Intelligent seasoning control system and method for diglyceride edible oil

Abstract

The invention discloses an intelligent seasoning control system and method for diglyceride edible oil, and the system comprises a processing data collection module, a cross validation module and a dynamic control module, and is characterized in that the processing data collection module is suitable for collecting data in a stir-frying process in a stir-frying seasoning process; the cross validation module is used for analyzing the aroma release degree in the stir-frying process and the coking degree of seasonings according to data acquired by the processing data acquisition module and carrying out cross validation on the processing process, and the processing data acquisition module is electrically connected with the cross validation module; the dynamic control module is used for dynamically controlling and adjusting the parameters of the stir-frying and seasoning process according to the verification result of the cross verification module, and the dynamic control module is electrically connected with the cross verification module.

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] Diacylcerol (DAG) is a lipid with a special structure. Compared with the common triglyceride (TAG), it has significantly different characteristics in the human body metabolism process. Studies have shown that diacylglycerol has health benefits such as lowering blood lipid levels, regulating fat metabolism, and reducing visceral fat accumulation. Therefore, it is widely used in the field of high-end edible oils and functional foods.

[0003] In the industrial production of diglyceride oil, in order to improve the added value and market competitiveness of the product, natural condiments (such as onions, ginger, garlic, pepper, fennel, etc.) are usually added to enhance the flavor. Natural condiments are rich in volatile aroma components, which can be fully released and dissolved in the oil through the stir-frying process, thus giving diglyceride oil a unique flavor. However, the traditional stir-frying process usually relies on manual operation, and the operator judges the stir-frying time and temperature based on experience. However, manual control often lacks precision and is significantly affected by the operator's technical level and subjective judgment, which can easily cause quality differences between production batches.

[0004] At the same time, the quality of natural seasonings is easily affected by many factors, such as the planting environment, climate conditions, storage methods and time, etc. These factors will cause significant changes in key parameters such as the volatile components and moisture content of the seasonings. Some fixed processing procedures and processing methods are difficult 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, 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, 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 pictures 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 degree of volatility according to the concentration of captured smoke combined with the characteristics of condiments, and the coking degree analysis module is used to analyze the coking degree during stir-frying according to visual imaging images.

[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 by using diglyceride edible oil as the base oil and natural seasoning, the processing program docking module is used to dock with the preset fixed processing program to obtain the initial parameters in the current processing program, wherein the initial parameters include the type and proportion of seasonings, processing progress and processing target, as well as oil temperature, stir-frying time and stirring rate;

[0012] Step S2: Then, the smoke concentration detection module is used to capture the changes in the concentration of volatile smoke during the stir-frying process in real time, and the detection data is transmitted to the volatility analysis module; at the same time, the visual detection module is started to collect image data during the stir-frying process of the condiment, 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 the 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 to obtain data 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 by formula (1):

[0019]

[0020] Where R voc (t) 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; C voc (t) is the change of volatile concentration over time; where is the integral from actual time t0 to current time t, that 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 R voc (t) is the ordinate, and the volatilization curve is drawn;

[0022] Step S34: In combination with 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, the electrical signal is transmitted to the intelligent temperature control module to reduce it, 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, that is, when H≤H0 and S≥S0, determine that the pixels with the coking feature are extracted, otherwise they are ignored; where H is the hue of the image pixel, H0 is the hue threshold feature stored in the condiment database; S is the saturation of the image pixel, S0 is the saturation threshold feature stored in the condiment database;

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

[0027] Step S3d: further divide the image into n×n small blocks, and calculate the standard deviation of the coking ratio of each block by formula (2):

[0028]

[0029] in, is the average carbonization ratio of each small piece; P 块i is the coking ratio of the ith small block; N is the number of small blocks; U j 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 U j Compared with the coking uniformity under the same time axis in history, if the current real-time coking uniformity U j 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 value R based on the volatilization curve max and its corresponding time t max ;

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

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

[0035] t 差 =|t max -t g |…(3);

[0036] Combine the current processing procedure and the historical big data of the condiment database to output the standard time difference t 标 Interval, if t 差 Less than or greater than the standard time difference t 标 interval, it is judged that the matching between volatility and coking degree is unqualified; if t 差 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 to synchronously control the intelligent temperature control reduction and stirring rate compensation; if t 差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 prior art, the beneficial effects achieved by the present invention are as follows: the present invention collects data in the stir-frying process in real time through smoke concentration and visual imaging technology, analyzes the degree of volatilization and the degree of coking, and ensures that the aroma release and coking state of the condiment during the stir-frying process are in the best match. And through further cross-validation, the heating power and stirring rate can be adjusted in time, the deviation caused by human factors and environmental changes can be eliminated, the consistency of the seasoning effect and high-quality output can be ensured, the transparency and controllability of the seasoning process can be effectively improved, the quality fluctuation caused by operational differences can be reduced, the stability and efficiency of the production process can be enhanced, and finally the diglyceride edible oil can be ensured to fully absorb the essence of the condiment, presenting a richer and more delicate taste and flavor, and some fault tolerance space of the processing program can be increased, thereby indirectly reducing the production management cost. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0039] In the attached picture:

[0040] Figure 1 It is a schematic diagram of the system module composition of the present invention. DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] See also Figure 1The 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 of the stir-frying process and the coking degree of the seasoning 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 result of the cross-validation module. The dynamic control module is electrically connected to the cross-validation module.

[0043] 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.

[0044] 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 combined with the characteristics of condiments, and the coking degree analysis module is used to analyze the coking degree during stir-frying according to visual imaging images.

[0045] 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.

[0046] A method for controlling the intelligent seasoning of diglyceride edible oil, comprising the following steps:

[0047] Step S1: After the stir-frying seasoning process is started by using diglyceride edible oil as the base oil and natural seasoning, the processing program docking module is used to dock with the preset fixed processing program to obtain the initial parameters in the current processing program, wherein the initial parameters include the type and proportion of seasonings, processing progress and processing target, as well as oil temperature, stir-frying time and stirring rate;

[0048] Step S2: Then, the smoke concentration detection module is used to capture the changes in the concentration of volatile smoke during the stir-frying process in real time, and the detection data is transmitted to the volatility analysis module; at the same time, the visual detection module is started to collect image data during the stir-frying process of the condiment, and the collected image data is transmitted to the coking degree analysis module;

[0049] 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;

[0050] 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;

[0051] Step S5: Adjust the heating power and the stirrer speed according to the real-time analysis results and the cross-validation results.

[0052] The volatility analysis method in step S3 further comprises:

[0053] Step S31: setting to obtain data 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;

[0054] Step S32: Based on the smoke concentration data, the cumulative concentration of volatiles is calculated by formula (1):

[0055]

[0056] Where R voc (t) 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; C voc (t) is the change of volatile concentration over time; where is the integral from actual time t0 to current time t, that 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 R output by the smoke concentration detection module is calculated. voc (t), calculate the cumulative concentration of volatile matter C voc (t) It 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;

[0057] Step S33: With time t as the horizontal axis, the volatilization rate R voc (t) is the ordinate to draw the volatilization curve; the volatilization rate curve is drawn according to time, and the release process of the volatile components is intuitively displayed through the dynamic changes of the curve, which is convenient for analyzing the relationship between the volatilization characteristics and the heating time and temperature, thereby improving the transparency and controllability of the processing process;

[0058] Step S34: Combine the current processing program and the historical big data of the seasoning database, compare the current real-time volatilization curve 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, the electrical signal is transmitted to the intelligent temperature control module to reduce it, otherwise it is increased; compare and analyze the current real-time volatilization curve with the historical volatilization curve in the seasoning database on the same time axis, so as to identify abnormal fluctuations in the current volatilization rate, and improve the adaptability to process changes by combining big data mining. In the case of too high or too low volatilization rate, the heating power is adjusted in time through the intelligent temperature control module to avoid premature or late release of aroma due to temperature fluctuations, so as to ensure the best seasoning effect.

[0059] The coking degree analysis method in step S3 further comprises:

[0060] Step S3a: Obtain an image of the seasoning stir-frying process, and convert the image from RGB to HSV space;

[0061] Step S3b: Set the color threshold, and segment and extract the dark area, and perform binarization processing, that is, when H≤H0 and S≥S0, determine that the pixels with the coking feature are extracted, otherwise they are ignored; wherein H is the hue of the image pixel, H0 is the hue threshold feature stored and set in the condiment database; S is the saturation of the image pixel, and S0 is the saturation threshold feature stored and set in the condiment database; by converting the image from the RGB space to the HSV space, and segmenting the dark area according to the thresholds of the hue H and the saturation S, the pixels with the coking feature are accurately extracted, which can avoid the interference of environmental factors such as lighting and shooting angle on the judgment of the degree of coking, and improve the robustness and accuracy of the detection;

[0062] Step S3c: Calculate the coking ratio P j , that is, extract the proportion of the coking feature pixels in the entire image, with time t as the horizontal axis, the coking feature pixel ratio P j As the ordinate, the coking progress curve is drawn; with the coking characteristic pixel ratio P j As the core indicator, a coking progress curve is constructed to track the coking dynamics of the condiment during stir-frying in real time. This data visualization method is not only convenient for operators to observe, but also provides basic data support for subsequent automated adjustments.

[0063] Step S3d: further divide the image into n×n small blocks, and calculate the standard deviation of the coking ratio of each block by formula (2):

[0064]

[0065] in, is the average carbonization ratio of each small piece; P 块i is the coking ratio of the ith small block; N is the number of small blocks; Uj is the coking uniformity; by dividing the image into n×n small blocks and calculating the standard deviation of the coking ratio U j , quantifying the coking uniformity, solving the problem that traditional methods are difficult to evaluate the coking distribution. This technical means can accurately identify the local over-coking or uneven coking problems that may exist during the processing process, ensuring the visual and flavor consistency of the product;

[0066] Step S3e: Combine the current processing procedure and the historical big data of the condiment database to calculate the real-time coking uniformity U j Compared with the coking uniformity under the same time axis in history, if the current real-time coking uniformity U j 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. 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.

[0067] Step S4 further comprises:

[0068] Step S41: Extracting the volatilization rate peak value R based on the volatilization curve max and its corresponding time t max ;

[0069] Step S42: Based on the coking progress curve, extract the time t corresponding to the inflection point of the coking degree g , where the coking degree inflection point is the starting point of the coking progress curve at a fast speed, which can be directly obtained from the coking progress curve;

[0070] Step S43: Calculate the time difference t between the peak time point of volatility and the inflection point of coking degree by using the delayed crossover calculation formula (3) 差 ;

[0071] t 差 =|t max -t g |…(3);

[0072] Combine the current processing procedure and the historical big data of the condiment database to output the standard time difference t 标 Interval, if t 差 Less than or greater than the standard time difference t 标 interval, it is judged that the matching between volatility and coking degree is unqualified; if t 差 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 to synchronously control the intelligent temperature control reduction and stirring rate compensation; if t 差If the error 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 for synchronous control to perform intelligent temperature control, heating and stirring rate compensation.

[0073] 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 coking, and ensures that the aroma release and coking state of the condiment 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 deviations 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 quality fluctuations caused by operational differences, and enhance the stability and efficiency of the production process. Ultimately, it ensures that diglyceride edible oil can fully absorb the essence of the seasoning, presenting a richer and more delicate taste and flavor, while also increasing some fault tolerance space in the processing program, thereby indirectly reducing production management costs.

[0074] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0075] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is 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 can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A diglyceride edible oil intelligent seasoning control system, 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.

2. The intelligent seasoning control system for diglyceride edible oil according to claim 1, 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 pictures 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.

3. The intelligent seasoning control system for diglyceride edible oil according to claim 1, 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 combined with the characteristics of condiments, and the coking degree analysis module is used to analyze the coking degree during stir-frying according to visual imaging pictures.

4. The intelligent seasoning control system for diglyceride edible oil according to claim 1, 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.

5. A method for controlling the intelligent seasoning of diglyceride edible oil, characterized in that: The diglyceride edible oil intelligent seasoning control method comprises the following steps: Step S1: After the stir-frying seasoning process is started by using diglyceride edible oil as the base oil and natural seasoning, the processing program docking module is used to dock with the preset fixed processing program to obtain the initial parameters in the current processing program, wherein the initial parameters include the type and proportion of seasonings, processing progress and processing target, as well as oil temperature, stir-frying time and stirring rate; Step S2: Then, the smoke concentration detection module is used to capture the changes in the concentration of volatile smoke during the stir-frying process in real time, and the detection data is transmitted to the volatility analysis module; at the same time, the visual detection module is started to collect image data during the stir-frying process of the condiment, 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: Adjust the heating power and the stirrer speed according to the real-time analysis results and the cross-validation results.

6. The method for controlling the intelligent seasoning of diglyceride edible oil according to claim 5, characterized in that: The volatility analysis method in step S3 further comprises: Step S31: setting to obtain data 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 by formula (1): Where R voc (t) 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; C voc (t) is the change of volatile concentration over time; where is the integral from actual time t0 to current time t, that 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 R voc (t) is the ordinate, and the volatilization curve is drawn; Step S34: In combination with 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, the electrical signal is transmitted to the intelligent temperature control module to reduce it, otherwise it is increased.

7. The method for controlling the intelligent seasoning of diglyceride edible oil according to claim 5, characterized in that: The coking degree analysis method in step S3 further comprises: 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, that is, when H≤H0 and S≥S0, determine that the pixels with the coking feature are extracted, otherwise they are ignored; where H is the hue of the image pixel, H0 is the hue threshold feature stored in the condiment database; S is the saturation of the image pixel, S0 is the saturation threshold feature stored in the condiment database; Step S3c: Calculate the coking ratio P j , that is, extract the proportion of the coking feature pixels in the entire image, with time t as the horizontal axis, the coking feature pixel ratio P j As the ordinate, draw the coking progress curve; Step S3d: further divide the image into n×n small blocks, and calculate the standard deviation of the coking ratio of each block by formula (2): in, is the average carbonization ratio of each small piece; P 块i is the coking ratio of the ith small block; N is the number of small blocks; U j 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 U j Compared with the coking uniformity under the same time axis in history, if the current real-time coking uniformity U j 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.

8. The method for controlling the intelligent seasoning of diglyceride edible oil according to claim 5, characterized in that: The step S4 further comprises: Step S41: Extracting the volatilization rate peak value R based on the volatilization curve max and its corresponding time t max ; Step S42: Based on the coking progress curve, extract the time t corresponding to the inflection point of the coking degree g , where the coking degree inflection point is the starting point of the coking progress curve at a fast speed, which can be directly obtained from the coking progress curve; Step S43: Calculate the time difference t between the peak time point of volatility and the inflection point of coking degree by using the delayed crossover calculation formula (3) 差 ; t 差 =|t max -t g |…(3); Combine the current processing procedure and the historical big data of the condiment database to output the standard time difference t 标 Interval, if t 差 Less than or greater than the standard time difference t 标 interval, it is judged that the matching between volatility and coking degree is unqualified; if t 差 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 to synchronously control the intelligent temperature control reduction and stirring rate compensation; if t 差 If the error range is greater than 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.

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