurry. Next, screening or filtering equipment can be used to remove large particles or impurities in the slurry to ensure the consistency of the slurry and reduce the risk of local overheating or overcooling during subsequent heating. In addition, the physical properties of the slurry can be monitored in real time by online viscosity sensors or density sensors to ensure that it meets the predetermined processing standards. The data collected by these sensors are analyzed by a software system to generate a viscosity coefficient or density index, and the stirring and screening parameters are automatically adjusted to optimize the uniformity of the slurry. The main purpose of the preliminary treatment is to allow the corn slurry to be heated evenly during the heating process to avoid insufficient degradation or local excessive degradation due to unevenness.

[0059] When the corn slurry is evenly divided into several areas and evenly distributed in each area, it can be achieved in the following ways. First, an automated distribution system is used to accurately distribute the mixed slurry to different heating areas according to the preset area size and shape. The system is adjusted in real time through flow control valves and sensors to ensure that each area receives a consistent amount of slurry. To further ensure uniform distribution, a vibration table or stirring device can be used during the distribution process to prevent the slurry from settling or stratifying during the flow process. These devices are controlled by a software system, combined with the fluidity index and distribution rate coefficient of the slurry, to dynamically adjust the vibration frequency or stirring speed to make the slurry more evenly distributed in each area. The main purpose of dividing the slurry evenly into multiple areas and ensuring uniform distribution is to accurately control the temperature changes in each area during the heating process, so as to effectively degrade vomitoxin and avoid the problem of inconsistent degradation effects caused by temperature differences between areas.

[0060] In the initial stage of the segmented heating process, the temperature information of the corn slurry in each area is obtained in real time, and analyzed after the acquisition to detect whether there is a phenomenon of uneven temperature distribution, and if the phenomenon is detected, the following steps are continued;

[0061] In this embodiment, in the initial stage of the segmented heating process, the temperature information of the corn slurry in each area is obtained in real time, and analyzed after acquisition to detect whether there is a phenomenon of uneven temperature distribution, which specifically includes the following steps:

[0062] In the initial stage of the segmented heating process, the temperature information of the corn slurry in each area is obtained in real time and pre-processed after being obtained;

[0063] In the initial stage of the segmented heating process, real-time acquisition of the temperature information of the corn slurry in each area can be achieved by installing high-precision temperature sensors in each area of ​​the heating equipment. These sensors are distributed in key locations in each area, can continuously monitor temperature changes, and transmit the collected temperature data to the central control system in real time via wireless or wired networks. The data received by the control system will be synchronized and summarized through dedicated software to form the dynamic distribution information of the temperature in each area. This process can be combined with a data acquisition frequency control algorithm to ensure that the acquired data is dense and accurate enough to reflect the temperature changes of the corn slurry in the initial stage of heating.

[0064] The purpose of preprocessing temperature information is to remove noise and outliers in the data and ensure the accuracy of the analysis results. Preprocessing includes steps such as smoothing, denoising and normalizing the temperature data. The specific operation can be smoothed by the filtering algorithm in the software to eliminate abnormal data points that may be caused by sensor errors or instantaneous fluctuations. Then, statistical methods are used to identify and remove extreme outliers in the data to ensure the continuity and stability of the data. Finally, the temperature data is normalized so that the temperature data in different areas can be compared and analyzed on the same scale. The results of the preprocessing will serve as the basis for subsequent temperature distribution analysis and unevenness detection to ensure the reliability and accuracy of the detection process.

[0065] The actual temperature of corn slurry in each area at different times within a period of time is extracted from the preprocessed temperature information and calibrated as , Indicates Corn slurry in each region over a period of time The actual temperature at the moment, , , and are all positive integers;

[0066] Extracting the actual temperature of corn slurry in each area at different times within a period of time from the preprocessed temperature information can be achieved in the following way: After the data preprocessing is completed, the software system will extract the temperature information of each area at a predetermined time interval to form a time series data set. This process can be achieved through timestamp indexing, that is, the system will extract the temperature value at a specific time based on the timestamp of each data point. In order to ensure the continuity and integrity of the data, the software will automatically filter out multiple time points within a period of time, extract the corresponding temperature values, and construct the temperature curves of each area within the time period. These temperature curves will reflect the temperature change trend of corn slurry during the heating process for subsequent analysis.

[0067] Calculate the average actual temperature of corn slurry in each area over a period of time according to the formula: , Indicates The average actual temperature of corn slurry in a certain area over a period of time;

[0068] Calculate the standard deviation of the actual temperature of corn slurry in all areas over a period of time. The specific calculation formula is as follows: In the formula, is the standard deviation of the actual temperature of corn slurry in all regions over a period of time;

[0069] Determine the preset temperature standard deviation threshold and calibrate it as ;

[0070] Determining the pre-set temperature standard deviation threshold can be achieved in the following ways: First, the software system will calculate the temperature standard deviation of each area under ideal heating conditions based on historical data and empirical formulas. This process can be done by analyzing a large amount of past production data, extracting the temperature standard deviation range under different environments and process parameters, and then deriving a statistically optimal standard deviation value. Then, the software system will adjust this standard deviation value through model prediction based on the specific conditions of the current processing batch (such as slurry viscosity, flow rate, ambient temperature, etc.) to adapt it to the current production situation. Ultimately, the determined temperature standard deviation threshold will serve as a reference standard for subsequent temperature distribution unevenness detection to ensure that abnormal temperature distribution can be effectively identified and controlled in actual operations.

[0071] The standard deviation of the actual temperature of corn slurry in all areas over a period of time The temperature standard deviation threshold is set For comparison, if , then there is no uneven temperature distribution between the corn slurry in each area. , there is an uneven temperature distribution in the corn slurry in each area.

[0072] like , which means that the temperature distribution of corn slurry in all areas during this time period is relatively uniform, that is, the temperature changes between various areas are within an acceptable range, and there is no significant temperature difference. This situation shows that the segmented heating process is operating normally, the temperature of each area is properly controlled, and the temperature uniformity of corn slurry in different areas is good, which can ensure that the degradation process of vomitoxin is consistent and effective. The advantage of uniform temperature is that it can avoid insufficient or excessive degradation caused by overheating or overcooling in local areas, thereby ensuring the quality and safety of the product.

[0073] like , which means that there is a significant temperature unevenness in the corn slurry in each area during this time period, and the temperature in some areas deviates from the preset ideal range, resulting in a large temperature difference in the entire system. This situation may cause the corn slurry in some areas to fail to reach the temperature that effectively degrades DON, while the temperature in other areas may be too high, resulting in excessive degradation and even the generation of new harmful byproducts. Uneven temperature not only affects the degradation effect, but may also increase energy consumption and production costs, and may require additional adjustments or remedial measures in subsequent processes, which in turn affects production efficiency and the quality stability of the final product.

[0074] The temperature dynamic distribution information of the corn slurry in each area is obtained, and analyzed after the acquisition to determine whether the corn slurry in each area has a phenomenon of over-rapid temperature rise or over-slow temperature rise, and each area is divided into a temperature increasing area, a temperature equilibrium area and a temperature hysteresis area according to the judgment result;

[0075] In this embodiment, the temperature dynamic distribution information of the corn slurry in each area is obtained, and after the acquisition, it is analyzed to determine whether the corn slurry in each area has a phenomenon of too fast temperature rise or too slow temperature rise, and each area is divided into a temperature increase area, a temperature balance area and a temperature hysteresis area according to the judgment result, which specifically includes the following steps:

[0076] Obtaining the temperature dynamic distribution information of corn slurry in each area and performing preprocessing after obtaining;

[0077] To obtain the temperature dynamic distribution information of corn slurry in each area, high-precision temperature sensors can be installed at various key locations of the heating equipment. These sensors can monitor the temperature changes in each area in real time. The data collected by these sensors are transmitted to the central control system in real time via wireless or wired networks. Specifically, the temperature dynamic distribution information includes the temperature value of each area at different time points, the ambient temperature response information (such as the temperature change rate and humidity data related to the external environment), and the thermal conduction effect information related to the thermal characteristics of the material (such as the temperature change over time and the heat transfer efficiency). Through these data, the system can fully grasp the temperature change trend and characteristics of each area, thereby providing a basis for subsequent analysis and processing.

[0078] Preprocessing is to ensure the accuracy and consistency of the temperature dynamic distribution information obtained from the sensor before analysis. Since the data acquisition process may be affected by sensor noise, environmental interference or instantaneous fluctuations, preprocessing can effectively remove these noises and outliers and improve data quality. Specific preprocessing steps include data smoothing (such as using filtering algorithms to eliminate noise), outlier detection and removal (such as identifying and deleting extreme temperature values), and data normalization (adjusting temperature data to a uniform scale for subsequent comparison and analysis). Through these preprocessing steps, it can be ensured that the data used in the analysis is reliable and comparable, thereby improving the accuracy and effectiveness of the evaluation.

[0079] Extracting the environmental temperature response information and heat conduction effect information of each region from the temperature dynamic distribution information of the corn slurry in each region that has been pretreated, and analyzing them after extraction to generate the temperature response coefficient and thermal effect index of each region respectively;

[0080] A temperature rise assessment model is constructed for the temperature response coefficient and thermal effect index generated for each area, and the temperature rise coefficient of each area is generated. The generated temperature rise coefficient of each area is compared with the pre-set temperature rise coefficient threshold range, and the comparison results are used to determine whether the corn slurry in each area has a phenomenon of heating up too fast or heating up too slow. According to the judgment results, each area is divided into a temperature acceleration area, a temperature balance area and a temperature hysteresis area.

[0081] The pre-set temperature rise coefficient threshold interval can be determined by analyzing historical data and experimental data. Specifically, the software system will first collect a large amount of data on the temperature response coefficient and thermal effect index of each area in the historical production process, especially under different operating conditions (such as different heating power, ambient temperature, material characteristics, etc.). By statistically analyzing these data, the software can generate a distribution curve of the temperature rise coefficient, from which the temperature rise coefficient range in the normal heating process can be determined. Then, the software will set a threshold interval on the distribution curve in combination with the process requirements in actual operation (such as degradation effect, production efficiency, safety standards, etc.) to ensure that the temperature rise coefficient within this interval can meet the process requirements. This process can also be dynamically adjusted through continuously updated real-time data to adapt to the requirements of different batches or different operating conditions, thereby ensuring the accuracy and applicability of the threshold interval.

[0082] In this embodiment, the logic for obtaining the temperature response coefficient and thermal effect index of each region is as follows:

[0083] The temperature dynamic distribution information of the corn slurry in each area after pretreatment is extracted, including the average temperature, average humidity and average heat capacity of the corn slurry in each area at different times within a period of time, and calibrated as , and , Indicates Corn slurry in each region over a period of time The average temperature at the time, Indicates Corn slurry in each region over a period of time The average humidity at the time, Indicates Corn slurry in each region over a period of time The average heat capacity at time, , , and are all positive integers;

[0084] To extract the temperature dynamic distribution information of the corn slurry in each area after pretreatment, including the average temperature, average humidity and average heat capacity, the following methods can be used. First, install high-precision temperature sensors, humidity sensors and material-related heat capacity calculation modules in each area. These sensors and modules can monitor and record the temperature, humidity and heat capacity data at different time points in real time. Specifically, the temperature data is recorded by the temperature value captured by the temperature sensor at each moment, and the average temperature over a period of time is calculated by the software; the humidity data is also collected and recorded in real time by the humidity sensor, and the software averages the data collected multiple times to obtain the average humidity; as for the heat capacity, the heat capacity calculation module uses the temperature data provided by the temperature sensor and the physical properties of the material (such as density and composition) to perform real-time calculations through the built-in heat capacity calculation model, and outputs the average heat capacity over a period of time. The data collected by these sensors and modules are transmitted to the central control system in real time via wireless or wired networks. The software in the system will pre-process the data, such as denoising, filtering and normalization, and then integrate and analyze the data according to the time dimension. Finally, the average temperature, average humidity and average heat capacity of each area at different times over a period of time are extracted to provide basic data for subsequent analysis.

[0085] Calculate the temperature response coefficient of each area. The specific calculation formula is as follows: In the formula, For the The temperature response coefficient of each region;

[0086] exist In the calculation formula, Indicates Corn slurry in each region over a period of time The average temperature at the time, Indicates Corn slurry in each region over a period of time The average humidity at the time, Indicates Corn slurry in each region over a period of time The average heat capacity at the time. This formula measures the temperature response characteristics of the area over the entire time period by weighted summing the temperature, humidity and heat capacity. The average temperature reflects the overall temperature level of the area, while humidity and heat capacity affect the ability to transmit and maintain temperature in the area. By weighted summing these factors and taking the average value, It can comprehensively evaluate the response of each area to temperature changes during the heating process, providing an important reference for subsequent temperature control and optimization. This calculation method helps to ensure the accuracy and effectiveness of temperature control and avoid problems such as overheating or uneven temperature.

[0087] No. Temperature response coefficient of the region The size of directly affects the judgment of whether the corn slurry in the area is heated too fast or too slowly. Specifically, It reflects the comprehensive changes in temperature, humidity and heat capacity. If the value is large, it means that the corn slurry in this area reacts quickly to temperature changes and may heat up too fast, which may cause overheating in some areas, excessive degradation of vomitoxin, and even the production of new harmful byproducts. If the value is small, it means that the temperature response is slow and the temperature may rise too slowly, which will cause the corn slurry to fail to reach the effective degradation temperature within the expected time, affecting the degradation effect. The size of the temperature can be used to determine whether the temperature in the area is rising normally, so that corresponding control measures can be taken to ensure the uniformity of temperature control and the effectiveness of the degradation process.

[0088] The temperature dynamic distribution information of the corn slurry in each area after pretreatment is extracted, including the average density and heat transfer coefficient of the corn slurry in each area at different times within a period of time, and calibrated as and , Indicates Corn slurry in each region over a period of time The average density at the moment, Indicates Corn slurry in each region over a period of time The heat transfer coefficient at the moment;

[0089] To extract the temperature dynamic distribution information of corn slurry in each area after pretreatment, including the average density and heat transfer coefficient, the following methods can be used. First, a density sensor and a heat transfer coefficient measurement module are installed in each area. The density sensor can monitor the density of corn slurry in real time, and usually obtains specific density data by detecting the mass-to-volume ratio of the slurry. The heat transfer coefficient measurement module is based on the heat flow method or other related measurement methods, and calculates the heat transfer coefficient in real time by detecting the relationship between the temperature change and heat flow of the slurry during the heating process. The data collected by these sensors and modules are transmitted to the central control system via wireless or wired networks. The software in the system will perform preliminary processing on the data, such as removing outliers and noise, and then perform time averaging processing on the data collected at different times to obtain the average density and heat transfer coefficient at each time point. For example, the software can average the density data collected by the density sensor at different time points to generate the average density over a period of time; for the heat transfer coefficient, the software will integrate multiple measurement data, consider the influence of temperature fluctuations and heat flow changes, and calculate the average heat transfer coefficient over a period of time. These processed average density and heat transfer coefficient data provide a key basis for subsequent thermal effect index and other analyses.

[0090] Calculate the thermal effect index of each area. The specific calculation formula is as follows: In the formula, For the Thermal effect index of a region.

[0091] exist In the calculation formula, Indicates Corn slurry in each region over a period of time The average density at the moment, Indicates Corn slurry in each region over a period of time The heat transfer coefficient at time is calculated by multiplying the temperature with the density and the heat transfer coefficient, and summing and averaging the total time period. Quantifies the thermal effect of a region during heating. Temperature reflects the level of thermal energy in the region, while density and heat transfer coefficient determine the transfer and distribution characteristics of thermal energy. It can accurately evaluate the degree of thermal effect in the area, help determine the effectiveness of the temperature control strategy and the direction of adjustment, and ensure that the material achieves the ideal processing effect under controlled temperature conditions.

[0092] No. Thermal effect index of the area The size of directly affects the judgment of whether the corn slurry in the area is heated too fast or too slowly. Specifically, It reflects the combined effect of temperature, density and heat transfer coefficient, indicating the efficiency of heat transfer in the area. A larger value indicates that the heat transfer efficiency in the corn slurry is higher, which may cause the temperature in the area to rise too fast, and then the local temperature may be too high, resulting in excessive degradation of vomitoxin or the production of harmful by-products; on the contrary, if A smaller value means that the heat transfer efficiency is low, which may lead to slow temperature rise and the slurry fails to reach the temperature that effectively degrades vomitoxin within the expected time. The size of the temperature can be used to determine whether there is an abnormal temperature rise in the area, so as to adjust the heating strategy to ensure that the temperature rise process of the corn slurry is uniform and effective.

[0093] In this embodiment, the temperature response coefficients of each region are generated. Thermal effect index Construct a temperature rise assessment model and generate the temperature rise coefficient of each region through weighted summation , the temperature rise coefficients of each region generated The pre-set temperature rise coefficient threshold range Compare and judge whether the corn slurry in each area has the phenomenon of too fast or too slow temperature rise according to the comparison results, and divide each area into temperature increase area, temperature balance area and temperature hysteresis area according to the judgment results. The specific comparison analysis and division process is as follows:

[0094] like , the corn slurry in this area has the phenomenon of slow temperature rise, so this area is divided into the temperature hysteresis area;

[0095] This means that the corn slurry in this area is heating up too slowly. Slow heating may cause the corn slurry in this area to fail to reach the required degradation temperature within the expected time, thus affecting the effective degradation of vomitoxin. This situation may cause the residual toxins to be unable to be completely decomposed, ultimately affecting the safety and quality of the product. In addition, slow heating may also lead to inefficiency in the entire heating process, extend the production cycle, and increase energy consumption and production costs.

[0096] like , the corn slurry in this area does not have the phenomenon of too fast or too slow temperature rise, and is within the normal range, so this area is divided into a temperature balance area;

[0097] This means that the temperature rise of the corn slurry in this area is within the normal range, and the temperature changes are balanced and in line with expectations. This situation shows that the temperature in this area is properly controlled, and the corn slurry reaches the temperature required to degrade DON in the appropriate time, ensuring the effectiveness and consistency of the degradation process. The temperature balance area helps to ensure stable product quality, reduce risks, and improve the operating efficiency and economy of the production line.

[0098] like , the corn slurry in this area has the phenomenon of heating up too fast, so this area is divided into the temperature increasing area.

[0099] This means that the corn slurry in this area is heating up too quickly. Heating up too quickly may cause the temperature in this area to be too high, resulting in excessive degradation of vomitoxin and even the generation of new harmful byproducts, which poses a threat to product safety. In addition, excessively high temperatures may also cause damage to equipment, increase maintenance costs, and may affect the stability of the entire production process, bringing potential production risks.

[0100] To "build a temperature rise assessment model for the generated temperature response coefficients and thermal effect indexes of each region, and generate the temperature rise coefficients of each region by weighted summation", the following method can be used. First, the construction of the temperature rise assessment model is based on two key parameters: temperature response coefficient Thermal effect index The temperature rise coefficient for each area is the weighted sum of these two parameters, where the weight coefficient and It is used to balance the relative importance of these two parameters in the heating process. Specifically, the weight coefficient is selected based on historical data and experimental analysis to determine which parameter has a greater impact on the heating process under different environmental conditions and process requirements. If the temperature response coefficient plays a more critical role in the actual process, it is assigned On the contrary, if the thermal effect index has a more significant impact on the temperature rise, it will be given a larger weight. Larger weight. Temperature rise coefficient The calculation formula is ;in, and The sum of is usually set to 1 to ensure that the weights of the two parameters in the overall evaluation are reasonably distributed. It reflects the comprehensive temperature rise in the area, combines the influence of temperature response speed and heat conduction efficiency, and provides a scientific basis for subsequent temperature control. This evaluation model can not only comprehensively consider the temperature dynamic characteristics of different areas, but also adjust the weight according to actual production needs to achieve more accurate temperature control and more effective vomitoxin degradation.

[0101] Based on the division results of each area, a heating power and time adjustment mechanism is constructed to make different temperature adjustments for the temperature acceleration area, temperature balance area and temperature hysteresis area respectively;

[0102] In this embodiment, based on the division results of each area, an adjustment mechanism for heating power and time is constructed, specifically: according to the division results of the temperature increase area, the temperature balance area and the temperature hysteresis area, different heating power and heating time parameters are set respectively to form an adjustment mechanism; the adjustment mechanism is based on the temperature rise coefficient and the current temperature state of each area, and automatically determines the adjustment range and method of the heating power and heating time through pre-set rules;

[0103] In order to "build an adjustment mechanism for heating power and time based on the division results of each area", it can be done in the following ways. First, the software system will automatically analyze the temperature change trend of each area according to the temperature rise coefficient and current temperature state of different areas. Specifically, the software will divide each area into a temperature increase area, a temperature balance area and a temperature hysteresis area, and set the initial heating power and time parameters for each area. Then, the system dynamically calculates the appropriate adjustment range based on the real-time temperature data and temperature rise coefficient of each area through the built-in rule engine. These rules may include: the temperature increase area needs to reduce power and shorten heating time to avoid overheating; the temperature balance area keeps power and time unchanged to maintain temperature stability; the temperature hysteresis area needs to increase power and extend heating time to speed up heating. In this way, the software system can accurately control the temperature changes in each area to ensure temperature uniformity and efficiency throughout the heating process. This dynamic adjustment mechanism based on real-time data can not only adapt to temperature changes in different areas, but also automatically optimize heating parameters during the production process, thereby improving production efficiency and ensuring stable product quality.

[0104] Different temperature adjustments are made to the temperature acceleration area, temperature balance area and temperature hysteresis area respectively, specifically including: in the temperature acceleration area, the low-power heating parameters in the adjustment mechanism are used to reduce the heating time and reduce the heating power; in the temperature balance area, the standard heating parameters in the adjustment mechanism are maintained without changing the current heating power and time; in the temperature hysteresis area, the high-power heating parameters in the adjustment mechanism are used to increase the heating time and increase the heating power.

[0105] To make different temperature adjustments for the temperature acceleration area, temperature balance area and temperature hysteresis area respectively, the following methods can be used. First, the software system monitors the temperature change and temperature rise coefficient of each area in real time, and automatically selects the appropriate heating power and time parameters through the preset adjustment mechanism. For the temperature acceleration area, after the system recognizes the phenomenon of too fast temperature rise, it will automatically reduce the heating power and shorten the heating time to avoid excessive degradation or harmful byproducts caused by local excessive temperature; this is achieved by adjusting the output power and heating cycle of the heating equipment. For the temperature balance area, the system recognizes that the temperature is maintained within a reasonable range, so the current heating power and time are kept unchanged to ensure temperature stability and a continuous and effective degradation process. For the temperature hysteresis area, after the system recognizes that the temperature rise is too slow, it will automatically increase the heating power and extend the heating time to ensure that the temperature reaches the predetermined value as soon as possible, thereby achieving effective degradation of vomitoxin. This adjustment mechanism relies on the automatic control of the software system, and dynamic adjustment through real-time data analysis and rule engine can not only improve the accuracy of the heating process, but also effectively respond to temperature changes in different areas, ensure the temperature uniformity and degradation effect of the entire production process, and thus ensure the quality and safety of the final product.

[0106] When the adjustment mechanism adjusts the temperature of each area, the temperature response information of each area is obtained in real time, and analyzed after acquisition to evaluate whether the temperature adjustment effect of the adjustment mechanism in each area meets expectations, and the adjustment mechanism is optimized according to the evaluation results;

[0107] In this embodiment, in the process of the adjustment mechanism adjusting the temperature of each area, the temperature response information of each area is obtained in real time, and analyzed after acquisition to evaluate whether the temperature adjustment effect of the adjustment mechanism in each area meets expectations, and the adjustment mechanism is optimized according to the evaluation results, which specifically includes the following steps:

[0108] In the process of adjusting the temperature of each area by the adjustment mechanism, the temperature response information of each area is obtained in real time and preprocessed after being obtained;

[0109] In the process of adjusting the temperature of each area by the adjustment mechanism, the temperature response information can be obtained in real time through high-precision sensors installed in each area. These sensors can capture data including temperature change rate, thermal conductivity, heating power, temperature deviation, specific heat capacity, and the difference between the current temperature and the target temperature. These data are transmitted to the central control system in real time via wireless or wired networks, and the software in the system integrates and analyzes the data. The temperature response information specifically includes two categories: temperature change response information (such as temperature change rate, thermal conductivity and heating power) and temperature control feedback information (such as temperature deviation, specific heat capacity and temperature target value). Through this information, the system can understand the temperature change dynamics of each area in real time, providing data support for subsequent analysis and adjustment.

[0110] Preprocessing is to ensure the accuracy and consistency of the temperature response information obtained in real time before analysis. Since the data collected by the sensor may be affected by noise, equipment fluctuations or environmental interference, preprocessing can effectively remove these interference factors. Specific preprocessing steps include data smoothing (using filtering algorithms to eliminate noise and abnormal fluctuations), outlier detection and elimination (identifying and removing extreme temperature data points), and data standardization (adjusting various types of data to a unified scale for subsequent comparison and analysis). Through these preprocessing steps, it can be ensured that the data used in the analysis is reliable, and the accuracy of the temperature adjustment response evaluation model is improved, thereby optimizing the entire adjustment mechanism.

[0111] Extracting the temperature change response information and temperature control feedback information of each region from the preprocessed temperature response information of each region, and analyzing them after extraction to generate a real-time temperature change response coefficient and a temperature control response index for each region respectively;

[0112] The real-time temperature change response coefficient and temperature control response index of each area are used to construct a temperature adjustment response evaluation model, and the temperature adjustment response index of each area is generated. The generated temperature adjustment response index of each area is compared with the pre-set temperature adjustment response index threshold to evaluate whether the temperature adjustment effect of the adjustment mechanism in each area meets the expectation, and optimize the adjustment mechanism according to the evaluation results.

[0113] The preset threshold of the temperature adjustment response index can be determined by analyzing historical data and experimental data. Specifically, the software system will first collect a large amount of data on the temperature response coefficient and control feedback information of each area in the historical production process, especially under different operating conditions (such as different heating power, ambient temperature, material characteristics, etc.). These data are processed by the statistical analysis tool in the software to generate a distribution curve of the temperature adjustment response index. Based on this curve, the system will identify the typical range of the temperature adjustment response index under normal production conditions, and then set a reasonable threshold interval on the distribution curve in combination with the process requirements in actual operation (such as temperature control accuracy, energy efficiency optimization, safety standards, etc.). This threshold interval can ensure that the temperature adjustment response index within this range meets the expected control effect, thereby achieving uniform adjustment and effective control of the temperature. This process is implemented by software and can be dynamically adjusted according to real-time data to adapt to changes in different batches or operating conditions, ensuring that the threshold interval always matches production needs.

[0114] In this embodiment, the logic for obtaining the real-time temperature change response coefficient and temperature control response index of each area is as follows:

[0115] The temperature change response information of each region in the preprocessed temperature response information of each region is extracted, specifically including the temperature change rate, thermal conductivity and heating power of the corn slurry in each region at different times within a period of time, and calibrated as , and , Indicates Corn slurry in each region over a period of time The temperature change rate at time, Indicates Corn slurry in each region over a period of time Thermal conductivity at time, Indicates Corn slurry in each region over a period of time Heating power at the moment, , , and are all positive integers;

[0116] To extract the temperature change response information from the pre-processed temperature response information of each area, it can be achieved in the following ways. First, the data of temperature change rate, thermal conductivity and heating power are obtained in real time through high-precision sensors and measuring equipment installed in each area. Specifically, the temperature change rate is calculated by the temperature data recorded by the temperature sensor at different time points. The software system calculates the temperature change rate by differential calculation of the temperature data at these time points. The thermal conductivity data comes from the physical properties of the material itself. Combined with the environmental conditions and the feedback of the measuring equipment, the software calculates the thermal conductivity of each area in real time through the built-in physical model. The heating power is directly obtained from the control system of the heating equipment. The equipment will feedback the current power output data in real time. The software system will record this data and match and integrate it with the temperature change rate and thermal conductivity data. Through the data collected by these sensors and equipment, the software can extract and process the temperature change response information to ensure the real-time and accuracy of the data, thereby providing reliable basic data for subsequent analysis and adjustment.

[0117] Calculate the real-time temperature change response coefficient of each area. The specific calculation formula is as follows: In the formula, For the Real-time temperature change response coefficient of each area;

[0118] exist In the calculation formula Indicates Corn slurry in each region over a period of time The temperature change rate at the moment, which reflects the temperature change trend over time. Indicates Corn slurry in each region over a period of time The thermal conductivity at the moment indicates the ability of the material in this area to transfer heat. Indicates Corn slurry in each region over a period of time The heating power at the moment reflects the intensity of the heat input applied by the system to the area; through this formula, the sensitivity of the temperature change of the area under given heating conditions and material properties can be evaluated. It is the average of all time points. The purpose of this is to comprehensively evaluate the temperature response during the entire time period and avoid the impact of fluctuations at a single moment on the results. This can accurately reflect the overall trend of temperature changes in the area during the entire heating process, thereby determining whether the current heating strategy is effective or needs to be adjusted.

[0119] No. Real-time temperature change response coefficient of the area The size of directly reflects the relationship between the sensitivity of temperature change and the regulation effect under the current heating conditions. Specifically, if A high value indicates that the temperature in this area changes rapidly, which usually means that the heating power is large or the thermal conductivity is small, resulting in a rapid temperature rise. However, a rapid temperature change may cause the temperature to exceed the expected target, and the temperature rise process cannot be effectively controlled, which may easily cause temperature overshoot, thereby affecting the temperature adjustment effect and even causing overheating, affecting the quality of the material. On the contrary, if A low value indicates that the temperature changes slowly, which may be due to insufficient heating power or a large thermal conductivity, resulting in a slow temperature rise. Slow temperature changes will prevent the area from reaching the expected temperature in time, affecting process efficiency and product uniformity. The size of the temperature can be evaluated in real time to see whether the temperature regulation effect of the current adjustment mechanism in this area meets the expectations, and according to The size of the heating power can be optimized or other control parameters can be adjusted in time to ensure that the temperature adjustment effect meets the expected requirements, thereby ensuring the stability of the production process and the consistency of product quality.

[0120] The temperature control feedback information of each area in the pre-processed temperature response information of each area is extracted, including the temperature deviation, specific heat capacity and temperature target value of the corn slurry in each area at different times within a period of time, and calibrated as , and , Indicates Corn slurry in each region over a period of time Temperature deviation at any time, Indicates Corn slurry in each region over a period of time The specific heat capacity at time, Indicates Corn slurry in each region over a period of time The temperature target value at the moment;

[0121] To extract the temperature control feedback information from the temperature response information of each pre-processed area, it can be achieved in the following ways. First, the temperature deviation is obtained by comparing the real-time measured temperature data with the preset temperature target value. The temperature sensor monitors the temperature of each area in real time and compares it with the target temperature set in the system. The software system calculates and records the temperature deviation at each moment. The specific heat capacity data comes from the physical properties of corn slurry, which is usually obtained by laboratory tests or literature data and stored in the system database. The software extracts the corresponding specific heat capacity value from the database according to the current material composition and state. The temperature target value is set in advance by the process requirements, which may be set in the system by the process engineer according to the temperature range required for corn slurry to degrade vomitoxin. Through the software system, these temperature target values, real-time temperature and specific heat capacity data are combined to extract and integrate into temperature control feedback information. The system will update these data regularly to ensure the accuracy and timeliness of the temperature control feedback information, thereby providing basic data for subsequent temperature control analysis.

[0122] Calculate the temperature control response index of each area. The specific calculation formula is as follows: In the formula, For the The temperature control response index of each area.

[0123] exist In the calculation formula, Indicates Corn slurry in each region over a period of time The temperature deviation at the moment is the difference between the current temperature and the target temperature. Indicates Corn slurry in each region over a period of time The specific heat capacity at a certain moment reflects the ease with which the temperature of the material changes after absorbing heat. Indicates Corn slurry in each region over a period of time The temperature target value at the moment represents the expected temperature during the control process. By combining the temperature deviation, specific heat capacity and temperature target value, the effectiveness of the current temperature control is evaluated. It is the average of all time points, ensuring a comprehensive evaluation of the temperature adjustment during the entire control process. The value can indicate the degree of deviation of the temperature in each area during the control process. If the value is too large, it means that the temperature control in this area is not accurate enough, and the control strategy may need to be further adjusted; if The smaller the value, the more successful the temperature control is, and the closer it is to the target temperature. The calculation helps to dynamically monitor and optimize the temperature control process to ensure that the corn slurry reaches the ideal temperature conditions in each area, thereby ensuring the effective degradation of vomitoxin.

[0124] No. Temperature control response index of each region The size of is directly related to whether the temperature adjustment effect of the adjustment mechanism in this area can achieve the expected goal. Specifically, The temperature deviation, specific heat capacity and temperature target value are combined to quantify the effectiveness of temperature control. If the value is large, it means that the temperature deviation in this area is large, the gap between the current temperature and the target temperature is significant, or the specific heat capacity of the material is large, which makes temperature adjustment more difficult. In this case, the effect of temperature control is obviously insufficient, and the temperature in this area may not reach or maintain within the set target temperature range in time, affecting the stability of the process and the consistency of product quality. This may mean that the heating power or time parameters need to be readjusted and the control strategy needs to be optimized to reduce the temperature deviation. On the contrary, if A smaller value indicates that the temperature control in this area is more successful, the temperature deviation is smaller, and the temperature is more stable close to or maintained within the target temperature range. This shows that the current adjustment mechanism has a good temperature control effect in this area, and the control strategy can effectively achieve the temperature target. Therefore, through real-time monitoring The size of the temperature control can be evaluated and ensured, and deficiencies in the control can be discovered and corrected in time, thereby ensuring the accuracy and reliability of the entire temperature adjustment process.

[0125] In this embodiment, the real-time temperature change response coefficients of each region are generated and temperature regulation response index Construct a temperature adjustment response evaluation model and generate the temperature adjustment response index of each area through weighted summation , and the temperature adjustment response index of each region is generated Adjustable response index with pre-set temperature thresholds Compare and evaluate whether the temperature adjustment effect of the adjustment mechanism in each area meets expectations, and optimize the adjustment mechanism based on the evaluation results. The specific comparison analysis is as follows:

[0126] like , the temperature adjustment effect of the adjustment mechanism in this area reaches the expected level, and the adjustment mechanism does not need to be optimized;

[0127] This means that the current temperature adjustment mechanism has achieved the expected temperature control effect in this area. Specifically, the speed, range and stability of temperature change are within acceptable ranges, and the temperature gradually approaches or remains near the target temperature, ensuring that the materials in this area are processed under ideal temperature conditions. Therefore, in this case, there is no need to further adjust the heating power or time parameters, which means that the temperature control process in this area is stable and efficient, which helps maintain the efficiency of the overall production line and the consistency of product quality.

[0128] like The temperature adjustment effect of the adjustment mechanism in this area did not meet expectations, and the adjustment mechanism needs to be optimized, including: for this area, reducing the heating power and shortening the heating time to prevent the temperature from being too high; increasing the heating power and extending the heating time to accelerate the temperature rise.

[0129] This indicates that the current temperature adjustment mechanism has not achieved the expected temperature control effect in this area. Specifically, the temperature may change too quickly or too slowly, resulting in large temperature deviations and failing to effectively approach or remain within the target temperature range. This situation may affect the handling effect of the material, lead to inefficient degradation processes, or cause other quality problems. To correct this situation, the adjustment mechanism must be optimized, such as reducing the heating power and shortening the heating time to avoid excessive temperatures, or increasing the heating power and extending the heating time to accelerate the temperature rise. Through these optimization measures, the temperature control can be made closer to the expected, ensuring the smooth progress of the process and the quality of the final product.

[0130] In order to "generate the real-time temperature change response coefficients of each area and temperature regulation response index Construct a temperature adjustment response evaluation model and generate the temperature adjustment response index of each area through weighted summation ", which can be achieved in the following ways. First, the software system will and As two key parameters, they are input into the temperature adjustment response evaluation model. The core of the model is a weighted summation calculation formula, where and Multiply by the preset weight coefficients and These weight coefficients and It is determined based on the importance of each parameter to the temperature control effect. For example, if the sensitivity of real-time temperature changes is more critical in the current process, then Weight will be larger; on the contrary, if the accuracy of temperature control is more important, then Weight will be larger. When calculating specifically, the system will use the formula ; The weighted sum of these two parameters generates the temperature adjustment response index of each area In this way, the system can comprehensively consider the sensitivity of temperature changes and the accuracy of regulation, evaluate the temperature adjustment effect of each area, and make subsequent optimization decisions based on this.

[0131] The temperature data, adjustment records and vomitoxin degradation effects during the entire heating process are recorded and analyzed in real time, and the temperature adjustment strategy is continuously improved and the adjustment mechanism is improved in real time.

[0132] High-precision temperature sensors installed in various areas can be used to collect temperature data of each area in real time during the entire heating process. The software system will transmit this data to the central control system through a wireless or wired network for real-time recording and storage. These temperature data include temperature change rate, temperature deviation, etc., and will be updated and saved regularly or continuously. The software system will conduct a comprehensive analysis of these data. By analyzing the trend, fluctuation and deviation from the preset target of temperature changes, the system can evaluate whether the current heating strategy is effective and identify potential temperature control problems, thereby providing a basis for subsequent strategy adjustments. The purpose of this is to ensure that the temperature control of the entire heating process is always under monitoring, and any abnormal conditions are discovered and corrected in a timely manner to ensure the stability of the process and the quality of the final product.

[0133] The adjustment record includes each change in heating power, adjustment of heating time, and optimization process of related parameters. These records can be automatically generated by the software system. Whenever the system performs an adjustment operation, the adjustment parameters and their corresponding timestamps will be automatically recorded. The software system will conduct a comprehensive analysis of these adjustment records, compare the effects of different adjustment strategies, and identify which adjustments are most effective for temperature control. Analyzing these records can help the system optimize the adjustment strategy and gradually improve the control model. Through this continuous recording and analysis, a dynamic optimization mechanism can be established to gradually improve the accuracy and stability of temperature control.

[0134] Real-time monitoring of the degradation of vomitoxin can be achieved through online analytical equipment or regular sampling and testing. The online equipment can monitor the changes in the concentration of vomitoxin in the corn slurry in real time, and the data will be transmitted to the software system in real time for recording and analysis. The software system will correlate the data of vomitoxin degradation with the temperature data and adjustment records to evaluate whether the current temperature adjustment effectively promotes the degradation of vomitoxin. If the degradation effect is not ideal, the system can identify the deficiencies in the temperature control or adjustment strategy and further optimize the control parameters. Doing so can ensure the ultimate goal of temperature adjustment, which is to effectively degrade vomitoxin, thereby ensuring the safety and quality of the product.

[0135] Based on the above data and analysis, the software system automatically generates a feedback loop to dynamically adjust the temperature control strategy by continuously monitoring and analyzing the temperature data, adjustment records and vomitoxin degradation effects of each area. Based on the analysis results, the system automatically optimizes the key parameters in the adjustment mechanism, such as the amplitude of the heating power adjustment, the frequency of the adjustment time, etc. Through continuous adjustment and optimization, the system can adaptively improve the temperature control model to ensure that the temperature control of each area can achieve the optimal effect, thereby achieving efficient degradation of vomitoxin. This real-time improvement mechanism can significantly improve the stability of the production process and the quality consistency of the final product, while reducing energy consumption and production costs.

[0136] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0137] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired or wireless (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0138] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean 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 the present application.

[0139] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0140] In the several embodiments provided in the present application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0141] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0142] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0143] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for degrading vomitoxin in the deep processing of corn, characterized in that: The specific steps include: Preliminarily treating the corn slurry for degrading vomitoxin, dividing it into a plurality of regions evenly, and evenly distributing the corn slurry for degrading vomitoxin in each region; In the initial stage of the segmented heating process, the temperature information of the corn slurry in each area is obtained in real time, and analyzed after the acquisition to detect whether there is a phenomenon of uneven temperature distribution, and if the phenomenon is detected, the following steps are continued; The temperature dynamic distribution information of the corn slurry in each area is obtained, and analyzed after the acquisition to determine whether the corn slurry in each area has a phenomenon of over-rapid temperature rise or over-slow temperature rise, and each area is divided into a temperature increasing area, a temperature equilibrium area and a temperature hysteresis area according to the judgment result; The temperature dynamic distribution information of corn slurry in each area is obtained, and analyzed after acquisition to determine whether the corn slurry in each area has the phenomenon of over-rapid temperature rise or over-slow temperature rise, and each area is divided into a temperature increase area, a temperature balance area and a temperature hysteresis area according to the judgment result, which specifically includes the following steps: Obtaining the temperature dynamic distribution information of corn slurry in each area and performing preprocessing after obtaining; Extracting the environmental temperature response information and heat conduction effect information of each region from the temperature dynamic distribution information of the corn slurry in each region that has been pretreated, and analyzing them after extraction to generate the temperature response coefficient and thermal effect index of each region respectively; The logic for obtaining the temperature response coefficient and thermal effect index of each area is as follows: The temperature dynamic distribution information of the corn slurry in each area after pretreatment is extracted, including the average temperature, average humidity and average heat capacity of the corn slurry in each area at different times within a period of time, and calibrated as and represents the average temperature of corn slurry in the mth area at time i within a period of time, represents the average humidity of corn slurry in the mth area at time i within a period of time, represents the average heat capacity of the corn slurry in the mth region at time i within a period of time, m = 1, 2, 3, ..., k, i = 1, 2, 3, ..., d, k and d are both positive integers; Calculate the temperature response coefficient of each area. The specific calculation formula is as follows: Where, TRC m is the temperature response coefficient of the mth region; The temperature dynamic distribution information of the corn slurry in each area after pretreatment is extracted, including the average density and heat transfer coefficient of the corn slurry in each area at different times within a period of time, and calibrated as and represents the average density of corn slurry in the mth region at time i within a period of time, represents the heat transfer coefficient of corn slurry in the mth region at time i within a period of time; Calculate the thermal effect index of each area. The specific calculation formula is as follows: Where, TEI m is the thermal effect index of the mth region; A temperature rise evaluation model is constructed for the generated temperature response coefficient and thermal effect index of each region, and a temperature rise coefficient of each region is generated. The generated temperature rise coefficient of each region is compared with a preset temperature rise coefficient threshold interval, and it is judged whether the corn slurry in each region has a phenomenon of over-rapid temperature rise or over-slow temperature rise according to the comparison result, and each region is divided into a temperature increase region, a temperature balance region and a temperature hysteresis region according to the judgment result; The temperature response coefficient TRC of each generated area m and thermal effect index TEI m Construct a temperature rise assessment model and generate the temperature rise coefficient HC for each area through weighted summation m , the temperature rise coefficient HC of each region generated m The pre-set temperature rise coefficient threshold range [HC min , HC max ] to compare, and judge whether the corn slurry in each area has the phenomenon of too fast or too slow temperature rise according to the comparison results, and divide each area into temperature increase area, temperature balance area and temperature hysteresis area according to the judgment results. The specific comparison analysis and division process is as follows: If HC m <HC min , the corn slurry in this area has the phenomenon of slow temperature rise, so this area is divided into the temperature hysteresis area; If HC min ≤HC m ≤HC max , the corn slurry in this area does not have the phenomenon of too fast or too slow temperature rise, and is within the normal range, so this area is divided into a temperature balance area; If HC m >HC max , the corn slurry in this area has a phenomenon of too fast temperature rise, so this area is divided into a temperature increase area; Based on the division results of each area, a heating power and time adjustment mechanism is constructed to make different temperature adjustments for the temperature acceleration area, temperature balance area and temperature hysteresis area respectively; When the adjustment mechanism adjusts the temperature of each area, the temperature response information of each area is obtained in real time, and analyzed after acquisition to evaluate whether the temperature adjustment effect of the adjustment mechanism in each area meets expectations, and the adjustment mechanism is optimized according to the evaluation results; The temperature data, adjustment records and vomitoxin degradation effects during the entire heating process are recorded and analyzed in real time, and the temperature adjustment strategy is continuously improved and the adjustment mechanism is improved in real time.

2. The method for degrading vomitoxin in the deep processing of corn according to claim 1, characterized in that: In the initial stage of the segmented heating process, the temperature information of the corn slurry in each area is obtained in real time, and analyzed after acquisition to detect whether there is a phenomenon of uneven temperature distribution, which specifically includes the following steps: In the initial stage of the segmented heating process, the temperature information of the corn slurry in each area is obtained in real time and pre-processed after being obtained; The actual temperature of corn slurry in each area at different times within a period of time is extracted from the preprocessed temperature information and calibrated as represents the actual temperature of the corn slurry in the mth area at time x within a period of time, m = 1, 2, 3, ..., k, x = 1, 2, 3, ..., g, k and g are both positive integers; Calculate the average actual temperature of corn slurry in each area over a period of time according to the formula: represents the average value of the actual temperature of the corn slurry in the mth area over a period of time; Calculate the standard deviation of the actual temperature of corn slurry in all areas over a period of time. The specific calculation formula is as follows: Where, WD ～ is the standard deviation of the actual temperature of corn slurry in all regions over a period of time; Determine the preset temperature standard deviation threshold and calibrate it as The standard deviation WD of the actual temperature of corn slurry in all areas over a period of time ～ The temperature standard deviation threshold is set For comparison, if There is no uneven temperature distribution between the corn slurry in each area. The corn slurry in each area has uneven temperature distribution.

3. The method for degrading vomitoxin in the deep processing of corn according to claim 2, characterized in that: Based on the division results of each area, an adjustment mechanism for heating power and time is constructed. Specifically, different heating power and heating time parameters are set according to the division results of the temperature increase area, the temperature balance area and the temperature hysteresis area to form an adjustment mechanism. The adjustment mechanism is based on the temperature rise coefficient and the current temperature state of each area, and automatically determines the adjustment range and method of the heating power and heating time through pre-set rules; Different temperature adjustments are made to the temperature acceleration area, temperature balance area and temperature hysteresis area respectively, specifically including: in the temperature acceleration area, the low-power heating parameters in the adjustment mechanism are used to reduce the heating time and reduce the heating power; in the temperature balance area, the standard heating parameters in the adjustment mechanism are maintained without changing the current heating power and time; in the temperature hysteresis area, the high-power heating parameters in the adjustment mechanism are used to increase the heating time and increase the heating power.

4. The method for degrading vomitoxin in the deep processing of corn according to claim 3, characterized in that: In the process of adjusting the temperature of each area by the adjustment mechanism, the temperature response information of each area is obtained in real time, and analyzed after acquisition to evaluate whether the temperature adjustment effect of the adjustment mechanism in each area meets expectations, and optimize the adjustment mechanism according to the evaluation results, which specifically includes the following steps: In the process of adjusting the temperature of each area by the adjustment mechanism, the temperature response information of each area is obtained in real time and preprocessed after being obtained; Extracting the temperature change response information and temperature control feedback information of each region from the preprocessed temperature response information of each region, and analyzing them after extraction to generate a real-time temperature change response coefficient and a temperature control response index for each region respectively; The real-time temperature change response coefficient and temperature control response index of each area are used to construct a temperature adjustment response evaluation model, and the temperature adjustment response index of each area is generated. The generated temperature adjustment response index of each area is compared with the pre-set temperature adjustment response index threshold to evaluate whether the temperature adjustment effect of the adjustment mechanism in each area meets the expectation, and optimize the adjustment mechanism according to the evaluation results.

5. The method for degrading vomitoxin in the deep processing of corn according to claim 4, characterized in that: The acquisition logic of the real-time temperature change response coefficient and temperature control response index of each area is as follows: The temperature change response information of each region in the preprocessed temperature response information of each region is extracted, specifically including the temperature change rate, thermal conductivity and heating power of the corn slurry in each region at different times within a period of time, and calibrated as and represents the temperature change rate of the corn slurry in the mth area at time j within a period of time, represents the thermal conductivity of the corn slurry in the mth region at time j within a period of time, represents the heating power of the corn slurry in the mth area at time j within a period of time, m=1, 2, 3, ..., k, j=1, 2, 3, ..., a, k and a are both positive integers; Calculate the real-time temperature change response coefficient of each area. The specific calculation formula is as follows: Where, RTCRC m is the real-time temperature change response coefficient of the mth region; The temperature control feedback information of each area in the pre-processed temperature response information of each area is extracted, including the temperature deviation, specific heat capacity and temperature target value of the corn slurry in each area at different times within a period of time, and calibrated as and represents the temperature deviation of the corn slurry in the mth area at time j within a period of time, represents the specific heat capacity of the corn slurry in the mth region at time j within a period of time, represents the temperature target value of the corn slurry in the mth area at time j within a period of time; Calculate the temperature control response index of each area. The specific calculation formula is as follows: Where, TRRI m is the temperature control response index of the mth region.

6. The method for degrading vomitoxin in the deep processing of corn according to claim 5, characterized in that: The real-time temperature change response coefficient RTCRC of each area will be generated m and Temperature Regulation Response Index TRRI m Construct a temperature adjustment response evaluation model and generate the temperature adjustment response index TARI for each area through weighted summation m , and the temperature adjustment response index TARI for each region is generated m With pre-set temperature adjusted response index threshold TARI yuzhi Compare and evaluate whether the temperature adjustment effect of the adjustment mechanism in each area meets expectations, and optimize the adjustment mechanism based on the evaluation results. The specific comparison analysis is as follows: If TARI m <TARI yuzhi , the temperature adjustment effect of the adjustment mechanism in this area reaches the expected level, and the adjustment mechanism does not need to be optimized; If TARI m ≥TARI yuzhi The temperature adjustment effect of the adjustment mechanism in this area did not meet expectations, and the adjustment mechanism needs to be optimized, including: for this area, reducing the heating power and shortening the heating time to prevent the temperature from being too high; increasing the heating power and extending the heating time to accelerate the temperature rise.

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