A production control method and system for improving the stability of black sesame food and medicine homologous cakes

Abstract

The present application relates to the technical field of food production, and in particular to a production control method and system for improving the stability of black sesame food and medicine homologous cakes, and provides a production control method for improving the stability of black sesame food and medicine homologous cakes, which comprises: calculating a first influence coefficient of temperature data on viscosity data and a second influence coefficient of temperature data on surface grease data according to the temperature distribution unevenness; correcting the uniform state data by using the first influence coefficient to obtain modified uniform state data, and correcting the grease state data by using the second influence coefficient to obtain modified grease state data; comparing the modified uniform state data with the uniform state data, comparing the modified grease state data with the grease state data, comparing the temperature distribution unevenness with a temperature distribution unevenness threshold, and judging whether there is stirring abnormality according to the comparison result; and when it is determined that there is stirring abnormality, generating and executing a stirring parameter adjustment instruction.

Description

Technical Field

[0001] This invention relates to the field of food production technology, and in particular to a production control method and system for improving the stability of black sesame-based food and medicinal pastries. Background Technology

[0002] In the industrial production of black sesame-based food and medicinal pastries, the mixing and blending process is a key step that determines the plasticity of the pastry and the quality of the finished product. Currently, production lines generally use closed-loop mixing equipment with quantitative weighing and variable frequency speed control functions. Operators preset a fixed ratio of black sesame powder to auxiliary powders (such as Poria cocos, yam, and Polygonatum sibiricum) based on experience, and input a constant stirring speed and mixing time. The equipment then performs batch open-loop mixing accordingly.

[0003] However, black sesame, as the core ingredient in this pastry recipe, has a high oil content and is rich in unsaturated fatty acids, which affects the stability of the pastry system and the storage quality of the finished product during the mixing process. In the closed mixing chamber, the black sesame powder is subjected to the combined effects of continuous shearing, extrusion, and frictional heat from the mixing paddle. Its cell structure gradually breaks down, and the internal oils gradually migrate and precipitate from the solid matrix to the surface of the powder particles and between the particles. Existing mixing equipment only uses open-loop control based on a fixed mixing speed and a preset single mixing time, lacking the ability to dynamically adjust based on the real-time oil precipitation status of the material. Under this fixed parameter control mode, if the preset mixing speed is too high or the mixing time is too long, a large amount of free oil from the black sesame will precipitate prematurely and excessively in the early stages of mixing. This free oil will quickly coat the surface of other medicinal and edible auxiliary material powder particles, forming a dense hydrophobic oil film. This oil film severely hinders the wetting and penetration of subsequent honey, syrup, etc. at the interface of powder particles, resulting not only in a rough and loose texture, but also in the expansion of the contact interface between oil and air due to the local high oil area, which greatly accelerates oxidative rancidity during storage and significantly shortens the shelf life.

[0004] Furthermore, the strong light absorption properties of black sesame's natural melanin further mask the true state within the mixing chamber. Even if there are agglomerates of dry powder or undispersed lumps of wet material, conventional machine vision or visual observation is difficult to accurately identify, causing the determination of the mixing endpoint to heavily rely on a fixed time rather than actual uniformity. This makes it easy for products from the same batch to exhibit uneven distribution of excipients, fluctuations in texture, and differences in the content of functional components, severely compromising batch consistency.

[0005] Therefore, how to adjust the stirring parameters according to the real-time state of the materials during the mixing process to achieve precise control of the mixing uniformity, thereby eliminating the quality instability caused by raw material fluctuations and environmental changes, has become a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0006] The problem solved by this invention is: how to adjust the stirring parameters according to the real-time state of the materials during the mixing process of black sesame medicinal and edible pastries, so as to achieve precise control of the mixing uniformity.

[0007] To address the above problems, this invention provides a production control method for improving the stability of black sesame-based food and medicinal pastries. The production control method includes the following steps:

[0008] Real-time acquisition of viscosity, surface grease, and temperature data of the mixed materials during the mixing process;

[0009] The uniformity data of the mixture is obtained based on the variation trend of viscosity data, and the oil state data of the mixture is obtained based on the variation trend of surface oil data.

[0010] The temperature distribution non-uniformity is obtained based on the temperature data, and the first influence coefficient of the temperature data on the viscosity data and the second influence coefficient on the surface grease data are calculated based on the temperature distribution non-uniformity.

[0011] Corrected uniform state data is obtained by correcting the uniform state data using the first influence coefficient, and corrected oil state data is obtained by correcting the oil state data using the second influence coefficient.

[0012] The corrected uniform state data is compared with the uniform state data, the corrected oil state data is compared with the oil state data, and the temperature distribution non-uniformity is compared with the temperature distribution non-uniformity threshold. Based on the comparison results, it is determined whether there is any stirring abnormality.

[0013] When an abnormal stirring condition is detected, a stirring parameter adjustment command is generated and executed.

[0014] Optionally, the viscosity, surface grease, and temperature data of the mixed materials during the mixing process can be collected in real time, specifically including:

[0015] Viscosity data of the mixture is obtained by acquiring real-time torque signals through a torque sensor. The viscosity data includes average viscosity and viscosity fluctuation range.

[0016] Surface grease data is obtained by continuously acquiring near-infrared spectral signals at each acquisition point at a preset sampling frequency. The surface grease data includes total surface grease data and grease distribution uniformity data.

[0017] Temperature data is collected by continuously acquiring temperature sensor signals from each acquisition point at a preset sampling frequency.

[0018] Optionally, the homogeneity data of the mixture can be obtained based on the trend of viscosity data, and the oil state data of the mixture can be obtained based on the trend of surface oil data, specifically including:

[0019] Within the target time period, the average viscosity change rate and viscosity fluctuation amplitude change rate of the mixture are obtained respectively.

[0020] The uniformity data of the mixture during the target time period are obtained based on the average viscosity change rate and the viscosity fluctuation amplitude change rate.

[0021] Within the target time period, the rate of change of the total surface oil content and the rate of change of oil distribution uniformity of the mixture were obtained respectively.

[0022] The oil state data of the mixture during the target time period are obtained based on the rate of change of total surface oil content and the rate of change of oil distribution uniformity.

[0023] Optionally, the temperature distribution non-uniformity is obtained based on the temperature data, and the first influence coefficient of the temperature data on the viscosity data and the second influence coefficient on the surface grease data are calculated based on the temperature distribution non-uniformity, specifically including:

[0024] The temperature values ​​of each sampling point within the target time period are obtained, the standard deviation of the temperature values ​​of each sampling point is calculated, and the temperature distribution non-uniformity is obtained based on the ratio of the standard deviation to the average temperature of each sampling point.

[0025] The contribution coefficient of the average viscosity change rate corresponding to each unit change in temperature distribution non-uniformity is denoted as the first sub-coefficient.

[0026] The contribution coefficient of the viscosity fluctuation rate corresponding to each unit change in temperature non-uniformity is denoted as the second sub-coefficient.

[0027] The first influence coefficient includes a first sub-coefficient and a second sub-coefficient;

[0028] The contribution coefficient of the rate of change of total surface grease corresponding to each unit change in temperature distribution non-uniformity is denoted as the third sub-coefficient.

[0029] The contribution coefficient of the rate of change of oil distribution uniformity corresponding to each unit change in temperature distribution non-uniformity is denoted as the fourth sub-coefficient.

[0030] The second influence coefficient includes the third sub-coefficient and the fourth sub-coefficient.

[0031] Optionally, the contribution coefficients for the rate of change of average viscosity, the rate of change of viscosity fluctuation amplitude, the rate of change of total surface grease, and the rate of change of grease distribution uniformity are obtained as follows:

[0032] The contribution coefficient of the average viscosity change rate is obtained by multiplying the material reference viscosity value, the temperature-viscosity sensitivity coefficient, and the standard deviation of the temperature distribution. The temperature-viscosity sensitivity coefficient is the result of dividing the ratio of the viscous flow activation energy to the gas constant by the square of the current average temperature.

[0033] The contribution coefficient of the viscosity fluctuation amplitude change rate is obtained by multiplying the material reference viscosity value by the temperature-viscosity sensitivity coefficient;

[0034] The contribution coefficient of the total surface oil change rate is obtained by multiplying the reference oil precipitation rate, the oil precipitation temperature sensitivity coefficient, and the temperature distribution standard deviation, wherein the oil precipitation temperature sensitivity coefficient is the result of dividing the ratio of oil precipitation activation energy to gas constant by the square of the current average temperature.

[0035] The contribution coefficient of the rate of change of oil distribution uniformity is obtained by multiplying the current oil distribution uniformity benchmark value, the oil migration temperature sensitive factor, the local oil precipitation rate gradient coefficient, and the temperature distribution standard deviation.

[0036] Optionally, the uniform state data is obtained by correcting the uniform state data using the first influence coefficient, and the oil state data is obtained by correcting the oil state data using the second influence coefficient, specifically including:

[0037] The average viscosity baseline change rate and viscosity fluctuation amplitude baseline change rate of the mixture under standard temperature conditions, as well as the baseline change rate of total surface grease and the baseline change rate of grease distribution uniformity, are obtained.

[0038] The correction value for the average viscosity rate of change is obtained by adding the additional correction value obtained by multiplying the average viscosity reference rate of change and the first sub-coefficient by the ratio of the temperature standard deviation change to the sampling period;

[0039] The correction value for the rate of change of viscosity fluctuation amplitude is obtained by adding the additional correction value obtained by multiplying the reference rate of change of viscosity fluctuation amplitude by the second sub-coefficient by the ratio of the change in temperature standard deviation to the sampling period;

[0040] Corrected uniform state data are obtained based on the correction values ​​for the rate of change of viscosity fluctuation amplitude and the average rate of change of viscosity.

[0041] The correction value for the rate of change of total surface grease is obtained by adding the additional correction value obtained by multiplying the baseline rate of change of total surface grease by the third sub-coefficient by the ratio of the change in temperature standard deviation to the sampling period;

[0042] The correction value for the rate of change of oil distribution uniformity is obtained by subtracting the fourth sub-coefficient multiplied by the ratio of the change in temperature standard deviation to the sampling period from the baseline rate of change of oil distribution uniformity.

[0043] Corrected oil state data are obtained based on the correction values ​​for the rate of change of total surface oil content and the rate of change of oil distribution uniformity.

[0044] Optionally, the corrected uniformity data can be compared with the uniformity data, the corrected oil state data can be compared with the oil state data, and the temperature distribution non-uniformity can be compared with the temperature distribution non-uniformity threshold. Based on the comparison results, it can be determined whether there is any stirring abnormality, specifically including:

[0045] The difference between the average viscosity change rate and the corrected value of the average viscosity change rate is denoted as the viscosity rate residual; the difference between the viscosity fluctuation amplitude change rate and the corrected value of the viscosity fluctuation amplitude change rate is denoted as the fluctuation rate residual; the difference between the change rate of the total surface oil and the corrected value of the total surface oil change rate is denoted as the oil precipitation residual; the difference between the change rate of the oil distribution uniformity and the corrected value of the oil distribution uniformity change rate is denoted as the uniformity residual; the difference between the change rate of the oil distribution uniformity and the corrected value of the oil distribution uniformity change rate is denoted as the uniformity residual.

[0046] When the absolute value of any residual exceeds the corresponding preset threshold, or when the temperature distribution non-uniformity is greater than the temperature distribution non-uniformity threshold, it is marked as a candidate anomaly.

[0047] Candidate anomalies are continuously verified at multiple points. If the residuals continuously exceed the limit in the same direction within multiple consecutive sampling periods, it is determined that there is a stirring anomaly.

[0048] Optionally, when an abnormal stirring condition is detected, a stirring parameter adjustment command is generated and executed, specifically including:

[0049] Based on the candidate anomalies, the stirring anomaly type is obtained, and a corresponding stirring parameter adjustment instruction is generated based on the stirring type.

[0050] After each adjustment command is executed, the changing trend of the four residuals is monitored in real time. If the residuals do not fall back to within the threshold within the set time, an alarm is issued.

[0051] Optionally, the average viscosity baseline change rate and viscosity fluctuation amplitude baseline change rate of the mixture under standard temperature conditions, as well as the baseline change rate of total surface grease and the baseline change rate of grease distribution uniformity, are obtained, specifically including:

[0052] During the mixing process, a time period is selected that is in the same mixing stage as the current time period, has a temperature distribution non-uniformity less than a preset non-uniformity threshold, and has a temperature average value that deviates from the temperature average value of the current time period less than a preset deviation tolerance, and is used as the standard temperature time period.

[0053] The average rate of change of average viscosity within the standard temperature time period is taken as the average viscosity reference rate of change.

[0054] The average rate of change of viscosity fluctuation amplitude within the standard temperature time period is taken as the reference rate of change of viscosity fluctuation amplitude.

[0055] The average rate of change of the total surface grease within the standard temperature time period is taken as the baseline rate of change of the total surface grease.

[0056] The average rate of change of oil distribution uniformity within the standard temperature time period is taken as the benchmark rate of change of oil distribution uniformity.

[0057] The present invention also provides a production control system for improving the stability of black sesame-based food and medicine pastries. The production control method described in the above embodiments is applied to the production control system. The production control system includes: a real-time acquisition module, which is equipped with a torque sensor, a near-infrared spectrometer and a distributed temperature sensor, for synchronously acquiring the viscosity, surface oil and temperature data of the mixed materials during the mixing process.

[0058] The state analysis module is used to calculate uniform state data, oil state data, and temperature distribution non-uniformity based on the collected data, and to perform temperature correction on the state data in conjunction with the influence coefficient.

[0059] The anomaly detection module is used to compare the corrected state data with the original state data and determine whether there is a stirring anomaly based on whether the residual exceeds the limit.

[0060] The parameter adjustment module is used to generate and execute corresponding stirring parameter adjustment instructions based on the type of the abnormality when an abnormality is detected.

[0061] In summary, compared with existing technologies, the technical effects achieved by adopting this technical solution are as follows:

[0062] First, it significantly improves the consistency of texture and the uniformity of oil distribution in the cake. The system detects different types of mixing anomalies through multiple sensors and adjusts the system according to the type of anomaly, avoiding quality defects such as dry powder fillings, localized oil accumulation, or loose structure caused by uneven mixing, thus effectively ensuring the texture characteristics and uniformity of oil distribution in the final cake.

[0063] Second, the determination of the mixing endpoint has been upgraded from "time-oriented" to "state-oriented". The system determines that the mixing is normal based on the simultaneous achievement of three criteria: corrected uniformity data, corrected oil state data, and temperature data. This ensures that the mechanical, chemical, and thermodynamic states of the mixture remain highly consistent with each batch of material discharged, eliminating process deviations caused by fluctuations in raw material batches or environmental changes.

[0064] Third, it reduces the scrap rate. The system can automatically identify the type of abnormality and execute corresponding adjustment instructions, avoiding quality fluctuations caused by differences in operator experience or misjudgment, and also avoiding equipment overload and mechanical wear caused by continuously high torque. Attached Figure Description

[0065] Figure 1 This is one of the flowcharts of a production control method for improving the stability of black sesame-based food and medicine pastries according to the present invention;

[0066] Figure 2 This is a schematic diagram of a production control system for improving the stability of black sesame-based food and medicinal pastries according to the present invention.

[0067] Explanation of reference numerals in the attached figures:

[0068] 100 - Production control system; 110 - Real-time acquisition module; 120 - Status analysis module; 130 - Anomaly detection module; 140 - Parameter adjustment module. Detailed Implementation

[0069] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0070] First Embodiment

[0071] See Figure 1 In one specific embodiment, the present invention provides a production control method for improving the stability of black sesame-based food and medicinal pastries. The production control method includes:

[0072] S100: Real-time acquisition of viscosity, surface grease, and temperature data of the mixed materials during the mixing process;

[0073] Furthermore, real-time data collection is performed on the viscosity, surface grease, and temperature of the mixed materials during the mixing process, specifically including:

[0074] The viscosity data of the mixture is obtained by acquiring real-time torque signals through a torque sensor. The viscosity data includes the average viscosity and the viscosity fluctuation range.

[0075] Surface grease data is obtained by continuously acquiring near-infrared spectral signals from each acquisition point at a preset sampling frequency. The surface grease data includes total surface grease data and grease distribution uniformity data.

[0076] Temperature data is collected by continuously acquiring temperature sensor signals from each acquisition point at a preset sampling frequency.

[0077] S200. Obtain the uniformity data of the mixture based on the variation trend of viscosity data, and obtain the oil state data of the mixture based on the variation trend of surface oil data.

[0078] Furthermore, the homogeneity data of the mixture is obtained based on the variation trend of viscosity data, and the oil state data of the mixture is obtained based on the variation trend of surface oil data, specifically including:

[0079] Within the target time period, the average viscosity change rate and viscosity fluctuation amplitude change rate of the mixture are obtained respectively.

[0080] The uniformity data of the mixture during the target time period are obtained based on the average viscosity change rate and the viscosity fluctuation amplitude change rate.

[0081] Within the target time period, the rate of change of the total surface oil content and the rate of change of oil distribution uniformity of the mixture were obtained respectively.

[0082] The oil state data of the mixture during the target time period are obtained based on the rate of change of total surface oil content and the rate of change of oil distribution uniformity.

[0083] S300. Obtain the temperature distribution non-uniformity based on the temperature data, and calculate the first influence coefficient of temperature data on viscosity data and the second influence coefficient of temperature data on surface grease data based on the temperature distribution non-uniformity.

[0084] S400. Corrected uniform state data is obtained by correcting uniform state data using the first influence coefficient, and corrected oil state data is obtained by correcting oil state data using the second influence coefficient.

[0085] S500: Compare the corrected uniform state data with the uniform state data, compare the corrected oil state data with the oil state data, compare the temperature distribution non-uniformity with the temperature distribution non-uniformity threshold, and determine whether there is any stirring abnormality based on the comparison results.

[0086] Furthermore, the difference between the average viscosity change rate and the corrected value of the average viscosity change rate is denoted as the viscosity rate residual; the difference between the viscosity fluctuation amplitude change rate and the corrected value of the viscosity fluctuation amplitude change rate is denoted as the fluctuation rate residual; the difference between the change rate of the total surface oil and the corrected value of the change rate of the total surface oil is denoted as the oil precipitation residual; and the difference between the change rate of the oil distribution uniformity and the corrected value of the change rate of the oil distribution uniformity is denoted as the uniformity residual.

[0087] When the absolute value of any residual exceeds the corresponding preset threshold, or when the temperature distribution non-uniformity is greater than the temperature distribution non-uniformity threshold, it is marked as a candidate anomaly.

[0088] Candidate anomalies are continuously verified at multiple points. If the residuals continuously exceed the limit in the same direction within multiple consecutive sampling periods, it is determined that there is a stirring anomaly.

[0089] S600 When an abnormal stirring condition is detected, a stirring parameter adjustment command is generated and executed.

[0090] In step S100, the mixing degree of the mixture in the high-black sesame medicinal and edible pastry mixing process is generally related to viscosity data reflecting the cohesiveness and dispersion uniformity of the system, surface oil data characterizing oil migration and distribution, and temperature data reflecting the thermodynamic stability. These state parameters are generally not isolated but interact with each other. For example, a local temperature increase can reduce material viscosity and accelerate oil precipitation; excessive oil precipitation can further lubricate the particle interface, leading to a decrease in torque and reduced viscosity fluctuations, thus masking the actual agglomeration phenomenon. Therefore, relying on a single parameter is insufficient to accurately determine the mixing state; multi-source data fusion and temperature correction mechanisms are necessary to reconstruct the true material behavior.

[0091] Viscosity data of the mixed materials during the mixing process can be acquired in real time by a torque sensor installed on the stirring shaft. Since material viscosity is positively correlated with the torque required for stirring, under constant rotational speed conditions, the torque signal can directly reflect the changing trend of the system's cohesion. The system can continuously acquire torque signals at a fixed sampling frequency, such as 1Hz or higher, and convert them into average viscosity and viscosity fluctuation amplitude through a preset algorithm. The average viscosity characterizes the overall mixing intensity, while the viscosity fluctuation amplitude reflects the presence of undispersed clumps or localized dry powder agglomerations within the material.

[0092] Surface oil data is simultaneously acquired using near-infrared spectral probes positioned at multiple locations on the inner wall of the mixing chamber. Near-infrared spectroscopy exhibits characteristic absorption at CH bonds, making it particularly suitable for the non-destructive testing of oily substances. The system continuously acquires spectral signals from each acquisition point at a preset sampling frequency (e.g., once every 5 seconds). After baseline correction and multivariate scattering correction, the surface oil content at each point is calculated using historical data. Furthermore, the total surface oil content and oil distribution uniformity of the batch are statistically analyzed. This indicator can effectively identify whether localized oil accumulation or migration lag occurs.

[0093] Typically, uneven temperature distribution easily occurs within the mixing chamber during the mixing process. This is due to the significant difference in shear rates between areas near and far from the agitator blades, resulting in concentrated mechanical energy dissipation and rapid heat generation in high-shear zones. Simultaneously, black sesame paste is a high-viscosity material with poor thermal conductivity, making it difficult for heat to be quickly and evenly distributed within the chamber through conduction. Furthermore, the material distribution within the chamber itself exhibits spatial differences—different thicknesses on the upward and downward rotating sides of the agitator, differences in flowability between the chamber walls and the central area, and potential flow dead zones—all contributing to varying degrees of shear and friction in different areas, naturally leading to uneven heat generation rates. To accurately capture such thermodynamic nonuniformity, the system deploys multiple distributed temperature sensors (e.g., 4-8) within the mixing chamber according to spatial symmetry, located on the cylinder walls, in the center, and near the agitator blades, synchronously collecting temperature data at a preset frequency. By comparing real-time temperature values ​​at different locations, abnormal temperature differences caused by uneven heat generation due to agitation or environmental heat dissipation can be effectively identified.

[0094] In actual operation, if the temperature in a certain area is significantly higher than that in other areas (e.g., the temperature difference exceeds 278.15K), it may indicate the presence of a high-shear zone or material accumulation, leading to accelerated local oil precipitation or even slight oxidation. Conversely, if the temperature in a certain area remains consistently low, it may mean that the material has not been fully mixed. Therefore, temperature data is not only used to correct for calculation deviations in viscosity and oil state, but it is also an important independent criterion for judging the uniformity of mixing.

[0095] The viscosity, surface grease, and temperature data of the mixture include: average viscosity (Pa·s), viscosity fluctuation range (standard deviation or range), total surface grease (%), grease distribution uniformity (standard deviation of surface grease content at all test points divided by average grease content), average temperature (K), temperature standard deviation, and temperature distribution non-uniformity.

[0096] In step S200, the trend of viscosity data typically includes the rate of change of average viscosity and the rate of change of viscosity fluctuation amplitude. The rate of change of average viscosity can be obtained by linear fitting or differential calculation of the average viscosity value within a continuous sampling window. A positive rate reflects the continuous enhancement of cohesion within the system, while a negative rate may indicate structural damage or overmixing. The rate of change of viscosity fluctuation amplitude is quantified by the rate of change of the standard deviation or peak-to-peak value of viscosity per unit time, characterizing the dissipation efficiency of heterogeneous structures within the material. When the fluctuation amplitude decays rapidly, it indicates that agglomerates have been effectively broken up and the material tends to be homogeneous; if the decay rate approaches zero but the absolute value is still higher than the threshold, it suggests the presence of hard agglomerates that are difficult to disperse or flow dead zones.

[0097] The changing trends of surface grease data include the rate of change of total surface grease and the rate of change of grease distribution uniformity. The rate of change of total surface grease characterizes the speed at which grease migrates and precipitates from within the system to the material surface per unit time. If this rate is too fast and continues to increase, it often indicates insufficient systemic stability and a risk of premature grease precipitation. The rate of change of grease distribution uniformity reflects the convergence speed of the differences in surface grease content at each detection point. A negative rate of change and a larger absolute value indicate a faster improvement in grease distribution uniformity; conversely, a slower rate suggests localized grease aggregation and stagnation in uniformity improvement.

[0098] In actual production, the homogeneity data and oily state data of the mixture can be categorized into the following types:

[0099] The homogeneity data of mixed materials typically exhibit the following typical patterns: First, the normal convergence type, characterized by a continuous rise in average viscosity to a stable plateau, with viscosity fluctuations decreasing monotonically over time and eventually falling below the set threshold, indicating that the internal structure of the material tends to be homogeneous, with no significant agglomeration or dry powder residue; Second, the stagnant fluctuation type, where although the average viscosity reaches the target range, the fluctuation amplitude remains high for a long time or oscillates repeatedly, suggesting the presence of local undispersed particles or flow dead zones, indicating insufficient mixing homogeneity; Third, the premature plateau type, where the viscosity rises rapidly in a short period and then enters a plateau period, but the plateau value is low and the fluctuation amplitude does not improve significantly, often corresponding to excessive stirring intensity leading to excessive particle breakage or premature precipitation of large amounts of oil, which inhibits the effective formation of the network structure; Fourth, the delayed growth type, where the viscosity rise rate is significantly lower than the historical qualified batch benchmark, possibly caused by abnormal raw material water absorption, deviation of auxiliary material ratio, or deviation of environmental temperature and humidity, reflecting an overall lag in the mixing process.

[0100] Correspondingly, the oil state data of the mixture can also be summarized into several characteristic patterns: First, the balanced release type, the total amount of surface oil steadily increases, the spatial distribution uniformity improves synchronously and eventually stabilizes within a reasonable range, indicating that the oil is released from the cells in an orderly manner and dispersed uniformly in the system; Second, the local enrichment type, the total amount of surface oil increases rapidly, but the uniformity index improves only slightly or even deteriorates, with significant differences between sampling points, often accompanied by local high temperature signals, suggesting that concentrated shear heat causes selective oil precipitation; Third, the release inhibition type, the oil content increases slowly, and the uniformity improvement is not obvious, which may be due to insufficient stirring shear force, low material temperature, or differences in the black sesame pretreatment process, resulting in insufficient cell wall rupture; Fourth, the secondary aggregation type, the oil content unexpectedly rebounds or the uniformity suddenly decreases in the later stage of mixing, which may be due to overmixing causing the dispersed oil to re-aggregate, or changes in the rheological properties of the system causing phase separation.

[0101] In steps S300 and S400, the temperature distribution non-uniformity is typically characterized by the combination of the standard deviation of the temperature values ​​at the sampling points and the average temperature value, reflecting the spatial heterogeneity of the thermal field and the overall thermal level within the mixing chamber. This uniformity not only reflects the shear efficiency of the equipment and the material flow state but also directly affects the accuracy of viscosity and surface grease measurements.

[0102] Temperature distribution non-uniformity is obtained by dividing the standard deviation of temperature values ​​at the sampling points by the average temperature value. The calculation formula is as follows:

[0103] ;

[0104] in, Let T be the standard deviation of the temperature values ​​at each sampling point, and T be the arithmetic mean of the temperature values ​​at each sampling point. This refers to the non-uniformity of temperature distribution.

[0105] Temperature distribution non-uniformity can eliminate the influence of the average temperature level on the judgment of dispersion. For example, under two working conditions, the overall temperature is relatively low in winter (e.g., 303.15K) and the overall temperature is relatively high in summer (e.g., 323.15K), if the temperature standard deviation is 275.15K in both cases, the relative uniformity is higher in winter than in summer, indicating that the same absolute temperature difference has a greater impact on the material state under low temperature conditions.

[0106] Generally, temperature non-uniformity drives the rate of change of average viscosity by affecting the intensity of heat exchange between hot and cold materials, and directly influences the rate of change of viscosity fluctuation amplitude by altering the resistance differences between different regions during the rotation of the agitator. For example, when the temperature non-uniformity is high, a significant rheological gradient forms between hot and cold regions in the mixing chamber, with high-viscosity low-temperature regions coexisting with low-viscosity high-temperature regions. This causes the periodic fluctuations in resistance experienced by the agitator during one rotation to intensify, thereby amplifying the rate of change of viscosity fluctuation amplitude. At the same time, materials in the hot zone preferentially participate in the flow due to their lower viscosity, while materials in the cold zone remain stagnant and form agglomeration nuclei, inhibiting the synchronous construction of the overall network structure, thus slowing down the rate of increase in average viscosity or even causing a phased plateau.

[0107] Furthermore, temperature distribution non-uniformity also affects the accuracy of surface oil state data by regulating the driving force of oil migration. In high-temperature regions, the thermal motion of oil molecules is enhanced, interfacial tension is reduced, and the precipitation rate is significantly accelerated; while in low-temperature regions, oil mobility is limited, making it difficult to effectively diffuse throughout the system. The resulting spatial differences in precipitation not only directly lower the oil distribution uniformity index but also cause systematic deviations in detection values ​​due to different near-infrared probe positions. For example, if a probe happens to be located in a high-temperature, oil-rich region, its reading may be much higher than the true average value of the batch, thus misleading the system's judgment of the overall oil release process.

[0108] Therefore, it is necessary to introduce temperature influence coefficients for the rate of change of different trend indicators in the uniform state data and the oil state data respectively, to correct the original data and obtain the theoretically achievable baseline rate of change at this average temperature, thus eliminating the interference of uneven temperature distribution on state judgment. The first influence coefficient includes a first sub-coefficient for the rate of change of average viscosity and a second sub-coefficient for the rate of change of viscosity fluctuation amplitude; the second influence coefficient includes a third sub-coefficient for the rate of change of total surface oil content and a fourth sub-coefficient for the rate of change of oil distribution uniformity.

[0109] In steps S500 and S600, the corrected uniformity data includes the corrected average viscosity change rate and the corrected viscosity fluctuation amplitude change rate, while the corrected grease state data includes the corrected surface grease total change rate and the corrected grease distribution uniformity change rate. The uniformity data includes the actually measured average viscosity change rate and viscosity fluctuation amplitude change rate, while the grease state data includes the actually measured surface grease total change rate and grease distribution uniformity change rate.

[0110] The system calculates the difference between the corrected indicators and the original indicators: the difference between the average viscosity change rate and the corrected average viscosity change rate is recorded as the viscosity rate residual; the difference between the viscosity fluctuation amplitude change rate and the corrected viscosity fluctuation amplitude change rate is recorded as the fluctuation rate residual; the difference between the surface oil total amount change rate and the corrected surface oil total amount change rate is recorded as the oil precipitation residual; the difference between the oil distribution uniformity change rate and the corrected oil distribution uniformity change rate is recorded as the uniformity residual. If the absolute value of any residual exceeds the preset residual threshold, it is considered that there may be an agitation anomaly and is marked as a candidate anomaly; otherwise, if all corrections are within the allowable range, the original data can be directly used for subsequent analysis.

[0111] The preset thresholds corresponding to viscosity rate residual, fluctuation rate residual, oil precipitation residual, and uniformity residual can be obtained by statistical analysis of historical qualified batch production data.

[0112] Simultaneously, the system compares the real-time calculated temperature distribution non-uniformity with a preset temperature distribution non-uniformity threshold. This threshold can be statistically derived from historical qualified batch data. When the temperature distribution non-uniformity is less than or equal to the temperature distribution non-uniformity threshold, it indicates that the thermal field distribution within the cavity is within an acceptable range; when the temperature distribution non-uniformity is greater than the temperature distribution non-uniformity threshold, it indicates that there may be an agitation abnormality, which is recorded as a candidate abnormality.

[0113] When candidate anomalies are present, they are verified at multiple consecutive points. If the residuals consistently exceed the limit in the same direction over multiple consecutive sampling periods, a stirring anomaly is determined to exist. For example, if residuals exceed the limit in the same direction for three consecutive sampling periods, a stirring anomaly can be confirmed.

[0114] It should be noted that the temperature distribution non-uniformity threshold is determined through statistical analysis of historical batches of qualified production data. In actual production, the threshold can be slightly calibrated and adjusted according to different product formulas, production seasons, and equipment specifications to ensure accuracy. The preset residual thresholds are obtained through statistical analysis of historical batches of qualified production data. Furthermore, if the formula or process parameters are adjusted during production, the thresholds can be adaptively modified based on qualified data from small-batch trial production to ensure that the residual thresholds always match the current production conditions and guarantee the accuracy of anomaly detection.

[0115] "Same-direction exceedance" refers to a situation where, over multiple consecutive sampling periods, a certain type of residual (such as viscosity rate residual, fluctuation rate residual, oil precipitation residual, or uniformity residual) not only continuously exceeds its corresponding preset threshold but also deviates in the same direction—that is, it is always positive or always negative. For example, if the viscosity rate residual is greater than the threshold for three consecutive periods (positive exceedance), it indicates that the actual average viscosity rise rate is significantly higher than the theoretical baseline value after temperature correction, which may reflect excessive stirring shear, local overheating of the material, or abnormally dense structure. Conversely, if it is continuously negative and exceeds the limit, it suggests that the mixing process is lagging or the cohesion of the system is hindered.

[0116] When all four residuals are larger in the same direction, it is judged as an abnormality of material properties.

[0117] When an agitation anomaly is detected, the type of agitation anomaly is determined based on candidate anomalies. Generally, agitation anomaly types can be categorized as follows:

[0118] ① When the fluctuation rate residual is positive, the uniformity residual is negative, and the viscosity rate residual is close to zero, it is judged as an anomaly of decreased stirring and mixing capacity;

[0119] ② When the residual of oil precipitation is positive, the residual of viscosity rate is negative, and the residual of fluctuation rate is positive, it is judged to be a local overheating type anomaly;

[0120] ③ When all four residuals are larger in the same direction, it is judged as an abnormality of material properties;

[0121] ④ When only the viscosity rate residual is abnormal while the other three items are normal, it is determined to be a sensor malfunction type abnormality;

[0122] ⑤ When the temperature distribution non-uniformity exceeds the temperature distribution non-uniformity threshold, it is judged as a thermal field imbalance anomaly.

[0123] Based on the type of agitation anomaly, corresponding control strategies are generated. For example, if the anomaly is due to a decrease in mixing capacity, the mixing time can be appropriately extended. If the mixing indicators still do not improve after extending the time, it indicates that the agitator blades are worn and the machine needs to be stopped for inspection and replacement. If the anomaly is due to local overheating, the agitator speed needs to be reduced and the chamber jacket cooling should be turned on. Production can be resumed after the temperature returns to the normal range. If the anomaly is due to abnormal material properties, the upstream raw material testing process needs to be linked to verify the oil content, water content, and particle size parameters of the raw materials. After confirming the deviation, the mixing process parameters should be adjusted proportionally or the materials should be re-produced. If the anomaly is due to sensor failure, a sensor calibration reminder should be triggered to notify the operation and maintenance personnel to inspect and maintain the viscosity detection unit. If the anomaly is due to thermal imbalance, the blade installation position and material feeding amount need to be checked to see if they meet the process requirements. Material bridging or flow dead zone problems should be eliminated, and the agitator speed should be adjusted to optimize the shear distribution if necessary.

[0124] In addition, if no abnormal stirring is found, monitoring should continue.

[0125] Second Embodiment

[0126] In one specific embodiment, the temperature distribution non-uniformity is obtained based on the temperature data, and the first influence coefficient of the temperature data on the viscosity data and the second influence coefficient on the surface grease data are calculated based on the temperature distribution non-uniformity, specifically including:

[0127] S310. Obtain the temperature values ​​of each sampling point within the target time period, calculate the standard deviation of the temperature values ​​of each sampling point, and obtain the temperature distribution non-uniformity based on the ratio of the standard deviation to the average temperature of each sampling point.

[0128] S320. The contribution coefficient of the average viscosity change rate corresponding to each unit change in temperature distribution non-uniformity is denoted as the first sub-coefficient.

[0129] S330. The contribution coefficient of the viscosity fluctuation amplitude change rate corresponding to each unit change in temperature distribution non-uniformity is denoted as the second sub-coefficient; the first influence coefficient includes the first sub-coefficient and the second sub-coefficient.

[0130] S340. The contribution coefficient of the rate of change of total surface grease corresponding to each unit change in temperature distribution non-uniformity is denoted as the third sub-coefficient.

[0131] S350, The contribution coefficient of the rate of change of oil distribution uniformity corresponding to each unit change in temperature distribution non-uniformity is denoted as the fourth sub-coefficient; the second influence coefficient includes the third sub-coefficient and the fourth sub-coefficient.

[0132] Furthermore, the contribution coefficients for the rate of change of average viscosity, the rate of change of viscosity fluctuation amplitude, the rate of change of total surface grease, and the rate of change of grease distribution uniformity were obtained as follows:

[0133] The contribution coefficient of the average viscosity change rate is obtained by multiplying the material reference viscosity value, the temperature-viscosity sensitivity coefficient, and the standard deviation of the temperature distribution. The temperature-viscosity sensitivity coefficient is the result of dividing the ratio of the viscous flow activation energy to the gas constant by the square of the current average temperature.

[0134] The contribution coefficient of the viscosity fluctuation amplitude change rate is obtained by multiplying the material reference viscosity value by the temperature-viscosity sensitivity coefficient;

[0135] The contribution coefficient of the total surface oil change rate is obtained by multiplying the reference oil precipitation rate, the oil precipitation temperature sensitivity coefficient, and the temperature distribution standard deviation, wherein the oil precipitation temperature sensitivity coefficient is the result of dividing the ratio of oil precipitation activation energy to gas constant by the square of the current average temperature.

[0136] The contribution coefficient of the rate of change of oil distribution uniformity is obtained by multiplying the current oil distribution uniformity benchmark value, the oil migration temperature sensitive factor, the local oil precipitation rate gradient coefficient, and the temperature distribution standard deviation.

[0137] In summary, the first sub-coefficient The calculation formula is:

[0138] ;

[0139] in, The average thermodynamic temperature is The corresponding material reference viscosity at that time It is the temperature-viscosity sensitivity coefficient of a material, which characterizes the magnitude of viscosity change caused by a unit change in temperature. Let R be the activation energy for viscous flow of the material, and R be the universal gas constant. The average thermodynamic temperature of the material in the mixing zone. This represents the standard deviation of the temperature values ​​at each sampling point.

[0140] It can be obtained by linear fitting through multiple sets of isothermal viscosity tests, where R is a fixed constant and the standard value is directly taken, such as 8.314 J / (mol·K).

[0141] Method for obtaining: Under the same mixing parameters, material loading, and process progress, data are collected on the steady-state operation of the mixing system, where the temperature standard deviation is less than a preset threshold (e.g., ...). The average temperature and online viscosity measured data under the <0.5K operating condition were used to construct the corresponding functional relationship between the average temperature and the reference viscosity based on multiple sets of matched data, so as to obtain the intrinsic reference viscosity values ​​corresponding to different average temperatures.

[0142] Second sub-coefficient The calculation formula is:

[0143] ;

[0144] Third sub-coefficient The calculation formula is:

[0145] ;

[0146] in, The average thermodynamic temperature is The rate of precipitation of oil as a benchmark, The activation energy for the oil precipitation reaction. This is the temperature sensitivity coefficient for oil precipitation. It can be obtained by fitting the results through experiments on the amount of oil extracted at different temperatures.

[0147] The method for obtaining the data is as follows: Under the same stirring parameters, material loading amount and mixing process, the average temperature and online measured oil precipitation rate data are collected under the condition that the system is in steady state and the temperature standard deviation is lower than the preset threshold. The temperature-reference precipitation rate function relationship is constructed by fitting multiple sets of matched data, thereby obtaining the intrinsic oil precipitation rate of the material corresponding to different average temperatures.

[0148] Fourth sub-coefficient The calculation formula is:

[0149] ;

[0150] in, This is the current baseline value for oil distribution uniformity. This is the temperature sensitivity coefficient for oil precipitation. This represents the gradient coefficient of the local oil precipitation rate.

[0151] The method for obtaining the data is as follows: Under the same stirring parameters, material loading amount, and mixing process, the average temperature and measured oil distribution homogenization rate data are collected under the condition that the system is in steady-state operation and the temperature standard deviation is lower than the preset threshold. Based on multiple sets of one-to-one corresponding measured data, a functional relationship between temperature and benchmark homogenization parameters is constructed to obtain the inherent oil dispersion benchmark level of the material system at different average temperatures.

[0152] The oil precipitation rate can be calculated by separately detecting the oil precipitation rate in the high-temperature region and the low-temperature region, and dividing the difference between the high and low temperature precipitation rates by the average oil precipitation rate over the entire region. In this case, multiple sets of fixed temperature measuring points are arranged along the axial and radial directions of the mixing tank cavity, which are divided into fixed monitoring areas: the area near the heating jacket layer of the tank wall, the liquid surface layer, and the outer edge of the stirring blades are designated as high-temperature areas; the flow field area in the center of the tank, the sedimentation area at the bottom of the tank, and the main body area of ​​the inner layer of the material are designated as low-temperature areas.

[0153] Third Embodiment

[0154] In one specific embodiment, the step of obtaining corrected uniform state data by correcting uniform state data using a first influence coefficient and obtaining corrected oil state data by correcting oil state data using a second influence coefficient specifically includes:

[0155] The average viscosity baseline change rate and viscosity fluctuation amplitude baseline change rate of the mixture under standard temperature conditions, as well as the baseline change rate of total surface grease and the baseline change rate of grease distribution uniformity, are obtained.

[0156] The correction value for the average viscosity rate of change is obtained by adding the additional correction value obtained by multiplying the average viscosity reference rate of change and the first sub-coefficient by the ratio of the temperature standard deviation change to the sampling period;

[0157] The correction value for the rate of change of viscosity fluctuation amplitude is obtained by adding the additional correction value obtained by multiplying the reference rate of change of viscosity fluctuation amplitude by the second sub-coefficient by the ratio of the change in temperature standard deviation to the sampling period;

[0158] Corrected uniform state data are obtained based on the correction values ​​for the rate of change of viscosity fluctuation amplitude and the average rate of change of viscosity.

[0159] The correction value for the rate of change of total surface grease is obtained by adding the additional correction value obtained by multiplying the baseline rate of change of total surface grease by the third sub-coefficient by the ratio of the change in temperature standard deviation to the sampling period;

[0160] The correction value for the rate of change of oil distribution uniformity is obtained by subtracting the fourth sub-coefficient multiplied by the ratio of the change in temperature standard deviation to the sampling period from the baseline rate of change of oil distribution uniformity.

[0161] Corrected oil state data are obtained based on the correction values ​​for the rate of change of total surface oil content and the rate of change of oil distribution uniformity.

[0162] Furthermore, the acquisition of the average viscosity reference rate and viscosity fluctuation amplitude reference rate of the mixture under standard temperature conditions, as well as the surface oil total amount reference rate and oil distribution uniformity reference rate, specifically includes:

[0163] During the mixing process, a time period is selected that is in the same mixing stage as the current time period, has a temperature distribution non-uniformity less than a preset non-uniformity threshold, and has a temperature average value that deviates from the temperature average value of the current time period less than a preset deviation tolerance, and is used as the standard temperature time period.

[0164] The average rate of change of average viscosity within the standard temperature time period is taken as the average viscosity reference rate of change.

[0165] The average rate of change of viscosity fluctuation amplitude within the standard temperature time period is taken as the reference rate of change of viscosity fluctuation amplitude.

[0166] The average rate of change of the total surface grease within the standard temperature time period is taken as the baseline rate of change of the total surface grease.

[0167] The average rate of change of oil distribution uniformity within the standard temperature time period is taken as the benchmark rate of change of oil distribution uniformity.

[0168] In summary, the correction value for the rate of change of average viscosity The calculation formula is:

[0169] ;

[0170] in, The average viscosity change rate is based on a uniform temperature field. This represents the change in the standard deviation of temperature. The sampling period.

[0171] Correction value for the rate of change of viscosity fluctuation amplitude The calculation formula is:

[0172] × ;

[0173] in, The viscosity fluctuation amplitude is the baseline rate of change.

[0174] Correction value for the rate of change of total surface grease The calculation formula is:

[0175] × ;

[0176] in, The rate of change of the total surface grease is the baseline.

[0177] Correction value for rate of change of oil distribution uniformity The calculation formula is:

[0178] × ;

[0179] in, The baseline rate of change for the uniformity of oil distribution.

[0180] Fourth embodiment

[0181] In one specific embodiment, when an abnormal stirring condition is detected, a stirring parameter adjustment command is generated and executed, specifically including:

[0182] S610. Obtain the stirring anomaly type based on the candidate anomalies, and generate a corresponding stirring parameter adjustment instruction based on the stirring type;

[0183] S620 After each adjustment command is executed, the changing trend of the four residuals is monitored in real time. If the residuals do not fall back to within the threshold within the set time, an alarm is issued.

[0184] In step S620, if multiple residuals continue to exceed limits, the alarm level is gradually increased. First, a process anomaly prompt is pushed to the central control system to guide the operator to review the parameter adjustment logic. If the indicators still do not improve after manual review and adjustment, an equipment operation and maintenance alarm is triggered to notify on-site personnel to carry out sensor calibration, wear inspection of stirring components, and thermal uniformity testing. The machine is stopped in time to investigate the root cause of the fault and prevent abnormal materials from entering subsequent processes, which would affect the storage stability and quality uniformity of the final black sesame medicinal and edible pastry.

[0185] It should be noted that after completing the above steps, it is also necessary to obtain the finished product quality inspection data of the dough obtained from the mixing process, and dynamically update the above control thresholds based on this data.

[0186] Fifth embodiment

[0187] See Figure 2In one specific embodiment, the present invention also provides a production control system for improving the stability of black sesame-based medicinal and edible pastries. The production control method described in the above embodiment is applied to this production control system. The production control system 100 includes: a real-time acquisition module 110, equipped with a torque sensor, a near-infrared spectrometer, and a distributed temperature sensor, used to simultaneously acquire viscosity, surface oil, and temperature data of the mixed materials during the mixing process; a state analysis module 120, used to calculate uniformity data, oil state data, and temperature distribution non-uniformity based on the acquired data, and to perform temperature correction on the state data in conjunction with an influence coefficient; an anomaly judgment module 130, used to compare the corrected state data with the original state data, and to determine whether there is a stirring anomaly based on whether the residual exceeds the limit; and a parameter adjustment module 140, used to generate and execute corresponding stirring parameter adjustment instructions according to the anomaly type when a stirring anomaly is determined to exist. This production control system 100 has all the technical features of the above-described production control method, which will not be described in detail here.

[0188] While the present invention has been disclosed above, it is not limited thereto. Any person skilled in the art can make various modifications and alterations without departing from the spirit and scope of the invention; therefore, the scope of protection of the present invention should be determined by the scope defined in the claims.

Claims

1. A production control method for improving the stability of black sesame-based medicinal and edible pastries, characterized in that, Includes the following steps: Real-time acquisition of viscosity, surface grease, and temperature data of the mixed materials during the mixing process; The uniformity data of the mixture is obtained based on the variation trend of viscosity data, and the oil state data of the mixture is obtained based on the variation trend of surface oil data. The temperature distribution non-uniformity is obtained based on the temperature data, and the first influence coefficient of the temperature data on the viscosity data and the second influence coefficient on the surface grease data are calculated based on the temperature distribution non-uniformity. Corrected uniform state data is obtained by correcting the uniform state data using the first influence coefficient, and corrected oil state data is obtained by correcting the oil state data using the second influence coefficient. The corrected uniform state data is compared with the uniform state data, the corrected oil state data is compared with the oil state data, and the temperature distribution non-uniformity is compared with the temperature distribution non-uniformity threshold. Based on the comparison results, it is determined whether there is any stirring abnormality. When an abnormal stirring condition is detected, a stirring parameter adjustment command is generated and executed.

2. The production control method according to claim 1, characterized in that, The real-time acquisition of viscosity, surface grease, and temperature data of the mixed materials during the mixing process specifically includes: The viscosity data of the mixture is obtained by acquiring real-time torque signals through a torque sensor. The viscosity data includes the average viscosity and the viscosity fluctuation range. Surface grease data is obtained by continuously acquiring near-infrared spectral signals from each acquisition point at a preset sampling frequency. The surface grease data includes total surface grease data and grease distribution uniformity data. Temperature data is collected by continuously acquiring temperature sensor signals from each acquisition point at a preset sampling frequency.

3. The production control method according to claim 2, characterized in that, The process of obtaining uniform state data of the mixture based on the variation trend of viscosity data and oil state data of the mixture based on the variation trend of surface oil data specifically includes: Within the target time period, the average viscosity change rate and viscosity fluctuation amplitude change rate of the mixture are obtained respectively. The uniformity data of the mixture during the target time period are obtained based on the average viscosity change rate and the viscosity fluctuation amplitude change rate. Within the target time period, the rate of change of the total surface oil content and the rate of change of oil distribution uniformity of the mixture were obtained respectively. The oil state data of the mixture during the target time period are obtained based on the rate of change of total surface oil content and the rate of change of oil distribution uniformity.

4. The production control method according to claim 3, characterized in that, The step of obtaining temperature distribution non-uniformity based on temperature data, and calculating the first influence coefficient of temperature data on viscosity data and the second influence coefficient of temperature data on surface grease data based on the temperature distribution non-uniformity, specifically includes: The temperature values ​​of each sampling point within the target time period are obtained, the standard deviation of the temperature values ​​of each sampling point is calculated, and the temperature distribution non-uniformity is obtained based on the ratio of the standard deviation to the average temperature of each sampling point. The contribution coefficient of the average viscosity change rate corresponding to each unit change in temperature distribution non-uniformity is denoted as the first sub-coefficient. The contribution coefficient of the viscosity fluctuation rate corresponding to each unit change in temperature non-uniformity is denoted as the second sub-coefficient. The first influence coefficient includes a first sub-coefficient and a second sub-coefficient; The contribution coefficient of the rate of change of total surface grease corresponding to each unit change in temperature distribution non-uniformity is denoted as the third sub-coefficient. The contribution coefficient of the rate of change of oil distribution uniformity corresponding to each unit change in temperature distribution non-uniformity is denoted as the fourth sub-coefficient. The second influence coefficient includes the third sub-coefficient and the fourth sub-coefficient.

5. The production control method according to claim 4, characterized in that, The contribution coefficients for the rate of change of average viscosity, the rate of change of viscosity fluctuation amplitude, the rate of change of total surface grease, and the rate of change of grease distribution uniformity are obtained as follows: The contribution coefficient of the average viscosity change rate is obtained by multiplying the material reference viscosity value, the temperature-viscosity sensitivity coefficient, and the standard deviation of the temperature distribution. The temperature-viscosity sensitivity coefficient is the result of dividing the ratio of the viscous flow activation energy to the gas constant by the square of the current average temperature. The contribution coefficient of the viscosity fluctuation amplitude change rate is obtained by multiplying the material reference viscosity value by the temperature-viscosity sensitivity coefficient; The contribution coefficient of the total surface oil change rate is obtained by multiplying the reference oil precipitation rate, the oil precipitation temperature sensitivity coefficient, and the temperature distribution standard deviation, wherein the oil precipitation temperature sensitivity coefficient is the result of dividing the ratio of oil precipitation activation energy to gas constant by the square of the current average temperature. The contribution coefficient of the rate of change of oil distribution uniformity is obtained by multiplying the current oil distribution uniformity benchmark value, the oil migration temperature sensitive factor, the local oil precipitation rate gradient coefficient, and the temperature distribution standard deviation.

6. The production control method according to claim 5, characterized in that, The process of obtaining corrected uniform state data by correcting uniform state data using a first influence coefficient and obtaining corrected oil state data by correcting oil state data using a second influence coefficient specifically includes: The average viscosity baseline change rate and viscosity fluctuation amplitude baseline change rate of the mixture under standard temperature conditions, as well as the baseline change rate of total surface grease and the baseline change rate of grease distribution uniformity, are obtained. The correction value for the average viscosity rate of change is obtained by adding the additional correction value obtained by multiplying the average viscosity reference rate of change and the first sub-coefficient by the ratio of the temperature standard deviation change to the sampling period; The correction value for the rate of change of viscosity fluctuation amplitude is obtained by adding the additional correction value obtained by multiplying the reference rate of change of viscosity fluctuation amplitude by the second sub-coefficient by the ratio of the change in temperature standard deviation to the sampling period; Corrected uniform state data are obtained based on the correction values ​​for the rate of change of viscosity fluctuation amplitude and the average rate of change of viscosity. The correction value for the rate of change of total surface grease is obtained by adding the additional correction value obtained by multiplying the baseline rate of change of total surface grease by the third sub-coefficient by the ratio of the change in temperature standard deviation to the sampling period; The correction value for the rate of change of oil distribution uniformity is obtained by subtracting the fourth sub-coefficient multiplied by the ratio of the change in temperature standard deviation to the sampling period from the baseline rate of change of oil distribution uniformity. Corrected oil state data are obtained based on the correction values ​​for the rate of change of total surface oil content and the rate of change of oil distribution uniformity.

7. The production control method according to claim 6, characterized in that, The process of comparing the corrected uniformity data with the uniformity data, comparing the corrected oil state data with the oil state data, and comparing the temperature distribution non-uniformity with the temperature distribution non-uniformity threshold, and determining whether there is an agitation abnormality based on the comparison results, specifically includes: The difference between the average viscosity change rate and the corrected value of the average viscosity change rate is denoted as the viscosity rate residual; the difference between the viscosity fluctuation amplitude change rate and the corrected value of the viscosity fluctuation amplitude change rate is denoted as the fluctuation rate residual; the difference between the change rate of the total surface oil and the corrected value of the change rate of the total surface oil is denoted as the oil precipitation residual; the difference between the change rate of the oil distribution uniformity and the corrected value of the change rate of the oil distribution uniformity is denoted as the uniformity residual. When the absolute value of any residual exceeds the corresponding preset threshold, or when the temperature distribution non-uniformity is greater than the temperature distribution non-uniformity threshold, it is marked as a candidate anomaly. Candidate anomalies are continuously verified at multiple points. If the residuals continuously exceed the limit in the same direction within multiple consecutive sampling periods, it is determined that there is a stirring anomaly.

8. The production control method according to claim 7, characterized in that, When an abnormal stirring condition is detected, a stirring parameter adjustment command is generated and executed, specifically including: Based on the candidate anomalies, the stirring anomaly type is obtained, and a corresponding stirring parameter adjustment instruction is generated based on the stirring type. After each adjustment command is executed, the changing trend of the four residuals is monitored in real time. If the residuals do not fall back to within the threshold within the set time, an alarm is issued.

9. The production control method according to claim 6, characterized in that, The acquisition of the average viscosity reference rate and viscosity fluctuation amplitude reference rate of the mixture under standard temperature conditions, as well as the reference rate of change of total surface oil content and the reference rate of change of oil distribution uniformity, specifically includes: During the mixing process, a time period is selected that is in the same mixing stage as the current time period, has a temperature distribution non-uniformity less than a preset non-uniformity threshold, and has a temperature average value that deviates from the temperature average value of the current time period less than a preset deviation tolerance, and is used as the standard temperature time period. The average rate of change of average viscosity within the standard temperature time period is taken as the average viscosity reference rate of change. The average rate of change of viscosity fluctuation amplitude within the standard temperature time period is taken as the reference rate of change of viscosity fluctuation amplitude. The average rate of change of the total surface grease within the standard temperature time period is taken as the baseline rate of change of the total surface grease. The average rate of change of oil distribution uniformity within the standard temperature time period is taken as the benchmark rate of change of oil distribution uniformity.

10. A production control system for improving the stability of black sesame-based medicinal and edible pastries, characterized in that, The production control system is used to implement the production control method as described in any one of claims 1 to 9, and the production control system includes: The real-time acquisition module is equipped with a torque sensor, a near-infrared spectrometer, and a distributed temperature sensor to simultaneously acquire viscosity, surface grease, and temperature data of the mixed materials during the mixing process. The state analysis module is used to calculate uniform state data, oil state data, and temperature distribution non-uniformity based on the collected data, and to perform temperature correction on the state data in conjunction with the influence coefficient. The anomaly detection module is used to compare the corrected state data with the original state data and determine whether there is a stirring anomaly based on whether the residual exceeds the limit. The parameter adjustment module is used to generate and execute corresponding stirring parameter adjustment instructions based on the type of the abnormality when an abnormality is detected.

Images