Fermented glutinous rice cake and chili steaming processing system based on temperature feedback

Abstract

The present invention relates to the field of food processing technology, and specifically to a fermented glutinous rice cake and chili steaming processing system based on temperature feedback. In the present invention, during the chili steaming process, by obtaining the temperature distribution of temperature control points at different heights in the cavity and constructing a layered gradient difference sequence, the spatial differences in temperature changes inside the cavity can be effectively identified, and then the advance or lag state of the heat response can be periodically distinguished, realizing the dynamic joint regulation of the heating frequency and the heat release rhythm. On this basis, time series structure data is constructed according to the top and bottom temperature and pressure change sequences, and the correlation between the upper and lower layer pressure responses is identified through trend matching, so that the heat pressure state prediction is closer to the actual change process. Finally, based on the prediction results, the opening and closing frequency and direction of the hot air circulation channel are rhythmically scheduled to ensure the balance and smoothness of the internal hot air circulation. The overall process enhances the thermal control accuracy and heat pressure adaptability during the steaming process.

Description

Technical Field

[0001] The invention relates to the technical field of food processing, in particular to a fermented glutinous rice cake and chili steaming processing system based on temperature feedback. Background Art

[0002] The field of food processing technology mainly involves physical or chemical treatment of raw materials to improve food safety, extend shelf life, improve taste and nutritional structure, and includes multiple key links such as pretreatment, sterilization, fermentation, packaging, storage and transportation.

[0003] Among them, the traditional fermented glutinous rice cake pepper steaming and processing system based on temperature feedback refers to a system that uses sensors to collect internal temperature data of the equipment during the steaming process, and adjusts the heat source or equipment operating parameters accordingly to achieve steaming processing of glutinous rice cake peppers.

[0004] Existing technologies rely solely on a single temperature feedback signal as a control basis, ignoring the temperature differences at different height levels in the cavity and the dynamic response process of heat transfer. During the actual cooking process, the top temperature inside the equipment may rise rapidly while the bottom responds slowly, resulting in frequent control errors of delayed or advanced heat source adjustment. Especially under conditions of large load or high initial temperature difference, the system's control capabilities are difficult to take into account the balance of overall heat distribution. For example, in a continuous cooking cycle, when the top temperature has reached the set threshold but the bottom is still at a low temperature, shutting down the heat source may cause insufficient bottom processing, thereby causing quality problems such as uneven softening of the peppers and disordered flavor release. In addition, the opening and closing control of the ventilation channel lacks the ability to predict based on the pressure change trend, and cannot timely guide the accumulation of hot air, increasing the local pressure load in the cavity, easily causing airflow direction disorder or abnormal fluctuations in cooking pressure, affecting the overall operation stability and product quality control accuracy. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a fermented glutinous rice cake chili steaming and processing system based on temperature feedback.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solution: a fermented glutinous rice cake chili steaming and processing system based on temperature feedback comprises:

[0007] Temperature gradient evaluation module: obtains the temperature set of multiple temperature control points in the chili steaming container cavity, and performs cluster gradient evaluation on the temperature set to form a temperature gradient cluster evaluation result;

[0008] Response delay determination module: based on the temperature gradient cluster evaluation result, determines the degree of advance of the heating response time of the current heat source in the pepper cooking container and generates a corresponding heat source response delay control instruction;

[0009] Heat joint adjustment module: based on the heat source response delay control instruction, jointly adjusts the heating frequency and heat release level of the pepper cooking container to obtain a heat adjustment result;

[0010] Thermal pressure trend prediction module: Based on the heat adjustment result, the future temperature and pressure state of the top and bottom cavities of the pepper cooking container during the cooking process is predicted and calculated to obtain the thermal pressure evolution trend prediction result;

[0011] Hot air channel opening and closing scheduling module: According to the thermal pressure evolution trend prediction result, the hot air circulation channel of the pepper cooking container is opened and closed at a frequency scheduling mode to obtain the cavity hot air circulation scheduling result.

[0012] As a further solution of the present invention, the temperature gradient clustering evaluation results include the cluster center position, temperature control point gradient classification, and temperature change trend type; the heat source response delay control instructions include heat source response time adjustment parameters, heating delay activation threshold, and power increase control; the heat adjustment results include heating frequency adjustment, single heat release amplitude, and heating cycle correction; the thermal pressure evolution trend prediction results include top temperature change trend, bottom pressure response trend, and top and bottom pressure difference change range; the cavity hot gas circulation scheduling results include opening and closing control timing, channel frequency adjustment, and hot gas circulation direction control mark.

[0013] As a further solution of the present invention, the temperature gradient assessment module includes:

[0014] Temperature point archiving submodule: This module obtains the temperature collection of multiple temperature control points in the chili steaming container cavity, arranges them in vertical order according to the position coordinates recorded by the infrared thermal probe, marks the corresponding height level of each temperature control point in the cavity, and generates temperature level archiving data;

[0015] Gradient difference construction submodule: based on the temperature between consecutive layers in the temperature layer archive data, calculate the change amplitude of adjacent temperature control points and obtain layered temperature gradient sequence data;

[0016] Gradient classification and identification submodule: calls all gradient values ​​in the layered temperature gradient sequence data, classifies them according to the amplitude aggregation situation, and generates temperature gradient clustering evaluation results.

[0017] As a further solution of the present invention, the response delay determination module includes:

[0018] Cluster position extraction submodule: Call the cluster category information in the temperature gradient cluster evaluation result, select the category with the largest number of temperature control points, and extract the spatial hierarchical position corresponding to the largest category, determine the hierarchical area with the largest thermal change amplitude in the current heating area, and generate the main control hierarchical positioning data;

[0019] Response time comparison submodule: Based on the area pointed by the main control level positioning data, the heat source response time series within the cooking cycle of the area is collected, and the standard heating response time of the current cycle is simultaneously obtained. The two types of time series are classified and compared according to the cycle to obtain a response time comparison dataset;

[0020] Response state discrimination submodule: calls the response time comparison data set to compare the time difference between the heat source response time of each cycle and the standard response time, identifies the early response cycle state, and outputs the heat source response delay control instruction.

[0021] As a further solution of the present invention, the heat joint adjustment module includes:

[0022] Frequency data extraction submodule: obtains the heating frequency of a specified period within the heat source regulation cycle corresponding to the heat source response delay control instruction, and generates a periodic frequency sequence;

[0023] Release level adjustment submodule: Combined with the initial set amount of single heat release in the current cycle in the periodic frequency sequence, the changing rhythm of heat output in the continuous response is reconstructed to obtain a variable release intensity sequence;

[0024] Heat regulation integration submodule: obtains the full-cycle information in the periodic frequency sequence and the variable release intensity sequence, establishes a regulation coupling relationship between frequency and release intensity, performs combined regulation in the heat source output, and obtains a heat adjustment result.

[0025] As a further solution of the present invention, the thermal pressure trend prediction module includes:

[0026] Temperature and pressure data acquisition submodule: collects the heating cycle data index located by the heat adjustment result, obtains the temperature and pressure records of the top and bottom areas of the pepper steaming container during the cycle, and generates the upper and lower temperature and pressure sequences;

[0027] Structural joint construction submodule: structurally integrates the temperature and pressure states corresponding to the time series nodes of the upper and lower temperature and pressure sequences with the frequency and release magnitude in the heat adjustment to form joint temperature and heat pairing structure data;

[0028] Trend similarity judgment submodule: The combined warm and hot pairing structure data is input into the grey correlation analysis model to calculate the response correlation distribution between the top and bottom time series, determine whether the top pressure change is in the leading rising interval and the bottom is delayed following, and perform periodic prediction of the hot pressure state of the cooking process according to the trend direction and duration, and output the hot pressure evolution trend prediction result.

[0029] As a further solution of the present invention, the hot gas channel opening and closing scheduling module includes:

[0030] An opening and closing record extraction submodule: calling the heat pressure evolution trend prediction result, obtaining the opening and closing frequency record of the hot air circulation channel of the pepper steaming container in the current cycle, and generating opening and closing frequency time series data;

[0031] Frequency deviation calculation submodule: extracts the opening and closing frequency corresponding to each time period in the opening and closing frequency time series data, compares and analyzes it with the joint characteristic interval composed of the top pressure rise rate and the bottom pressure lag time in the thermal pressure evolution trend prediction result, identifies the change deviation of the opening and closing frequency within the time period, and obtains the opening and closing frequency deviation identification result;

[0032] Frequency adjustment execution submodule: extracts the time period exceeding the characteristic tolerance range in the opening and closing frequency deviation identification result, locates the abnormal frequency interval, and performs scheduling operations on the opening and closing frequency in combination with the channel response capability within the cycle, adjusts the opening and closing rhythm, and outputs the cavity hot air circulation scheduling result.

[0033] Compared with the prior art, the advantages and positive effects of the present invention are:

[0034] In the present invention, during the pepper cooking process, by obtaining the temperature distribution of temperature control points at different heights in the cavity and constructing a layered gradient difference sequence, the spatial differences in temperature changes inside the cavity can be effectively identified, and then the advance or lag state of the heat response can be periodically judged, thereby realizing dynamic joint adjustment of the heating frequency and the heat release rhythm. On this basis, time series structure data is constructed according to the top and bottom temperature and pressure change sequences, and the correlation between the upper and lower layer pressure responses is identified through trend matching, so that the heat pressure state prediction is closer to the real change process. Finally, based on the prediction results, the opening and closing frequency and direction of the hot air circulation channel are rhythmically scheduled to ensure the balance and smoothness of the internal hot air circulation. The overall process enhances the thermal control accuracy and hot pressure adaptability during the steaming process, improves the thermal efficiency during the steaming process and the consistency of the pepper quality, avoids the adverse effects of local temperature heat accumulation and bottom pressure lag, promotes the stable evolution of the thermal environment inside the cavity, and improves the balanced release of pepper flavor substances and the quality consistency of the final product. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 is a system flow chart of the present invention;

[0036] Figure 2 This is a flow chart of the temperature gradient evaluation module of the present invention;

[0037] Figure 3 This is a flow chart of the response delay determination module of the present invention;

[0038] Figure 4 This is a flow chart of the heat joint adjustment module of the present invention;

[0039] Figure 5This is a flow chart of the heat pressure trend prediction module of the present invention;

[0040] Figure 6 This is a flow chart of the hot gas channel opening and closing scheduling module of the present invention. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0042] In the description of the present invention, it should be understood that the terms "length," "width," "up," "down," "front," "back," "left," "right," "vertical," "horizontal," "top," "bottom," "inside," "outside," and the like, indicating positions or relationships, are based on the positions or relationships shown in the accompanying drawings and are intended only to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, in the description of the present invention, "plurality" means two or more, unless otherwise expressly and specifically defined.

[0043] See also Figure 1 The fermented glutinous rice cake and chili steaming processing system based on temperature feedback includes:

[0044] Temperature gradient evaluation module: obtains the temperature set of multiple temperature control points in the chili steaming container cavity, and performs cluster gradient evaluation on the temperature set to form a temperature gradient cluster evaluation result;

[0045] Response delay determination module: Based on the temperature gradient clustering evaluation results, it determines the degree of advance of the current heat source heating response time in the pepper steaming container and generates the corresponding heat source response delay control instructions;

[0046] Heat joint adjustment module: Based on the heat source response delay control instruction, the heating frequency and heat release level of the pepper steaming container are jointly adjusted to obtain the heat adjustment result;

[0047] Thermal pressure trend prediction module: Based on the heat adjustment results, the future temperature and pressure state of the top and bottom cavities of the pepper cooking container during the cooking process is predicted and calculated to obtain the thermal pressure evolution trend prediction results;

[0048] Hot air channel opening and closing scheduling module: Based on the prediction results of the thermal pressure evolution trend, the hot air flow channel of the pepper cooking container is opened and closed at a frequency that is scheduled to obtain the cavity hot air flow scheduling results;

[0049] The temperature gradient clustering assessment results include the cluster center location, temperature control point gradient classification, and temperature change trend type. The heat source response delay control instructions include heat source response time adjustment parameters, heating delay activation threshold, and power increase control. The heat adjustment results include heating frequency adjustment, single heat release amplitude, and heating cycle correction. The thermal pressure evolution trend prediction results include top temperature change trend, bottom pressure response trend, and top and bottom pressure difference change range. The cavity hot gas circulation scheduling results include opening and closing control timing, channel frequency adjustment, and hot gas circulation direction control mark.

[0050] See also Figure 2 , the temperature gradient evaluation module includes:

[0051] Temperature point archiving submodule: This module obtains the temperature collection of multiple temperature control points in the chili steaming container cavity, arranges them in vertical order according to the position coordinates recorded by the infrared thermal probe, marks the corresponding height level of each temperature control point in the cavity, and generates temperature level archiving data;

[0052] The temperature sets of multiple temperature control points in the chili steaming container cavity are obtained and arranged in vertical order according to the position coordinates recorded by the infrared thermal probe. The height level corresponding to each temperature control point in the cavity is marked to generate temperature level archiving data. During the specific implementation process, multiple infrared thermal probes need to be arranged at different heights in the cooking chamber. Each probe needs to be assigned a unique number and marked with coordinates to establish a correspondence between the temperature control point and the chamber height. For example, in a vertical container with a height of 1 meter, five probes are set at 0.1 meters, 0.3 meters, 0.5 meters, 0.7 meters, and 0.9 meters respectively. Their coordinates can be marked as P1 to P5, and defined as L1 to L5 levels respectively. Then the steaming program is started, and the temperature readings of all probes are collected at each time node (such as every 5 seconds) during the pepper steaming process and saved by timestamp. The obtained original temperature data includes: time, probe number, coordinate position, and corresponding temperature value. After that, the data set needs to be sorted from low to high according to the coordinates, and archived in layers according to the height layer corresponding to each coordinate, and finally a structured temperature hierarchy file is generated.

[0053] Gradient difference construction submodule: Based on the temperature between consecutive layers in the temperature layer archive data, the change amplitude of adjacent temperature control points is calculated to obtain layered temperature gradient sequence data;

[0054] Based on the temperatures between consecutive layers in the temperature hierarchical archive data, the change amplitude of adjacent temperature control points is calculated to obtain the layered temperature gradient sequence data. In the specific implementation process, the hierarchically sorted temperature archive data generated by the previous submodule is first read. The temperature data of two adjacent layers is extracted according to the hierarchical order and the difference operation is performed, that is, the temperature difference is calculated layer by layer. For example, if the archived L1-L5 temperature sequence is 85℃, 88℃, 90℃, 93℃, and 96℃, the difference between L1 and L2 is 3℃, the difference between L2 and L3 is 2℃, the difference between L3 and L4 is 3℃, and the difference between L4 and L5 is 3℃, resulting in a gradient sequence of [3, 2, 3, 3]. During this process, the data records at all time nodes should be traversed through programmatic means to ensure that a complete gradient sequence can be calculated at each time point. The above process can be automatically executed using a data analysis script to output formatted gradient difference sequence records. For example, the current gradient list and time stamp are output for each 5-second time period, thus forming a complete layered temperature gradient historical data set.

[0055] Gradient classification and identification submodule: calls all gradient values ​​in the layered temperature gradient sequence data, classifies them according to the amplitude clustering, and generates temperature gradient clustering evaluation results;

[0056] To call all the gradient values ​​in the layered temperature gradient sequence data, first extract the temperature gradient data generated at all time points in a specific cooking period and unify them into a complete gradient set. For example, in a 30-second cooking time, if the data is recorded every 5 seconds and each time contains 4 gradient differences, then 24 gradient values ​​will be obtained in this period, which is set as ,in Indicates the temperature gradient value between a certain level at a certain time point, the i-th data, Then, the distribution range of all gradient values ​​is counted. By performing distribution analysis on the set G, the main concentrated intervals are identified and the category boundaries are determined. The K-means clustering method is used to classify the gradient values. The initial number of categories K can be preset by the user. The common setting is K=3, which represents the three gradient levels of low, medium, and high. Then, the K-means classification steps are performed:

[0057] Initial stage: Randomly select 3 initial centers from the gradient value set , , , respectively represent the center values ​​of low gradient class, medium gradient class, and high gradient class. Here represents the jth class ( ) The current cluster center, initially selected =1.5, =3.0, =4.5 as low, medium, and high category centers respectively;

[0058] Distribution phase: for each gradient value in G Calculate the Euclidean distance from each center:

[0059] ;

[0060] in: : The distance between the i-th gradient value and the j-th class center, : the i-th gradient data, : The cluster center of the j-th class.

[0061] If the distance is minimum, then Assigned to class , that is, in the jth class; for example: =1.6, then its distance from each center is: , , .

[0062] therefore Classify to The low gradient class represented;

[0063] Update phase: for each category Recalculate the mean of all data within as the new cluster center:

[0064] ;

[0065] in: : updated class center of class j, : The number of members contained in the jth class (that is, the number of data in the class), : The set of all gradient values ​​belonging to this class.

[0066] For example, if category 1 contains =1.6, =1.4, =1.5, then: .

[0067] After updating all the class centers, repeat steps 2 and 3 until all The change of satisfies the convergence condition: , It is the convergence threshold, which is usually set to 0.01 (that is, the iteration stops when the change is less than 0.01).

[0068] Assume that the following 6 sets of gradient data are recorded within 30 seconds, each set contains 4 values, and the total is:

[0069] ;

[0070] The initial cluster centers are set as follows: =1.3 (low gradient class), =3.0 (medium gradient class), =4.9 (high gradient class).

[0071] The clustering results after allocation according to the above formula are as follows:

[0072] Low gradient class : ;

[0073] Medium gradient class : ;

[0074] High gradient class : .

[0075] Calculate the new center value:

[0076] ;

[0077] ;

[0078] .

[0079] After further iterations, the center values ​​are basically stable, satisfying: , that is, the convergence condition is reached, and the final clustering interval is output as follows: Low gradient interval: , mid-gradient interval: , high gradient interval: Finally, the category and interval results of each gradient value are output to form a complete temperature gradient clustering evaluation table.

[0080] See also Figure 3 , the response delay determination module includes:

[0081] Cluster position extraction submodule: Call the cluster category information in the temperature gradient cluster evaluation result, select the category with the largest number of temperature control points, and extract the spatial hierarchical position corresponding to the largest category, determine the hierarchical area with the largest thermal change amplitude in the current heating area, and generate the main control hierarchical positioning data;

[0082] The clustering category information in the temperature gradient clustering evaluation result is called. During the specific operation, the system first extracts the mapping table of the category label corresponding to each temperature control point and its spatial position level from the gradient clustering evaluation. The mapping table lists the gradient categories and the number of temperature control points contained in each level in a structured manner. Then, the number of temperature control points in each category is counted, and the category with the largest number is selected as the current target category. For example, if there are three categories in total, namely low, medium, and high, which contain 3, 7, and 5 temperature control points respectively, the medium category is selected as the target category. Then, the spatial hierarchical distribution of all medium-category temperature control points is extracted. For example, if the 7 points are located at L2, L2, L3, L3, L3, L4, and L5, respectively, the distribution density is counted and sorted, and L3 is obtained as a dense level. The system determines that L3 is the level area with the largest thermal change amplitude in the current heating area, and then generates a main control level positioning data for subsequent response judgment. The level position information can be combined with the structural coordinate system of the container cavity to output as a spatial positioning parameter.

[0083] Response time comparison submodule: Based on the area pointed by the main control level positioning data, the heat source response time series within the cooking cycle of the area is collected, and the standard heating response time of the current cycle is simultaneously obtained. The two types of time series are classified and compared according to the cycle to obtain a response time comparison dataset;

[0084] Based on the area pointed by the main control level positioning data, the heat source response data of this area is collected for each time period within the cooking cycle. In specific operations, the trigger time of the heat source start signal and the time when the corresponding temperature control point first reaches the heating threshold are recorded in each cycle, which is defined as the actual response time. At the same time, the standard response time series set for the cycle is read from the equipment heating standard library to construct a standard reference table. For example, within a 60-second cooking cycle, the system can collect data every 10 seconds and record the response times such as 12 seconds, 11.8 seconds, 11.6 seconds, 12.2 seconds, 11.9 seconds, and 12.1 seconds respectively. At the same time, the standard response time is 11.5 seconds. The system compares all actual times with the standard time and uses point-by-point subtraction to obtain the response difference in each cycle. For example, the first segment is 12-11.5=0.5 seconds, and the second segment is 11.8-11.5=0.3 seconds. This generates a set of cycle response time comparison data sets.

[0085] Response state identification submodule: calls the response time comparison data set to compare the time difference between the heat source response time of each cycle and the standard response time, identifies the early response cycle state, and outputs the heat source response delay control instruction;

[0086] The difference information between the heat source response time and the standard response time in each cycle in the response time comparison data set is called. The system reads the time difference of each set of data cycle by cycle, and sets the upper and lower limits of the standard threshold to identify the state offset interval. For example, the system sets the normal response time error tolerance to ±0.5 seconds. If the actual response time is more than 0.5 seconds earlier than the standard response time, it is classified as an early response state, and if it is more than 0.5 seconds later, it is classified as a delayed response state. If it is within the threshold range, it is regarded as a normal response. During specific execution, the system traverses the entire comparison data set and determines whether the early condition is met item by item. For example, if the actual time of a cycle is 10.6 seconds and the standard is 11.5 seconds, the difference is -0.9 seconds, which is more than 0.5 seconds earlier than the standard and is classified as an early response cycle. Subsequently, after each early cycle is identified, the system generates an identification record with its number, timestamp, etc., and outputs a heat source response delay control instruction for the equipment to execute logical switching or adjust the control process.

[0087] See also Figure 4 , the heat joint adjustment module includes:

[0088] Frequency data extraction submodule: obtains the heating frequency of the specified period within the heat source control cycle corresponding to the heat source response delay control instruction, and generates a periodic frequency sequence;

[0089] Obtain the heating frequency of the specified time period within the heat source control cycle corresponding to the heat source response delay control instruction. In specific operations, the system first confirms the starting cycle time window that triggers the control instruction. For example, if the current response delay occurs between 20 seconds and 30 seconds in the 5th cycle, the system locks this time period as the data extraction range, and then extracts the actual heating frequency data within this period from the heat source control record. The unit of heating frequency is "times / minute" or "times / second", which is determined by the heating control granularity set by the system. If it is a pulse heating system, the heating trigger frequency is usually recorded in "times / second". For example, the heat source can trigger 1 to 10 heating pulses per second. During actual sampling, the number of pulses per second is recorded in units of 1 second to form a time series. This sequence is the frequency distribution structure of the cycle. At the same time, the system pairs and archives it with the standard frequency distribution.

[0090] Release level adjustment submodule: Combined with the initial set amount of single heat release in the current cycle in the periodic frequency sequence, the changing rhythm of heat output in the continuous response is reconstructed to obtain a variable release intensity sequence;

[0091] Based on the initial set amount of heat released per pulse in the current cycle of the cyclic frequency sequence, during implementation, the system reads the baseline heat release of the heat source during a single pulse of heating. This baseline value is measured in kilojoules per pulse (kJ / pulse). For example, if 0.5 kJ is set per pulse, the heat source outputs 0.5 kJ of heat energy each time it triggers a pulse. This set value is called the "initial release intensity." Release intensity is defined as the intensity of the heat output of a heat source per unit time. Specifically, it can be understood as the level of thermal power released by the heat source per unit time at a certain heating frequency. Based on actual frequency analysis, the system reduces the release intensity to 0.4 kJ / pulse during a period of high frequency (such as 10 pulses / second for 3 consecutive seconds) to prevent excessive heat accumulation. If the frequency is low (such as less than 6 pulses / second), the release intensity is adjusted to 0.6 kJ / pulse to maintain a stable overall heat output. The system updates the release intensity once per second, ultimately forming a series of variable release intensities, with each time point in the sequence corresponding to a release value.

[0092] Heat regulation integration submodule: obtains full-cycle information from the periodic frequency sequence and the variable release intensity sequence, establishes a regulatory coupling relationship between frequency and release intensity, performs combined regulation in the heat source output, and obtains the heat adjustment result;

[0093] The system obtains full-cycle information from the periodic frequency sequence and the variable release intensity sequence. During execution, the system synchronizes and merges the two sequences under the same cycle timeline, pairing them in seconds. For each second, it reads the corresponding frequency value (in "times / second") and release intensity value (in "kJ / time") to construct a combination adjustment table. For example, if the frequency at the 6th second is 9 times / second and the release intensity is 0.5 kJ / time, the system records it as "6th second: 9 × 0.5 = 4.5 kJ / second," indicating a total heat output of 4.5 kJ in that second. This logic executes once per second, generating multiple combination records over the entire cycle. The system then calls the adjustment rule table to determine whether each combination falls within the specified control range. For example, if the rule sets a frequency ≥ 9 times / second and a release intensity ≥ 0.5 kJ / time, maximum output is allowed; otherwise, conservative output mode is entered. The system evaluates each second and compiles the heat output results for each second into a heat adjustment result sequence. Each result is uniformly recorded in "kJ / second." This ultimately generates a complete cycle heat output log for reference and traceability during the execution of equipment heat source adjustment.

[0094] See also Figure 5 , the thermal pressure trend prediction module includes:

[0095] Temperature and pressure data acquisition submodule: This module collects the heating cycle data index located by the heat adjustment result, obtains the temperature and pressure records of the top and bottom areas of the pepper steaming container during the cycle, and generates the upper and lower temperature and pressure sequences;

[0096] Collect the heating cycle data index located by the heat adjustment result. In the specific implementation, the system first calls the heat adjustment record generated in the previous stage to determine the heating cycle number and its specific time period. For example, the 12th heating cycle corresponds to the time range from 120 seconds to 180 seconds. Based on this, the system searches for the sensor record data corresponding to the top and bottom areas of the pepper steaming container during this time period. The top area data comes from the temperature sensor and pressure probe in the top layer of the cavity, and the bottom data comes from the corresponding sensing device in the bottom layer. The system collects temperature and pressure readings once per second, in units of "degrees Celsius" and "kilopascals" respectively, and stores them in sequence according to the time axis to form two synchronized upper and lower temperature and pressure sequence data sets. For example, at the 120th second, the top record is 92°C and 103 kPa, and the bottom record is 85°C and 101 kPa. The recording lasts for 60 seconds per second to form a complete sequence. All data are archived uniformly and associated with the index number of the cycle.

[0097] Structural joint construction submodule: structurally integrates the temperature and pressure states corresponding to the time series nodes of the upper and lower temperature and pressure sequences with the frequency and release magnitude in the heat adjustment to form joint temperature and heat pairing structure data;

[0098] The temperature and pressure data of each time node in the upper and lower temperature and pressure sequences are structurally integrated with the frequency and release magnitude of the corresponding moment in the heat adjustment results. In actual operation, the system uses each second as the time node, and pairs the top temperature, top pressure, bottom temperature, and bottom pressure at that time point with the heating frequency and release magnitude of that second. For example, at the 130th second, the upper temperature is 94°C and the pressure is 104 kPa, and the lower temperature is 88°C and the pressure is 102 kPa. The heating frequency of that second is 8 times / second, and the release magnitude is 0.5 kJ / time. The system then generates a structural record containing paired data of 6 indicators. All structural combination results within 60 seconds are unified into a joint thermal pairing structure data table, which structurally shows the temporal coupling relationship between temperature, pressure and heat source output.

[0099] Trend similarity judgment submodule: This module inputs the combined warm-hot paired structure data into the grey correlation analysis model to calculate the response correlation distribution between the top and bottom time series, determines whether the top pressure change is in the leading rising range and the bottom follows with a delay, and performs a periodic prediction of the hot pressure state of the cooking process based on the trend direction and duration, outputting the hot pressure evolution trend prediction results.

[0100] The combined warm-hot paired structure data are input into the grey correlation analysis model to calculate the response correlation distribution between the top and bottom time series. In the specific implementation process, the top pressure or temperature sequence and the corresponding bottom sequence in the combined data are first extracted and input into the grey correlation system model as the main sequence and comparison sequence. The time step is uniformly set per second to construct a set of sequences of equal length. For example, the top pressure sequence is set as the reference main sequence and the bottom pressure sequence is set as the comparison sequence. If the acquisition time period is 60 seconds, the length of each sequence is 60 items.

[0101] The system first confirms whether the two sequence data are of the same length and there are no missing data, and sorts them in chronological order. If there are missing values, linear interpolation is required to ensure that there are main sequence values ​​and comparison sequence values ​​at each time point, and complete the data structure standardization.

[0102] Since physical quantities such as pressure or temperature may have inconsistent dimensions or different numerical scales, the two sequences need to be dimensionlessly normalized. A common method is range normalization, which involves scaling each data point to the interval [0, 1] as follows: subtract the minimum value of the sequence from the current value and divide it by the range (maximum value minus minimum value). After this processing, the data of the main sequence and the comparison sequence fall into the same interval, eliminating the influence of the value itself on the association judgment.

[0103] For the two normalized sequences, the absolute difference between the main sequence and the comparison sequence values ​​is calculated at each time k to obtain a difference sequence for subsequent correlation calculation.

[0104] After the difference sequence is calculated, the system uses the standard grey correlation coefficient formula to process it and calculate the similarity of the response between the top and bottom at each time point. The following correlation coefficient calculation formula is used:

[0105] ;

[0106] in, : represents the grey correlation coefficient of the hth (only one) comparison sequence at the kth time point, reflecting the similarity of the responses of the top and bottom at that moment; : represents the absolute difference between the main sequence and the h-th comparison sequence at the k-th moment; : represents the minimum absolute difference among all time points; : Indicates the maximum absolute difference among all time points; : It is the grey relational coefficient resolution coefficient, which usually takes a value between 0 and 1 and is generally set to 0.5.

[0107] Assuming the sampling period is 4 seconds, the normalized top and bottom pressure sequences are as follows:

[0108] Top (main sequence): , bottom (comparing sequences): .

[0109] Compute the absolute difference every second: , , , .

[0110] get: , , .

[0111] Substitute the formula to calculate the grey correlation coefficient: , , , .

[0112] Finally, the response similarity distribution between the top and bottom is obtained as follows: This sequence shows that the top pressure is completely synchronized with the bottom response at the first moment, and then gradually deviates. Based on this distribution, we can further determine whether there is a leading upward trend at the top and whether the bottom has a lagging response. We can also make a periodic prediction based on the direction of change and the degree of delay within the time period, and finally output the prediction result of the hot pressure evolution trend for reference by the cooking control system in formulating the next periodic response strategy.

[0113] See also Figure 6 , the hot gas channel opening and closing scheduling module includes:

[0114] Opening and closing record extraction submodule: Calls the thermal pressure evolution trend prediction results to obtain the opening and closing frequency records of the hot air circulation channel of the pepper steaming container in the current cycle, and generates opening and closing frequency time series data;

[0115] The prediction results of the thermal pressure evolution trend are called. In the specific operation, the system first identifies the thermal pressure trend characteristics in the current heating cycle. For example, the cycle number is the 15th cycle, and the time period is from the 300th second to the 360th second. The trend results show that the top pressure is in a rapid rising state and the bottom reaction delay is obvious. Based on this, the system calls the opening and closing control records of the cavity hot gas circulation channel in this time period, and obtains the opening and closing operation frequency of the channel valve from the equipment log. The frequency unit is "times / minute" or "times / cycle segment". Each opening and closing event includes the timestamp of opening or closing, the status label and the operation number. The system organizes all opening and closing events in this period at intervals of 1 second, calculates the number of openings and closings per second, and generates an opening and closing frequency time series data sequence. The sequence structure is a combination of "time point-opening and closing times" and is stored in time axis order to form a complete opening and closing frequency distribution record.

[0116] Frequency deviation calculation submodule: extracts the opening and closing frequency corresponding to each time period in the opening and closing frequency time series data, and compares and analyzes it with the joint characteristic interval composed of the top pressure rise rate and the bottom pressure lag time in the thermal pressure evolution trend prediction result, identifies the change deviation of the opening and closing frequency within the time period, and obtains the opening and closing frequency deviation identification result;

[0117] The opening and closing frequencies corresponding to each time period in the opening and closing frequency time series data are extracted. The system reads all second-level opening and closing frequencies in the period, and compares the top pressure rise rate and the bottom pressure lag time in the thermal pressure evolution trend prediction results to construct a joint characteristic interval per second as a comparison benchmark. For example, in the time period where the top pressure rise rate per second is greater than 2 kPa and the bottom response lag exceeds 5 seconds, the ideal range of the opening and closing frequency should be set to 10 to 12 times / minute. The system uses second as the analysis unit and compares the actually recorded opening and closing frequency with the characteristic interval. If the opening and closing frequency in a certain second is only 7 times / minute or higher than 15 times / minute, the system identifies the second as a deviation period, forms the opening and closing frequency deviation identification result data, and records the deviation time point, deviation amplitude and corresponding trend feature number.

[0118] Frequency adjustment execution submodule: This module extracts the time periods exceeding the characteristic tolerance range from the on / off frequency deviation identification results, locates the abnormal frequency intervals, and performs scheduling operations on the on / off frequencies based on the channel response capabilities within the cycle, adjusts the on / off rhythm, and outputs the cavity hot air flow scheduling results.

[0119] The time periods marked as exceeding the tolerance range in the identification results of the opening and closing frequency deviation are extracted, and the system analyzes the time period structure of these abnormal frequency points one by one, locates the duration of the opening and closing fluctuation abnormality in seconds, and reads the opening and closing response capability parameters of the channel within the cycle, such as the maximum response frequency, the minimum effective opening and closing time interval and other parameters. In the scheduling stage, the system compares and matches the opening and closing frequency of the abnormal period with the response capability, and selects the appropriate opening and closing rhythm correction method according to the strategy library, such as shortening the opening and closing interval, merging repeated operations, limiting the instantaneous frequency peak and other operations. After completion, the original opening and closing frequency record table is updated, and a new opening and closing scheduling plan table is output to form a complete cavity hot air circulation scheduling result.

[0120] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A fermented glutinous rice cake and chili cooking processing system based on temperature feedback, characterized in that: The system comprises: Temperature gradient evaluation module: obtains the temperature set of multiple temperature control points in the chili steaming container cavity, and performs cluster gradient evaluation on the temperature set to form a temperature gradient cluster evaluation result; The temperature gradient assessment module includes: Temperature point archiving submodule: This module obtains the temperature collection of multiple temperature control points in the chili steaming container cavity, arranges them in vertical order according to the position coordinates recorded by the infrared thermal probe, marks the corresponding height level of each temperature control point in the cavity, and generates temperature level archiving data; Gradient difference construction submodule: based on the temperature between consecutive layers in the temperature layer archive data, calculate the change amplitude of adjacent temperature control points and obtain layered temperature gradient sequence data; Gradient classification and identification submodule: calls all gradient values ​​in the layered temperature gradient sequence data, classifies them according to the amplitude aggregation, and generates temperature gradient clustering evaluation results; Response delay determination module: based on the temperature gradient cluster evaluation result, determines the degree of advance of the heating response time of the current heat source in the pepper cooking container and generates a corresponding heat source response delay control instruction; The response delay determination module includes: Cluster position extraction submodule: Call the cluster category information in the temperature gradient cluster evaluation result, select the category with the largest number of temperature control points, and extract the spatial hierarchical position corresponding to the largest category, determine the hierarchical area with the largest thermal change amplitude in the current heating area, and generate the main control hierarchical positioning data; Response time comparison submodule: Based on the area pointed by the main control level positioning data, the heat source response time series within the cooking cycle of the area is collected, and the standard heating response time of the current cycle is simultaneously obtained. The two types of time series are classified and compared according to the cycle to obtain a response time comparison dataset; Response state identification submodule: calls the response time comparison data set to compare the time difference between the heat source response time of each cycle and the standard response time, identifies the early response cycle state, and outputs the heat source response delay control instruction; Heat joint adjustment module: based on the heat source response delay control instruction, jointly adjusts the heating frequency and heat release level of the pepper cooking container to obtain a heat adjustment result; The heat joint adjustment module includes: Frequency data extraction submodule: obtains the heating frequency of a specified period within the heat source regulation cycle corresponding to the heat source response delay control instruction, and generates a periodic frequency sequence; Release level adjustment submodule: Combined with the initial set amount of single heat release in the current cycle in the periodic frequency sequence, the changing rhythm of heat output in the continuous response is reconstructed to obtain a variable release intensity sequence; Heat regulation integration submodule: obtains the full-cycle information in the periodic frequency sequence and the variable release intensity sequence, establishes a regulation coupling relationship between frequency and release intensity, performs combined regulation in the heat source output, and obtains a heat adjustment result; Thermal pressure trend prediction module: Based on the heat adjustment result, the future temperature and pressure state of the top and bottom cavities of the pepper cooking container during the cooking process is predicted and calculated to obtain the thermal pressure evolution trend prediction result; Hot air channel opening and closing scheduling module: According to the thermal pressure evolution trend prediction result, the hot air circulation channel of the pepper cooking container is opened and closed at a frequency scheduling mode to obtain the cavity hot air circulation scheduling result.

2. The fermented glutinous rice cake and chili cooking processing system based on temperature feedback according to claim 1 is characterized in that: The temperature gradient clustering evaluation result includes the cluster center position, temperature control point gradient classification, and temperature change trend type; the heat source response delay control instruction includes the heat source response time adjustment parameter, heating delay activation threshold, and power increase control; the heat adjustment result includes heating frequency adjustment, single heat release amplitude, and heating cycle correction; the thermal pressure evolution trend prediction result includes the top temperature change trend, the bottom pressure response trend, and the top and bottom pressure difference change range; the cavity hot gas circulation scheduling result includes the opening and closing control timing, channel frequency adjustment, and hot gas circulation direction control mark.

3. The fermented glutinous rice cake and chili cooking processing system based on temperature feedback according to claim 1 is characterized in that: The thermal pressure trend prediction module includes: Temperature and pressure data acquisition submodule: collects the heating cycle data index located by the heat adjustment result, obtains the temperature and pressure records of the top and bottom areas of the pepper steaming container during the cycle, and generates the upper and lower temperature and pressure sequences; Structural joint construction submodule: structurally integrates the temperature and pressure states corresponding to the time series nodes of the upper and lower temperature and pressure sequences with the frequency and release magnitude in the heat adjustment to form joint temperature and heat pairing structure data; Trend similarity judgment submodule: The combined warm and hot pairing structure data is input into the grey correlation analysis model to calculate the response correlation distribution between the top and bottom time series, determine whether the top pressure change is in the leading rising interval and the bottom is delayed following, and perform periodic prediction of the hot pressure state of the cooking process according to the trend direction and duration, and output the hot pressure evolution trend prediction result.

4. The fermented glutinous rice cake and chili cooking processing system based on temperature feedback according to claim 3 is characterized in that: The hot gas channel opening and closing scheduling module includes: An opening and closing record extraction submodule: calling the heat pressure evolution trend prediction result, obtaining the opening and closing frequency record of the hot air circulation channel of the pepper steaming container in the current cycle, and generating opening and closing frequency time series data; Frequency deviation calculation submodule: extracts the opening and closing frequency corresponding to each time period in the opening and closing frequency time series data, compares and analyzes it with the joint characteristic interval composed of the top pressure rise rate and the bottom pressure lag time in the thermal pressure evolution trend prediction result, identifies the change deviation of the opening and closing frequency within the time period, and obtains the opening and closing frequency deviation identification result; Frequency adjustment execution submodule: extracts the time period exceeding the characteristic tolerance range in the opening and closing frequency deviation identification result, locates the abnormal frequency interval, and performs scheduling operations on the opening and closing frequency in combination with the channel response capability within the cycle, adjusts the opening and closing rhythm, and outputs the cavity hot air circulation scheduling result.

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