A method and system for controlling the slow processing temperature of fresh corn

Abstract

The invention discloses a control method and system for the slowing processing temperature of fresh corn, which specifically relates to the technical field of food preservation and processing, and is used to solve the problem of different sterilization effects caused by uneven steam transmission in existing slowing and sterilization processes; stress data of the internal structure of corn kernels are collected before slowing treatment, and stress distribution parameters are calculated based on a finite element analysis algorithm; temperature parameters of the slowing process are set in stages through a proportional-integral-differential control algorithm according to the stress distribution parameters; steam temperature data in a sterilization device are collected after the slowing is completed, and steam flow field parameters are generated based on computational fluid dynamics algorithm analysis; heating power output and steam pressure are dynamically adjusted according to the flow field parameters; steam residence time distribution is monitored, and steam residence time uniformity deviations are classified using an adaptive resonance theory network; steam injection parameters are controlled based on the classification results; temperature control from slowing to sterilization is achieved, and the efficiency of sterilization is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of food preservation and processing, and more specifically, to a method and system for controlling the slow processing temperature of fresh corn. Background Art

[0002] The thawing process in fresh corn processing is an important step to warm the corn from a frozen state to a suitable processing state. Temperature control during the thawing process has a significant impact on the internal structure of the corn kernels. After the thawing process is completed, sterilization is an important step to ensure the safety and quality of fresh corn products. Sterilization is usually carried out by treating the corn with high-temperature steam to kill the microorganisms inside and on the surface of the kernels.

[0003] In the actual easing process, changes in temperature gradients will produce tiny cracks inside the corn kernels. These cracks are not easily detected under normal observation, but will form irregular channels during the sterilization process, resulting in uneven transfer of high-temperature steam inside the kernels, thereby affecting the uniformity and effectiveness of sterilization and increasing the probability of residual microorganisms in some kernels. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present invention provide a method and system for controlling the slow processing temperature of fresh corn to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A method for controlling the slow processing temperature of fresh corn comprises the following steps:

[0007] S1: Before the slowing treatment, the stress data of the internal structure of corn kernels were collected, and the stress data were calculated based on the finite element analysis algorithm to obtain the stress distribution parameters;

[0008] S2: According to the stress distribution parameters, the temperature parameters of the easing process are set in grades through the proportional-integral-differential control algorithm;

[0009] S3: placing the slowed fresh corn into a sterilization device, obtaining steam temperature data in the sterilization device, and analyzing the steam temperature data based on a computational fluid dynamics algorithm to obtain steam flow field parameters;

[0010] S4: adjusting the heating power output and steam pressure of the sterilization device according to the steam flow field parameters;

[0011] S5: Monitor the distribution of steam residence time on the corn surface and classify the deviation of steam residence time uniformity using an adaptive resonance theory network;

[0012] S6: Based on the classification result of the steam residence time uniformity deviation, the injection parameters of the steam injection are controlled.

[0013] In a preferred embodiment, before the slowing treatment, stress data of the internal structure of the corn kernels is collected, and the stress data is calculated based on a finite element analysis algorithm to obtain stress distribution parameters, specifically including:

[0014] Scanning the internal structure of corn kernels by ultrasonic sensors to obtain initial stress data inside the corn kernels;

[0015] The initial stress data were calculated based on the finite element analysis algorithm to obtain the stress distribution parameters inside the corn kernels. The stress distribution parameters included stress magnitude distribution and stress direction distribution.

[0016] In a preferred embodiment, the temperature parameters of the easing process are set in stages according to the stress distribution parameters through a proportional-integral-differential control algorithm, specifically including:

[0017] Divide the target temperature intervals in the easing process according to the stress distribution, and define the initial temperature, target temperature and time span of each target temperature interval;

[0018] The proportional-integral-differential control algorithm is used to dynamically adjust the temperature change during the slowing process;

[0019] The output result of the proportional-integral-derivative control algorithm is executed to dynamically adjust the heating power inside the mitigation device to achieve temperature classification setting.

[0020] In a preferred embodiment, the temperature change of the slowing process is dynamically adjusted using a proportional-integral-differential control algorithm, specifically:

[0021] The proportional-integral-derivative control algorithm combines the proportional, integral and differential parts to calculate the temperature adjustment value using the following formula: ;in, Indicates time The control output value at represents the proportionality coefficient, represents the integral coefficient, represents the differential coefficient, Indicates the integral value of temperature deviation, represents the derivative of temperature deviation with respect to time, Indicates time The temperature deviation at Indicates at a point in time The temperature deviation at Indicates any time point in the integral calculation. The temperature deviation refers to the difference between the current temperature and the target temperature.

[0022] In a preferred embodiment, the slowed fresh corn is placed in a sterilization device, steam temperature data in the sterilization device is obtained, and the steam temperature data is analyzed based on a computational fluid dynamics algorithm to obtain steam flow field parameters, specifically including:

[0023] The slowed fresh corn is placed in a sterilization device, and multiple temperature collection points are arranged in the sterilization device;

[0024] During the sterilization process, steam temperature data of each temperature collection point is collected, and the steam temperature data includes the position of the temperature collection point and the corresponding temperature value;

[0025] Construct a three-dimensional spatial model of steam flow in the sterilization device, using steam temperature data as boundary conditions;

[0026] The flow field characteristics of steam in the sterilization device are calculated to generate steam flow field parameters, which include steam velocity distribution parameters, pressure distribution parameters and temperature gradient distribution parameters.

[0027] In a preferred embodiment, the heating power output and steam pressure of the sterilization device are adjusted according to the steam flow field parameters, specifically including:

[0028] According to the steam velocity distribution parameters, the heating power output in the sterilization device is adjusted to maintain thermal energy compensation in the high velocity area of ​​the steam;

[0029] Adjust the steam pressure in the sterilization device according to the pressure distribution parameters;

[0030] The heating area of ​​the steam in the sterilizer is dynamically optimized according to the temperature gradient distribution parameters.

[0031] In a preferred embodiment, the distribution of steam residence time on the corn surface is monitored, and the uniformity deviation of steam residence time is classified using an adaptive resonance theory network, specifically including:

[0032] During the sterilization process, multiple time monitoring points are set at different positions on the corn surface, and each time monitoring point is used to record the steam residence time data at the corresponding position;

[0033] Associating the steam residence time data with the spatial position coordinates of the corresponding time monitoring points one by one to form a steam residence time distribution data set;

[0034] The steam residence time distribution data set is input into the adaptive resonance theory network, and the adaptive resonance theory network classifies the steam residence time uniformity deviation according to the input steam residence time distribution data, and the classification includes: dividing the time residence data into several intervals according to the range of time values, and classifying the time monitoring points in the same interval into the same category;

[0035] The classification results are matched with the spatial positions of the time monitoring points to generate time uniformity deviation information of the area where each time monitoring point is located;

[0036] Each time monitoring point on the corn surface is divided into a high residence time deviation area, a medium residence time deviation area and a low residence time deviation area.

[0037] In a preferred embodiment, based on the classification result of the steam residence time uniformity deviation, the injection parameters of the steam injection are controlled, specifically including:

[0038] The spray direction of the steam spray is set according to the high residence time deviation area, so that the spray device can direct more steam to the high residence time deviation area;

[0039] The spray direction of the steam spray is set according to the mid-dwelling time deviation area, so that the spray device maintains uniform steam coverage;

[0040] The injection direction of steam injection is set according to the low residence time deviation area to reduce the steam supply in the low residence time deviation area;

[0041] The injection time of steam injection is dynamically adjusted, the steam injection time is extended in the high residence time deviation area, and the steam injection time is shortened in the low residence time deviation area.

[0042] On the other hand, the present invention provides a control system for slow processing temperature of fresh corn, including a stress data acquisition module, a temperature parameter setting module, a steam temperature analysis module, a power pressure adjustment module, a residence time classification module and an injection parameter control module;

[0043] Stress data acquisition module: Before the slowing down treatment, the stress data of the internal structure of the corn kernels is collected, and the stress data is calculated based on the finite element analysis algorithm to obtain the stress distribution parameters;

[0044] Temperature parameter setting module: According to the stress distribution parameters, the temperature parameters of the easing process are set in grades through the proportional-integral-differential control algorithm;

[0045] Steam temperature analysis module: Place the slowed fresh corn into the sterilization device, obtain the steam temperature data in the sterilization device, and analyze the steam temperature data based on the computational fluid dynamics algorithm to obtain the steam flow field parameters;

[0046] Power and pressure regulating module: adjusts the heating power output and steam pressure of the sterilization device according to the steam flow field parameters;

[0047] Residence time classification module: monitors the time distribution of steam on the corn surface and classifies the uniformity deviation of steam residence time using an adaptive resonance theory network;

[0048] Injection parameter control module: based on the classification result of the uniformity deviation of the steam residence time, the injection parameters of the steam injection are controlled.

[0049] The technical effects and advantages of the method and system for controlling the slow processing temperature of fresh corn of the present invention are as follows:

[0050] 1. By accurately collecting stress data inside corn kernels and calculating stress distribution parameters based on the finite element analysis algorithm, the influence of temperature changes on the internal structure during the easing process can be understood from the source; the easing temperature parameters are graded and set by the proportional-integral-differential control algorithm, which can effectively regulate the temperature gradient changes during the easing process and reduce the internal stress concentration and crack generation; when the corn is sterilized after easing, the steam flow field parameters are generated by collecting steam temperature data and analyzing it based on the computational fluid dynamics algorithm, and the heating power and steam pressure of the sterilization device are dynamically adjusted according to the flow field parameters, thereby optimizing the uniformity of steam distribution.

[0051] 2. By monitoring the distribution of steam residence time, the adaptive resonance theory network is used to classify the residence time uniformity deviation, and the direction and time of steam injection are dynamically adjusted based on the classification results, so that the use of steam in the sterilization process is more accurate and efficient; by comprehensively controlling the temperature and steam in the entire process from easing to sterilization, the uniformity and effectiveness of sterilization can be significantly improved, the probability of residual microorganisms in the grain can be reduced, and the processing quality and safety of fresh corn products can be ensured. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 This is a schematic diagram of a method for controlling the slow processing temperature of fresh corn according to the present invention;

[0053] Figure 2 The present invention is a schematic structural diagram of a control system for slow processing temperature of fresh corn. DETAILED DESCRIPTION

[0054] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0055] Example 1: The present invention gradually optimizes the easing and sterilization process of fresh corn through multiple levels of steps. First, before easing, stress data analysis is performed, and the easing temperature is graded and set based on the stress distribution parameters using a proportional-integral-differential control algorithm to reduce cracks caused by temperature gradients or mitigate their effects from the source. Secondly, in the sterilization step after easing, steam temperature data is obtained and steam flow field parameters are generated through computational fluid dynamics analysis, and the heating power and steam pressure are further adjusted to achieve a more uniform steam distribution and solve the problem of uneven heat transfer during the sterilization process. Finally, by monitoring the steam residence time distribution, the time uniformity deviation is classified using an adaptive resonance theory network, and the injection direction and time of the injection device are controlled to accurately adjust the steam distribution.

[0056] Figure 1 A method for controlling the slow processing temperature of fresh corn is provided, which comprises the following steps:

[0057] S1: Before the retardation treatment, the stress data of the internal structure of the corn kernels were collected, and the stress data were calculated based on the finite element analysis algorithm to obtain the stress distribution parameters.

[0058] S2: According to the stress distribution parameters, the temperature parameters of the easing process are set in stages through the proportional-integral-differential control algorithm.

[0059] S3: placing the slowed fresh corn into a sterilization device, obtaining steam temperature data in the sterilization device, and analyzing the steam temperature data based on a computational fluid dynamics algorithm to obtain steam flow field parameters.

[0060] S4: Adjust the heating power output and steam pressure of the sterilization device according to the steam flow field parameters.

[0061] S5: The distribution of steam residence time on the corn surface is monitored and the deviation from steam residence time uniformity is classified using an adaptive resonance theory network.

[0062] S6: Based on the classification result of the steam residence time uniformity deviation, the injection parameters of the steam injection are controlled.

[0063] Before the slowing treatment, the stress data of the internal structure of the corn kernels were collected, and the stress data were calculated based on the finite element analysis algorithm to obtain the stress distribution parameters, including:

[0064] The internal structure of corn kernels is scanned by ultrasonic sensors to obtain the initial stress data inside the corn kernels:

[0065] The corn kernels to be processed are fixed on the scanning platform. The scanning platform can adjust the position and angle of the corn through a robotic arm to ensure that the ultrasonic sensor covers the surface of the corn kernels in all directions. The ultrasonic sensor emits sound wave signals and records the internal structural characteristics of the kernels by receiving the reflected signals from inside the corn kernels.

[0066] The reflected signal is converted into a digital signal through time domain signal processing technology to generate an initial data set for describing the internal stress state of corn kernels. This initial data set is based on multiple points and records the acoustic response intensity and time delay value of each point as input parameters for subsequent analysis.

[0067] The initial stress data are extracted by analyzing the acoustic response intensity and time delay of the reflected signal. The initial stress data are recorded in the form of three-dimensional coordinates, representing the preliminary stress characteristics of each position inside the corn kernel.

[0068] The format of the initial stress data includes the spatial position coordinates of each sampling point and the corresponding reflection signal intensity.

[0069] The initial stress data is calculated based on the finite element analysis algorithm to obtain the stress distribution parameters inside the corn kernels. The stress distribution parameters include stress magnitude distribution and stress direction distribution:

[0070] The collected initial stress data is used as input, and the stress distribution inside the corn kernel is calculated based on the finite element analysis algorithm. In the finite element analysis, the shape of the corn kernel is simplified into a mesh model composed of multiple finite elements. The stress characteristics of each element in the model are expressed by the following formula: ;in, is the stress tensor in the element; Indicates that the unit is Strain gradient in direction; Indicates that the unit is Strain gradient in direction; It represents the displacement of the unit node, which is calculated from the ultrasonic reflection signal; Indicates the coordinate position of a certain direction in the internal space coordinate system of the corn kernel; Representation and Different coordinate position in another direction; An index used to mark a direction in the coordinate system; Used to mark the coordinate system Different index in the other direction.

[0071] By solving the stress tensor, the stress magnitude distribution and direction distribution of the corn kernels are obtained, which are expressed in the form of scalar field and vector field, respectively. The stress magnitude distribution represents the stress intensity in each unit, and the direction distribution represents the direction of stress action.

[0072] The stress distribution results are stored in a three-dimensional array format. Assuming that the stress magnitude at a point is , the stress direction is vector , the complete output is: ;in, Indicates point Stress distribution parameters of Indicates the stress magnitude at that point; , and They represent the three components of stress direction respectively; Represents the position coordinates of a point inside a corn kernel in three-dimensional space, and is used to locate the specific position of the point in the stress distribution.

[0073] According to the stress distribution parameters, the temperature parameters of the easing process are set in stages through the proportional-integral-differential control algorithm, including:

[0074] The target temperature intervals in the easing process are divided according to the stress distribution, and the initial temperature, target temperature and time span of each target temperature interval are defined:

[0075] The stress distribution parameters calculated by the finite element analysis algorithm represent the stress intensity of different areas inside the corn kernel, reflecting the heat treatment requirements that may be required for each area during the easing process; the easing temperature is usually adjusted between 10 degrees Celsius and 40 degrees Celsius, with the lowest temperature being 10 degrees Celsius and the highest temperature being 40 degrees Celsius. Temperature adjustment within this range helps to homogenize the internal structure of the kernel and avoid new cracks caused by temperature gradients.

[0076] Corresponding to the areas where stress values ​​are in the minimum range (such as 0.1 to 0.3 MPa), these areas are less sensitive to temperature. The target temperature range is set to 10 degrees Celsius to 20 degrees Celsius, the time span is 10 minutes, and the temperature rise is slow.

[0077] For areas with stress values ​​in the middle range (such as 0.3 to 0.7 MPa), these areas require a more stable temperature increase. The target temperature range is set to 20 degrees Celsius to 30 degrees Celsius, the time span is 15 minutes, and the temperature rise rate is moderate.

[0078] For areas with higher stress values ​​(e.g., 0.7 to 1.0 MPa), which may be more susceptible to temperature gradient-induced cracking, the target temperature range is set at 30 to 40 degrees Celsius, with a time span of 20 minutes, and the temperature rise is slower.

[0079] The above division results are stored in a standardized data format, including the initial temperature, target temperature and time span of each interval, and are associated with the corresponding stress magnitude distribution parameters.

[0080] The proportional-integral-differential control algorithm is used to dynamically adjust the temperature change during the slowing process:

[0081] The core of temperature control is to achieve real-time adjustment of the heating process of the easing equipment through the proportional-integral-differential control algorithm (PID algorithm).

[0082] Temperature deviation is the difference between the current temperature and the target temperature.

[0083] The proportional-integral-derivative control algorithm combines the proportional, integral and differential parts to calculate the temperature adjustment value using the following formula: ;in, Indicates time The control output value when ; Represents the proportional coefficient, which is used to adjust the weight of the current temperature deviation; Indicates the integral coefficient, which is used to correct the influence of the accumulated temperature difference on the system; Represents the differential coefficient, which is used to suppress the fluctuation of the temperature change rate that is too fast; Indicates the integral value of temperature deviation, indicating the accumulated temperature difference; It represents the derivative of temperature deviation with respect to time, indicating the rate of change of the deviation; Indicates time Temperature deviation at ; Indicates at a point in time Temperature deviation at Represents any time point in the integral calculation, used to describe the change process from the initial time to the current time range.

[0084] The goal of the proportional-integral-derivative control algorithm is to Gradually reduce to near zero, ensuring that the slowing temperature is stable within the target range.

[0085] The specific values ​​of the proportional coefficient, integral coefficient and differential coefficient are determined through experimental debugging. For example: the proportional coefficient reflects the sensitivity of the current temperature change and is usually set between 1.5 and 2.5; the integral coefficient corrects the speed of the accumulated temperature difference and is usually set between 0.1 and 0.5; the differential coefficient suppresses the response speed of temperature difference fluctuations and is usually set between 0.01 and 0.1.

[0086] The output of the proportional-integral-derivative control algorithm is executed to dynamically adjust the heating power inside the mitigation device to achieve temperature classification settings:

[0087] Collect current temperature data in the mitigation environment in real time.

[0088] Time The control output value at that time is converted into a dynamic adjustment instruction for the heating power. For example, if the current heating power is , the power after the control command is adjusted is: ;in, Indicates the adjusted heating power.

[0089] After performing the above temperature adjustment, the mitigation device gradually enters the next target temperature range until all temperature range settings are completed.

[0090] The slowed fresh corn is placed in the sterilization device, the steam temperature data in the sterilization device is obtained, and the steam temperature data is analyzed based on the computational fluid dynamics algorithm to obtain the steam flow field parameters, specifically including:

[0091] Place the slowed fresh corn into the sterilization device, and arrange multiple temperature collection points in the sterilization device:

[0092] After the slowing down treatment is completed, the fresh corn is neatly arranged on multiple shelves inside the sterilizer, ensuring that there is an appropriate distance between each corn to avoid stacking and affecting the sterilization effect. At the same time, in order to accurately collect the temperature data of the steam distribution in the sterilizer, multiple temperature collection points are arranged in the sterilizer.

[0093] The positions of the temperature collection points are arranged according to the structural characteristics of the sterilization device and the law of steam flow. For example, the temperature collection points are arranged at different heights such as the bottom, middle, and top of the device, as well as near the wall area and the center area inside the device.

[0094] During the sterilization process, the steam temperature data of each temperature collection point is collected. The steam temperature data includes the location of the temperature collection point and the corresponding temperature value:

[0095] Start the sterilization device and introduce saturated steam into the device to make the inside of the device reach the working temperature required for sterilization; during the sterilization process, the steam temperature data of each temperature collection point is collected in real time through the temperature sensor.

[0096] The data recorded at each temperature collection point includes its position (spatial coordinates) and the corresponding real-time temperature value. For example, the position of a point is "(0.3, 0.5, 0.7)", and the collected temperature value is 121 degrees Celsius.

[0097] In order to obtain sufficient temperature data samples, it is recommended to collect temperature data at 1 second intervals and continue collecting until the sterilization process is completed.

[0098] Construct a three-dimensional spatial model of steam flow in the sterilization device, using steam temperature data as boundary conditions:

[0099] According to the physical size and internal structure of the sterilization device, a three-dimensional spatial model of steam flow inside the device is constructed.

[0100] Among them, the geometric shape of the sterilization device is used as the basis, including key structures such as the bottom heating area and the top steam outlet; the location of the temperature collection point and the steam temperature data are used as boundary conditions to simulate the distribution and flow state of steam in space.

[0101] The temperature data distribution in the model will be used to describe the actual temperature characteristics of steam in different areas and reflect the law of local temperature gradient changes.

[0102] The flow field characteristics of steam in the sterilization device are calculated to generate steam flow field parameters, which include steam velocity distribution parameters, pressure distribution parameters and temperature gradient distribution parameters:

[0103] Based on the three-dimensional space model, the steam flow field characteristics in the sterilization device are calculated through computational fluid dynamics analysis to obtain the steam flow field parameters.

[0104] The steam temperature data obtained from the aforementioned temperature collection points are used as the initial conditions of the three-dimensional space model.

[0105] The input data include: the location coordinates of each temperature collection point, the corresponding steam temperature value (such as 121 degrees Celsius), and the steam inlet and outlet conditions, such as the inlet steam flow rate and temperature (such as flow rate 1.5 m / s, temperature 125 degrees Celsius).

[0106] Set the steam inlet area (such as the bottom of the sterilization device) and define the flow rate and temperature of the inlet steam; set the steam outlet area (such as the exhaust port at the top) and define the outlet pressure as standard atmospheric pressure; determine the internal space as a closed system and simulate the flow of steam in different areas.

[0107] By using the momentum conservation principle of fluid mechanics and simulating the flow path and velocity changes of steam in space, the velocity distribution parameters of steam are calculated.

[0108] The focus of the steam velocity distribution calculation is to analyze the change in steam velocity as it diffuses from the inlet area to other areas inside the device: in the near-wall area, the steam velocity is lower due to boundary friction; in the central area, the steam velocity is higher because the fluid encounters less resistance.

[0109] Using the principle of pressure conservation in fluid mechanics, the pressure distribution at different locations is calculated based on the steam flow path and velocity distribution. The key to calculating steam pressure distribution is to analyze the pressure difference caused by velocity changes in steam flow: in areas with higher velocity, the pressure is usually lower; in areas with lower velocity, the pressure is relatively higher.

[0110] The temperature gradient distribution in space is calculated using the data from the steam temperature collection points. The temperature gradient reflects the variation of the steam temperature in space: in the area close to the steam inlet, the temperature is higher and the gradient variation is smaller; in the area far from the inlet or in the obstructed area, the temperature gradient is larger.

[0111] The steam velocity distribution parameters describe the flow velocity of steam at different positions, for example, the velocity at a certain point (0.2, 0.3, 0.5) is 1.2 m / s; the pressure distribution parameters describe the pressure of steam at different positions, for example, the pressure at a certain point (0.4, 0.6, 0.8) is 0.15 MPa; the temperature gradient distribution parameters describe the change of steam temperature in space, for example, the temperature gradient at a certain point (0.5, 0.4, 0.3) is 5 degrees Celsius / meter.

[0112] The heating power output and steam pressure of the sterilizer are adjusted according to the steam flow field parameters, including:

[0113] According to the steam velocity distribution parameters, the heating power output in the sterilizer is adjusted to maintain thermal energy compensation in the high velocity area of ​​steam:

[0114] The steam velocity distribution parameter calculated in step S3 is input into the control system of the sterilization device. The parameter reflects the flow velocity distribution of steam at different positions in the device. The flow velocity will directly affect the steam heat energy transfer efficiency in the area.

[0115] In the steam velocity distribution parameters, the area with higher flow velocity may cause insufficient heat accumulation in this area due to the rapid flow of steam. Therefore, thermal energy compensation is performed in the high flow velocity area by increasing the heating power.

[0116] For example, in a certain area, the steam flow rate is 2 m / s, which is significantly higher than that in other areas. The control system increases the heating power in this area by 10% based on the velocity distribution parameters to maintain the thermal energy balance of the steam in the area.

[0117] For areas with lower flow rates, the heating power can be appropriately reduced to avoid overheating due to the longer residence time of the steam.

[0118] For example, in an area with a flow rate of 0.5 m / s, the system will reduce the power output in that area by 20%, thereby optimizing the overall energy consumption distribution.

[0119] According to the pressure distribution parameters, adjust the steam pressure in the sterilizer to ensure sufficient steam is added in the low-pressure area:

[0120] The pressure distribution parameter shows how much steam pressure there is at different locations within the sterilizer. The uniformity of steam pressure is critical to sterilization effectiveness, as low-pressure areas can lead to inadequate steam distribution.

[0121] In the pressure distribution parameters, the low-pressure area is identified and the steam volume is supplemented by adjusting the opening of the steam inlet valve. For example, in a certain area, the steam pressure is only 0.08 MPa, which is 0.12 MPa lower than the average value. Based on this information, the control system increases the opening of the inlet valve from 50% to 70%, increases the steam input, and ensures that the low-pressure area is fully compensated.

[0122] For high-pressure areas, the steam supply can be appropriately reduced to prevent excessive steam accumulation. For example, in an area with a pressure of 0.15 MPa, the control system will adjust the valve opening from 50% to 40% to avoid excessive steam concentration and uneven distribution.

[0123] According to the temperature gradient distribution parameters, the heating area of ​​the steam in the sterilizer is dynamically optimized so that the area with a larger temperature gradient can obtain additional heat energy supplement:

[0124] The temperature gradient distribution parameter indicates the change gradient of steam temperature at different locations inside the device. Areas with larger temperature gradients are often the key areas for temperature control during the sterilization process.

[0125] For areas with large temperature gradients, additional thermal compensation is required to balance the temperature due to the significant temperature differences within the area. For example, in a certain area, the temperature gradient is 6 degrees Celsius / meter, which is significantly higher than other areas; the control system will increase the heating power output of this area, for example, by 15%, to balance the temperature distribution.

[0126] For areas with lower temperature gradients, it means that the steam temperature is more uniform and the heating power can be appropriately reduced. For example, in an area with a gradient of 2 degrees Celsius per meter, the control system will reduce the power output of the area by 10%, thereby achieving energy saving optimization.

[0127] The distribution of steam residence time on the corn surface is monitored, and the deviation from steam residence time uniformity is classified using an adaptive resonance theory network, including:

[0128] During the sterilization process, multiple time monitoring points are set at different positions on the corn surface. Each time monitoring point is used to record the steam residence time data at the corresponding position:

[0129] Each time monitoring point is used to record the residence time of steam at that location. The location of the time monitoring point is through a unified spatial coordinate.

[0130] The time monitoring point records the steam residence time data in real time. The steam residence time data is obtained by recording the residence time of steam at various positions on the corn surface in real time through the time monitoring point. The time monitoring point uses a high-sensitivity sensor to capture the time difference between steam contact and departure.

[0131] The steam residence time data is associated with the spatial position coordinates of the corresponding time monitoring points one by one to form a steam residence time distribution data set:

[0132] The steam residence time distribution dataset contains the spatial coordinates of each time monitoring point and the corresponding residence time: ;in, represents the steam residence time distribution data set, represents the spatial position of the time monitoring point, Indicates the steam residence time value, Indicates the index number of the time monitoring point. Indicates the number of time monitoring points.

[0133] The steam residence time distribution data set is input into the adaptive resonance theory network. The adaptive resonance theory network classifies the steam residence time uniformity deviation according to the input steam residence time distribution data. The classification includes: dividing the time residence data into several intervals according to the range of time values, and classifying the time monitoring points in the same interval into the same category:

[0134] Initialize the adaptive resonance theory network and set the classification threshold to control the granularity of classification.

[0135] For each data pair in the steam residence time distribution data set Input the input layer of the network, and the steam residence time value at each time monitoring point is used as the main classification basis.

[0136] The adaptive resonance theory network calculates the similarity between the input data and the existing classification center through the following formula: ;in, Indicates the similarity between the steam residence time value of the input data and the classification center; Indicates the current Steam residence time value for each classification center; Represents the minimum value between the input value and the classification center value. The index number representing the cluster center (used to identify a specific cluster group in the adaptive resonance theory network).

[0137] If the similarity between the steam residence time value of the input data and the classification center is greater than the classification threshold, the data point is classified as the current classification center; otherwise, a new classification center is generated ( ).

[0138] Among them, the classification threshold is a parameter used to control the classification granularity in the adaptive resonance theory network. It represents the minimum requirement for the similarity between the input data and the existing classification center. When the similarity is greater than the classification threshold, the input data is classified into the current classification; otherwise, a new classification center is generated. The larger the classification threshold, the more refined the classification results and the more obvious the differences between groups; otherwise, the classification is more rough and the similarity between groups is higher.

[0139] The classification results are matched with the spatial positions of the time monitoring points to generate the time uniformity deviation information of the area where each time monitoring point is located:

[0140] The classification results output by the adaptive resonance theory network are matched with the spatial positions of the time monitoring points to generate the time uniformity deviation information of each time monitoring point.

[0141] The generated time uniformity deviation information is: ;in, Indicates Deviation information of each time monitoring point.

[0142] The time monitoring points on the corn surface are divided into high residence time deviation area, medium residence time deviation area and low residence time deviation area:

[0143] After analyzing the time distribution data, the adaptive resonance theory network groups the steam residence time of the time monitoring points according to their similarity with the classification center (the classification center represents different residence time ranges); each time monitoring point is classified into a classification center, and the classification result shows the degree of difference between the residence time of the time monitoring point and other time monitoring points.

[0144] High Residence Time Deviation Region: Contains time monitoring points where the residence time is significantly higher than the average, which may correspond to areas of long steam residence time.

[0145] Medium residence time deviation area: Contains time monitoring points where the residence time is close to the average value and the steam distribution is relatively uniform.

[0146] Low Residence Time Deviation Region: Contains time monitoring points where the residence time is significantly lower than the average, and steam may be flowing through faster.

[0147] Based on the classification results of each time monitoring point, the spatial locations of the time monitoring points at the same classification center are combined and delineated into a region. For example, all the spatial locations of the time monitoring points classified as "high residence time deviation" constitute a "high deviation region".

[0148] Based on the classification results of the uniformity deviation of the steam residence time, the injection parameters of the steam injection are controlled, including:

[0149] Set the injection direction of steam injection according to the high residence time deviation area, so that the injection device can focus more steam to the high residence time deviation area:

[0150] According to the position of the high residence time deviation area, the injection direction of the steam injection device is adjusted to direct the steam flow to this area. During the adjustment, the center position of the high residence time deviation area is taken as the target, and the angle of the steam injection device is optimized so that the main direction of the injection beam covers this area.

[0151] Set the direction of the steam jet according to the mid-dwelling time deviation area so that the jet device maintains uniform steam coverage:

[0152] Adjust the steam injection direction for uniform coverage in the area with deviation in residence time: by analyzing the spatial distribution of monitoring points in the area, determine the direction of the steam injection device so that the steam covers the entire area. During the adjustment process, ensure that the injection angle is evenly distributed to avoid excessive bias towards one part.

[0153] Set the injection direction of steam injection according to the low residence time deviation area to reduce the steam supply in the low residence time deviation area:

[0154] According to the location of the low residence time deviation area, the injection direction is adjusted: reduce the steam supply to the area by the injection device, and avoid the low residence time deviation area by changing the injection angle; if this area cannot be completely avoided, the diversion device can be used to reduce the impact of the steam flow on it.

[0155] Dynamically adjust the injection time of steam injection, extend the steam injection time in the high residence time deviation area, and shorten the steam injection time in the low residence time deviation area:

[0156] High residence time deviation area: extend the steam injection time to ensure uniform steam distribution; low residence time deviation area: shorten the steam injection time to avoid excessive steam retention.

[0157] Combined with the residence time data and classification results of the time monitoring point, a parameter adjustment instruction is sent to the time control module of the injection device.

[0158] The upper and lower limits of the injection time are set dynamically based on the maximum and minimum values ​​of the residence time in the area. For example, the injection time in the high residence time deviation area is set to 1.2 times the standard time, and the injection time in the low residence time deviation area is set to 0.8 times the standard time.

[0159] The control of steam injection time and steam injection direction directly affects the distribution and residence time of steam during the sterilization process, thereby achieving precise control of the processing temperature. By adjusting the steam injection direction, the steam can be concentrated to the area with high residence time deviation, thereby improving the temperature uniformity of the area; at the same time, by extending or shortening the steam injection time, the temperature difference in different areas can be compensated to avoid overheating or insufficient temperature. The uniform distribution and dynamic adjustment of steam ensure that the temperature of each area during the processing process remains within a reasonable range, thereby achieving efficient temperature control and sterilization effect.

[0160] Example 2: The difference between Example 2 of the present invention and Example 1 is that this example introduces a control system for the slow processing temperature of fresh corn.

[0161] Figure 2 A structural schematic diagram of a control system for the slow processing temperature of fresh corn of the present invention is given, a control system for the slow processing temperature of fresh corn, including a stress data acquisition module, a temperature parameter setting module, a steam temperature analysis module, a power pressure regulation module, a residence time classification module and an injection parameter control module.

[0162] Stress data acquisition module: Before the easing treatment, the stress data of the internal structure of the corn kernels is collected, and the stress data is calculated based on the finite element analysis algorithm to obtain the stress distribution parameters.

[0163] Temperature parameter setting module: According to the stress distribution parameters, the temperature parameters of the easing process are set in grades through the proportional-integral-differential control algorithm.

[0164] Steam temperature analysis module: Place the slowed fresh corn into the sterilization device, obtain the steam temperature data in the sterilization device, and analyze the steam temperature data based on the computational fluid dynamics algorithm to obtain the steam flow field parameters.

[0165] Power and pressure regulation module: regulates the heating power output and steam pressure of the sterilization device according to the steam flow field parameters.

[0166] Residence time classification module: monitors the time distribution of steam residence time on the corn surface and classifies the uniformity deviation of steam residence time using an adaptive resonance theory network.

[0167] Injection parameter control module: based on the classification result of the uniformity deviation of the steam residence time, the injection parameters of the steam injection are controlled.

[0168] 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 and thresholds in the formula are set by technicians in this field according to actual conditions.

[0169] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may 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 may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or may be transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium may be a solid-state hard disk.

[0170] Those of ordinary skill in the art will appreciate that the modules 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.

[0171] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0172] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules 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 through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0173] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0174] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0175] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0176] 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.

[0177] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for controlling the slow processing temperature of fresh corn, characterized in that: The steps include: S1: Before the slowing treatment, the stress data of the internal structure of corn kernels were collected, and the stress data were calculated based on the finite element analysis algorithm to obtain the stress distribution parameters; S2: According to the stress distribution parameters, the temperature parameters of the easing process are set in stages through the proportional-integral-differential control algorithm; S3: placing the slowed fresh corn into a sterilization device, obtaining steam temperature data in the sterilization device, and analyzing the steam temperature data based on a computational fluid dynamics algorithm to obtain steam flow field parameters; S4: adjusting the heating power output and steam pressure of the sterilization device according to the steam flow field parameters; S5: Monitor the distribution of steam residence time on the corn surface and classify the deviation of steam residence time uniformity using an adaptive resonance theory network; S6: Based on the classification result of the steam residence time uniformity deviation, the injection parameters of the steam injection are controlled; According to the stress distribution parameters, the temperature parameters of the easing process are set in stages through the proportional-integral-differential control algorithm, including: Divide the target temperature intervals in the easing process according to the stress distribution, and define the initial temperature, target temperature and time span of each target temperature interval; The proportional-integral-differential control algorithm is used to dynamically adjust the temperature change during the slowing process; Execute the output of the proportional-integral-derivative control algorithm to dynamically adjust the heating power inside the mitigation device to achieve temperature classification settings; The proportional-integral-differential control algorithm is used to dynamically adjust the temperature change of the slowing process, specifically: The proportional-integral-derivative control algorithm combines the proportional, integral and differential parts to calculate the temperature adjustment value using the following formula: ;in, Indicates time The control output value at represents the proportionality coefficient, represents the integral coefficient, represents the differential coefficient, Indicates the integral value of temperature deviation, represents the derivative of temperature deviation with respect to time, Indicates time The temperature deviation at Indicates at a point in time The temperature deviation at Indicates any time point in the integral calculation. The temperature deviation refers to the difference between the current temperature and the target temperature. The slowed fresh corn is placed in the sterilization device, the steam temperature data in the sterilization device is obtained, and the steam temperature data is analyzed based on the computational fluid dynamics algorithm to obtain the steam flow field parameters, specifically including: The slowed fresh corn is placed in a sterilization device, and multiple temperature collection points are arranged in the sterilization device; During the sterilization process, steam temperature data of each temperature collection point is collected, and the steam temperature data includes the position of the temperature collection point and the corresponding temperature value; Construct a three-dimensional spatial model of steam flow in the sterilization device, using steam temperature data as boundary conditions; Calculating the flow field characteristics of steam in the sterilization device to generate steam flow field parameters, the steam flow field parameters including steam velocity distribution parameters, pressure distribution parameters and temperature gradient distribution parameters; The distribution of steam residence time on the corn surface is monitored, and the deviation from steam residence time uniformity is classified using an adaptive resonance theory network, including: During the sterilization process, multiple time monitoring points are set at different positions on the corn surface, and each time monitoring point is used to record the steam residence time data at the corresponding position; Associating the steam residence time data with the spatial position coordinates of the corresponding time monitoring points one by one to form a steam residence time distribution data set; The steam residence time distribution data set is input into the adaptive resonance theory network, and the adaptive resonance theory network classifies the steam residence time uniformity deviation according to the input steam residence time distribution data, and the classification includes: dividing the time residence data into several intervals according to the range of time values, and classifying the time monitoring points in the same interval into the same category; The classification results are matched with the spatial positions of the time monitoring points to generate time uniformity deviation information of the area where each time monitoring point is located; Each time monitoring point on the corn surface is divided into a high residence time deviation area, a medium residence time deviation area and a low residence time deviation area.

2. The method for controlling the slow processing temperature of fresh corn according to claim 1, characterized in that: Before the slowing treatment, the stress data of the internal structure of the corn kernels were collected, and the stress data were calculated based on the finite element analysis algorithm to obtain the stress distribution parameters, including: Scanning the internal structure of corn kernels by ultrasonic sensors to obtain initial stress data inside the corn kernels; The initial stress data were calculated based on the finite element analysis algorithm to obtain the stress distribution parameters inside the corn kernels. The stress distribution parameters included stress magnitude distribution and stress direction distribution.

3. The method for controlling the slow processing temperature of fresh corn according to claim 2, characterized in that: The heating power output and steam pressure of the sterilizer are adjusted according to the steam flow field parameters, including: According to the steam velocity distribution parameters, the heating power output in the sterilization device is adjusted to maintain thermal energy compensation in the high velocity area of ​​the steam; Adjust the steam pressure in the sterilization device according to the pressure distribution parameters; Dynamically optimize the heating area of ​​steam in the sterilizer based on the temperature gradient distribution parameters.

4. The method for controlling the slow processing temperature of fresh corn according to claim 3, characterized in that: Based on the classification results of the uniformity deviation of the steam residence time, the injection parameters of the steam injection are controlled, including: The spray direction of the steam spray is set according to the high residence time deviation area, so that the spray device can direct more steam to the high residence time deviation area; The spray direction of the steam spray is set according to the mid-dwelling time deviation area, so that the spray device maintains uniform steam coverage; The spray direction of steam injection is set according to the low residence time deviation area, and the steam supply in the low residence time deviation area is reduced; The injection time of steam injection is dynamically adjusted, the steam injection time is extended in the high residence time deviation area, and the steam injection time is shortened in the low residence time deviation area.

5. A control system for slow processing temperature of fresh corn, used to implement the control method for slow processing temperature of fresh corn according to any one of claims 1 to 4, characterized in that: It includes a stress data acquisition module, a temperature parameter setting module, a steam temperature analysis module, a power pressure adjustment module, a residence time classification module and an injection parameter control module; Stress data acquisition module: Before the slowing down treatment, the stress data of the internal structure of the corn kernels is collected, and the stress data is calculated based on the finite element analysis algorithm to obtain the stress distribution parameters; Temperature parameter setting module: According to the stress distribution parameters, the temperature parameters of the easing process are set in grades through the proportional-integral-differential control algorithm; Steam temperature analysis module: Place the slowed fresh corn into the sterilization device, obtain the steam temperature data in the sterilization device, and analyze the steam temperature data based on the computational fluid dynamics algorithm to obtain the steam flow field parameters; Power and pressure regulating module: adjusts the heating power output and steam pressure of the sterilization device according to the steam flow field parameters; Residence time classification module: monitors the time distribution of steam on the corn surface and classifies the uniformity deviation of steam residence time using an adaptive resonance theory network; Injection parameter control module: based on the classification result of the uniformity deviation of the steam residence time, the injection parameters of the steam injection are controlled.

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