A diglyceride edible oil intelligent seasoning control system and method

Abstract

The present invention discloses an intelligent seasoning control system and method for diglyceride edible oil, comprising a processing data acquisition module, a cross-validation module and a dynamic control module. The method is characterized in that: the processing data acquisition module is suitable for collecting data of the stir-frying process during the stir-frying seasoning process; the cross-validation module is used to analyze the aroma release degree and the coking degree of the seasoning in the stir-frying process according to the data collected by the processing data acquisition module and to cross-validate the processing process; the processing data acquisition module is electrically connected to the cross-validation module; the dynamic control module is used to dynamically control and adjust the parameters of the stir-frying seasoning process according to the verification result of the cross-validation module; the dynamic control module is electrically connected to the cross-validation module. The present invention has the characteristics of strong practicality and high intelligence.

Description

Technical Field

[0001] The present invention relates to the technical field of food processing, and in particular to an intelligent seasoning control system and method for diglyceride edible oil. Background Art

[0002] Diacylceride (DAG) is a lipid with a unique structure that exhibits significantly different properties during human metabolism compared to more common triglycerides (TAG). Studies have shown that diacylglyceride has health benefits such as lowering blood lipid levels, regulating fat metabolism, and reducing visceral fat accumulation. Therefore, it is widely used in high-end edible oils and functional foods.

[0003] In the industrial production of diglyceride oil, natural seasonings (such as scallions, ginger, garlic, Sichuan peppercorns, and fennel) are often added to enhance the product's added value and market competitiveness. Natural seasonings are rich in volatile aroma components, which are fully released and dissolved in the oil during the stir-frying process, giving the diglyceride oil its unique flavor. However, traditional stir-frying often relies on manual operation, with operators using experience to determine the stir-frying time and temperature. However, manual control often lacks precision and is significantly affected by the operator's skill level and subjective judgment, which can easily lead to quality variations between production batches.

[0004] At the same time, the quality of natural seasonings is easily affected by a variety of factors, such as the growing environment, climatic conditions, storage methods, and duration. These factors can cause significant variations in key parameters such as volatile components and moisture content. Some processing methods using fixed procedures are unable to adapt to these differences, resulting in unstable seasoning effects. Summary of the Invention

[0005] The object of the present invention is to provide a diglyceride edible oil intelligent seasoning control system and method to solve the problems raised in the above background technology.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a diglyceride edible oil intelligent seasoning control system and method, comprising a processing data acquisition module, a cross-validation module and a dynamic control module, wherein the processing data acquisition module is suitable for collecting data of the stir-frying process during the stir-frying seasoning process, the cross-validation module is used to analyze the aroma release degree and the carbonization degree of the seasoning in the stir-frying process according to the data collected by the processing data acquisition module and to cross-validate the processing process, the processing data acquisition module is electrically connected to the cross-validation module, and the dynamic control module is used to dynamically control and adjust the parameters of the stir-frying seasoning process according to the verification results of the cross-validation module, and the dynamic control module is electrically connected to the cross-validation module.

[0007] According to the above technical solution, the processing data acquisition module includes a smoke concentration detection module, a visual detection module, a seasoning database and a processing program docking module. The smoke concentration detection module is used to detect the smoke concentration value during the stir-frying process in real time. The visual detection module is used to collect visual imaging images during the stir-frying process. The seasoning database is used to record and store the volatilization and coking characteristics of various seasonings. The processing program docking module is used to dock with a preset fixed processing program to obtain real-time processing status data.

[0008] According to the above technical solution, the cross-validation module includes a volatility analysis module and a coking degree analysis module. The volatility analysis module is used to analyze the volatility degree based on the concentration of captured smoke and the characteristics of the seasoning. The coking degree analysis module is used to analyze the coking degree during the stir-frying process based on the visual imaging picture.

[0009] According to the above technical solution, the dynamic control module includes an intelligent temperature control module and a stirring rate compensation module. The intelligent temperature control module is suitable for dynamically adjusting the heating power during the stir-frying process, and the stirring rate compensation module is suitable for adjusting the stirring rate of the stirrer during the stir-frying process.

[0010] A method for controlling the intelligent seasoning of diglyceride edible oil, comprising the following steps:

[0011] Step S1: After the stir-frying seasoning process is started, the process program docking module is used to dock with a preset fixed process program to obtain the initial parameters of the current process program, where the initial parameters include the type and proportion of seasoning, processing progress and processing target, as well as oil temperature, stir-frying time, and stirring rate;

[0012] Step S2: The smoke concentration detection module then captures the changes in the concentration of volatile smoke during the stir-frying process in real time, and transmits the detection data to the volatility analysis module. At the same time, the visual detection module is activated to collect image data of the seasoning during the stir-frying process, and the collected image data is transmitted to the coking degree analysis module.

[0013] Step S3: Based on the acquired smoke concentration detection data and visual imaging data, respectively analyze the volatilization degree and the coking degree, and generate a volatilization curve and a coking progress curve;

[0014] Step S4: fusing and analyzing the volatilization curve and the coking progress curve to determine the matching between the volatilization degree and the coking degree;

[0015] Step S5: Adjust the heating power and stirrer speed according to the real-time analysis results and the cross-validation results.

[0016] According to the above technical solution, the volatility analysis method in step S3 further includes:

[0017] Step S31: setting the data acquisition rate to once per second to form a time series data point, and obtaining the concentration of volatile smoke collected each time during the stir-frying process;

[0018] Step S32: Based on the smoke concentration data, the cumulative concentration of volatiles is calculated using formula (1):

[0019] …(1);

[0020] in is the volatilization rate, which indicates the concentration of volatile matter released per unit time and is the output value of the smoke concentration detection module; is the change of volatile concentration over time; where is the starting time The integral up to the current time t is the total amount of all volatile components released from the start of heating to the current moment;

[0021] Step S33: With time t as the horizontal axis, the volatilization rate As the vertical axis, draw the volatility curve;

[0022] Step S34: Based on the current processing program and the historical big data of the seasoning database, the current real-time volatilization curve is compared with the historical volatilization curve on the same time axis. If the volatilization rate of the current real-time volatilization curve is higher than that of the historical volatilization curve, an electrical signal is transmitted to the intelligent temperature control module to reduce the volatilization rate, otherwise it is increased.

[0023] According to the above technical solution, the coking degree analysis method in step S3 further includes:

[0024] Step S3a: Obtain an image of the seasoning stir-frying process and convert the image from RGB to HSV space;

[0025] Step S3b: Set the color threshold, segment and extract the dark area, and perform binarization processing. and When , it is determined that the pixels with coking characteristics are extracted, otherwise they are ignored; where H is the hue of the image pixel, The hue threshold feature set for the condiment database; S is the saturation of the image pixel, Storing a set saturation threshold feature for a condiment database;

[0026] Step S3c: Counting the coking ratio , that is, extract the proportion of the coking feature pixels in the entire image, with time t as the horizontal axis, the proportion of the coking feature pixels As the vertical axis, draw the coking progress curve;

[0027] Step S3d: Divide the image into For small pieces, the standard deviation of the coking ratio of each piece is calculated as the coking uniformity using formula (2):

[0028] …(2);

[0029] in, is the average coking ratio of each small piece; is the coking ratio of the i-th small block; N is the number of small blocks divided into N; is the coking uniformity;

[0030] Step S3e: Combine the current processing procedure and the historical big data of the condiment database to calculate the real-time coking uniformity. Compared with the coking uniformity under the same time axis in history, if the current real-time coking uniformity If the coking uniformity is lower than the historical coking uniformity on the same time axis, the electrical signal is transmitted to the stirring rate compensation module for rate compensation.

[0031] According to the above technical solution, step S4 further includes:

[0032] Step S41: Extracting the volatilization rate peak based on the volatilization curve and its corresponding time ;

[0033] Step S42: Based on the coking progress curve, extract the time corresponding to the coking degree inflection point , where the coking degree inflection point is the starting point of the coking progress curve at a rapid speed, and can be directly obtained from the coking progress curve;

[0034] Step S43: Calculate the time difference between the peak time point of volatility and the inflection point of coking degree by using the delayed cross calculation formula (3) ;

[0035] …(3);

[0036] Combine the current processing procedures and the historical big data of the condiment database to output the standard time difference Interval, if Less than or greater than the standard time difference interval, it is judged that the matching between volatility and coking degree is unqualified; if If the time difference is less than the allowable error range of the standard time difference, it means that the delay is too short and premature coking occurs. The electrical signal is transmitted to the dynamic control module, and the dynamic control module is used for synchronous control to perform intelligent temperature control reduction and stirring rate compensation; if If the error is greater than the allowable range of the standard time difference, it means that the delay is too long and the aroma release is insufficient. The electrical signal is transmitted to the dynamic control module, and the dynamic control module is used for synchronous control to perform intelligent temperature control, heating and stirring rate compensation.

[0037] Compared with the existing technology, the present invention achieves the following beneficial effects: By using smoke concentration and visual imaging technology to collect real-time data during the stir-frying process, the present invention analyzes the degree of volatilization and carbonization, ensuring that the aroma release and carbonization state of the seasoning during the stir-frying process are optimally matched. Furthermore, through further cross-validation, the present invention can timely adjust the heating power and stirring rate, eliminating deviations caused by human factors and environmental changes, ensuring the consistency of the seasoning effect and high-quality output. This effectively improves the transparency and controllability of the seasoning process, reduces quality fluctuations caused by operational differences, and enhances the stability and efficiency of the production process. Ultimately, this ensures that the diglyceride cooking oil can fully absorb the essence of the seasoning, presenting a richer and more delicate taste and flavor, while also increasing the processing error tolerance, thereby indirectly reducing production management costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0039] Figure 1 It is a schematic diagram of the system module composition of the present invention. DETAILED DESCRIPTION

[0040] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0041] See also Figure 1 The present invention provides a technical solution: a diglyceride edible oil intelligent seasoning control system and method, including a processing data acquisition module, a cross-validation module and a dynamic control module. The processing data acquisition module is suitable for collecting data of the stir-frying process during the stir-frying seasoning process. The cross-validation module is used to analyze the aroma release degree and the coking degree of the seasoning in the stir-frying process according to the data collected by the processing data acquisition module and cross-validate the processing process. The processing data acquisition module is electrically connected to the cross-validation module. The dynamic control module is used to dynamically control and adjust the parameters of the stir-frying seasoning process according to the verification results of the cross-validation module. The dynamic control module is electrically connected to the cross-validation module.

[0042] The processing data acquisition module includes a smoke concentration detection module, a visual detection module, a seasoning database and a processing program docking module. The smoke concentration detection module is used to detect the smoke concentration value during the stir-frying process in real time. The visual detection module is used to collect visual imaging images during the stir-frying process. The seasoning database is used to record and store the volatilization and coking characteristics of various seasonings. The processing program docking module is used to dock with the preset fixed processing program to obtain real-time processing status data.

[0043] The cross-validation module includes a volatility analysis module and a coking degree analysis module. The volatility analysis module is used to analyze the volatility according to the concentration of captured smoke and the characteristics of seasonings. The coking degree analysis module is used to analyze the coking degree during the stir-frying process according to the visual imaging picture.

[0044] The dynamic control module includes an intelligent temperature control module and a stirring rate compensation module. The intelligent temperature control module is suitable for dynamically adjusting the heating power during the stir-frying process, and the stirring rate compensation module is suitable for adjusting the stirring rate of the stirrer during the stir-frying process.

[0045] A method for controlling the intelligent seasoning of diglyceride edible oil, comprising the following steps:

[0046] Step S1: After the stir-frying seasoning process is started, the process program docking module is used to dock with a preset fixed process program to obtain the initial parameters of the current process program, where the initial parameters include the type and proportion of seasoning, processing progress and processing target, as well as oil temperature, stir-frying time, and stirring rate;

[0047] Step S2: The smoke concentration detection module then captures the changes in the concentration of volatile smoke during the stir-frying process in real time, and transmits the detection data to the volatility analysis module. At the same time, the visual detection module is activated to collect image data of the seasoning during the stir-frying process, and the collected image data is transmitted to the coking degree analysis module.

[0048] Step S3: Based on the acquired smoke concentration detection data and visual imaging data, respectively analyze the volatilization degree and the coking degree, and generate a volatilization curve and a coking progress curve;

[0049] Step S4: fusing and analyzing the volatilization curve and the coking progress curve to determine the matching between the volatilization degree and the coking degree;

[0050] Step S5: Adjust the heating power and stirrer speed according to the real-time analysis results and the cross-validation results.

[0051] The volatility analysis method in step S3 further includes:

[0052] Step S31: setting the data acquisition rate to once per second to form a time series data point, and obtaining the concentration of volatile smoke collected each time during the stir-frying process;

[0053] Step S32: Based on the smoke concentration data, the cumulative concentration of volatiles is calculated using formula (1):

[0054] …(1);

[0055] in is the volatilization rate, which indicates the concentration of volatile matter released per unit time and is the output value of the smoke concentration detection module; is the change of volatile concentration over time; where is the starting time The integral to the current time t is the total amount of all volatile components released from the start of heating to the current moment; by setting the data to be acquired once per second, a time series data point is formed, and the volatilization rate output by the smoke concentration detection module is calculated. , calculate the cumulative concentration of volatiles , can accurately quantify the total amount of volatile components released during the stir-frying process in real time, avoiding the subjectivity and uncertainty of traditional manual judgment;

[0056] Step S33: With time t as the horizontal axis, the volatilization rate The volatilization curve is drawn as the vertical axis; the volatilization rate curve is drawn according to time. The dynamic change of the curve can intuitively show the release process of volatile components, which is convenient for analyzing the relationship between volatilization characteristics and heating time and temperature, thereby improving the transparency and controllability of the processing process;

[0057] Step S34: Combining the current processing program with historical data from the seasoning database, the current real-time volatilization curve is compared with the historical volatilization curves along the same timeline. If the current real-time volatilization curve's volatilization rate is higher than the historical volatilization curve, an electrical signal is transmitted to the intelligent temperature control module to reduce the volatilization rate; otherwise, it is increased. This timeline comparison and analysis of the current real-time volatilization curve and the historical volatilization curves in the seasoning database can identify abnormal fluctuations in the current volatilization rate and, combined with big data mining, improve adaptability to process changes. If the volatilization rate is too high or too low, the intelligent temperature control module promptly adjusts the heating power to avoid premature or delayed aroma release due to temperature fluctuations, ensuring optimal seasoning results.

[0058] The coking degree analysis method in step S3 further includes:

[0059] Step S3a: Obtain an image of the seasoning stir-frying process and convert the image from RGB to HSV space;

[0060] Step S3b: Set the color threshold, segment and extract the dark area, and perform binarization processing. and When , it is determined that the pixels with coking characteristics are extracted, otherwise they are ignored; where H is the hue of the image pixel, The hue threshold feature set for the condiment database; S is the saturation of the image pixel, The condiment database stores the set saturation threshold features. By converting the image from RGB space to HSV space and segmenting the dark areas based on the hue H and saturation S thresholds, pixels with burnt characteristics are accurately extracted. This can avoid interference with burnt degree judgment caused by environmental factors such as lighting and shooting angle, thereby improving the robustness and accuracy of detection.

[0061] Step S3c: Counting the coking ratio , that is, extract the proportion of the coking feature pixels in the entire image, with time t as the horizontal axis, the proportion of the coking feature pixels As the vertical axis, draw the coking progress curve; with the coking characteristic pixel ratio With the coking progress curve as the core indicator, the coking dynamics of the condiment during stir-frying can be tracked in real time. This data visualization method not only facilitates operator observation but also provides basic data support for subsequent automated adjustments.

[0062] Step S3d: Divide the image into For small pieces, the standard deviation of the coking ratio of each piece is calculated as the coking uniformity using formula (2):

[0063] …(2);

[0064] in, is the average coking ratio of each small piece; is the coking ratio of the i-th small block; N is the number of small blocks divided into N; is the coking uniformity; by dividing the image into small pieces and calculate the standard deviation of the coking ratio , quantifying the degree of coking uniformity, addressing the difficulty of traditional methods in assessing coking distribution. This technology can accurately identify potential localized over-coking or uneven coking issues during processing, ensuring visual and flavor consistency of the product.

[0065] Step S3e: Combine the current processing procedure and the historical big data of the condiment database to calculate the real-time coking uniformity. Compared with the coking uniformity under the same time axis in history, if the current real-time coking uniformity If the coking uniformity is lower than the historical coking uniformity on the same time axis, the electrical signal is transmitted to the stirring rate compensation module for rate compensation; the real-time comparative analysis of the coking uniformity and historical big data is used to determine whether the current coking progress meets expectations, and the stirring rate is adjusted through intelligent feedback to improve the dynamic controllability of the coking process, thereby significantly reducing the quality fluctuations caused by differences in human operations.

[0066] Step S4 further comprises:

[0067] Step S41: Extracting the volatilization rate peak based on the volatilization curve and its corresponding time ;

[0068] Step S42: Based on the coking progress curve, extract the time corresponding to the coking degree inflection point , where the coking degree inflection point is the starting point of the coking progress curve at a rapid speed, and can be directly obtained from the coking progress curve;

[0069] Step S43: Calculate the time difference between the peak time point of volatility and the inflection point of coking degree by using the delayed cross calculation formula (3) ;

[0070] …(3);

[0071] Combine the current processing procedures and the historical big data of the condiment database to output the standard time difference Interval, if Less than or greater than the standard time difference interval, it is judged that the matching between volatility and coking degree is unqualified; if If the time difference is less than the allowable error range of the standard time difference, it means that the delay is too short and premature coking occurs. The electrical signal is transmitted to the dynamic control module, and the dynamic control module is used for synchronous control to perform intelligent temperature control reduction and stirring rate compensation; if If the time difference is greater than the allowable error range of the standard time difference, it means that the delay is too long and the aroma release is insufficient. The electrical signal is transmitted to the dynamic control module, and the dynamic control module is used to synchronously control the intelligent temperature control, heating and stirring rate compensation.

[0072] This application uses smoke concentration and visual imaging technology to collect data in the stir-frying process in real time, analyzes the degree of volatilization and carbonization, and ensures that the aroma release and carbonization state of the seasoning during the stir-frying process are in the best match. And through further cross-validation, the system can adjust the heating power and stirring rate in time, eliminate the deviation caused by human factors and environmental changes, ensure the consistency of the seasoning effect and high-quality output, effectively improve the transparency and controllability of the seasoning process, reduce the quality fluctuations caused by operational differences, enhance the stability and efficiency of the production process, and ultimately ensure that diglyceride cooking oil can fully absorb the essence of the seasoning, presenting a richer and more delicate taste and flavor. It can also increase some fault tolerance space in the processing program, thereby indirectly reducing production management costs.

[0073] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0074] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art will be able to modify the technical solutions described in the aforementioned embodiments or substitute equivalents for some of the technical features. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A method for controlling the intelligent seasoning of diglyceride edible oil, characterized by: The diglyceride edible oil intelligent seasoning control method comprises the following steps: Step S1: After the stir-frying seasoning process is started, the process program docking module is used to dock with a preset fixed process program to obtain the initial parameters of the current process program, where the initial parameters include the type and proportion of seasoning, processing progress and processing target, as well as oil temperature, stir-frying time, and stirring rate; Step S2: The smoke concentration detection module then captures the changes in the concentration of volatile smoke during the stir-frying process in real time, and transmits the detection data to the volatility analysis module. At the same time, the visual detection module is activated to collect image data of the seasoning during the stir-frying process, and the collected image data is transmitted to the coking degree analysis module. Step S3: Based on the acquired smoke concentration detection data and visual imaging data, respectively analyze the volatilization degree and the coking degree, and generate a volatilization curve and a coking progress curve; Step S4: fusing and analyzing the volatilization curve and the coking progress curve to determine the matching between the volatilization degree and the coking degree; Step S5: adjusting the heating power and the stirrer speed according to the real-time analysis results and the cross-validation results; The step S4 further comprises: Step S41: Extracting the volatilization rate peak based on the volatilization curve and its corresponding time ; Step S42: Based on the coking progress curve, extract the time corresponding to the coking degree inflection point , where the coking degree inflection point is the starting point of the coking progress curve at a rapid speed, and can be directly obtained from the coking progress curve; Step S43: Calculate the time difference between the peak time point of volatility and the inflection point of coking degree by using the delayed cross calculation formula (3) ; …(3)； Combine the current processing procedures and the historical big data of the condiment database to output the standard time difference Interval, if Less than or greater than the standard time difference interval, it is judged that the matching between volatility and coking degree is unqualified; if If the time difference is less than the allowable error range of the standard time difference, it means that the delay is too short and premature coking occurs. The electrical signal is transmitted to the dynamic control module, and the dynamic control module is used for synchronous control to perform intelligent temperature control reduction and stirring rate compensation; if If the error is greater than the allowable range of the standard time difference, it means that the delay is too long and the aroma release is insufficient. The electrical signal is transmitted to the dynamic control module, and the dynamic control module is used for synchronous control to perform intelligent temperature control, heating and stirring rate compensation.

2. The method for controlling the intelligent seasoning of diglyceride edible oil according to claim 1, wherein: The volatility analysis method in step S3 further includes: Step S31: setting the data acquisition rate to once per second to form a time series data point, and obtaining the concentration of volatile smoke collected each time during the stir-frying process; Step S32: Based on the smoke concentration data, the cumulative concentration of volatiles is calculated using formula (1): …(1); in is the volatilization rate, which indicates the concentration of volatile matter released per unit time and is the output value of the smoke concentration detection module; is the change of volatile concentration over time; where is the starting time The integral up to the current time t is the total amount of all volatile components released from the start of heating to the current moment; Step S33: With time t as the horizontal axis, the volatilization rate As the vertical axis, draw the volatility curve; Step S34: Based on the current processing program and the historical big data of the seasoning database, the current real-time volatilization curve is compared with the historical volatilization curve on the same time axis. If the volatilization rate of the current real-time volatilization curve is higher than that of the historical volatilization curve, an electrical signal is transmitted to the intelligent temperature control module to reduce the volatilization rate, otherwise it is increased.

3. The method for controlling the intelligent seasoning of diglyceride edible oil according to claim 1, wherein: The coking degree analysis method in step S3 further includes: Step S3a: Obtain an image of the seasoning stir-frying process and convert the image from RGB to HSV space; Step S3b: Set the color threshold, segment and extract the dark area, and perform binarization processing. and When , it is determined that the pixels with coking characteristics are extracted, otherwise they are ignored; where H is the hue of the image pixel, The hue threshold feature set for the condiment database; S is the saturation of the image pixel, Storing a set saturation threshold feature for a condiment database; Step S3c: Counting the coking ratio , that is, extract the proportion of the coking feature pixels in the entire image, with time t as the horizontal axis, the proportion of the coking feature pixels As the vertical axis, draw the coking progress curve; Step S3d: Divide the image into For small pieces, the standard deviation of the coking ratio of each piece is calculated as the coking uniformity using formula (2): …(2)； in, is the average coking ratio of each small piece; is the coking ratio of the i-th small block; N is the number of small blocks divided into N; is the coking uniformity; Step S3e: Combine the current processing procedure and the historical big data of the condiment database to calculate the real-time coking uniformity. Compared with the coking uniformity under the same time axis in history, if the current real-time coking uniformity If the coking uniformity is lower than the historical coking uniformity on the same time axis, the electrical signal is transmitted to the stirring rate compensation module for rate compensation.

4. A diglyceride cooking oil intelligent seasoning control system for implementing the method of claim 1, comprising a processing data acquisition module, a cross-validation module, and a dynamic control module, characterized in that: The processing data acquisition module is suitable for collecting data of the stir-frying process during the stir-frying and seasoning process. The cross-validation module is used to analyze the aroma release degree and the coking degree of the seasoning in the stir-frying process according to the data collected by the processing data acquisition module and to cross-validate the processing process. The processing data acquisition module is electrically connected to the cross-validation module. The dynamic control module is used to dynamically control and adjust the parameters of the stir-frying and seasoning process according to the verification results of the cross-validation module. The dynamic control module is electrically connected to the cross-validation module.

5. The intelligent seasoning control system for diglyceride edible oil according to claim 4, characterized in that: The processing data acquisition module includes a smoke concentration detection module, a visual detection module, a seasoning database and a processing program docking module. The smoke concentration detection module is used to detect the smoke concentration value during the stir-frying process in real time. The visual detection module is used to collect visual imaging images during the stir-frying process. The seasoning database is used to record and store the volatilization and coking characteristics of various seasonings. The processing program docking module is used to dock with a preset fixed processing program to obtain real-time processing status data.

6. The intelligent seasoning control system for diglyceride edible oil according to claim 4, characterized in that: The cross-validation module includes a volatility analysis module and a coking degree analysis module. The volatility analysis module is used to analyze the volatility according to the concentration of captured smoke and the characteristics of the seasoning. The coking degree analysis module is used to analyze the coking degree during the stir-frying process according to the visual imaging picture.

7. The intelligent seasoning control system for diglyceride edible oil according to claim 4, characterized in that: The dynamic control module includes an intelligent temperature control module and a stirring rate compensation module. The intelligent temperature control module is suitable for dynamically adjusting the heating power during the stir-frying process, and the stirring rate compensation module is suitable for adjusting the stirring rate of the stirrer during the stir-frying process.

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