A temperature and humidity linkage control method of a cake proofing box

Abstract

This invention relates to the field of food processing control technology and discloses a temperature and humidity linkage control method for a pastry proofing box, comprising: acquiring ambient temperature and humidity and surface temperature and reconstructing real-time volume, extracting microclimate condensation margin by combining phase transition constant; generating expansion activity by normalizing adjacent cycle volume differences, extracting compensation terms by combining thermal volume coefficient, and inversely compensating for and limiting the target humidity; quantifying the actuator time constant mismatch index and feeding it forward into the pre-control quantity, constructing a rheological fracture risk index by fusing temperature and humidity cross gradient, expansion deformation and condensation margin; obtaining the modulation factor based on risk threshold nonlinear mapping, and executing power-based double-sided limiting output instructions on the error data; this technical solution can effectively eliminate inertial delay mismatch, avoid damage to gluten structure, and achieve high-precision adaptive dynamic safety control throughout the fermentation cycle.

Description

Technical Field

[0001] This invention relates to the field of food processing control technology, specifically to a method for temperature and humidity linkage control of a pastry proofing box. Background Technology

[0002] Pastry proofing boxes are core equipment in modern baking to ensure the quality of dough fermentation. The internal temperature and humidity conditions directly determine the degree of gluten network expansion and yeast metabolic activity. However, in industrial and commercial fermentation scenarios, existing temperature and humidity control strategies generally have significant inherent defects. Conventional equipment typically employs independent temperature and humidity control mechanisms, focusing solely on achieving the required parameters within the macroscopic space of the chamber. However, it overlooks the objective law that the saturated vapor pressure of air changes nonlinearly with temperature. This leads to a situation where, at the dynamic boundary between temperature rise and fall, the microclimate on the surface of the dough is prone to supersaturation and condensation or relative dryness and drying. Secondly, dough fermentation is not an inertial load; it involves intense exothermic yeast metabolism and moisture migration. Traditional static parameter control methods cannot detect the transient volume expansion activity of the dough, and rigid targets are easily broken by the dough's own heat source, leading to loss of control. Furthermore, the thermal inertia hysteresis of heating elements is usually much greater than the moisture diffusion time of ultrasonic humidifiers. Existing technology fails to provide feedforward compensation for the inertial mismatch of heterogeneous actuators before driving, resulting in a severe phase difference when temperature and humidity waves come into contact with the dough, instantly widening the local temperature and humidity gradient. This microscopic shear will directly apply highly unstable rheological fracture stress to the surface network of the dough. Once the elastic limit is exceeded, it will cause the fermentation pores to rupture or even the entire structure to collapse. Therefore, there is an urgent need for a temperature and humidity linkage control method for pastry proofing boxes to solve the problems of microclimate imbalance, sluggish dynamic response, actuator inertia mismatch, and easy damage to gluten structure in the existing technologies. Summary of the Invention

[0003] This invention provides a method for temperature and humidity linkage control of a pastry proofing box, which helps to solve the problems mentioned in the background art.

[0004] This invention provides the following technical solution: a method for temperature and humidity linkage control of a pastry proofing box, comprising: The system acquires ambient temperature and humidity and dough surface temperature, obtains real-time volume using surface integral algorithm, and retrieves historical state and underlying parameter data. Based on the phase transition constant, the ambient temperature and humidity are converted to obtain the actual water vapor pressure. Combined with the dough surface temperature, the microclimate condensation margin is calculated and extracted. By combining real-time and historical volume to extract instantaneous expansion velocity, the volume normalization of the expansion velocity difference between adjacent periods is performed to generate expansion activity. A thermodynamic compensation term is generated using the conversion coefficient, expansion activity, and condensation margin, and based on this, the humidity of the basic target is inversely compensated and the boundary is limited. Extract the inertial time constants of the two types of actuators to calculate the mismatch index, and feed this index forward into the target temperature and humidity setting to generate a pre-control quantity; Based on the cross-gradient parameter quantized by the difference between the pre-control quantity and the environmental state, a risk index is constructed by integrating it with the volume expansion ratio and condensation residue. By combining the risk benchmark threshold, the risk index is nonlinearly mapped and calculated to obtain the modulation factor, and the pre-control error is scaled using the gain coefficient. The error scaling data is subjected to double-sided limiting and truncation based on the maximum rated power and zero value, the drive command is output, and the history register is overwritten.

[0005] Optionally, the steps of obtaining ambient temperature and humidity and dough surface temperature, obtaining real-time volume through surface integral algorithm, and retrieving historical state and underlying parameter data include: Activate the high-frequency environmental sensor to obtain the ambient temperature and relative humidity inside the chamber during the current control cycle; The infrared structured light topography scanner is activated to simultaneously acquire the surface temperature of the dough, and the built-in three-dimensional point cloud surface integral algorithm is used to perform integral reconstruction calculations to extract the real-time volume of the dough. Retrieve the stored discrete control sampling period duration, the real-time volume of the dough in the previous sampling period, and the volume expansion rate in the previous sampling period, and retrieve the preset underlying physical parameters and control reference parameters for the execution calculation.

[0006] Optionally, the step of converting ambient temperature and humidity based on the phase transition constant to obtain the actual water vapor pressure, and then calculating and extracting the microclimate condensation margin in conjunction with the dough surface temperature, includes: Read the physical phase transition reference constants of the first, second, and third pure water; The denominator is constructed by summing the ambient temperature inside the chamber with the second pure water physical phase change reference constant. The numerator is constructed by multiplying the ambient temperature inside the chamber with the first pure water physical phase change reference constant. The numerator is divided by the denominator and a natural exponential transformation is performed. The transformation result is multiplied by the percentage value of the ambient relative humidity and the third pure water physical phase change reference constant to obtain the actual water vapor pressure inside the chamber. Divide the actual water vapor pressure inside the box by the third pure water physical phase change reference constant, and further perform a natural logarithmic operation. Then, perform an algebraic transformation on the logarithmic operation result with the first and second pure water physical phase change reference constants to construct a fractional subtraction term. Subtract this fractional subtraction term from the surface temperature of the dough to obtain the microclimate condensation margin.

[0007] Optionally, the step of combining real-time and historical volume extraction of instantaneous expansion velocity, normalizing the volume difference between adjacent periods, and generating expansion activity includes: Extract the discrete control sampling period duration, the real-time volume of the dough, and the real-time volume of the dough in the previous sampling period; The instantaneous volume expansion rate of the current control cycle is calculated by subtracting the real-time volume of the dough from the real-time volume of the previous sampling period, dividing the resulting volume deviation by the duration of the discrete control sampling period. The instantaneous volume expansion rate of the current control cycle is subtracted from the volume expansion rate of the previous sampling cycle. The second-order velocity deviation obtained by the subtraction is divided by the duration of the discrete control sampling cycle. The result of the division operation is then divided by the normalization of the real-time volume execution characteristics of the dough to generate the yeast transient expansion activity index.

[0008] Optionally, the step of generating a thermal compensation term using the conversion coefficient, expansion activity, and condensation margin, and then performing inverse compensation and boundary limiting on the baseline target humidity, includes: Read the heat volume conversion coefficient, the physical slope constant of absolute moisture content, the base target temperature, the base target humidity, and the anti-condensation reference margin; Divide the microclimate condensation margin by the anti-condensation reference margin, and then perform a natural index transformation after taking the negative of the quotient. Multiply the result of the index transformation with the heat volume conversion coefficient and the yeast transient expansion activity index to extract the temperature thermodynamic compensation term. Divide the temperature thermal compensation term by the base target temperature, multiply the quotient by the physical slope constant of the absolute moisture content and the full-scale percentage constant, extract the humidity intervention term, and subtract the humidity intervention term from the base target humidity to generate the first intermediate humidity. The first intermediate humidity is compared with the full-scale percentage constant, and the smaller value between the two is extracted to generate the second intermediate humidity. The second intermediate humidity is compared with the zero reference value, and the larger value between the two is extracted. The dynamic target humidity constrained by the physical boundary saturation limit is output.

[0009] Optionally, the step of extracting the inertial time constants of the two types of actuators to calculate the mismatch index, and then feeding this index forward into the target temperature and humidity setting to generate a pre-control quantity, includes: Read the thermal inertia time constant of the heating element and the diffusion inertia time constant of the ultrasonic humidifier; Subtract the diffusion inertia time constant of the ultrasonic humidifier from the thermal inertia time constant of the heating tube to obtain the inertia difference value. Add the two together to obtain the inertia sum value. Divide the inertia difference value by the inertia sum value to obtain the inertia mismatch feedforward compensation index. Subtract the ambient temperature inside the chamber from the base target temperature, multiply by the inertial mismatch feedforward compensation index, and add the base target temperature and the temperature thermodynamic compensation term to the product to generate the phase shift temperature pre-control quantity. Subtract the ambient relative humidity from the dynamic target humidity, multiply by the inertial mismatch feedforward compensation index, and subtract the product from the dynamic target humidity to generate the phase-shifted humidity pre-control quantity.

[0010] Optionally, the step of quantizing the cross-gradient parameter based on the difference between the pre-control quantity and the environmental state, and fusing it with the volume expansion ratio and condensation residue to construct a risk index includes: Read the baseline volume of the dough; Calculate the absolute deviation between the phase-shift temperature pre-control value and the ambient temperature inside the chamber, and the absolute deviation between the phase-shift humidity pre-control value and the relative humidity of the environment. Divide the two absolute deviations by the thermal inertia time constant of the heating tube and the diffusion inertia time constant of the ultrasonic humidifier, respectively. Multiply the two division quotients to obtain the temperature-humidity cross gradient index. Divide the real-time volume of the dough by the baseline volume of the dough to construct the expansion deformation characteristic quantity. Multiply the temperature and humidity cross gradient index, the expansion deformation characteristic quantity and the microclimate condensation margin together to generate the rheological fracture risk index.

[0011] Optionally, the step of performing a nonlinear mapping calculation on the risk index based on a risk benchmark threshold to obtain a modulation factor, and scaling the pre-control error using a gain coefficient, includes: Read the risk baseline threshold, the electrothermal conversion gain coefficient of the heating element, and the electro-atomization conversion gain coefficient of the humidifier; Subtract the risk benchmark threshold from the rheological fracture risk index, perform a natural exponential transformation on the difference, add a digital benchmark constant to the transformation result, calculate its overall reciprocal, and generate a nonlinear output modulation factor. The temperature pre-control error is extracted by subtracting the ambient temperature inside the chamber from the phase-shift temperature pre-control value. This error is then multiplied by the electrothermal conversion gain coefficient of the heating tube and the nonlinear output modulation factor to extract the physical scaling of the heating channel. The humidity pre-control error is extracted by subtracting the ambient relative humidity from the phase-shifted humidity pre-control amount. This error is then multiplied by the humidifier electro-atomization conversion gain coefficient and the nonlinear output modulation factor to extract the humidification channel physical scaling amount.

[0012] Optionally, the step of performing double-sided limiting and truncation processing on the error scaling data based on the maximum rated power and zero value, outputting drive instructions, and overwriting the history register includes: Read the rated maximum heating power and rated maximum humidification power; The physical scaling of the heating channel is compared with the zero reference value, and the maximum value of the two is extracted. The extracted result is compared with the rated maximum heating power, and the minimum value of the two is extracted. The physically bounded heating power command is then output. The physical scaling of the humidification channel is compared with the zero reference value, and the maximum value of the two is extracted. The extracted result is compared with the rated maximum humidification power, and the minimum value of the two is extracted. The physically bounded humidification power command is then output. At the end of the control cycle, a state storage update instruction is triggered to overwrite the real-time volume of the dough to the storage location of the real-time volume of the dough in the previous sampling cycle, and to overwrite the instantaneous volume expansion rate to the storage location of the volume expansion rate in the previous sampling cycle.

[0013] The present invention has the following beneficial effects: 1. To address the challenges of microclimate imbalance in pastry proofing boxes and the control difficulties arising from dough self-heating that can damage the gluten network, this solution integrates environmental moisture conversion, transient expansion characteristics, and actuator inertial time constants. It extracts microclimate condensation margin and thermal compensation terms, constructs feedforward control variables and a rheological fracture risk index, and then performs nonlinear modulation and dual-sided limiting on the output power. This control logic breaks the isolated state of independent temperature and humidity regulation, cross-coupling the dough's biological metabolic activity with the physical actuator's hysteresis inertia, enabling macroscopic environmental temperature and humidity compensation to adaptively track the thermodynamic boundary of the dough's gas-liquid interface. This multi-dimensional parametric composite soft-measurement risk constraint mechanism allows the system to not only avoid local hydrodynamic shocks caused by the phase difference in the arrival of thermal and humid waves when dealing with temperature abrupt changes or moisture migration disturbances, but also flexibly conform to the rheological limit of the gluten network with bounded convergent driving commands. This nonlinear composite of multi-source information establishes a deep linkage between anti-condensation surface intervention and anti-fracture internal constraints, achieving comprehensive and coordinated protective control that takes into account both biological fermentation kinetics and material rheological boundaries.

[0014] 2. By integrating environmental sensing devices and structured light morphology scanning devices, the system simultaneously acquires the ambient temperature and humidity inside the chamber and the actual surface temperature of the dough within a specific control cycle. It then utilizes a surface integral reconstruction algorithm to perform deep three-dimensional morphology analysis on the spatial point cloud data, accurately extracting the real-time physical volume reflecting the dough's fermentation state. This effectively avoids local perception errors caused by traditional contact measurement or two-dimensional visual estimation. Simultaneously, by rationally adjusting the discrete sampling time interval in conjunction with system baseline parameters, the system precisely matches the slowly changing physical characteristics of dough biological expansion. This prevents the introduction of high-frequency environmental noise due to excessively high sampling frequency, which could cause severe oscillations in subsequent differential calculations. It also avoids missing transient expansion acceleration characteristics due to excessively low sampling frequency, which could lead to control response lag. This provides a solid foundation of sensing data for the entire temperature and humidity linkage control system. 3. By reading the objective benchmark constant of the physical phase transition law of pure water, the system performs natural exponential transformation and proportional scaling algebraic deduction on the collected macroscopic environmental temperature and relative humidity inside the chamber, accurately constructing the actual water vapor pressure state characteristics that reflect the current real water pressure in the space. This breaks the limitation of traditional proofing equipment that relies solely on coarse spatial averages for static settings. Furthermore, the system performs logarithmic operations and cross-fractional integration on the calculated and analyzed actual water vapor pressure state characteristics and objective phase transition constants, and deeply integrates the physical temperature parameters of the dough surface. It then calculates the microclimate condensation margin used to characterize the thermodynamic safety boundary of the dough's gas-liquid interface, achieving advanced quantitative perception and accurate prediction of potential water vapor supersaturation and condensation risks, as well as drying and dehydration hazards in the microenvironment of the dough surface. This allows the system to obtain reliable intervention basis before physical phase transitions. 4. By jointly retrieving the real-time physical space volume of the current control cycle and the historical volume state of the previous sampling cycle, time-domain differential deduction is performed on its macroscopic physical expansion behavior to accurately capture the instantaneous volume expansion evolution speed reflecting the dynamic characteristics of dough biological fermentation. This overcomes the traditional misconception of treating fermented dough as an inert thermodynamic load. To completely eliminate the interference in basic volume calculation caused by the differences in initial dough weight and forming specifications under different process formulas, the expansion speed difference characteristics of adjacent sampling cycles are further extracted. Using the current real-time volume base, a rigorous volume fraction normalization dimensionality reduction process is performed on its second-order discrete change trend, thereby refining the transient expansion activity parameter of the dough. This truly reflects the intensity of yeast community metabolic heat production and water migration, providing a control index for subsequent implementation of thermodynamic adaptive compensation. 5. By reading the thermal volume conversion constant determined based on physical components in pre-experimental studies, and deeply combining and exponentially extrapolating it with the transient expansion activity of dough and the microclimate condensation margin, a dynamically adaptive temperature and thermodynamic compensation feature term is constructed. This solves the rigid problem that fixed parameters cannot be automatically adjusted according to the self-heating intensity of yeast. Furthermore, by utilizing the physical slope characteristics of absolute moisture content and the previously obtained temperature and thermodynamic compensation term, reverse intervention is initiated on the basic target humidity command to ensure that the system can provide sufficient and pre-emptive moisture pressure replenishment when facing sudden changes in box temperature, preventing the dough surface from drying and cracking. On this basis, strict physical boundary constraints are introduced, and a two-sided extreme value chiseling comparison mechanism is used to implement saturation limiting on the humidity index after intervention, preventing over-adjustment from causing the output command to overflow and fail, and effectively ensuring the robust and safe operation of the overall temperature and humidity linkage control system. 6. By analyzing the inherent physical drive hysteresis characteristics of the underlying adjustment hardware, the slow thermal inertia time constant of the heating tube and the rapid diffusion inertia time constant of the ultrasonic humidifier are extracted. Based on the ratio of the difference and sum of the two, the inertia mismatch feedforward compensation parameter is accurately extracted, objectively quantifying the contradiction of inconsistent start-up rates between the system's electrothermal and electro-atomization mechanisms. Subsequently, this mismatch compensation parameter is injected into the adjustment deviation of the control target and the environmental state in the form of feedforward correction, and is superimposed and fused with the pre-compensated dynamic setting target to independently generate a phase shift pre-control quantity with time axis time delay correction characteristics. From the source of the drive command, the two differentiated physical channels achieve synchronous alignment of time phase when they reach the surface space of the dough, thus blocking the microclimate damage chain induced by the temperature and humidity interaction delay misalignment. 7. By leveraging the physical limitation that gluten stress cannot be measured online non-destructively, the absolute deviation of temperature and humidity pre-control parameters relative to the measured environmental conditions is calculated. Combined with the time constant, the temperature and humidity cross-gradient parameter per unit time is quantified, thereby objectively characterizing the local hydrodynamic and thermodynamic shear impact intensity induced by the underlying intervention measures. Furthermore, a benchmark volume reflecting the original characteristics of the dough fermentation process is introduced, and it is combined with the real-time physical volume to construct the expansion deformation rate characteristic. In conjunction with the microclimate condensation margin and the aforementioned cross-gradient, deep phenomenological multi-source information fusion is performed, and cross-multiplication is used to construct a real-time rheological fracture risk index for the dough surface. This process utilizes cross-dimensional external measurable parameters to establish a soft measurement proxy boundary that maps the limit of microscopic damage to the dough, providing mechanical assessment coordinates for subsequent implementation of safety constraint control. 8. By introducing a physical benchmark safety threshold characterizing the critical fracture characteristics of the elastic modulus of gluten energy storage, and based on the flexible extension limit that the gluten network can withstand, a nonlinear clamping command is issued to the entire dynamic system. The real-time rheological fracture risk difference is subjected to natural exponential transformation and reciprocal deduction to smoothly generate a nonlinear output modulation parameter with soft isolation characteristics. This effectively prevents the skin from drying and cracking or the collapse of fermentation pores caused by excessive temperature and humidity response. Furthermore, the objective energy conversion gain coefficients of electrothermal and electro-atomization of industrial equipment are read. The modulation parameter is used in conjunction with the inherent energy gain to perform multiplicative gain scaling on the pre-control physical error of the temperature and humidity dual channels. This ensures that the system converges smoothly to the target temperature and humidity steady state while preventing the execution hardware from generating large and repeated oscillations due to gain mismatch, thus improving the buffering capacity of the overall linkage command. 9. By extracting the rated maximum heating and humidification power with a safety margin design, a physical isolation protection wall is deployed for the terminal drive link. For the dual-channel physical scaling data calculated in the previous step, the chimeric function is screened by extreme value comparison, and strict double-boundary clamping is implemented. This avoids unimplementable negative power logic overflow by eliminating invalid values ​​below zero, and prevents accidents such as hardware overvoltage burnout or system crashes and power failures under extreme environmental error conditions through the upper limit truncation mechanism. This ensures that the finally issued drive instructions are objectively safe and physically bounded and executable. At the same time, at the end of the instruction issuance of the discrete control cycle, the register state update mechanism is triggered in a timely manner to overwrite the currently captured real-time volume and expansion activity of the dough into the historical data storage array, ensuring the stable operation and progress of the timing control chain on the time axis of adjacent cycles. Attached Figure Description

[0015] Figure 1 This is the main flowchart of the temperature and humidity linkage control method of the present invention.

[0016] Figure 2 This is a topology diagram of the sensing and execution hardware architecture of the wake-up box of the present invention.

[0017] Figure 3 This is a block diagram of the core control logic and data closed-loop flow of this invention.

[0018] Figure 4 This is a graph showing the output characteristics of the nonlinear modulation factor for rheological fracture risk in this invention.

[0019] Figure 5 This is a schematic diagram of the dual-sided amplitude limiting protection control logic of the heterogeneous actuator of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Example 1, refer to Figure 1 A method for temperature and humidity linkage control of a pastry proofing box, comprising: The system acquires ambient temperature and humidity and dough surface temperature, obtains real-time volume using surface integral algorithm, and retrieves historical state and underlying parameter data. Based on the phase transition constant, the ambient temperature and humidity are converted to obtain the actual water vapor pressure. Combined with the dough surface temperature, the microclimate condensation margin is calculated and extracted. By combining real-time and historical volume to extract instantaneous expansion velocity, the volume normalization of the expansion velocity difference between adjacent periods is performed to generate expansion activity. A thermodynamic compensation term is generated using the conversion coefficient, expansion activity, and condensation margin, and based on this, the humidity of the basic target is inversely compensated and the boundary is limited. Extract the inertial time constants of the two types of actuators to calculate the mismatch index, and feed this index forward into the target temperature and humidity setting to generate a pre-control quantity; Based on the cross-gradient parameter quantized by the difference between the pre-control quantity and the environmental state, a risk index is constructed by integrating it with the volume expansion ratio and condensation residue. By combining the risk benchmark threshold, the risk index is nonlinearly mapped and calculated to obtain the modulation factor, and the pre-control error is scaled using the gain coefficient. The error scaling data is subjected to double-sided limiting and truncation based on the maximum rated power and zero value, the drive command is output, and the history register is overwritten.

[0022] The process of acquiring ambient temperature and humidity and dough surface temperature, obtaining real-time volume through surface integral algorithm, and retrieving historical state and underlying parameter data includes: Activate the high-frequency environmental sensor to obtain the ambient temperature and relative humidity inside the chamber during the current control cycle; The infrared structured light topography scanner is activated to simultaneously acquire the surface temperature of the dough, and the built-in three-dimensional point cloud surface integral algorithm is used to perform integral reconstruction calculations to extract the real-time volume of the dough. Retrieve the stored discrete control sampling period duration, the real-time volume of the dough in the previous sampling period, and the volume expansion rate in the previous sampling period, and retrieve the preset underlying physical parameters and control reference parameters for the execution calculation.

[0023] The process of converting ambient temperature and humidity based on the phase transition constant to obtain the actual water vapor pressure, and then calculating and extracting the microclimate condensation margin by combining it with the dough surface temperature, includes: Read the physical phase transition reference constants of the first, second, and third pure water; The denominator is constructed by summing the ambient temperature inside the chamber with the second pure water physical phase change reference constant. The numerator is constructed by multiplying the ambient temperature inside the chamber with the first pure water physical phase change reference constant. The numerator is divided by the denominator and a natural exponential transformation is performed. The transformation result is multiplied by the percentage value of the ambient relative humidity and the third pure water physical phase change reference constant to obtain the actual water vapor pressure inside the chamber. Divide the actual water vapor pressure inside the box by the third pure water physical phase change reference constant, and further perform a natural logarithmic operation. Then, perform an algebraic transformation on the logarithmic operation result with the first and second pure water physical phase change reference constants to construct a fractional subtraction term. Subtract this fractional subtraction term from the surface temperature of the dough to obtain the microclimate condensation margin.

[0024] The step of extracting instantaneous expansion velocity by combining real-time and historical volume data, normalizing the volume difference between expansion velocities of adjacent periods, and generating expansion activity includes: Extract the discrete control sampling period duration, the real-time volume of the dough, and the real-time volume of the dough in the previous sampling period; The instantaneous volume expansion rate of the current control cycle is calculated by subtracting the real-time volume of the dough from the real-time volume of the previous sampling period, dividing the resulting volume deviation by the duration of the discrete control sampling period. The instantaneous volume expansion rate of the current control cycle is subtracted from the volume expansion rate of the previous sampling cycle. The second-order velocity deviation obtained by the subtraction is divided by the duration of the discrete control sampling cycle. The result of the division operation is then divided by the normalization of the real-time volume execution characteristics of the dough to generate the yeast transient expansion activity index.

[0025] The process of generating a thermal compensation term using the conversion coefficient, expansion activity, and condensation margin, and then performing inverse compensation and boundary limiting on the baseline target humidity, includes: Read the heat volume conversion coefficient, the physical slope constant of absolute moisture content, the base target temperature, the base target humidity, and the anti-condensation reference margin; Divide the microclimate condensation margin by the anti-condensation reference margin, and then perform a natural index transformation after taking the negative of the quotient. Multiply the result of the index transformation with the heat volume conversion coefficient and the yeast transient expansion activity index to extract the temperature thermodynamic compensation term. Divide the temperature thermal compensation term by the base target temperature, multiply the quotient by the physical slope constant of the absolute moisture content and the full-scale percentage constant, extract the humidity intervention term, and subtract the humidity intervention term from the base target humidity to generate the first intermediate humidity. The first intermediate humidity is compared with the full-scale percentage constant, and the smaller value between the two is extracted to generate the second intermediate humidity. The second intermediate humidity is compared with the zero reference value, and the larger value between the two is extracted. The dynamic target humidity constrained by the physical boundary saturation limit is output.

[0026] The process involves extracting the inertial time constants of the two types of actuators to calculate the mismatch index, then feeding this index forward into the target temperature and humidity setting to generate a pre-control quantity, including: Read the thermal inertia time constant of the heating element and the diffusion inertia time constant of the ultrasonic humidifier; Subtract the diffusion inertia time constant of the ultrasonic humidifier from the thermal inertia time constant of the heating tube to obtain the inertia difference value. Add the two together to obtain the inertia sum value. Divide the inertia difference value by the inertia sum value to obtain the inertia mismatch feedforward compensation index. Subtract the ambient temperature inside the chamber from the base target temperature, multiply by the inertial mismatch feedforward compensation index, and add the base target temperature and the temperature thermodynamic compensation term to the product to generate the phase shift temperature pre-control quantity. Subtract the ambient relative humidity from the dynamic target humidity, multiply by the inertial mismatch feedforward compensation index, and subtract the product from the dynamic target humidity to generate the phase-shifted humidity pre-control quantity.

[0027] The risk index is constructed by quantizing the cross-gradient parameter based on the difference between the pre-control quantity and the environmental state, and integrating it with the volume expansion ratio and condensation residue. This includes: Read the baseline volume of the dough; Calculate the absolute deviation between the phase-shift temperature pre-control value and the ambient temperature inside the chamber, and the absolute deviation between the phase-shift humidity pre-control value and the relative humidity of the environment. Divide the two absolute deviations by the thermal inertia time constant of the heating tube and the diffusion inertia time constant of the ultrasonic humidifier, respectively. Multiply the two division quotients to obtain the temperature-humidity cross gradient index. Divide the real-time volume of the dough by the baseline volume of the dough to construct the expansion deformation characteristic quantity. Multiply the temperature and humidity cross gradient index, the expansion deformation characteristic quantity and the microclimate condensation margin together to generate the rheological fracture risk index.

[0028] The step of combining a risk benchmark threshold with a nonlinear mapping calculation of the risk index to obtain a modulation factor, and scaling the pre-control error using a gain coefficient, includes: Read the risk baseline threshold, the electrothermal conversion gain coefficient of the heating element, and the electro-atomization conversion gain coefficient of the humidifier; Subtract the risk benchmark threshold from the rheological fracture risk index, perform a natural exponential transformation on the difference, add a digital benchmark constant to the transformation result, calculate its overall reciprocal, and generate a nonlinear output modulation factor. The temperature pre-control error is extracted by subtracting the ambient temperature inside the chamber from the phase-shift temperature pre-control value. This error is then multiplied by the electrothermal conversion gain coefficient of the heating tube and the nonlinear output modulation factor to extract the physical scaling of the heating channel. The humidity pre-control error is extracted by subtracting the ambient relative humidity from the phase-shifted humidity pre-control amount. This error is then multiplied by the humidifier electro-atomization conversion gain coefficient and the nonlinear output modulation factor to extract the humidification channel physical scaling amount.

[0029] The process of performing double-sided clipping of the error scaling data based on the maximum rated power and zero value, outputting drive instructions, and overwriting the history register includes: Read the rated maximum heating power and rated maximum humidification power; The physical scaling of the heating channel is compared with the zero reference value, and the maximum value of the two is extracted. The extracted result is compared with the rated maximum heating power, and the minimum value of the two is extracted. The physically bounded heating power command is then output. The physical scaling of the humidification channel is compared with the zero reference value, and the maximum value of the two is extracted. The extracted result is compared with the rated maximum humidification power, and the minimum value of the two is extracted. The physically bounded humidification power command is then output. At the end of the control cycle, a state storage update instruction is triggered to overwrite the real-time volume of the dough to the storage location of the real-time volume of the dough in the previous sampling cycle, and to overwrite the instantaneous volume expansion rate to the storage location of the volume expansion rate in the previous sampling cycle. To address the challenges of microclimate imbalance in pastry proofing boxes and the control difficulties arising from dough self-heating that can damage the gluten network, this solution integrates environmental moisture conversion, transient expansion characteristics, and actuator inertial time constants. It extracts microclimate condensation margin and thermal compensation terms, constructs feedforward control variables and a rheological fracture risk index, and then performs nonlinear modulation and dual-sided limiting on the output power. This control logic breaks the isolated state of independent temperature and humidity regulation, cross-coupling the dough's biological metabolic activity with the hysteresis inertia of the physical actuator. This allows macroscopic environmental temperature and humidity compensation to adaptively track the thermodynamic boundary of the dough's gas-liquid interface. This multi-dimensional parametric composite soft-measurement risk constraint mechanism enables the system to not only avoid local hydrodynamic shocks caused by the phase difference in the arrival of thermal and humid waves when dealing with temperature abrupt changes or moisture migration disturbances, but also to flexibly conform to the rheological limits of the gluten network with bounded convergent driving commands. This nonlinear composite of multi-source information establishes a deep linkage between anti-condensation surface intervention and anti-fracture internal constraints, achieving comprehensive and coordinated protective control that takes into account both biological fermentation kinetics and material rheological boundaries.

[0030] Example 2, as Figure 3 The core control logic and data closed-loop flow diagram shown, a method for temperature and humidity linkage control of a pastry proofing box, further includes the following steps: Step 1: Obtain ambient temperature and humidity and surface temperature, extract real-time volume using surface integral algorithm, and retrieve historical state and underlying physical parameters; like Figure 2 The sensing and execution hardware architecture shown activates a high-frequency environmental sensor upon system startup to acquire the ambient temperature inside the chamber during the current control cycle. relative humidity of the environment ; Activate the infrared structured light morphology scanner to simultaneously acquire the surface temperature of the dough. The built-in 3D point cloud surface integral algorithm is used to perform integral reconstruction calculations, thereby extracting the real-time volume of the dough.

[0031] In the formula, To calculate the real-time volume of the extracted dough, which is a basic measurement obtained from the underlying morphology data; The two-dimensional projection integral region of the dough on the tray plane obtained by the topography scanner; The vertical height function of each three-dimensional coordinate point on the surface of the dough, which is composed of point cloud data, relative to the bottom surface; Retrieve the stored discrete control sampling period duration Real-time volume of dough in the previous sampling period and the volume expansion rate of the previous sampling period. Simultaneously, it retrieves the preset underlying physical parameters and control reference parameters of the hardware for the operation. For discrete control sampling period duration Parameters: It is recommended to set the value within the range of 1 to 5 seconds; Basis for setting the value: The fermentation and expansion of dough is a slow-changing biophysical process, and this range fully meets the truncation requirements of the Nyquist sampling law for slow-changing signals; Sensitivity analysis: If the value is too large, the control system will not be able to capture the transient expansion acceleration in time, thus causing control phase lag; if the value is too small, high-frequency noise from the environmental sensor will be introduced, causing the subsequent differential calculation results to oscillate violently; By integrating environmental sensing devices and structured light morphology scanning devices, the system simultaneously acquires the ambient temperature and humidity inside the chamber and the actual surface temperature of the dough within a specific control cycle. It then uses a surface integral reconstruction algorithm to perform deep three-dimensional morphology analysis on the spatial point cloud data, accurately extracting the real-time physical volume reflecting the dough's fermentation state. This effectively avoids local perception errors caused by traditional contact measurement or two-dimensional visual estimation. Simultaneously, by rationally adjusting the discrete sampling time interval in conjunction with the system's baseline parameters, the system precisely matches the slowly changing physical characteristics of the dough's biological expansion. This prevents the introduction of high-frequency environmental noise due to excessively high sampling frequency, which could cause severe oscillations in subsequent differential calculations. It also avoids missing transient expansion acceleration characteristics due to excessively low sampling frequency, which could lead to control response lag. This provides a solid foundation of sensing data for the entire temperature and humidity linkage control system. Step 2: Based on the phase transition constant, convert the ambient temperature and humidity to obtain the actual water vapor pressure, and combine it with the dough surface temperature to calculate and extract the microclimate condensation margin. Read the physical phase transition reference constants of the first, second, and third pure water, i.e. , as well as ; To accurately quantify the risk of water vapor condensation in the microenvironment of the dough surface, macroscopic environmental parameters need to be extracted. Based on these parameters, a natural exponential transformation and multiplicative scaling calculation are performed to construct the actual water vapor pressure inside the chamber.

[0032] In the formula, The actual water vapor pressure inside the box is obtained through calculation; it is an intermediate calculation quantity. To obtain the ambient relative humidity; To obtain the ambient temperature inside the chamber; , , All of these are objective physical phase transition reference constants for pure water, with fixed values ​​of 0.6112, 17.62, and 243.12, respectively. The actual vapor pressure and the objective phase transition constant are extracted, and logarithmic operations and cross divisions are performed on both. The dough surface temperature is used to perform algebraic derivation of the above calculation results to obtain the microclimate condensation margin, which is used to characterize the thermodynamic safety boundary of the dough's gas-liquid interface.

[0033] In the formula, The microclimate condensation margin obtained for calculation is a target quantity to be obtained; To obtain the surface temperature of the dough; This is the actual water vapor pressure calculated previously; , , The same as the reference constant for the physical phase transition of pure water; By reading the objective benchmark constant of the physical phase transition law of pure water, the system performs natural exponential transformation and proportional scaling algebraic deduction on the collected macroscopic environmental temperature and relative humidity inside the chamber, accurately constructing the actual water vapor pressure state characteristics that reflect the current real water pressure in the space. This breaks the limitation of traditional proofing equipment that relies solely on coarse spatial averages for static settings. Furthermore, the system performs logarithmic operations and cross-fractional integration on the calculated and analyzed actual water vapor pressure state characteristics and objective phase transition constants, and deeply integrates the physical temperature parameters of the dough surface. It then calculates the microclimate condensation margin used to characterize the thermodynamic safety boundary of the dough's gas-liquid interface, achieving advanced quantitative perception and accurate prediction of potential water vapor supersaturation and condensation risks, as well as drying and dehydration hazards in the microenvironment of the dough surface. This allows the system to obtain reliable intervention basis before physical phase transitions. Step 3: Combine real-time and historical volumes to extract instantaneous expansion velocity, and normalize the volume difference between adjacent periods to generate expansion activity; Extract the above discrete control sampling period duration Real-time volume of dough and the real-time volume of dough from the previous sampling period. ; By performing time-domain differentiation on the macroscopic expansion of the dough, the instantaneous volume expansion rate of the current control cycle can be calculated:

[0034] In the formula, The instantaneous volume expansion rate is the calculated value, which is an intermediate calculation quantity; This represents the current real-time volume of the dough. This is the real-time volume of the dough recorded in the previous cycle. This refers to the sampling period duration; To eliminate the cardinal interference caused by differences in initial dough weight, it is necessary to extract the second-order discrete change feature of dough volume; this feature is then normalized using a volume fraction to generate the yeast transient expansion activity index with the reciprocal of the square of time.

[0035] In the formula, The target quantity is to be obtained for calculating the transient swelling activity index of yeast; This represents the volume expansion rate for the current cycle; The volume expansion rate of the previous sampling period is retrieved; This represents the current real-time volume. This refers to the sampling period duration. By jointly retrieving the real-time physical volume of the current control cycle and the historical volume state of the previous sampling cycle, time-domain differential deduction is performed on its macroscopic physical expansion behavior. This accurately captures the instantaneous volume expansion evolution rate, which reflects the dynamic characteristics of dough biological fermentation, overcoming the traditional misconception of treating fermented dough as an inert thermodynamic load. To completely eliminate the interference in basic volume calculation caused by differences in initial dough weight and forming specifications under different process formulas, the expansion rate difference characteristics of adjacent sampling cycles are further extracted. Using the current real-time volume base, a rigorous volume fraction normalization dimensionality reduction process is performed on its second-order discrete change trend, thereby refining the transient expansion activity parameter of the dough. This truly reflects the intensity of yeast community metabolic heat production and water migration, providing a control index for subsequent implementation of thermodynamic adaptive compensation. Step 4: Generate a thermal compensation term using the conversion coefficient, expansion activity, and condensation margin, and then perform reverse compensation and boundary limiting on the basic target humidity. Reading the thermal volume conversion coefficient Absolute moisture content physical slope constant Basic target temperature Basic target humidity and anti-condensation reference margin ; The expansion activity index and the dew point condensation boundary are physically and logically combined; natural index transformation and multiplication are performed on the above combined characteristics to derive a dynamic temperature-thermal compensation term:

[0036] In the formula, The temperature thermodynamic compensation term is calculated and is an intermediate calculation item; The transient swelling activity index of yeast; This represents the condensation margin of the microclimate. To prevent condensation, a margin of safety should be maintained. This is the heat volume conversion coefficient; Regarding the heat volume conversion coefficient Parameters: Based on the reciprocal of the specific heat capacity of flour, it is recommended to set it to a value between 0.5 and 1.5; Basis for value selection: Based on the yeast fermentation heat generation equation and the preliminary experimental determination rules of the physical components of general flour; Sensitivity analysis: If the value is too large, it will lead to an excessive temperature compensation intervention, inducing thermodynamic overshoot of the system; if the value is too small, it will not be able to effectively respond to the self-heating characteristics of active fermentation of dough, resulting in the failure of the compensation mechanism. Reference margin for anti-condensation Parameters: According to empirical physical laws, it is recommended to take values ​​of 1.5 to 3.0 degrees Celsius; Basis for setting values: Based on the critical temperature difference between the threshold of the water activity jump of the dough surface and the latent heat of condensation phase change; Sensitivity analysis: If the value is too large, the system will be too slow to respond to the danger boundary of condensation; if the value is too small, the temperature intervention will be too frequent and conservative, causing the dough surface to be repeatedly in a state of alternating dryness and wetness. By utilizing the extracted temperature thermodynamic compensation term, the humidity setpoint is inversely affected; by introducing percentage physical boundary constraints and performing bilateral extreme value chirping comparison and screening calculations, a dynamic target humidity that does not exceed the limit is constructed.

[0037] In the formula, The dynamic target humidity is calculated, and it is the target quantity obtained. The set baseline target humidity; The base target temperature; This is a temperature and thermal compensation term; The absolute moisture content is the physical slope constant; and The constructed chimeric function is used to ensure that the full-scale percentage physical boundary never overflows; For the physical slope constant of absolute moisture content Parameters: For ambient pressure fermentation, it is recommended to set the value between 2.0 and 4.0; Value basis: Strictly derived from the partial derivative slope of the coupling relationship between absolute moisture content and relative humidity in the enthalpy-humidity diagram of humid air under standard atmospheric pressure; Sensitivity analysis: A value that is too large will not only cause violent fluctuations in the humidity intervention target, affecting fermentation stability and making it easy to exceed the limit; a value that is too small will not be able to provide sufficient reverse moisture pressure compensation when the temperature of the fermentation chamber changes abruptly, thus causing skin formation. By reading the thermal volume conversion constant determined based on physical components in a pre-experimental study, and deeply combining and exponentially extrapolating it with the transient expansion activity of dough and the microclimate condensation margin, a dynamically adaptive temperature and thermodynamic compensation feature term is constructed. This solves the rigid problem that fixed parameters cannot be automatically adjusted according to the self-heating intensity of yeast. Furthermore, by leveraging the physical slope characteristics of absolute moisture content and using the previously obtained temperature and thermodynamic compensation term, a reverse intervention is initiated on the basic target humidity command. This ensures that the system can provide sufficient and pre-emptive moisture pressure replenishment when faced with sudden changes in chamber temperature, preventing the dough surface from drying out and cracking. On this basis, strict physical boundary constraints are introduced, and a two-sided extreme value chiseling comparison mechanism is used to implement saturation limiting on the humidity index after intervention, preventing over-adjustment from causing the output command to overflow and fail. This effectively ensures the robust and safe operation of the overall temperature and humidity linkage control system. Step 5: Extract the inertial time constants of the two types of actuators, calculate the mismatch index, and feed this index forward into the target temperature and humidity setting to generate the pre-control quantity; Read the factory-preset thermal inertia time constant of the heating element. With the diffusion inertial time constant of ultrasonic humidifier ; Extracting the characteristic inertia of both, and by calculating the ratio of the difference term to the sum term, the inertia mismatch feedforward compensation index is extracted:

[0038] In the formula, The obtained inertial mismatch compensation index is used as an intermediate calculation. This is the inherent thermal inertia time constant of the heating element; The time constant of moisture diffusion in an ultrasonic humidifier; The mismatch index is multiplied into the corresponding environmental deviations in the form of feedforward compensation; the product results are then superimposed and injected into the temperature and humidity control target to generate a phase-shift pre-control quantity with time compensation characteristics.

[0039]

[0040] In the formula, To calculate the obtained phase shift temperature pre-control value, Both are target quantities and are pre-control quantities for phase-shifted humidity. The base target temperature; This is a thermal compensation item; For dynamic target humidity; and These are the measured ambient temperature and relative humidity inside the chamber, respectively. This refers to the mismatch compensation index extracted earlier. By analyzing the inherent physical drive hysteresis characteristics of the underlying adjustment hardware, the slow thermal inertia time constant of the heating element and the rapid diffusion inertia time constant of the ultrasonic humidifier are extracted. Based on the ratio of the difference and the sum of the two, the inertia mismatch feedforward compensation parameter is accurately extracted, objectively quantifying the contradiction of inconsistent start-up rates between the system's electrothermal and electro-atomization mechanisms. Subsequently, this mismatch compensation parameter is injected into the adjustment deviations of the control target and the environmental state in the form of feedforward correction, and is superimposed and fused with the dynamically set target after prior compensation to independently generate a phase shift pre-control quantity with time axis time delay correction characteristics. From the source of the drive command, the two differentiated physical channels achieve synchronous alignment of time phase when they reach the surface space of the dough, thus blocking the microclimate damage chain induced by the temperature and humidity interaction delay misalignment. Step 6: Quantify the cross-gradient parameters based on the difference between the pre-control quantity and the environmental state, and integrate them with the volume expansion ratio and condensation allowance to construct a risk index; Read the initial dough baseline volume ; Based on dough reference volume Parameters: Recommended values: Set the theoretical volume base based on the initial weight and density of the dough in each process unit; Basis for values: Pure physical calibration volume base value of the dough when it is placed in the box without any fermentation or biochemical reaction; Sensitivity analysis: If the value is too large, it will artificially reduce the volume expansion ratio denominator, resulting in a serious underestimation of the risk characteristics; if the value is too small, it will erroneously amplify the expansion ratio, making the control system very prone to triggering erroneous and frequent safety clamping logic; Due to the physical limitation that gluten stress cannot be measured online non-destructively, it is necessary to calculate the absolute values ​​of temperature deviation and humidity deviation separately; then, divide these absolute values ​​by the corresponding inertial constants to obtain the temperature and humidity cross-gradient exponent per unit time.

[0041] In the formula, The temperature and humidity cross gradient index is calculated, which is an intermediate calculation. and The aforementioned pre-control quantity; and The current ambient temperature and humidity; and The inertial time constant for reading; Macroscopic disturbance gradients of temperature and humidity, dough expansion deformation rate, and microclimate boundary state characteristics were extracted; the expansion deformation rate was characterized by the ratio of the baseline volume to the real-time volume; the above multi-source characteristics were phenomenologically fused and multiplied to construct a real-time rheological fracture risk index for the dough surface.

[0042] In the formula, To calculate the obtained surface rheological fracture risk index of dough, it is used as the phenomenological proxy mapping target quantity of the system damage limit; Cross-gradient exponent; The measured real-time volume of the dough; The set dough baseline volume; This represents the condensation margin of the microclimate. By leveraging the physical limitation that gluten stress cannot be measured online non-destructively, the absolute deviation of temperature and humidity pre-control parameters relative to the measured environmental conditions is calculated. Combined with the time constant, the temperature and humidity cross-gradient parameter per unit time is quantified, thereby objectively characterizing the local hydrodynamic and thermodynamic shear impact intensity induced by the underlying intervention measures. Furthermore, a benchmark volume reflecting the original characteristics of the dough fermentation process is introduced, and it is combined with the real-time physical volume to construct an expansion deformation rate feature. In conjunction with the microclimate condensation margin and the aforementioned cross-gradient, deep phenomenological multi-source information fusion is performed, and cross-multiplication is used to construct a real-time rheological fracture risk index for the dough surface. This process utilizes cross-dimensional external measurable parameters to establish a soft measurement proxy boundary that maps the limit of microscopic damage to the dough, providing mechanical assessment coordinates for subsequent implementation of safety constraint control. Step 7: Combine the risk benchmark threshold, perform nonlinear mapping calculation on the risk index to obtain the modulation factor, and use the gain coefficient to scale the pre-control error; Read the calibration risk benchmark threshold Thermoelectric conversion gain coefficient of heating tube and the gain coefficient of humidifier electro-atomization conversion ; For the calibration risk benchmark threshold Parameters: It is recommended to calibrate within the range of 0.6 to 1.2 for high-gluten flour; Basis for value selection: derived from the physical product of gluten storage elastic modulus and critical fracture strain, the absolute safety threshold of the soft measurement proxy boundary is derived; Sensitivity analysis: if the value is too large, the subsequent nonlinear clamping gating function will become ineffective, inducing micro-cracks and dehydration of the dough; if the value is too small, the control system will be too sensitive to small fluctuations, thereby significantly reducing the output power and slowing down the overall fermentation rate; Regarding the conversion gain coefficient and Parameters: It is recommended to set them according to the energy conversion rate of the hardware's rated parameters; the basis for the value is the objective physical efficiency of the conversion of electrical energy to heat energy and the conversion of ultrasonic atomization kinetic energy in industrial equipment; sensitivity analysis: values ​​that are too large are likely to cause the system to repeatedly start and stop and oscillate; values ​​that are too small will cause the entire nonlinear adjustment system to take too long to reach the set temperature and humidity target. Based on the physical limits of the gluten network, a safety clamp command is issued to the system; the natural exponential transformation of the risk difference is calculated, and the reciprocal of the transformation result is taken to generate a result such as... Figure 4 The nonlinear output modulation factor of the output characteristic curve shown:

[0043] In the formula, The nonlinear output modulation factor is calculated, and is the intermediate target quantity used for amplitude clamping; This is a rheological fracture risk index. This serves as a risk benchmark threshold. Using the nonlinear output modulation factor and the corresponding gain coefficient, the pre-control error of each channel is derived and calculated; the physical scaling of the heating channel is then constructed. Physical scaling of humidification channel :

[0044]

[0045] In the formula, and These are the obtained intermediate-state physical scaling error characteristic data; Modulation factor; and The gain coefficient for the electrothermal to electroatomization conversion; and For pre-control quantity; and The current ambient temperature and humidity; By introducing a physical benchmark safety threshold characterizing the critical fracture characteristics of the elastic modulus of gluten energy storage, and based on the flexible extension limit that the gluten network can withstand, a nonlinear clamping command is issued to the entire dynamic system. The real-time rheological fracture risk difference is subjected to natural exponential transformation and reciprocal deduction to smoothly generate a nonlinear output modulation parameter with soft isolation characteristics. This effectively prevents the dough from drying and cracking or the collapse of fermentation pores due to excessive temperature and humidity response. Furthermore, the objective energy conversion gain coefficients of electrothermal and electro-atomization of industrial equipment are read. The modulation parameter is used in conjunction with the inherent energy gain to perform multiplicative gain scaling on the pre-control physical error of the temperature and humidity dual channels. This ensures that the system converges smoothly to the target temperature and humidity steady state while preventing the execution hardware from generating large and repeated oscillations due to gain mismatch, thus improving the buffering capacity of the overall linkage command. Step 8: Perform double-sided limiting and truncation processing based on the maximum rated power and zero value on the error scaling data, output the drive instruction, and then overwrite the history register; Read the factory-preset rated maximum heating power and rated maximum humidification power ; For rated maximum power and Parameters: It is recommended to conservatively set the value to 80% to 90% of the rated safe operating power as indicated on the actuator nameplate; Basis for value selection: Safety design margin that industrial equipment must reserve to cope with long-term continuous heavy-load operation; Sensitivity analysis: If the value is too large or not limited, it will directly cause hard physical shutdown failures such as equipment overload burnout or power grid fluctuations under extreme error conditions; If the value is too small, it will artificially limit the ultimate potential of the equipment hardware, resulting in insufficient heating and humidification compensation capability under extreme temperature difference environments; Based on the pre-control error scaling data, an extreme value clamping function is used to increase the physical limit clamping of the actuator's dual-side drive; the control quantity of each channel is calculated independently, and reference is made to... Figure 5 The dual-sided limiting protection control logic shown outputs the final device power drive command within the rated operating boundary:

[0046]

[0047] In the formula, To ultimately obtain and issue the heating power command to the heating element execution end; This is the final humidification power command sent to the execution end of the ultrasonic humidifier; and This refers to the physical scaling of the heating and humidification channels obtained in the previous steps; and This is the rated maximum heating and humidifying power; Chip functions are used to avoid non-executable negative power overflow. Chip functions are used to avoid the risk of overpower overload; At the end of the control cycle, the state storage update logic is triggered, performing a memory overwrite operation on the control cycle data: the current state quantity is overwritten and updated to the storage address corresponding to the previous sampling cycle, specifically: let The stored value is updated to And let The stored value is updated to ; By extracting the rated maximum heating and humidification power with a safety margin design, a physical isolation protection wall is deployed for the terminal drive link. For the dual-channel physical scaling data calculated in the previous step, the chimeric function is screened by extreme value comparison, and strict double-boundary clamping is implemented. This avoids unfeasible negative power logic overflow by eliminating invalid values ​​below zero, and prevents accidents such as hardware overvoltage burnout or system crashes and power failures under extreme environmental error conditions through the upper limit truncation mechanism. This ensures that the finally issued drive instructions are objectively safe and physically bounded and executable. At the end of the instruction issuance in the discrete control cycle, the register state update mechanism is triggered in a timely manner to overwrite the currently captured real-time dough volume and expansion activity into the historical data storage array, ensuring the stable operation and progress of the timing control chain on the time axis of adjacent cycles.

[0048] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0049] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for temperature and humidity linkage control of a pastry proofing box, characterized in that, include: The system acquires ambient temperature and humidity and dough surface temperature, obtains real-time volume using surface integral algorithm, and retrieves historical state and underlying parameter data. Based on the phase transition constant, the ambient temperature and humidity are converted to obtain the actual water vapor pressure. Combined with the dough surface temperature, the microclimate condensation margin is calculated and extracted. By combining real-time and historical volume to extract instantaneous expansion velocity, the volume normalization of the expansion velocity difference between adjacent periods is performed to generate expansion activity. A thermodynamic compensation term is generated using the conversion coefficient, expansion activity, and condensation margin, and based on this, the humidity of the basic target is inversely compensated and the boundary is limited. Extract the inertial time constants of the two types of actuators to calculate the mismatch index, and feed this index forward into the target temperature and humidity setting to generate a pre-control quantity; Based on the cross-gradient parameter quantized by the difference between the pre-control quantity and the environmental state, a risk index is constructed by integrating it with the volume expansion ratio and condensation residue. By combining the risk benchmark threshold, the risk index is nonlinearly mapped and calculated to obtain the modulation factor, and the pre-control error is scaled using the gain coefficient. The error scaling data is subjected to double-sided limiting and truncation based on the maximum rated power and zero value, the drive command is output, and the history register is overwritten.

2. The temperature and humidity linkage control method for a pastry proofing box according to claim 1, characterized in that, The process of acquiring ambient temperature and humidity and dough surface temperature, obtaining real-time volume through surface integral algorithm, and retrieving historical state and underlying parameter data includes: Activate the high-frequency environmental sensor to obtain the ambient temperature and relative humidity inside the chamber during the current control cycle; The infrared structured light topography scanner is activated to simultaneously acquire the surface temperature of the dough, and the built-in three-dimensional point cloud surface integral algorithm is used to perform integral reconstruction calculations to extract the real-time volume of the dough. Retrieve the stored discrete control sampling period duration, the real-time volume of the dough in the previous sampling period, and the volume expansion rate in the previous sampling period, and retrieve the preset underlying physical parameters and control reference parameters for the execution calculation.

3. The temperature and humidity linkage control method for a pastry proofing box according to claim 1, characterized in that, The process of converting ambient temperature and humidity based on the phase transition constant to obtain the actual water vapor pressure, and then calculating and extracting the microclimate condensation margin by combining it with the dough surface temperature, includes: Read the physical phase transition reference constants of the first, second, and third pure water; The denominator is constructed by summing the ambient temperature inside the chamber with the second pure water physical phase change reference constant. The numerator is constructed by multiplying the ambient temperature inside the chamber with the first pure water physical phase change reference constant. The numerator is divided by the denominator and a natural exponential transformation is performed. The transformation result is multiplied by the percentage value of the ambient relative humidity and the third pure water physical phase change reference constant to obtain the actual water vapor pressure inside the chamber. Divide the actual water vapor pressure inside the box by the third pure water physical phase change reference constant, and further perform a natural logarithmic operation. Then, perform an algebraic transformation on the logarithmic operation result with the first and second pure water physical phase change reference constants to construct a fractional subtraction term. Subtract this fractional subtraction term from the surface temperature of the dough to obtain the microclimate condensation margin.

4. The temperature and humidity linkage control method for a pastry proofing box according to claim 1, characterized in that, The step of extracting instantaneous expansion velocity by combining real-time and historical volume data, normalizing the volume difference between expansion velocities of adjacent periods, and generating expansion activity includes: Extract the discrete control sampling period duration, the real-time volume of the dough, and the real-time volume of the dough in the previous sampling period; The instantaneous volume expansion rate of the current control cycle is calculated by subtracting the real-time volume of the dough from the real-time volume of the previous sampling period, dividing the resulting volume deviation by the duration of the discrete control sampling period. The instantaneous volume expansion rate of the current control cycle is subtracted from the volume expansion rate of the previous sampling cycle. The second-order velocity deviation obtained by the subtraction is divided by the duration of the discrete control sampling cycle. The result of the division operation is then divided by the normalization of the real-time volume execution characteristics of the dough to generate the yeast transient expansion activity index.

5. The temperature and humidity linkage control method for a pastry proofing box according to claim 1, characterized in that, The process of generating a thermal compensation term using the conversion coefficient, expansion activity, and condensation margin, and then performing inverse compensation and boundary limiting on the baseline target humidity, includes: Read the heat volume conversion coefficient, the physical slope constant of absolute moisture content, the base target temperature, the base target humidity, and the anti-condensation reference margin; Divide the microclimate condensation margin by the anti-condensation reference margin, and then perform a natural index transformation after taking the negative of the quotient. Multiply the result of the index transformation with the heat volume conversion coefficient and the yeast transient expansion activity index to extract the temperature thermodynamic compensation term. Divide the temperature thermal compensation term by the base target temperature, multiply the quotient by the physical slope constant of the absolute moisture content and the full-scale percentage constant, extract the humidity intervention term, and subtract the humidity intervention term from the base target humidity to generate the first intermediate humidity. The first intermediate humidity is compared with the full-scale percentage constant, and the smaller value between the two is extracted to generate the second intermediate humidity. The second intermediate humidity is compared with the zero reference value, and the larger value between the two is extracted. The dynamic target humidity constrained by the physical boundary saturation limit is output.

6. The temperature and humidity linkage control method for a pastry proofing box according to claim 1, characterized in that, The process involves extracting the inertial time constants of the two types of actuators to calculate the mismatch index, then feeding this index forward into the target temperature and humidity setting to generate a pre-control quantity, including: Read the thermal inertia time constant of the heating element and the diffusion inertia time constant of the ultrasonic humidifier; Subtract the diffusion inertia time constant of the ultrasonic humidifier from the thermal inertia time constant of the heating tube to obtain the inertia difference value. Add the two together to obtain the inertia sum value. Divide the inertia difference value by the inertia sum value to obtain the inertia mismatch feedforward compensation index. Subtract the ambient temperature inside the chamber from the base target temperature, multiply by the inertial mismatch feedforward compensation index, and add the base target temperature and the temperature thermodynamic compensation term to the product to generate the phase shift temperature pre-control quantity. Subtract the ambient relative humidity from the dynamic target humidity, multiply by the inertial mismatch feedforward compensation index, and subtract the product from the dynamic target humidity to generate the phase-shifted humidity pre-control quantity.

7. The temperature and humidity linkage control method for a pastry proofing box according to claim 1, characterized in that, The risk index is constructed by quantizing the cross-gradient parameter based on the difference between the pre-control quantity and the environmental state, and integrating it with the volume expansion ratio and condensation residue. This includes: Read the baseline volume of the dough; Calculate the absolute deviation between the phase-shift temperature pre-control value and the ambient temperature inside the chamber, and the absolute deviation between the phase-shift humidity pre-control value and the relative humidity of the environment. Divide the two absolute deviations by the thermal inertia time constant of the heating tube and the diffusion inertia time constant of the ultrasonic humidifier, respectively. Multiply the two division quotients to obtain the temperature-humidity cross gradient index. Divide the real-time volume of the dough by the baseline volume of the dough to construct the expansion deformation characteristic quantity. Multiply the temperature and humidity cross gradient index, the expansion deformation characteristic quantity and the microclimate condensation margin together to generate the rheological fracture risk index.

8. The temperature and humidity linkage control method for a pastry proofing box according to claim 1, characterized in that, The step of combining a risk benchmark threshold with a nonlinear mapping calculation of the risk index to obtain a modulation factor, and scaling the pre-control error using a gain coefficient, includes: Read the risk baseline threshold, the electrothermal conversion gain coefficient of the heating element, and the electro-atomization conversion gain coefficient of the humidifier; Subtract the risk benchmark threshold from the rheological fracture risk index, perform a natural exponential transformation on the difference, add a digital benchmark constant to the transformation result, calculate its overall reciprocal, and generate a nonlinear output modulation factor. The temperature pre-control error is extracted by subtracting the ambient temperature inside the chamber from the phase-shift temperature pre-control value. This error is then multiplied by the electrothermal conversion gain coefficient of the heating tube and the nonlinear output modulation factor to extract the physical scaling of the heating channel. The humidity pre-control error is extracted by subtracting the ambient relative humidity from the phase-shifted humidity pre-control amount. This error is then multiplied by the humidifier electro-atomization conversion gain coefficient and the nonlinear output modulation factor to extract the humidification channel physical scaling amount.

9. The temperature and humidity linkage control method for a pastry proofing box according to claim 1, characterized in that, The process of performing double-sided clipping of the error scaling data based on the maximum rated power and zero value, outputting drive instructions, and overwriting the history register includes: Read the rated maximum heating power and rated maximum humidification power; The physical scaling of the heating channel is compared with the zero reference value, and the maximum value of the two is extracted. The extracted result is compared with the rated maximum heating power, and the minimum value of the two is extracted. The physically bounded heating power command is then output. The physical scaling of the humidification channel is compared with the zero reference value, and the maximum value of the two is extracted. The extracted result is compared with the rated maximum humidification power, and the minimum value of the two is extracted. The physically bounded humidification power command is then output. At the end of the control cycle, a state storage update instruction is triggered to overwrite the real-time volume of the dough to the storage location of the real-time volume of the dough in the previous sampling cycle, and to overwrite the instantaneous volume expansion rate to the storage location of the volume expansion rate in the previous sampling cycle.

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