Intelligent control method and system for wake-up and steaming integrated cabinet

Abstract

The application relates to the technical field of adaptive control, in particular to an intelligent control method and system for a proofing and steaming integrated cabinet, which comprises the following steps: taking the concentration of carbon dioxide in the proofing and steaming integrated cabinet as a fermentation degree, dynamically adjusting the membership function of each output fuzzy set in a fuzzy PID control model according to the fermentation degree, and obtaining an initial output; taking the concentration change rate of carbon dioxide as a fermentation activity index representing the metabolic activity of yeast, determining an integral inhibition coefficient according to the fermentation activity index, and inhibiting the integral gain adjustment amount in the initial output according to the integral inhibition coefficient to obtain a control output; and adjusting the parameters of a PID controller according to the control output to realize adaptive control of the proofing and steaming integrated cabinet. Through the technical scheme, accurate adaptive control of the proofing and steaming integrated cabinet can be realized in the food fermentation process.

Description

Technical Field

[0001] This application relates to the field of adaptive control technology, and in particular to an intelligent control method and system for an integrated steaming and waking cabinet. Background Technology

[0002] In the food industry, the steaming and proofing cabinet generates steam by controlling a steam generator and uses the steam as a heat source to allow food or dough inside the cabinet to ferment naturally in an environment with a certain temperature and humidity. After fermentation, the dough is steamed, thus realizing the automatic processing of food.

[0003] Currently, patent application CN119575828A discloses a food processing control method, which includes the following steps: S1, acquiring an infrared image of dough during fermentation, removing the background area from the infrared image to obtain the dough infrared image; S2, dividing the dough infrared image into a central area, a middle area, and an edge area; S3, calculating the central fermentation activity, the middle fermentation activity, and the edge fermentation activity in the central area, the middle area, and the edge area, respectively; S4, obtaining the compensation power based on a third-order feedback control model according to the difference between the target activity and the central fermentation activity, the middle fermentation activity, and the edge fermentation activity, respectively; S5, adding the compensation power to the current heating power to obtain the heating power required for the fermentation process.

[0004] The above method uses infrared images of the dough during fermentation to determine the differences in fermentation activity in different parts of the dough and adjusts the heating power during fermentation. However, during the fermentation of food or dough, the steam generator produces high-temperature steam in the steamer cabinet. This steam causes the infrared images to fail to accurately reflect the dough temperature, thus affecting the accuracy of the central fermentation activity, intermediate fermentation activity, and edge fermentation activity, making it impossible to achieve precise adaptive control during food fermentation. Summary of the Invention

[0005] To address the technical problem of achieving precise adaptive control during food fermentation, this application provides an intelligent control method and system for an integrated proofing and steaming cabinet, which can achieve precise adaptive control of the integrated proofing and steaming cabinet during food fermentation by collecting the concentration and rate of change of carbon dioxide.

[0006] In a first aspect, this application provides an intelligent control method for an integrated proofing and steaming cabinet. The control method includes: using the concentration of carbon dioxide within the integrated proofing and steaming cabinet as the fermentation level; dynamically adjusting the membership functions of each output fuzzy set in a fuzzy PID control model according to the fermentation level to obtain an initial output, the initial output including a proportional gain adjustment, an integral gain adjustment, and a derivative gain adjustment; using the rate of change of carbon dioxide concentration as a fermentation activity index characterizing yeast metabolic activity; determining an integral suppression coefficient negatively correlated with the fermentation activity index; and suppressing the integral gain adjustment in the initial output according to the integral suppression coefficient to obtain a control output; and adjusting the parameters of the PID controller according to the control output to achieve adaptive control of the integrated proofing and steaming cabinet.

[0007] Using carbon dioxide concentration as the degree of fermentation and the rate of change of concentration as the fermentation activity index, these were used to adjust the fuzzy PID control strategy and suppress the integral gain, respectively. This not only allows for adjustments to the aggressiveness of control based on the fermentation process but also proactively addresses the instability caused by the yeast's own heat production, eliminating the influence of heat generated by internal heat sources on the integral gain (I). This achieves adaptive control of the fermentation and steaming integrated cabinet, significantly improving fermentation quality.

[0008] Preferably, the step of dynamically adjusting the membership function of each output fuzzy set in the fuzzy PID control model according to the degree of fermentation includes: calculating an adjustment factor based on the current carbon dioxide concentration, wherein the adjustment factor is negatively correlated with the carbon dioxide concentration; and adjusting the center point based on the adjustment factor within a preset interval of the center point of the membership function in any output fuzzy set, wherein the minimum and maximum values ​​of the preset interval are preset conservative center point and aggressive center point, respectively.

[0009] The membership function center point is dynamically adjusted within a preset interval, thereby specifying and quantifying the adjustment process of the control strategy. This allows the control strategy to smoothly transition from aggressive to conservative as fermentation progresses, ensuring fermentation efficiency and quality throughout the entire fermentation cycle.

[0010] Preferably, the conservative center point is obtained by reducing the original center point of the membership function by a ratio less than 1, and the radical center point is obtained by enlarging the original center point of the membership function by a ratio greater than 1.

[0011] Preferably, the rate of change of carbon dioxide concentration is the difference between the carbon dioxide concentration at the current sampling time and the carbon dioxide concentration at the previous sampling time.

[0012] Preferably, the step of determining the integral inhibition coefficient includes: comparing the fermentation activity index and the saturation activity threshold; in response to the fermentation activity index being less than the saturation activity threshold, the integral inhibition coefficient is equal to 1; otherwise, the integral inhibition coefficient is negatively correlated with the fermentation activity index and satisfies that the integral inhibition coefficient is not less than the minimum allowable value.

[0013] When the fermentation activity index is less than the saturation activity threshold, the yeast activity is normal, the integral inhibition coefficient is 1, and the PID controller maintains its complete error elimination capability. Inhibition is only initiated when the activity is abnormally enhanced (i.e., the fermentation activity index is not less than the saturation activity threshold), which avoids the continuous weakening of the integral action. While effectively preventing integral saturation, the steady-state control accuracy of the PID controller is preserved to the maximum extent.

[0014] Preferably, suppressing the integral gain adjustment in the initial output based on the integral suppression coefficient includes multiplying the integral suppression coefficient by the integral gain adjustment in the initial output.

[0015] Multiplying the integral suppression coefficient by the integral gain adjustment allows changes in the fermentation activity index to be applied to the integral gain adjustment in real time, ensuring the real-time nature of the suppression effect and thus effectively improving the dynamic stability of the control system.

[0016] Preferably, the control method further includes: determining that the fermentation process ends when the carbon dioxide concentration in the integrated steaming and waking cabinet reaches a preset concentration threshold or the fermentation time reaches a preset time.

[0017] By setting a carbon dioxide concentration threshold or fermentation time as the basis for determining the end of the fermentation process, the determination of the fermentation endpoint is automated.

[0018] Preferably, after determining that the fermentation process has ended, the steaming stage is started using a preset steam temperature.

[0019] Preferably, each output fuzzy set includes at least positive adjustment, zero adjustment, and negative adjustment.

[0020] In a second aspect, this application also provides an intelligent control system for a steamer / proof cabinet, including a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, an intelligent control method for a steamer / proof cabinet according to the first aspect of this application is implemented.

[0021] The technical solution of this application has the following beneficial technical effects:

[0022] First, the fermentation level of the dough is measured based on the carbon dioxide concentration inside the steamer. The membership function of the output fuzzy set in the fuzzy PID control model is then dynamically adjusted accordingly. This allows the control strategy to smoothly transition from an aggressive mode in the early stages of fermentation to a conservative mode in the later stages, adapting to the dough's varying sensitivity to environmental changes at different stages. Second, the fermentation activity index, which characterizes yeast metabolic activity, is calculated based on the rate of change in carbon dioxide concentration. When excessive yeast activity leads to internal heat generation and heat source interference, an integral suppression coefficient is calculated based on the fermentation activity index and used to suppress the integral gain adjustment in the PID controller. This effectively eliminates the influence of heat generated by internal heat sources on the integral gain (I), achieving precise control of the fermentation process. Finally, the parameters of the PID controller are updated in real time based on the dynamically adjusted and suppressed control output, thus achieving precise and stable adaptive control of the steamer throughout the entire fermentation process. Attached Figure Description

[0023] Figure 1 This is a flowchart of an intelligent control method for a steamer / proof cabinet according to an embodiment of this application.

[0024] Figure 2 This is a schematic diagram of the changes in fermentation degree and fermentation activity index according to embodiments of this application.

[0025] Figure 3 A schematic diagram of the variation curves of the adjustment factor and the integral inhibition coefficient according to an embodiment of this application.

[0026] Figure 4 This is a comparison diagram of the control effects according to the embodiments of this application.

[0027] Figure 5 This is a structural block diagram of an intelligent control system for a steamer / proof cabinet according to an embodiment of this application. Detailed Implementation

[0028] According to the first aspect of this application, this application provides an intelligent control method for an integrated steaming and proofing cabinet, which is used to realize adaptive control in the food fermentation process in the food industry. Figure 1 This is a flowchart of an intelligent control method for a steaming and proofing cabinet according to an embodiment of this application. Figure 1 As shown, the intelligent control method for the steaming and waking cabinet includes steps S101 to S103, which are described in detail below.

[0029] S101, the concentration of carbon dioxide in the steaming and evaporating cabinet is taken as the fermentation degree. After dynamically adjusting the membership function of each output fuzzy set in the fuzzy PID control model according to the fermentation degree, the initial output is obtained. The initial output includes the proportional gain adjustment, integral gain adjustment and derivative gain adjustment.

[0030] In one embodiment, one or more doughs to be fermented are placed in a steamer, which controls the temperature and humidity inside the steamer by controlling the power of the steam generator, thereby achieving the fermentation of the dough.

[0031] In the early stages of fermentation, the dough is in a high-density state. At this time, the dough is not sensitive to changes in temperature and humidity, and a more aggressive control strategy can be adopted, which can accelerate the fermentation process without affecting the fermentation quality. However, during fermentation, the carbon dioxide produced by the continuous metabolism of yeast inside the dough will gradually transform it from the initial high-density state into a porous medium full of air pockets. This causes the dough's sensitivity to changes in temperature and humidity to increase non-linearly over time. If a more aggressive control strategy is continued at this point, it will affect the fermentation quality. Therefore, in order to ensure the fermentation quality of the dough, the fermentation degree of the dough is monitored based on the concentration of carbon dioxide in the proofing and steaming cabinet. As the fermentation degree gradually increases, the control strategy is tightened, gradually transitioning from aggressive large-scale adjustments to conservative small adjustments.

[0032] To adjust the control strategy, the membership function of each output fuzzy set in the fuzzy PID control model is dynamically adjusted according to the fermentation degree; the output fuzzy set includes at least positive adjustment, zero adjustment and negative adjustment.

[0033] The fuzzy PID control model uses a fuzzy control algorithm to determine the adjustment values ​​of the proportional gain (P), integral gain (I), and derivative gain (D) in the PID controller, thereby achieving precise control of the steam generator in the steam-heating integrated cabinet. Specifically, in the PID controller, the proportional gain (P) can be adjusted according to the current error magnitude, providing a fast response but generating steady-state error; the integral gain (I) accumulates historical errors and is used to eliminate steady-state errors, but it is prone to causing system overshoot and oscillation; the derivative gain (D) can be adjusted according to the error change trend, providing predictability and suppressing overshoot, but it is sensitive to noise. Precise control of the steam generator is achieved through a linear combination of the three actions: proportional gain (P), integral gain (I), and derivative gain (D).

[0034] To facilitate understanding, the fuzzy PID control model is introduced here: First, the input fuzzy sets and output fuzzy sets of each input information and output result are defined. The input information is temperature error and the rate of change of temperature error. The output result is the proportional gain adjustment, integral gain adjustment, and derivative gain adjustment. Taking temperature error as an example, it corresponds to five input fuzzy sets: negative large, negative small, zero, positive small, and positive large. Among them, positive small and positive large are positive adjustments, negative large and negative small are negative adjustments, and zero is a zero adjustment. The membership function of each input fuzzy set and output fuzzy set is determined. The membership function can be a triangular function or a trapezoidal function. After fuzzifying each input information according to the input fuzzy set, fuzzy inference is performed to calculate the activation degree of each fuzzy rule in the fuzzy rule base, resulting in multiple fuzzy outputs. Then, the multiple fuzzy outputs are defuzzified using the output fuzzy set. The defuzzification process uses the centroid method to convert the multiple fuzzy outputs into specific values ​​of proportional gain adjustment, integral gain adjustment, and derivative gain adjustment.

[0035] One of the fuzzy rules is the “IF-THEN” structure, such as “IF temperature error is negative large AND temperature error change rate is negative large, THEN proportional gain adjustment is positive large, proportional gain adjustment is negative large, proportional gain adjustment is positive small”.

[0036] The calculation formula for defuzzification using the centroid method is as follows:

[0037] ;

[0038] For proportional gain Output fuzzy set The center point, and For proportional gain Output fuzzy set and output fuzzy set The degree of activation; To output the number of fuzzy sets, For proportional gain The corresponding proportional gain adjustment amount.

[0039] From the above formula, it can be seen that the proportional gain The center point of each output fuzzy set directly affects the magnitude of the proportional gain adjustment. Therefore, the control strategy can be dynamically adjusted by regulating the center point of the membership function in the output fuzzy set. Specifically, the dynamic adjustment of the membership function of each output fuzzy set in the fuzzy PID control model based on the fermentation degree includes: calculating an adjustment factor based on the current carbon dioxide concentration, wherein the adjustment factor is negatively correlated with the carbon dioxide concentration; and adjusting the center point based on the adjustment factor within a preset interval of the center point of the membership function in any output fuzzy set, wherein the minimum and maximum values ​​of the preset interval are preset conservative and aggressive center points, respectively.

[0040] Wherein, the regulating factor Satisfying the relation:

[0041] ; The target concentration threshold for carbon dioxide. This represents the initial concentration of carbon dioxide at the start of fermentation. For the current moment Carbon dioxide concentration; target concentration threshold for carbon dioxide It is a pre-set concentration of carbon dioxide at the end of fermentation, based on the specific pastry-making process (such as dough type, weight, and desired expansion rate). In the initial stage of fermentation, the carbon dioxide concentration is close to... , A value approaching 1 indicates that the most aggressive control strategy is adopted to ensure fermentation efficiency; as fermentation progresses, the carbon dioxide concentration increases and approaches [a certain value]. , The value smoothly transitions to 0, indicating that the system should adopt the most conservative control strategy.

[0042] The minimum and maximum values ​​of the preset interval are respectively the preset conservative center point and radical center point. The radical center point and the conservative center point are located on both sides of the original center point of the membership function, and the radical center point is larger than the conservative center point. The conservative center point is obtained by reducing the original center point of the membership function by a ratio less than 1, and the radical center point is obtained by enlarging the original center point of the membership function by a ratio greater than 1. The conservative center point is 80% of the original center point of the membership function, and the radical center point is 120% of the original center point of the membership function.

[0043] Adjusting the center point based on the aforementioned adjustment factor includes:

[0044] ;in, For the current moment proportional gain Output fuzzy set Adjusted center point; and Representing proportional gain Output fuzzy set Radical and conservative central points. When At that time, the center point is Fuzzy PID control models tend to output larger control adjustments; when At that time, the center point is Fuzzy PID control models tend to output smaller control adjustments.

[0045] In this way, by linking the carbon dioxide concentration with the control strategy of the fuzzy PID control model, the control strategy of the fuzzy PID control model is adaptively adjusted as the fermentation progresses, significantly improving the control accuracy.

[0046] S102, the rate of change in carbon dioxide concentration is used as the fermentation activity index to characterize the metabolic activity of yeast. An integral inhibition coefficient that is negatively correlated with the fermentation activity index is determined based on the fermentation activity index. The integral gain adjustment in the initial output is suppressed according to the integral inhibition coefficient to obtain the control output.

[0047] In one embodiment, the rate of change of carbon dioxide concentration is the difference between the carbon dioxide concentration at the current sampling time and the previous sampling time. A larger rate of change indicates that a large amount of carbon dioxide was generated between two adjacent sampling times, and the more vigorous the yeast metabolic activity. See also... Figure 2 This is a schematic diagram of the changes in fermentation degree and fermentation activity index according to the embodiments of this application, which can reflect the changes in fermentation degree and fermentation activity index during the fermentation process.

[0048] Understandably, when yeast exhibits vigorous metabolic activity, it releases a large amount of heat, forming an unpredictable internal heat source. This internal heat source can cause the temperature to exceed the setpoint, at which point the fuzzy PID control model will attempt to cool the yeast. However, the integral gain (I) will continuously accumulate due to the persistent positive error, eventually reaching integral saturation. Once the heat production peak has passed, this saturated integral gain (I) will cause the fuzzy PID control model to continuously suppress heating, leading to a significant drop in temperature below the setpoint and reduced fermentation quality. Therefore, it is necessary to calculate an integral suppression coefficient based on the fermentation activity index to suppress the integral gain adjustment and eliminate the impact of heat generated by the internal heat source on the integral gain (I).

[0049] Specifically, the step of determining the integral inhibition coefficient includes: comparing the fermentation activity index and the saturation activity threshold; in response to the fermentation activity index being less than the saturation activity threshold, the integral inhibition coefficient is equal to 1; otherwise, the integral inhibition coefficient is negatively correlated with the fermentation activity index and satisfies that the integral inhibition coefficient is not less than the minimum allowable value.

[0050] When a significant increase in fermentation activity is detected, the integral gain (I) is actively reduced to prevent integral saturation caused by endogenous thermal perturbation. integral suppression coefficient for:

[0051] ;

[0052] in, For the current moment Fermentation activity index The saturation vitality threshold, This is the minimum allowable value. When the fermentation activity index is less than the saturation activity threshold, As the integral gain (I) approaches 1, it remains unaffected; however, as the fermentation activity index increases and approaches the saturation activity threshold, Gradually decreasing, it inhibits the integral gain (I); when the fermentation activity index exceeds the saturation activity threshold, Stay Apply maximum suppression. The minimum value is allowed to be a value greater than zero; for example, A value of 0.3 can be chosen; when maximum suppression is applied, the basic integral regulation capability of the PID controller is still retained. See also... Figure 3 This is a schematic diagram of the change curves of the regulation factor and the integral inhibition coefficient according to the embodiments of this application, which can reflect the changes of the regulation factor and the integral inhibition coefficient during the fermentation process.

[0053] Suppressing the integral gain adjustment in the initial output based on the integral suppression coefficient includes multiplying the integral suppression coefficient by the integral gain adjustment in the initial output.

[0054] Thus, by applying the fermentation activity index to the integral gain (I), the problem of increased cumulative error caused by the yeast's own heat production is solved, the influence of heat generated by the internal heat source on the integral gain (I) is eliminated, and the control output of the fuzzy PID control model is obtained. The control output includes the proportional gain adjustment, the suppressed integral gain adjustment, and the derivative gain adjustment.

[0055] S103 adjusts the parameters of the PID controller based on the control output to achieve adaptive control of the steaming and proofing cabinet.

[0056] In one embodiment, the parameters of the PID controller are adjusted according to the control output, including proportional gain, integral gain, and derivative gain; thus achieving adaptive control of the fermentation and steaming integrated cabinet.

[0057] It should be noted that during the fermentation process, a control frequency can be set. For example, if the control frequency is set to one per minute, the concentration and rate of change of carbon dioxide in the fermentation chamber will be collected every minute, and the parameters of the PID controller will be adjusted accordingly. Please refer to [link / reference]. Figure 4 The figure is a comparison diagram of the control effects according to the embodiments of this application. It intuitively compares the temperature control effects of the standard fuzzy PID control model and the improved fuzzy PID control model in this application. It can be seen from the figure that the improved fuzzy PID control model in this application can achieve more smooth and stable precise temperature control during the fermentation process.

[0058] The fermentation process is considered complete when the carbon dioxide concentration in the steam fermenter reaches a preset threshold, or when the fermentation time reaches a preset duration. The preset threshold is the target carbon dioxide concentration threshold. And at the end of the fermentation process, the steaming stage will automatically begin using a preset steam temperature.

[0059] According to a second aspect of this application, this application also provides an intelligent control system for an integrated steaming and waking cabinet. Figure 5 This is a structural block diagram of an intelligent control system for an integrated steaming and proofing cabinet according to an embodiment of this application. Figure 5 As shown, the system 50 includes a processor and a memory. The memory stores computer program instructions, which, when executed by the processor, implement the intelligent control method for an integrated steamer and proofer cabinet according to the first aspect of this application. The system also includes other components well-known to those skilled in the art, such as a communication bus and a communication interface. Their configurations and functions are known in the art and will not be described further here.

[0060] It should be noted that, for those skilled in the art, several modifications and improvements can be made without departing from the concept of this application, and these all fall within the scope of protection of this application.

Claims

1. An intelligent control method for a steamer / proofing cabinet, characterized in that, The control method includes: The concentration of carbon dioxide in the fermentation cabinet is used as the fermentation degree. Based on the fermentation degree, the membership functions of each output fuzzy set in the fuzzy PID control model are dynamically adjusted to obtain the initial output, which includes proportional gain adjustment, integral gain adjustment, and derivative gain adjustment. The dynamic adjustment of the membership functions of each output fuzzy set in the fuzzy PID control model based on the fermentation degree includes: calculating an adjustment factor based on the current carbon dioxide concentration, where the adjustment factor is negatively correlated with the carbon dioxide concentration; adjusting the center point within a preset interval of the membership function center point in any output fuzzy set based on the adjustment factor, where the minimum and maximum values ​​of the preset interval are preset conservative and aggressive center points, respectively; the conservative center point is obtained by reducing the original membership function center point by a ratio less than 1, and the aggressive center point is obtained by amplifying the original membership function center point by a ratio greater than 1. The rate of change in carbon dioxide concentration is used as the fermentation activity index to characterize the metabolic activity of yeast. An integral inhibition coefficient that is negatively correlated with the fermentation activity index is determined based on the fermentation activity index. The integral gain adjustment in the initial output is suppressed based on the integral inhibition coefficient to obtain the control output. The parameters of the PID controller are adjusted according to the control output to achieve adaptive control of the steaming and proofing cabinet.

2. The intelligent control method for a steaming and proofing integrated cabinet according to claim 1, characterized in that, The rate of change of carbon dioxide concentration is the difference between the carbon dioxide concentration at the current sampling time and the previous sampling time.

3. The intelligent control method for a proofing and steaming integrated cabinet according to claim 1, characterized in that, The step of determining the integral inhibition coefficient includes: comparing the fermentation activity index and the saturation activity threshold; in response to the fermentation activity index being less than the saturation activity threshold, the integral inhibition coefficient is equal to 1; otherwise, the integral inhibition coefficient is negatively correlated with the fermentation activity index and satisfies that the integral inhibition coefficient is not less than the minimum allowable value.

4. The intelligent control method for a steaming and proofing integrated cabinet according to claim 1, characterized in that, Suppressing the integral gain adjustment in the initial output based on the integral suppression coefficient includes multiplying the integral suppression coefficient by the integral gain adjustment in the initial output.

5. The intelligent control method for a steaming and proofing integrated cabinet according to claim 1, characterized in that, The control method further includes: determining that the fermentation process ends when the carbon dioxide concentration in the integrated steaming and waking cabinet reaches a preset concentration threshold or the fermentation time reaches a preset time.

6. The intelligent control method for a proofing and steaming integrated cabinet according to claim 5, characterized in that, After determining that the fermentation process is complete, the steaming stage begins using a preset steam temperature.

7. The intelligent control method for a steaming and proofing integrated cabinet according to claim 1, characterized in that, Each output fuzzy set includes at least positive adjustment, zero adjustment, and negative adjustment.

8. An intelligent control system for a steamer / proofing cabinet, characterized in that, It includes a processor and a memory, the memory storing computer program instructions, which, when executed by the processor, implement an intelligent control method for a steamer cabinet according to any one of claims 1 to 7.

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