Danish pizza base making method and system based on variable temperature proofing control

Abstract

The application relates to the technical field of food processing automation, and relates to a Danish cancon pizza bottom manufacturing method and system based on variable-temperature proofing control, which comprises the following steps: weighing flour, calculating the target quality of other raw materials, proportionally weighing and conveying the raw materials to a mixing bin, stirring the raw materials according to a stirring frequency set according to the total material quality to obtain a to-be-proofed dough blank, feeding the to-be-proofed dough blank into a variable-temperature proofing box, setting a plurality of proofing time periods and target temperatures and target time lengths of each time period, collecting actual temperatures and proofing time lengths, calculating the real-time output power of a semiconductor temperature regulator and synchronously operating the semiconductor temperature regulator, obtaining a proofed dough blank after proofing, sequentially performing pre-baking and cooling operations to obtain a finished product, and constructing a variable-temperature control model based on proofing data for subsequent batch power calculation. The application can realize accurate staged closed-loop control of temperature in the proofing process, and improves the consistency of the quality of Danish cancon pizza bottoms and the automatic production efficiency.

Description

Technical Field

[0001] This invention relates to the field of food processing automation technology, and in particular to a method and system for making Danish croissant pizza crust based on variable temperature proofing control. Background Technology

[0002] With the rapid development of industrial baking, Danish croissant pizza crusts have become a popular product in the pizza industry due to their crisp, layered texture and wide applicability. Against this backdrop, the proofing process in pizza crust making faces the challenge of precise temperature control. Variable-temperature proofing control technology aims to allow the dough to undergo low-temperature oil-locking, medium-temperature fermentation, and high-temperature shaping at different stages, which is of crucial practical significance for ensuring the product's flaky layers, volume expansion, and consistent taste.

[0003] Traditional proofing equipment often uses a constant temperature chamber or a simple multi-segment timed temperature control method. This is achieved by operators setting several fixed temperature values ​​and time periods based on experience, and the equipment switches the temperatures sequentially. However, this open-loop control with fixed parameters cannot dynamically adjust the heating or cooling power based on the actual temperature feedback of the green billet. This results in a large deviation between the actual and target temperatures, severe temperature overshoot or lag, and the initial temperature differences between different batches of green billets and environmental fluctuations cannot be automatically compensated for. Summary of the Invention

[0004] This invention provides a method and system for making Danish croissant pizza bases based on variable temperature proofing control. Its main purpose is to achieve precise, phased, closed-loop control of temperature during the proofing process, thereby improving the consistency of Danish croissant pizza base quality and the efficiency of automated production.

[0005] To achieve the above objectives, the present invention provides a method for making Danish croissant pizza crust based on variable temperature proofing control, comprising:

[0006] Receive a pizza base preparation instruction and start a pizza base preparation device according to the pizza base preparation instruction, wherein the pizza base preparation device includes a conveyor, a mixing chamber, a mixer and a variable temperature proofing chamber;

[0007] Obtain basic materials, including flour, butter, eggs and drinking water. Flour is transported to a mixing silo via a conveyor and its mass is weighed after transport. The mass ratios of flour to butter, eggs and drinking water are set. Based on the flour mass and the mass ratios, the target masses of butter, eggs and drinking water are calculated.

[0008] Weigh out the target mass of butter, eggs and drinking water from the base materials, and transport the weighed butter, eggs and drinking water to the mixing silo via a conveyor. Set the stirring frequency based on the target mass, and command the stirrer to stir at the set stirring frequency to obtain the preform to be awakened.

[0009] The blank to be awakened is sent into a variable temperature awakening chamber, which is equipped with a semiconductor temperature controller and a digital sensor. Multiple awakening periods are set, and each awakening period has a target temperature and a target duration preset. The digital sensor collects the actual temperature inside the variable temperature awakening chamber and the awakening time of the blank to be awakened. Based on the awakening time, target temperature, target duration and actual temperature, the real-time output power of the semiconductor temperature controller is calculated, and the semiconductor temperature controller is commanded to run synchronously at the real-time output power. After all awakening periods have passed, the awakened blank is obtained.

[0010] The proofed dough is pre-baked and cooled sequentially to obtain the target Danish croissant pizza base. A variable temperature control model is constructed based on the proofing time, real-time output power, target temperature and actual temperature. The real-time output power is then calculated using the variable temperature control model to complete the production of the Danish croissant pizza base based on variable temperature proofing control.

[0011] Optionally, the step of using the digital sensor to collect the actual temperature inside the variable-temperature proofing chamber and the proofing time of the blank to be proofed, and calculating the real-time output power of the semiconductor temperature controller based on the proofing time, target temperature, target duration, and actual temperature, includes:

[0012] The digital sensor is used to collect the actual temperature inside the variable temperature proofing box and the proofing time of the blank to be proofed. The collection time interval and the number of data collections of the digital sensor are recorded simultaneously. After each collection, the temperature difference between the target temperature and the actual temperature is calculated.

[0013] Confirm the current wake-up period and read the target temperature and target duration corresponding to the current wake-up period;

[0014] A temperature difference threshold is set. Based on the temperature difference threshold, the temperature difference, the existing warming time, and the target duration corresponding to the current warming period, the power regulation intensity of the semiconductor temperature controller is calculated according to the following formula:

[0015]

[0016] in, For the first Power adjustment intensity during the second acquisition The duration of wakefulness. The target duration corresponding to the current wake-up period. For the first Temperature difference value at each sampling point Temperature difference threshold It is a natural constant;

[0017] Calculate the ratio of the duration of the current warm-up period to the target duration of the current warm-up period. Determine the current warm-up sub-stage based on the temperature difference and the duration ratio. Calculate the real-time output power of the semiconductor temperature controller based on the determination result of the warm-up sub-stage, combined with the power adjustment intensity, the sampling time interval, and the number of data samplings.

[0018] Optionally, the step of calculating the real-time output power of the semiconductor temperature controller based on the determination result of the waking-up sub-stage, combined with the power adjustment intensity, the sampling time interval, and the number of data samplings, includes:

[0019] Obtain the reference power of the semiconductor temperature controller and confirm the current wake-up sub-stage, wherein the wake-up sub-stage corresponds to one of the stage types: heating stage, steady state stage, and cooling stage.

[0020] If the current waking sub-stage is in the heating stage, the product of the power regulation intensity and the reference power is used as the real-time output power.

[0021] If the current wake-up sub-stage is in a steady state, the real-time output power is calculated according to the following formula:

[0022]

[0023] in, For the first Real-time output power during the first data acquisition. For the first Power adjustment intensity during the second acquisition As the reference power, The time interval for data collection. The number of times data was collected. For the first Temperature difference value at the time of the second data collection;

[0024] If the current waking sub-stage is in the cooling stage, calculate the rate of change of the actual temperature over time to obtain the temperature change rate. Use the absolute value of the temperature change rate as the power reduction coefficient. Calculate the product of the absolute value of the temperature difference and the power reduction coefficient to obtain the power reduction amount. Calculate the difference between the reference power and the power reduction amount to obtain the real-time output power.

[0025] Optionally, the step of sequentially performing pre-baking and cooling operations on the proofed dough to obtain the target Danish croissant pizza base includes:

[0026] Set a pre-baking parameter set and send the proofed dough into a hot air circulating oven. The hot air circulating oven has multiple temperature monitoring points built in and performs a pre-baking operation on the proofed dough according to the set pre-baking parameter set.

[0027] The real-time temperature of each temperature monitoring point is continuously collected, and the temperature standard deviation and temperature non-uniformity are calculated based on the real-time temperature.

[0028] Based on the temperature standard deviation and temperature non-uniformity, the pre-baking parameter set is iteratively updated. After each iteration, an updated pre-baking parameter set is obtained, and the hot air circulation oven is immediately adjusted in real time with the updated pre-baking parameter set until the optimal pre-baking parameter set is iteratively updated. The hot air circulation oven is then adjusted with the optimal pre-baking parameter set, and the pre-baked crust is obtained after the pre-baking operation is completed.

[0029] The pre-baked dough is removed from the hot air circulating oven and transferred to a preset cooling device to perform a cooling operation, thereby obtaining the target Danish croissant pizza base.

[0030] Optionally, the step of performing an iterative update operation on the pre-baking parameter set, obtaining an updated pre-baking parameter set after each iteration, and immediately adjusting the hot air circulation oven in real time with the updated pre-baking parameter set until the optimal pre-baking parameter set is obtained through iterative updates, includes:

[0031] Confirm the pre-baking parameter set, wherein the pre-baking parameter set consists of multiple parameters, and sequentially set the adjustment step size and value range of each parameter;

[0032] The temperature standard deviation and temperature non-uniformity are weighted and summed to obtain a comprehensive evaluation index. The comprehensive evaluation index is marked as the current index, and a convergence threshold for the comprehensive evaluation index is set.

[0033] Select individual parameters from the pre-baking parameter set in sequence, mark them as the current parameters, and then perform the following parameter tuning operations in sequence:

[0034] Increase the current parameter by one adjustment step and calculate the corresponding comprehensive evaluation index to obtain the positive index;

[0035] Reduce the current parameter by one adjustment step and calculate the corresponding comprehensive evaluation index to obtain the negative index.

[0036] The positive and negative indicators are compared numerically. If the positive indicator is smaller, the current parameter is increased by one step. If the negative indicator is smaller, the current parameter is decreased by one step. If the positive and negative indicators are equal, the current parameter remains unchanged.

[0037] After adjusting the current parameter, continue to make a judgment: if the value of the current parameter is less than the lower limit of the corresponding value range, then assign the current parameter the lower limit of the value range.

[0038] If the value of the current parameter is greater than the upper limit of its value range, then the current parameter is set to the upper limit of its value range;

[0039] After traversing and performing parameter tuning operations on all parameters in the pre-baking parameter set, an updated pre-baking parameter set is obtained. The comprehensive evaluation index corresponding to the updated pre-baking parameter set is calculated and marked as the updated index. The absolute value of the difference between the updated index and the current index is calculated. If the absolute value of the difference is less than the set convergence threshold, the updated index is used as the current index for the new round of iteration. The parameter adjustment process is repeated until the absolute value of the difference between the updated index and the current index is less than the convergence threshold, and the optimal pre-baking parameter set is generated.

[0040] Optionally, the step of removing the pre-baked dough from the hot air circulating oven and transferring it to a preset cooling device to perform a cooling operation on the pre-baked dough to obtain the target Danish croissant pizza dough includes:

[0041] The pre-baked dough is removed from the hot air circulating oven and transferred to a preset cooling device, wherein the preset cooling device has a built-in circulating fan and a temperature sensor;

[0042] Set a first wind speed value and a second wind speed value for the circulating fan, wherein the first wind speed value is greater than the second wind speed value;

[0043] A first temperature threshold and a second temperature threshold are set, wherein the first temperature threshold is greater than the second temperature threshold;

[0044] The circulating fan is started at the first wind speed value to perform forced convection cooling, and the temperature sensor is used to continuously monitor the center temperature of the pre-baked dough.

[0045] When the temperature at the center of the pie crust drops to the first temperature threshold, the fan speed is reduced to the second speed, and forced convection cooling continues until the difference between the temperature at the center of the pie crust and the current ambient temperature is less than the second temperature threshold, thus completing the cooling operation.

[0046] Let the pre-baked dough rest at room temperature for 15 to 30 minutes to obtain the desired Danish croissant pizza base.

[0047] Optionally, the step of constructing a variable temperature control model based on the wake-up time, real-time output power, target temperature, and actual temperature includes:

[0048] Collect multiple time-by-time operational data during the wake-up process. The time-by-time operational data consists of the wake-up duration, target temperature, actual temperature, and real-time output power at each time point.

[0049] Each of the time-by-time running data is used to form a sample, and the real-time output power in the sample is used as the sample real-time output power.

[0050] A variable temperature control model is constructed based on a neural network algorithm, wherein the variable temperature control model includes an input layer, a processing layer and an output layer;

[0051] Each sample is selected sequentially, and the input layer is used to construct an input parameter sequence from the wake-up time, target temperature, and the calculated temperature difference between the target temperature and the actual temperature in the training sample. The input parameter sequence is then input into the variable temperature control model.

[0052] By setting processing weights, the processing layer sequentially performs weighted operations, feature extraction, and summation on the input parameter sequence based on the processing weights to obtain the original output value.

[0053] By setting output weights and output bias values, the original output values ​​are weighted based on the output weights and then superimposed with the output bias values ​​to obtain the predicted real-time output power.

[0054] The difference between the predicted real-time output power and the sample real-time output power is calculated to obtain the power deviation value. The processing weight and output weight are iteratively adjusted based on the power deviation value until the predicted real-time output power is equal to the sample real-time output power.

[0055] The variable temperature control model is constructed when the predicted real-time output power of all samples is equal to the real-time output power of the samples.

[0056] Optionally, the setting of processing weights, utilizing the processing layer to sequentially perform weighted operations, feature extraction, and summation operations on the input parameter sequence based on the processing weights to obtain the original output value, includes:

[0057] The processing layer is confirmed and the processing weights are set. The processing layer includes a preprocessing layer, an intermediate processing layer and a postprocessing layer. The processing weights include preprocessing weights, intermediate processing weights and postprocessing weights.

[0058] The preprocessing layer receives the input parameter sequence, and performs a weighted operation on the wake-up time, target temperature, and calculated temperature difference between the target temperature and the actual temperature in the input parameter sequence according to the preprocessing weight to obtain multiple preprocessing values. The multiple preprocessing values ​​are then combined to generate a preprocessing sequence.

[0059] The intermediate processing layer receives the preprocessed sequence, calculates the difference between each group of adjacent preprocessed values ​​in the preprocessed sequence as the trend feature value, and calculates the difference between the maximum and minimum values ​​of the preprocessed values ​​in the preprocessed sequence to obtain the sequence deviation. The trend feature value and the sequence deviation are concatenated in the order of generation to obtain the trend sequence.

[0060] The trend sequence is weighted according to the intermediate processing weight to obtain multiple intermediate processing values. The intermediate processing values ​​are then arranged according to the original splicing order of the trend sequence to obtain the intermediate processing sequence.

[0061] The intermediate processing sequence is received by the post-processing layer, and the intermediate processing values ​​are weighted by matching them with post-processing weights to obtain multiple post-processing values. The original output value is obtained by summing all the post-processing values.

[0062] Optionally, the iterative adjustment of the processing weights and output weights based on the power deviation value includes:

[0063] If the predicted real-time output power is less than the sample real-time output power, based on the power deviation value, the output weight, output bias value, post-processing weight, intermediate processing weight and pre-processing weight are adjusted downward in sequence.

[0064] If the predicted real-time output power is greater than the sample real-time output power, based on the power deviation value, the output weight, output bias value, post-processing weight, intermediate processing weight and pre-processing weight are adjusted upward in sequence.

[0065] The input parameter sequence of the corresponding sample is repeatedly input into the temperature control model, and the predicted real-time output power is recalculated.

[0066] The numerical adjustment process of the above input parameter sequence is iterated repeatedly until the predicted real-time output power is equal to the sample real-time output power.

[0067] To achieve the above objectives, the present invention also provides a Danish croissant pizza crust making system based on variable temperature proofing control, comprising:

[0068] The instruction receiving and material preparation module is used to receive pizza base making instructions and start the pizza base preparation device according to the pizza base making instructions. The pizza base preparation device includes a conveyor, a mixing bin, a mixer, and a variable temperature proofing box.

[0069] The material weighing and proportioning module is used to obtain basic materials, including flour, butter, eggs and drinking water. The flour is transported to the mixing bin by the conveyor and the flour mass is weighed after the conveying is completed. The mass ratio of flour to butter, eggs and drinking water is set respectively. Based on the flour mass and the mass ratio, the target mass of butter, eggs and drinking water is calculated respectively.

[0070] The mixing and forming module is used to weigh out the target mass of butter, eggs and drinking water from the base materials, and transport the weighed butter, eggs and drinking water to the mixing bin via a conveyor. Based on the target mass, the mixing frequency is set, and the stirrer is commanded to stir at the set mixing frequency to obtain the preform to be prepared.

[0071] A variable-temperature proofing and forming module is used to feed the blank to be proofed into a variable-temperature proofing chamber, which is equipped with a semiconductor temperature controller and a digital sensor. Multiple proofing periods are set, and each proofing period has a pre-set target temperature and target duration. The digital sensor collects the actual temperature inside the variable-temperature proofing chamber and the proofing time of the blank. Based on the proofing time, target temperature, target duration, and actual temperature, the real-time output power of the semiconductor temperature controller is calculated, and the semiconductor temperature controller is commanded to run synchronously at the real-time output power. After all proofing periods, the proofed blank is obtained. The proofed blank is then subjected to pre-baking and cooling operations in sequence to obtain the target Danish croissant pizza base. A variable-temperature control model is constructed based on the proofing time, real-time output power, target temperature, and actual temperature. The variable-temperature control model is used to continue calculating the real-time output power to complete the production of the Danish croissant pizza base based on variable-temperature proofing control.

[0072] To address the above problems, the present invention also provides an electronic device, the electronic device comprising:

[0073] Memory, storing at least one instruction;

[0074] The processor executes the instructions stored in the memory to implement the above-described method for making Danish croissant pizza crust based on variable temperature proofing control.

[0075] To address the aforementioned problems, the present invention also provides a computer-readable storage medium storing at least one instruction, which is executed by a processor in an electronic device to implement the above-described method for making Danish croissant pizza crust based on variable temperature proofing control.

[0076] To address the problems described in the background art, this invention first receives a pizza base preparation instruction and then starts a pizza base preparation device according to the instruction. This device includes a conveyor, a mixing chamber, a mixer, and a variable-temperature proofing chamber. This invention addresses the problem that traditional pizza base preparation relies on manual, step-by-step operations, and the lack of a unified start-up sequence between devices can easily lead to chaotic process connections. Therefore, by uniformly starting an integrated preparation device with a single instruction, the sequential automation of raw material delivery, mixing, stirring, and proofing is achieved, laying the hardware foundation for intelligent control of the entire process. Then, the present invention obtains basic materials, including flour, butter, eggs, and drinking water. The flour is conveyed to the mixing chamber by the conveyor, and the flour mass is weighed after conveying. The mass ratio of flour to butter, eggs, and drinking water is set. Based on the flour mass and the mass ratio, the target mass of butter, eggs, and drinking water is calculated. The corresponding target mass of butter, eggs, and drinking water is weighed from the basic materials and conveyed to the mixing chamber by the conveyor. The stirring frequency is set based on the target mass, and the stirrer is commanded to stir at the set stirring frequency to obtain the dough to be proofed. It can be seen that the present invention takes into account that the traditional proportioning relies on manual weighing, which is prone to errors and inefficient. Moreover, the mismatch between the stirring intensity and the total amount of materials will lead to uneven gluten development. Therefore, by weighing the flour mass after conveying, the target mass of other raw materials is automatically calculated according to the proportion based on the flour mass, and the stirring frequency is dynamically set according to the total material mass, the precise control of the raw material ratio and the adaptive adjustment of the stirring intensity are achieved, thereby ensuring the uniformity and stability of each batch of dough. Secondly, this invention sends the dough to be proofed into a variable-temperature proofing chamber, which is equipped with a semiconductor thermostat and a digital sensor. Multiple proofing periods are set, each of which is pre-set with a target temperature and a target duration. The digital sensor collects the actual temperature inside the variable-temperature proofing chamber and the proofing time of the dough. Based on the proofing time, target duration, target temperature, and actual temperature, the real-time output power of the semiconductor thermostat is calculated, and the semiconductor thermostat is commanded to operate synchronously at the real-time output power. After all proofing periods, the proofed dough is obtained. It can be seen that this invention takes into account that traditional constant temperature or simple timed proofing cannot meet the sensitive requirements of croissant dough for staged temperature changes, and that fixed power output cannot cope with the real-time deviation between the actual temperature and the target temperature. Therefore, the proofing process is divided into multiple periods, each with a preset target temperature and target duration. The actual temperature and proofing time are fed back in real time by the digital sensor, and the real-time output power of the semiconductor thermostat is dynamically calculated and adjusted synchronously. This effectively solves the problems of poor puff pastry layers and insufficient volume expansion caused by temperature overshoot or lag.Finally, the present invention sequentially performs pre-baking and cooling operations on the proofed dough to obtain the target Danish croissant pizza base. A variable temperature control model is constructed based on the proofing time, real-time output power, target temperature, and actual temperature. This model is used to calculate the real-time output power for subsequent batches during proofing, completing the production of the Danish croissant pizza base based on variable temperature proofing control. It is evident that the present invention considers that traditional methods require repeating the same formula calculations for each proofing, making it impossible to utilize historical data to optimize subsequent batch control. Furthermore, pre-baking parameters rely on manual experience and lack closed-loop optimization. Therefore, by collecting time-series data from the entire proofing process to construct a variable temperature control model, subsequent batches can directly input the current parameters to obtain the optimal output power, achieving intelligent control. Simultaneously, during pre-baking, parameters are iteratively optimized based on multi-point temperature standard deviation and non-uniformity to ensure baking uniformity. Therefore, the present invention can achieve precise, staged, closed-loop temperature control during the proofing process, improving the consistency of Danish croissant pizza base quality and the efficiency of automated production. Attached Figure Description

[0077] Figure 1 This is a schematic flowchart of a method for making Danish croissant pizza crust based on variable temperature proofing control according to an embodiment of the present invention.

[0078] Figure 2 This is a functional block diagram of a Danish croissant pizza crust making system based on variable temperature proofing control provided in an embodiment of the present invention;

[0079] Figure 3 This is a schematic diagram of the structure of an electronic device for implementing the method of making Danish croissant pizza crust based on variable temperature proofing control, according to an embodiment of the present invention.

[0080] Explanation of reference numerals in the attached figures:

[0081] 10. Electronic device; 11. Processor; 12. Memory; 13. Bus.

[0082] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0083] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0084] This application provides a method for making Danish croissant pizza bases based on variable-temperature proofing control. The execution entity of this method includes, but is not limited to, at least one electronic device that can be configured to execute the method provided in this application, such as a server or a terminal. In other words, the method can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0085] Reference Figure 1 The diagram shown is a flowchart illustrating a method for making a Danish croissant pizza base based on variable-temperature proofing control according to an embodiment of the present invention. In this embodiment, the method for making a Danish croissant pizza base based on variable-temperature proofing control includes:

[0086] S1. Receive pizza base making instruction and start pizza base preparation device according to the pizza base making instruction, wherein the pizza base preparation device includes conveyor, mixing chamber, stirrer and variable temperature proofing chamber.

[0087] Understandably, the pizza base preparation instructions are digital control signals issued by operators in industrialized food production plants through a human-machine interface on a control terminal. The pizza base preparation unit is a collection of equipment used to prepare Danish croissant pizza bases, consisting of multiple functional units connected sequentially in the process flow. Conveyors are used for automated transport of base materials, avoiding contamination and inefficiency caused by manual handling. The mixing chamber receives the base materials and pre-mixes them, ensuring that butter and eggs are relatively evenly dispersed in the flour, reducing the workload of subsequent mixers and improving mixing uniformity. The mixer vigorously stirs the base materials to obtain a proofing blank with appropriate elasticity and extensibility. The variable-temperature proofing chamber is an environmental simulation device used to perform variable-temperature proofing on the proofing blank; it is internally sealed and equipped with a semiconductor temperature controller and digital sensors, enabling precise temperature control.

[0088] S2. Obtain basic materials, wherein the basic materials include flour, butter, eggs and drinking water. The flour is conveyed to the mixing silo by the conveyor and the flour mass is weighed after the conveying is completed. The mass ratio of flour to butter, eggs and drinking water is set respectively. Based on the flour mass and the mass ratio, the target mass of butter, eggs and drinking water is calculated respectively.

[0089] Understandably, the basic ingredients are the raw, unmixed combination of ingredients necessary for making the Danish croissant pizza base, specifically including flour, butter, eggs, and drinking water. These ingredients are prepared for subsequent weighing and conveying. A conveyor belt transports the flour to the mixing hopper; during this process, the conveyor belt only handles material transport. After conveying, the flour in the mixing hopper is weighed to obtain a precise flour mass. Then, based on the established mass ratio between flour and butter, eggs, and drinking water, and combined with the flour mass, the target masses of butter, eggs, and drinking water to be weighed are calculated separately, providing a quantitative basis for the precise weighing and addition of each ingredient.

[0090] It should be explained that the mass ratios are fixed proportions between flour and butter, flour and eggs, and flour and drinking water, determined by the Danish croissant pizza base recipe. Since the water absorption of different batches of flour may vary slightly, maintaining these fixed proportions ensures the consistency of the final Danish croissant pizza base. The target mass is the precise weight of each ingredient—butter, eggs, and drinking water—obtained through multiplication based on the weighed flour mass and the mass ratios. The target mass provides a specific quantitative indicator for subsequent weighing operations, ensuring that each ingredient is added in a uniform proportion.

[0091] S3. Weigh out the target mass of butter, eggs and drinking water from the base materials, and transport the weighed butter, eggs and drinking water to the mixing silo via a conveyor. Set the stirring frequency based on the target mass, and command the stirrer to stir at the set stirring frequency to obtain the preform to be awakened.

[0092] Understandably, based on the target quality of butter, eggs, and drinking water, the corresponding weights of raw materials are precisely weighed from their respective base materials. These are then conveyed to a mixing hopper already containing flour via a conveyor. Subsequently, the mixing frequency is determined based on the linear relationship between the total material mass and the mixing frequency, and the mixer is instructed to operate at that frequency. This ensures that the flour, water, butter, and eggs are thoroughly mixed and form a gluten network, ultimately resulting in a dough with a certain degree of elasticity and extensibility, ready for proofing. The linear relationship between the total material mass and the mixing frequency is determined through calibration experiments before the equipment leaves the factory; for example, for every 1kg increase in total material mass, the mixing frequency increases by 5Hz. The dough ready for proofing is the raw dough formed after thorough mixing by the mixer, but before it has undergone proofing.

[0093] S4. The blank to be awakened is sent into a variable temperature awakening chamber, wherein a semiconductor temperature controller and a digital sensor are installed inside the variable temperature awakening chamber. Multiple awakening periods are set, wherein a target temperature and a target duration are preset for each awakening period. The actual temperature inside the variable temperature awakening chamber and the awakening time of the blank to be awakened are collected by the digital sensor. Based on the awakening time, target temperature, target duration and actual temperature, the real-time output power of the semiconductor temperature controller is calculated, and the semiconductor temperature controller is commanded to run synchronously at the real-time output power. After all awakening periods have passed, the awakened blank is obtained.

[0094] It should be explained that this invention uses a variable-temperature proofing box, unlike traditional constant-temperature proofing boxes, supporting dynamic temperature adjustment. The core components built into the proofing box are a semiconductor temperature controller and a digital sensor. The semiconductor temperature controller uses semiconductor elements to achieve precise temperature control within the box; the digital sensor is responsible for capturing real-time environmental data within the box, providing a basis for subsequent accurate calculations and intelligent control. Since Danish croissants are a type of puff pastry dough, the proofing process of the pizza base is very sensitive to temperature changes. Initially, a low temperature is needed to lock in the butter and prevent it from melting and separating; in the middle stage, a medium temperature is needed for fermentation to promote yeast gas production and increase volume; and in the later stage, a slow rise is needed to set the shape, fix the bubble structure, and form the crisp precursor. Therefore, segmented temperature control is necessary, which is the key difference between this invention and the ordinary pizza base proofing process.

[0095] Understandably, compared to traditional pizza dough proofing methods, this invention does not use a single temperature and a single time to complete the proofing process. Instead, it breaks down the entire proofing process into multiple independent stages. Each stage has two fixed standard parameters: a target temperature and a target time. The target temperature is the ideal temperature that the proofing box needs to maintain in that stage, and the target time is the time that the dough needs to continue to proof at that temperature.

[0096] Furthermore, during the proofing process, digital sensors continuously monitor two sets of real-time data: the actual temperature and the proofing time. The actual temperature is the true ambient temperature inside the proofing box, which may deviate from the preset target temperature. The proofing time is the cumulative fermentation time since the dough entered the proofing box. By combining the target temperature and target time, and the actual temperature and proofing time, logical judgments and calculations are performed to ultimately determine the appropriate power level for the semiconductor temperature controller to operate at.

[0097] Furthermore, the step of using the digital sensor to collect the actual temperature inside the variable-temperature proofing chamber and the proofing time of the blank to be proofed, and calculating the real-time output power of the semiconductor temperature controller based on the proofing time, target temperature, target duration, and actual temperature, includes:

[0098] The digital sensor is used to collect the actual temperature inside the variable temperature proofing box and the proofing time of the blank to be proofed. The collection time interval and the number of data collections of the digital sensor are recorded simultaneously. After each collection, the temperature difference between the target temperature and the actual temperature is calculated.

[0099] Confirm the current wake-up period, and read the target temperature and target duration corresponding to the current wake-up period;

[0100] A temperature difference threshold is set. Based on the temperature difference threshold, the temperature difference, the existing warming time, and the target duration corresponding to the current warming period, the power regulation intensity of the semiconductor temperature controller is calculated according to the following formula:

[0101]

[0102] in, For the first Power adjustment intensity during the second acquisition The duration of wakefulness. The target duration corresponding to the current wake-up period. For the first Temperature difference value at each sampling point Temperature difference threshold It is a natural constant;

[0103] Calculate the ratio of the duration of the current warm-up period to the target duration of the current warm-up period. Determine the current warm-up sub-stage based on the temperature difference and the duration ratio. Calculate the real-time output power of the semiconductor temperature controller based on the determination result of the warm-up sub-stage, combined with the power adjustment intensity, the sampling time interval, and the number of data samplings.

[0104] It should be explained that the digital sensor records two fundamental parameters at each sampling moment: the sampling time interval and the number of data acquisitions, while simultaneously calculating the difference between the target temperature and the actual temperature in real time. The sampling time interval is used to normalize the discrete sampling data to a continuous time axis, and the number of data acquisitions is used to determine the range of integral accumulation. The temperature difference directly reflects the degree of deviation between the current actual temperature and the desired temperature. Since both the wake-up time and the temperature difference change dynamically over time, the power adjustment intensity is different at each sampling moment. Similarly, the subsequent real-time output power is also updated in real time with each sampling moment.

[0105] Understandably, because different stages have different temperature requirements, this embodiment of the invention identifies the current proofing period and reads the preset target temperature and target duration for that period, enabling subsequent calculations to adapt to the process requirements of different stages. The target duration is used to normalize the proofing time. Simultaneously, a temperature difference threshold is set, and the ratio of the absolute value of the temperature difference to the temperature difference threshold is used as a temperature difference attenuation term. When the actual temperature deviates too much from the target temperature, the power adjustment intensity is automatically reduced to prevent temperature overshoot.

[0106] Furthermore, after calculating the power adjustment intensity based on the above parameters, the current warming sub-stage needs to be determined based on the current temperature difference and the ratio of the current warming time to the target time. Specifically, when the temperature difference is positive and its absolute value is greater than a preset deviation threshold, it is determined to be a heating stage; when the temperature difference is negative and its absolute value is greater than the deviation threshold, it is determined to be a cooling stage; and when the absolute value is less than or equal to the deviation threshold, it is determined to be a steady-state stage. Different power calculation strategies will be used for different sub-stages to achieve precise control of the semiconductor temperature controller's output power.

[0107] Furthermore, the step of calculating the real-time output power of the semiconductor temperature controller based on the determination result of the waking-up sub-stage, combined with the power adjustment intensity, the sampling time interval, and the number of data samplings, includes:

[0108] Obtain the reference power of the semiconductor temperature controller and confirm the current wake-up sub-stage, wherein the wake-up sub-stage corresponds to one of the following stage types: heating stage, steady state stage, and cooling stage.

[0109] If the current waking sub-stage is in the heating stage, the product of the power regulation intensity and the reference power is used as the real-time output power.

[0110] If the current wake-up sub-stage is in a steady state, the real-time output power is calculated according to the following formula:

[0111]

[0112] in, For the first Real-time output power during the first data acquisition. For the first Power adjustment intensity during the second acquisition As the reference power, The time interval for data collection. The number of times data was collected. For the first Temperature difference value at the time of the second data collection;

[0113] If the current waking sub-stage is in the cooling stage, calculate the rate of change of the actual temperature over time to obtain the temperature change rate. Use the absolute value of the temperature change rate as the power reduction coefficient. Calculate the product of the absolute value of the temperature difference and the power reduction coefficient to obtain the power reduction amount. Calculate the difference between the reference power and the power reduction amount to obtain the real-time output power.

[0114] It should be explained that the reference power is the stable output power value of the semiconductor temperature controller under standard operating conditions. It can be directly read from the equipment's factory parameters and also represents the power regulation intensity. The dimensional reference for converting into actual executable power commands.

[0115] Understandably, during the heating phase, the temperature difference is large and the actual temperature is lower than the target temperature. The primary goal is to quickly eliminate the deviation, so the power adjustment intensity is directly multiplied by the reference power. The power adjustment intensity automatically changes according to the relative position of the aging time within the time period. When the temperature difference is large, the output is close to full power, and when the temperature difference is small, the output is moderate, thereby achieving rapid and stable heating.

[0116] Furthermore, in the steady-state phase, the temperature difference is already very small, and there may be a tiny difference between the actual temperature and the target temperature that cannot be eliminated by the proportional term alone. Therefore, an integral term is added in addition to the proportional term. This integral term automatically compensates for the long-standing tiny deviations by accumulating all historical temperature differences from the start of waking up to the current moment, so that the actual temperature stabilizes near the target value.

[0117] Furthermore, during the cooling phase, if the actual temperature is higher than the target temperature, active cooling is required. At this point, the power reduction is calculated based on the rate of temperature change, and this reduction is subtracted from the reference power to obtain the output power. When the reduction exceeds the reference power, the output power is negative, indicating that the semiconductor temperature controller has switched to cooling mode. Through these three phased calculations, the real-time output power can accurately match the control requirements of each stage—heating, steady-state, and cooling—significantly improving temperature tracking accuracy and anti-interference capability.

[0118] Understandably, different power calculation methods are used for each proofing stage to obtain the real-time output power of the semiconductor temperature controller and operate synchronously, so that the temperature inside the variable temperature proofing box can change precisely throughout the proofing process, providing a stable quality of proofed blanks for subsequent pre-baking operations.

[0119] S5. Perform pre-baking and cooling operations on the proofed dough in sequence to obtain the target Danish croissant pizza base. Based on the proofing time, real-time output power, target temperature and actual temperature, construct a variable temperature control model. Use the variable temperature control model to continue to calculate the real-time output power and complete the production of Danish croissant pizza base based on variable temperature proofing control.

[0120] It should be explained that this step undertakes two core tasks: first, to transform the proofed dough into the target finished product through pre-baking and cooling; second, to build a variable temperature control model using all historical data recorded during the proofing process, providing a predictive tool for the intelligent control of the subsequent batch pizza dough production process.

[0121] Furthermore, unlike traditional baking methods that rely on single-point temperature measurement and pre-set fixed parameters, this invention monitors multiple temperatures in real time during the baking process. It can collect temperatures from different areas within the oven cavity of a hot air circulating oven and calculate two quantitative indicators: temperature standard deviation and temperature non-uniformity. These indicators are used to evaluate the uniformity of temperature distribution within the oven and to iteratively correct the pre-baking parameter set in real time. Each time a parameter update is completed, the new parameters are immediately sent to the hot air circulating oven for synchronous adjustment. This iterative optimization continues until the optimal pre-baking parameter set is obtained. The remaining pre-baking is then completed using the optimal parameters, ensuring that the oven always operates under optimal conditions.

[0122] Furthermore, the pre-baking and cooling operations performed sequentially on the awakened dough to obtain the target Danish croissant pizza base include:

[0123] Set a pre-baking parameter set and send the proofed dough into a hot air circulating oven. The hot air circulating oven has multiple temperature monitoring points built in and performs a pre-baking operation on the proofed dough according to the set pre-baking parameter set.

[0124] The real-time temperature of each temperature monitoring point is continuously collected, and the temperature standard deviation and temperature non-uniformity are calculated based on the real-time temperature.

[0125] Based on the temperature standard deviation and temperature non-uniformity, the pre-baking parameter set is iteratively updated. After each iteration, an updated pre-baking parameter set is obtained, and the hot air circulation oven is immediately adjusted in real time with the updated pre-baking parameter set until the optimal pre-baking parameter set is iteratively updated. The hot air circulation oven is then adjusted with the optimal pre-baking parameter set, and the pre-baked crust is obtained after the pre-baking operation is completed.

[0126] The pre-baked dough is removed from the hot air circulating oven and transferred to a preset cooling device to perform a cooling operation, thereby obtaining the target Danish croissant pizza base.

[0127] It should be explained that this invention calculates the temperature standard deviation and temperature non-uniformity through temperature measurement, and obtains a comprehensive evaluation index by weighted summation of the two. Based on the comprehensive evaluation index, the oven pre-baking parameters are automatically and iteratively optimized. The entire process aims to optimize the oven pre-baking parameters by setting the smaller the comprehensive evaluation index as the optimization goal, indicating a more uniform temperature field and better baking effect. Each baking parameter is fine-tuned one by one, and the index changes are compared after each round of parameter adjustment. If the index change is less than the convergence threshold, the optimization is considered complete, and the optimal pre-baking parameter set is obtained. Since Danish croissant dough contains a large amount of butter, uneven temperature field can easily lead to localized oil leakage, flaky texture collapse, etc. Therefore, this invention sets up an automatic update and iteration of the pre-baking parameter set, which can replace the tedious manual repeated trial baking and parameter adjustment.

[0128] Furthermore, the step of performing an iterative update operation on the pre-baking parameter set, obtaining an updated pre-baking parameter set after each iteration, and immediately adjusting the hot air circulation oven in real time based on the updated pre-baking parameter set until the optimal pre-baking parameter set is obtained through iterative updates, includes:

[0129] Confirm the pre-baking parameter set, wherein the pre-baking parameter set consists of multiple parameters, and sequentially set the adjustment step size and value range of each parameter;

[0130] The temperature standard deviation and temperature non-uniformity are weighted and summed to obtain a comprehensive evaluation index. The comprehensive evaluation index is marked as the current index, and a convergence threshold for the comprehensive evaluation index is set.

[0131] Select individual parameters from the pre-baking parameter set in sequence, mark them as the current parameters, and then perform the following parameter tuning operations in sequence:

[0132] Increase the current parameter by one adjustment step and calculate the corresponding comprehensive evaluation index to obtain the positive index;

[0133] Reduce the current parameter by one adjustment step and calculate the corresponding comprehensive evaluation index to obtain the negative index.

[0134] The positive and negative indicators are compared numerically. If the positive indicator is smaller, the current parameter is increased by one step. If the negative indicator is smaller, the current parameter is decreased by one step. If the positive and negative indicators are equal, the current parameter remains unchanged.

[0135] After adjusting the current parameter, continue to make a judgment: if the value of the current parameter is less than the lower limit of the corresponding value range, then assign the current parameter the lower limit of the value range.

[0136] If the value of the current parameter is greater than the upper limit of its value range, then the current parameter is set to the upper limit of its value range;

[0137] After traversing and performing parameter tuning operations on all parameters in the pre-baking parameter set, an updated pre-baking parameter set is obtained. The comprehensive evaluation index corresponding to the updated pre-baking parameter set is calculated and marked as the updated index. The absolute value of the difference between the updated index and the current index is calculated. If the absolute value of the difference is less than the set convergence threshold, the updated index is used as the current index for the new round of iteration. The parameter adjustment process is repeated until the absolute value of the difference between the updated index and the current index is less than the convergence threshold, and the optimal pre-baking parameter set is generated.

[0138] It should be explained that this invention uses a hill-climbing optimization algorithm. By conducting perturbation experiments on each parameter in both positive and negative directions, the adjustment direction of the parameters is determined based on the changing trend of the comprehensive evaluation index of temperature distribution, gradually approaching the optimal parameter combination. The pre-baking parameter set consists of multiple controllable parameters such as hot air circulation speed, guide vane angle, pre-baking temperature, and tray spacing. Each parameter has a set adjustment step size and allowable value range. The adjustment step size determines the magnitude of each parameter change, while the value range ensures that the parameter does not exceed the physical limits of the equipment.

[0139] Understandably, the comprehensive evaluation index is a value obtained by weighted summing of temperature standard deviation and temperature non-uniformity; the smaller the value, the more uniform the temperature distribution inside the oven. The convergence threshold is a preset positive number used to determine whether the iteration has approached the optimum: when the change in the index after parameter updates is less than this threshold, further adjustments no longer significantly improve uniformity, and iteration can be stopped. A positive index is the comprehensive evaluation index calculated by increasing the current parameter by one adjustment step size, while a negative index is the comprehensive evaluation index calculated by decreasing the current parameter by one adjustment step size. By comparing the magnitudes of the positive and negative indices, the direction of parameter adjustment can be determined: if the positive index is smaller, it indicates that increasing the parameter is beneficial to improving temperature uniformity, so an actual increase of one step size is made; if the negative index is smaller, a decrease of one step size is made; if the two are equal, the parameter remains unchanged.

[0140] Furthermore, the equipment cannot operate outside its designated range; forcibly exceeding this range may damage the equipment or cause control failure. Therefore, after each adjustment, it is necessary to check whether the parameters exceed their range. After all parameters have undergone one round of adjustment, an updated pre-baking parameter set is obtained, and its comprehensive evaluation index is recalculated as the updated index. The absolute value of the difference between the updated index and the previous current index is calculated. If this absolute value is less than the convergence threshold, it indicates that the index has stabilized, and the current updated parameter set is determined as the optimal pre-baking parameter set. If it is still greater than the threshold, the updated index is used as the new current index, and the above perturbation-comparison-adjustment process is repeated. Through multiple iterations, the index gradually approaches the minimum value, ultimately obtaining the optimal parameter combination that results in the most uniform temperature distribution.

[0141] Furthermore, the step of removing the pre-baked dough from the hot air circulating oven and transferring it to a preset cooling device to perform a cooling operation on the pre-baked dough to obtain the target Danish croissant pizza dough includes:

[0142] The pre-baked dough is removed from the hot air circulating oven and transferred to a preset cooling device, wherein the preset cooling device has a built-in circulating fan and a temperature sensor;

[0143] Set a first wind speed value and a second wind speed value for the circulating fan, wherein the first wind speed value is greater than the second wind speed value;

[0144] A first temperature threshold and a second temperature threshold are set, wherein the first temperature threshold is greater than the second temperature threshold;

[0145] The circulating fan is started at the first wind speed value to perform forced convection cooling, and the temperature sensor is used to continuously monitor the center temperature of the pre-baked dough.

[0146] When the temperature at the center of the pie crust drops to the first temperature threshold, the fan speed is reduced to the second speed, and forced convection cooling continues until the difference between the temperature at the center of the pie crust and the current ambient temperature is less than the second temperature threshold, thus completing the cooling operation.

[0147] Let the pre-baked dough rest at room temperature for 15 to 30 minutes to obtain the desired Danish croissant pizza base.

[0148] It should be explained that the preset cooling device is designed to lower the temperature of the crust, and it includes a built-in circulating fan and temperature sensor. Because the surface temperature of the pre-baked crust is very high, direct exposure to room temperature could cause the crust to crack or lead to uneven moisture distribution due to the large temperature difference between the inside and outside. Therefore, the circulating fan is first activated at a first fan speed to perform forced convection cooling, rapidly dissipating heat from the crust surface, while the temperature sensor continuously monitors the center temperature of the crust. When the center temperature of the crust drops to the first temperature threshold, it indicates that the crust has passed the rapid heat dissipation phase. At this point, the fan speed is reduced to a second speed, entering a slow equilibrium phase to prevent the crust from hardening due to excessively rapid cooling. Cooling continues until the difference between the center temperature of the crust and the ambient temperature is less than the second temperature threshold, indicating that the crust temperature is close to room temperature, at which point cooling is complete.

[0149] Furthermore, after cooling, the dough is left to rest at room temperature for 15 to 30 minutes to allow residual heat to dissipate and moisture to redistribute, ensuring the final product's crispy texture and stable internal structure. At this point, the pre-baked dough, after cooling and resting, yields the target Danish croissant pizza base.

[0150] Furthermore, the construction of the variable temperature control model based on the awakening time, real-time output power, target temperature, and actual temperature includes:

[0151] Collect multiple time-by-time operational data during the wake-up process. The time-by-time operational data consists of the wake-up duration, target temperature, actual temperature, and real-time output power at each time point.

[0152] Each of the time-by-time running data is used to form a sample, and the real-time output power in the sample is used as the sample real-time output power.

[0153] A variable temperature control model is constructed based on a neural network algorithm, wherein the variable temperature control model includes an input layer, a processing layer and an output layer;

[0154] Each sample is selected sequentially, and the input layer is used to construct an input parameter sequence from the wake-up time, target temperature, and the calculated temperature difference between the target temperature and the actual temperature in the training sample. The input parameter sequence is then input into the variable temperature control model.

[0155] By setting processing weights, the processing layer sequentially performs weighted operations, feature extraction, and summation on the input parameter sequence based on the processing weights to obtain the original output value.

[0156] By setting output weights and output bias values, the original output values ​​are weighted based on the output weights and then superimposed with the output bias values ​​to obtain the predicted real-time output power.

[0157] The difference between the predicted real-time output power and the sample real-time output power is calculated to obtain the power deviation value. The processing weight and output weight are iteratively adjusted based on the power deviation value until the predicted real-time output power is equal to the sample real-time output power.

[0158] The variable temperature control model is constructed when the predicted real-time output power of all samples is equal to the real-time output power of the samples.

[0159] It should be explained that by using all the historical data recorded during the proofing process to build a variable temperature control model, it is possible to quickly calculate the real-time output power directly based on the currently collected proofing time, target temperature and actual temperature during the proofing process of subsequent batches, thereby improving the control response speed and achieving adaptability.

[0160] Understandably, the variable temperature control model employs a neural network structure, comprising a three-layer architecture: an input layer, a processing layer, and an output layer. For each sample, the input parameter sequence consists of three components in sequence: the warm-up time, the target temperature, and the temperature difference between the target and actual temperatures. The model's calculation process is as follows: the input parameter sequence is fed into the processing layer through the input layer; the processing layer performs weighted operations on each component of the input parameter sequence according to the set processing weights, extracts the feature change trend from the weighted operation results, and finally sums all processing results to obtain the original output value; the output layer weights the original output value according to the set output weights, and then sums it with the set output bias value to obtain the predicted real-time output power.

[0161] Furthermore, the predicted real-time output power is compared with the sample real-time output power, and the difference between the two is calculated to obtain the power deviation value. Based on the magnitude and sign of the power deviation value, the processing weights and output weights are iteratively adjusted: if the predicted real-time output power is less than the sample real-time output power, the processing weights and output weights are increased; otherwise, the processing weights and output weights are decreased. After each adjustment, the predicted real-time output power is recalculated and compared with the sample real-time output power again, and this process is repeated iteratively until the two are equal, completing the training of this sample. For each sample, when the predicted real-time output power of all samples is equal to the real-time output power of their corresponding samples, the construction of the variable temperature control model is complete. Afterwards, for the aging process of a new batch, only the current aging time, target temperature, and actual temperature need to be input into the model to directly obtain the required real-time output power.

[0162] Furthermore, the setting of processing weights, and the use of the processing layer to sequentially perform weighted operations, feature extraction, and summation operations on the input parameter sequence based on the processing weights to obtain the original output value, includes:

[0163] The processing layer is confirmed and the processing weights are set. The processing layer includes a preprocessing layer, an intermediate processing layer and a postprocessing layer. The processing weights include preprocessing weights, intermediate processing weights and postprocessing weights.

[0164] The preprocessing layer receives the input parameter sequence, and performs a weighted operation on the wake-up time, target temperature, and calculated temperature difference between the target temperature and the actual temperature in the input parameter sequence according to the preprocessing weight to obtain multiple preprocessing values. The multiple preprocessing values ​​are then combined to generate a preprocessing sequence.

[0165] The intermediate processing layer receives the preprocessed sequence, calculates the difference between each group of adjacent preprocessed values ​​in the preprocessed sequence as the trend feature value, and calculates the difference between the maximum and minimum values ​​of the preprocessed values ​​in the preprocessed sequence to obtain the sequence deviation. The trend feature value and the sequence deviation are concatenated in the order of generation to obtain the trend sequence.

[0166] The trend sequence is weighted according to the intermediate processing weight to obtain multiple intermediate processing values. The intermediate processing values ​​are then arranged according to the original splicing order of the trend sequence to obtain the intermediate processing sequence.

[0167] The intermediate processing sequence is received by the post-processing layer, and the intermediate processing values ​​are weighted by matching them with post-processing weights to obtain multiple post-processing values. The original output value is obtained by summing all the post-processing values.

[0168] It should be explained that the processing layer consists of three sub-layers connected in series: a preprocessing layer, an intermediate processing layer, and a post-processing layer. Each sub-layer is configured with corresponding processing weights, namely preprocessing weights, intermediate processing weights, and post-processing weights. The initial values ​​of these weights can be randomly set. During model training, the weight values ​​are repeatedly adjusted by comparing the difference between the model's prediction results and the actual sample values, so that the prediction results gradually approach the actual values.

[0169] Understandably, the preprocessing layer receives the input parameter sequence and multiplies each parameter value in the sequence by its corresponding preprocessing weight, resulting in multiple preprocessed values. These preprocessed values ​​are arranged according to the original order of the parameters in the input sequence, forming a preprocessed sequence. The weighting operation of the preprocessing layer is equivalent to scaling the input parameters, allowing input parameters with different dimensions to participate in subsequent calculations on the same scale.

[0170] Furthermore, the intermediate processing layer receives the preprocessed sequence and extracts two types of features: the first is trend feature values, which calculate the difference between every two adjacent preprocessed values ​​in the preprocessed sequence, reflecting the magnitude and direction of changes in the input parameters; the second is sequence bias, which calculates the difference between the maximum and minimum values ​​of all preprocessed values ​​in the preprocessed sequence, reflecting the numerical dispersion of the entire sequence. Then, the trend feature values ​​and sequence bias are concatenated in chronological order to form a trend sequence. Next, each component in the trend sequence is multiplied by its corresponding intermediate processing weight to obtain multiple intermediate processing values, which are then arranged in their original order to obtain an intermediate processing sequence. The role of the intermediate processing layer is to extract features more strongly correlated with the output power from the preprocessed sequence, enriching the model's expressive power.

[0171] Furthermore, the post-processing layer receives the intermediate processing sequence, multiplies each intermediate processing value in the sequence by its corresponding post-processing weight, obtaining multiple post-processed values. These post-processed values ​​are then summed to obtain the original output value. The weighted summation of the post-processing layer is equivalent to linearly combining the extracted features to form the final intermediate result, which is then processed by the output layer. Through these three progressive weighting, feature extraction, and summation operations, the processing layer can gradually transform the original input parameter sequence into original output values ​​containing deep features, providing a sufficient information foundation for the subsequent output layer to generate predicted real-time output power.

[0172] Furthermore, the iterative adjustment of the processing weights and output weights based on the power deviation value includes:

[0173] If the predicted real-time output power is less than the sample real-time output power, based on the power deviation value, the output weight, output bias value, post-processing weight, intermediate processing weight and pre-processing weight are adjusted downward in sequence.

[0174] If the predicted real-time output power is greater than the sample real-time output power, based on the power deviation value, the output weight, output bias value, post-processing weight, intermediate processing weight and pre-processing weight are adjusted upward in sequence.

[0175] The input parameter sequence of the corresponding sample is repeatedly input into the temperature control model, and the predicted real-time output power is recalculated.

[0176] The numerical adjustment process of the above input parameter sequence is iterated repeatedly until the predicted real-time output power is equal to the sample real-time output power.

[0177] It should be explained that during model training, when the predicted real-time output power is not equal to the sample real-time output power, the weights and biases need to be adjusted according to the direction of the deviation between the two, so that the predicted value gradually approaches the sample value. The output weights are coefficients that scale the original output value, and the output bias is a constant added to the weighted result. The post-processing weights, intermediate processing weights, and pre-processing weights correspond to the scaling factors of each feature in the post-processing layer, intermediate processing layer, and pre-processing layer, respectively.

[0178] Understandably, the adjustment rules fall into two categories: if the predicted real-time output power is less than the sample real-time output power, it indicates that the model's output power value is too low, requiring an increase in the output weights, output bias values, and processing weights of each layer to increase the predicted value in the next calculation. Therefore, these weights and bias values ​​are increased one by one. If the predicted real-time output power is greater than the sample real-time output power, it indicates that the model's output power value is too high, requiring a decrease in these weights and bias values ​​one by one. The magnitude of each adjustment is determined by the size of the power deviation; the larger the deviation, the larger the adjustment; the smaller the deviation, the smaller the adjustment. This method of adjusting in the same direction based on the deviation allows the predicted value to quickly approach the sample value.

[0179] Furthermore, after each adjustment of the weights and biases, the forward computation of the input layer, processing layer, and output layer is re-executed to obtain a new predicted real-time output power, which is then compared with the sample real-time output power. This process of comparison, adjustment, and recalculation is repeated until the predicted real-time output power is exactly equal to the sample real-time output power, at which point the training of that sample is complete. This process is repeated for all samples. When the predicted value for each sample is equal to its corresponding sample value, the construction of the variable temperature control model is complete.

[0180] To address the problems described in the background art, this invention first receives a pizza base preparation instruction and then starts a pizza base preparation device according to the instruction. This device includes a conveyor, a mixing chamber, a mixer, and a variable-temperature proofing chamber. This invention addresses the problem that traditional pizza base preparation relies on manual, step-by-step operations, and the lack of a unified start-up sequence between devices can easily lead to chaotic process connections. Therefore, by uniformly starting an integrated preparation device with a single instruction, the sequential automation of raw material delivery, mixing, stirring, and proofing is achieved, laying the hardware foundation for intelligent control of the entire process. Then, the present invention obtains basic materials, including flour, butter, eggs, and drinking water. The flour is conveyed to the mixing chamber by the conveyor, and the flour mass is weighed after conveying. The mass ratio of flour to butter, eggs, and drinking water is set. Based on the flour mass and the mass ratio, the target mass of butter, eggs, and drinking water is calculated. The corresponding target mass of butter, eggs, and drinking water is weighed from the basic materials and conveyed to the mixing chamber by the conveyor. The stirring frequency is set based on the target mass, and the stirrer is commanded to stir at the set stirring frequency to obtain the dough to be proofed. It can be seen that the present invention takes into account that the traditional proportioning relies on manual weighing, which is prone to errors and inefficient. Moreover, the mismatch between the stirring intensity and the total amount of materials will lead to uneven gluten development. Therefore, by weighing the flour mass after conveying, the target mass of other raw materials is automatically calculated according to the proportion based on the flour mass, and the stirring frequency is dynamically set according to the total material mass, the precise control of the raw material ratio and the adaptive adjustment of the stirring intensity are achieved, thereby ensuring the uniformity and stability of each batch of dough. Secondly, this invention sends the dough to be proofed into a variable-temperature proofing chamber, which is equipped with a semiconductor thermostat and a digital sensor. Multiple proofing periods are set, each of which is pre-set with a target temperature and a target duration. The digital sensor collects the actual temperature inside the variable-temperature proofing chamber and the proofing time of the dough. Based on the proofing time, target duration, target temperature, and actual temperature, the real-time output power of the semiconductor thermostat is calculated, and the semiconductor thermostat is commanded to operate synchronously at the real-time output power. After all proofing periods, the proofed dough is obtained. It can be seen that this invention takes into account that traditional constant temperature or simple timed proofing cannot meet the sensitive requirements of croissant dough for staged temperature changes, and that fixed power output cannot cope with the real-time deviation between the actual temperature and the target temperature. Therefore, the proofing process is divided into multiple periods, each with a preset target temperature and target duration. The actual temperature and proofing time are fed back in real time by the digital sensor, and the real-time output power of the semiconductor thermostat is dynamically calculated and adjusted synchronously. This effectively solves the problems of poor puff pastry layers and insufficient volume expansion caused by temperature overshoot or lag.Finally, the present invention sequentially performs pre-baking and cooling operations on the proofed dough to obtain the target Danish croissant pizza base. A variable temperature control model is constructed based on the proofing time, real-time output power, target temperature, and actual temperature. This model is used to calculate the real-time output power for subsequent batches during proofing, completing the production of the Danish croissant pizza base based on variable temperature proofing control. It is evident that the present invention considers that traditional methods require repeating the same formula calculations for each proofing, making it impossible to utilize historical data to optimize subsequent batch control. Furthermore, pre-baking parameters rely on manual experience and lack closed-loop optimization. Therefore, by collecting time-series data from the entire proofing process to construct a variable temperature control model, subsequent batches can directly input the current parameters to obtain the optimal output power, achieving intelligent control. Simultaneously, during pre-baking, parameters are iteratively optimized based on multi-point temperature standard deviation and non-uniformity to ensure baking uniformity. Therefore, the present invention can achieve precise, staged, closed-loop temperature control during the proofing process, improving the consistency of Danish croissant pizza base quality and the efficiency of automated production.

[0181] like Figure 2 The diagram shown is a functional block diagram of a Danish croissant pizza crust making system based on variable temperature proofing control provided in an embodiment of the present invention.

[0182] The Danish croissant pizza crust making system 100 based on variable temperature proofing control described in this invention can be installed in an electronic device. Depending on the functions implemented, the Danish croissant pizza crust making system 100 may include an instruction receiving and material preparation module 101, a material weighing and proportioning module 102, a mixing and forming module 103, and a variable temperature proofing and molding module 104. The module described in this invention can also be called a unit, which is a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, and is stored in the memory of the electronic device.

[0183] The instruction receiving and material preparation module 101 is used to receive pizza base making instructions and start the pizza base preparation device according to the pizza base making instructions. The pizza base preparation device includes a conveyor, a mixing chamber, a mixer and a variable temperature proofing box.

[0184] The material weighing and proportioning module 102 is used to obtain basic materials, including flour, butter, eggs and drinking water. The flour is conveyed to the mixing silo by the conveyor and the flour mass is weighed after the conveying is completed. The mass ratio of flour to butter, eggs and drinking water is set respectively. Based on the flour mass and the mass ratio, the target mass of butter, eggs and drinking water is calculated respectively.

[0185] The mixing and forming module 103 is used to weigh out the corresponding target mass of butter, eggs and drinking water from the base materials, and transport the weighed butter, eggs and drinking water to the mixing silo through a conveyor. Based on the target mass, the mixing frequency is set, and the stirrer is commanded to stir at the set mixing frequency to obtain the preform to be generated.

[0186] The variable-temperature proofing and forming module 104 is used to send the blank to be proofed into a variable-temperature proofing chamber. The variable-temperature proofing chamber is equipped with a semiconductor temperature controller and a digital sensor. Multiple proofing periods are set, and each proofing period has a pre-set target temperature and target duration. The digital sensor collects the actual temperature inside the variable-temperature proofing chamber and the proofing time of the blank. Based on the proofing time, target temperature, target duration, and actual temperature, the real-time output power of the semiconductor temperature controller is calculated, and the semiconductor temperature controller is commanded to run synchronously at the real-time output power. After all proofing periods, the proofed blank is obtained. The proofed blank is then subjected to pre-baking and cooling operations in sequence to obtain the target Danish croissant pizza base. A variable-temperature control model is constructed based on the proofing time, real-time output power, target temperature, and actual temperature. The variable-temperature control model is used to continue calculating the real-time output power to complete the production of the Danish croissant pizza base based on variable-temperature proofing control.

[0187] In detail, the modules in the Danish croissant pizza crust making system 100 based on variable temperature proofing control described in this embodiment of the invention employ the same methods as described above. Figure 1 The method used is the same as the one described above for making Danish croissant pizza crust based on variable temperature proofing control, and can produce the same technical effect, so it will not be repeated here.

[0188] like Figure 3 The diagram shown is a schematic representation of an electronic device for implementing a method for making Danish croissant pizza crust based on variable temperature proofing control, according to an embodiment of the present invention.

[0189] The electronic device 1 may include a processor 10, a memory 11 and a bus 12, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a method program for making Danish croissant pizza crust based on variable temperature proofing control.

[0190] The memory 11 includes at least one type of readable storage medium, such as flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of the electronic device 1, such as the portable hard drive of the electronic device 1. In other embodiments, the memory 11 can be an external storage device of the electronic device 1, such as a plug-in portable hard drive, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the electronic device 1. Furthermore, the memory 11 includes both internal storage units and external storage devices of the electronic device 1. The memory 11 can be used not only to store application software and various types of data installed on the electronic device 1, such as the code of a method for making a Danish croissant pizza crust based on temperature-controlled proofing, but also to temporarily store data that has been output or will be output.

[0191] In some embodiments, the processor 10 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 10 is the control unit of the electronic device, connecting various components of the entire electronic device through various interfaces and lines. It executes programs or modules stored in the memory 11 (e.g., a method for making a Danish croissant pizza base based on variable-temperature proofing control), and calls data stored in the memory 11 to perform various functions of the electronic device 1 and process data.

[0192] The bus 12 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus 12 can be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to realize the connection and communication between the memory 11 and at least one processor 10, etc.

[0193] Figure 3 Only electronic devices with components are shown; those skilled in the art will understand that... Figure 3The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0194] For example, although not shown, the electronic device 1 may also include a power supply (such as a battery) to power the various components. Preferably, the power supply can be logically connected to the at least one processor 10 through a power management device, thereby enabling functions such as charging management, discharging management, and power consumption management. The power supply may also include one or more DC or AC power supplies, recharging devices, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device 1 may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.

[0195] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a Wi-Fi interface, a Bluetooth interface, etc.), which is typically used to establish communication connections between the electronic device 1 and other electronic devices.

[0196] Optionally, the electronic device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), and optionally, a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen, etc. The display may also be appropriately referred to as a screen or display unit, used to display information processed in the electronic device 1 and to display a visual user interface.

[0197] The program for making Danish croissant pizza crust based on variable temperature proofing control, stored in the memory 11 of the electronic device 1, is a combination of multiple instructions. When run in the processor 10, it can achieve the following:

[0198] Receive a pizza base preparation instruction and start a pizza base preparation device according to the pizza base preparation instruction, wherein the pizza base preparation device includes a conveyor, a mixing chamber, a mixer and a variable temperature proofing chamber;

[0199] Obtain basic materials, including flour, butter, eggs and drinking water. Flour is transported to a mixing silo via a conveyor and its mass is weighed after transport. The mass ratios of flour to butter, eggs and drinking water are set. Based on the flour mass and the mass ratios, the target masses of butter, eggs and drinking water are calculated.

[0200] Weigh out the target mass of butter, eggs and drinking water from the base materials, and transport the weighed butter, eggs and drinking water to the mixing silo via a conveyor. Set the stirring frequency based on the target mass, and command the stirrer to stir at the set stirring frequency to obtain the preform to be awakened.

[0201] The blank to be awakened is sent into a variable temperature awakening chamber, which is equipped with a semiconductor temperature controller and a digital sensor. Multiple awakening periods are set, and each awakening period has a target temperature and a target duration preset. The digital sensor collects the actual temperature inside the variable temperature awakening chamber and the awakening time of the blank to be awakened. Based on the awakening time, target temperature, target duration and actual temperature, the real-time output power of the semiconductor temperature controller is calculated, and the semiconductor temperature controller is commanded to run synchronously at the real-time output power. After all awakening periods have passed, the awakened blank is obtained.

[0202] The proofed dough is pre-baked and cooled sequentially to obtain the target Danish croissant pizza base. A variable temperature control model is constructed based on the proofing time, real-time output power, target temperature and actual temperature. The real-time output power is then calculated using the variable temperature control model to complete the production of the Danish croissant pizza base based on variable temperature proofing control.

[0203] Specifically, the processor 10's implementation method for the above instructions can be found in [reference needed]. Figures 1 to 3 The descriptions of the relevant steps in the corresponding embodiments are not repeated here.

[0204] Furthermore, if the modules / units integrated in the electronic device 1 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).

[0205] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor of an electronic device, can perform the following:

[0206] Receive a pizza base preparation instruction and start a pizza base preparation device according to the pizza base preparation instruction, wherein the pizza base preparation device includes a conveyor, a mixing chamber, a mixer and a variable temperature proofing chamber;

[0207] Obtain basic materials, including flour, butter, eggs and drinking water. Flour is transported to a mixing silo via a conveyor and its mass is weighed after transport. The mass ratios of flour to butter, eggs and drinking water are set. Based on the flour mass and the mass ratios, the target masses of butter, eggs and drinking water are calculated.

[0208] Weigh out the target mass of butter, eggs and drinking water from the base materials, and transport the weighed butter, eggs and drinking water to the mixing silo via a conveyor. Set the stirring frequency based on the target mass, and command the stirrer to stir at the set stirring frequency to obtain the preform to be awakened.

[0209] The blank to be awakened is sent into a variable temperature awakening chamber, which is equipped with a semiconductor temperature controller and a digital sensor. Multiple awakening periods are set, and each awakening period has a target temperature and a target duration preset. The digital sensor collects the actual temperature inside the variable temperature awakening chamber and the awakening time of the blank to be awakened. Based on the awakening time, target temperature, target duration and actual temperature, the real-time output power of the semiconductor temperature controller is calculated, and the semiconductor temperature controller is commanded to run synchronously at the real-time output power. After all awakening periods have passed, the awakened blank is obtained.

[0210] The proofed dough is pre-baked and cooled sequentially to obtain the target Danish croissant pizza base. A variable temperature control model is constructed based on the proofing time, real-time output power, target temperature and actual temperature. The real-time output power is then calculated using the variable temperature control model to complete the production of the Danish croissant pizza base based on variable temperature proofing control.

[0211] In the embodiments provided by this invention, it should be understood that the disclosed devices, systems, and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative, and actual implementations may have other classification methods.

[0212] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0213] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0214] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0215] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for making Danish croissant pizza crust based on variable temperature proofing control, characterized in that, The method includes: Receive a pizza base preparation instruction and start a pizza base preparation device according to the pizza base preparation instruction, wherein the pizza base preparation device includes a conveyor, a mixing chamber, a mixer and a variable temperature proofing chamber; Obtain basic materials, including flour, butter, eggs and drinking water. Flour is transported to a mixing silo via a conveyor and its mass is weighed after transport. The mass ratios of flour to butter, eggs and drinking water are set. Based on the flour mass and the mass ratios, the target masses of butter, eggs and drinking water are calculated. Weigh out the target mass of butter, eggs and drinking water from the base materials, and transport the weighed butter, eggs and drinking water to the mixing silo via a conveyor. Set the stirring frequency based on the target mass, and command the stirrer to stir at the set stirring frequency to obtain the preform to be awakened. The blank to be awakened is sent into a variable temperature awakening chamber, which is equipped with a semiconductor temperature controller and a digital sensor. Multiple awakening periods are set, and each awakening period has a target temperature and a target duration preset. The digital sensor collects the actual temperature inside the variable temperature awakening chamber and the awakening time of the blank to be awakened. Based on the awakening time, target temperature, target duration and actual temperature, the real-time output power of the semiconductor temperature controller is calculated, and the semiconductor temperature controller is commanded to run synchronously at the real-time output power. After all awakening periods have passed, the awakened blank is obtained. The proofed dough is pre-baked and cooled sequentially to obtain the target Danish croissant pizza base. A variable temperature control model is constructed based on the proofing time, real-time output power, target temperature and actual temperature. The real-time output power is then calculated using the variable temperature control model to complete the production of the Danish croissant pizza base based on variable temperature proofing control.

2. The method for making Danish croissant pizza crust based on variable temperature proofing control as described in claim 1, characterized in that, The process of using the digital sensor to collect the actual temperature inside the variable-temperature proofing chamber and the proofing time of the blank to be proofed, and calculating the real-time output power of the semiconductor temperature controller based on the proofing time, target temperature, target duration, and actual temperature, includes: The digital sensor is used to collect the actual temperature inside the variable temperature proofing box and the proofing time of the blank to be proofed. The collection time interval and the number of data collections of the digital sensor are recorded simultaneously. After each collection, the temperature difference between the target temperature and the actual temperature is calculated. Confirm the current wake-up period and read the target temperature and target duration corresponding to the current wake-up period; A temperature difference threshold is set. Based on the temperature difference threshold, the temperature difference, the existing warming time, and the target duration corresponding to the current warming period, the power regulation intensity of the semiconductor temperature controller is calculated according to the following formula: in, For the first Power adjustment intensity during the second acquisition The duration of wakefulness. The target duration corresponding to the current wake-up period. For the first Temperature difference value at each sampling point Temperature difference threshold It is a natural constant; Calculate the ratio of the duration of the current warm-up period to the target duration of the current warm-up period. Determine the current warm-up sub-stage based on the temperature difference and the duration ratio. Calculate the real-time output power of the semiconductor temperature controller based on the determination result of the warm-up sub-stage, combined with the power adjustment intensity, the sampling time interval, and the number of data samplings.

3. The method for making Danish croissant pizza crust based on variable temperature proofing control as described in claim 2, characterized in that, The calculation of the real-time output power of the semiconductor temperature controller based on the determination result of the waking-up sub-stage, combined with the power adjustment intensity, the sampling time interval, and the number of data samplings, includes: Obtain the reference power of the semiconductor temperature controller and confirm the current wake-up sub-stage, wherein the wake-up sub-stage corresponds to one of the stage types: heating stage, steady state stage, and cooling stage. If the current waking sub-stage is in the heating stage, the product of the power regulation intensity and the reference power is used as the real-time output power. If the current wake-up sub-stage is in a steady state, the real-time output power is calculated according to the following formula: in, For the first Real-time output power during the first data acquisition. For the first Power adjustment intensity during the second acquisition As the reference power, The time interval for data collection. The number of times data was collected. For the first Temperature difference value at each sampling time; If the current waking sub-stage is in the cooling stage, calculate the rate of change of the actual temperature over time to obtain the temperature change rate. Use the absolute value of the temperature change rate as the power reduction coefficient. Calculate the product of the absolute value of the temperature difference and the power reduction coefficient to obtain the power reduction amount. Calculate the difference between the reference power and the power reduction amount to obtain the real-time output power.

4. The method for making Danish croissant pizza crust based on variable temperature proofing control as described in claim 1, characterized in that, The process of sequentially pre-baking and cooling the proofed dough to obtain the target Danish croissant pizza base includes: Set a pre-baking parameter set and send the proofed dough into a hot air circulating oven. The hot air circulating oven has multiple temperature monitoring points built in and performs a pre-baking operation on the proofed dough according to the set pre-baking parameter set. The real-time temperature of each temperature monitoring point is continuously collected, and the temperature standard deviation and temperature non-uniformity are calculated based on the real-time temperature. Based on the temperature standard deviation and temperature non-uniformity, the pre-baking parameter set is iteratively updated. After each iteration, an updated pre-baking parameter set is obtained, and the hot air circulation oven is immediately adjusted in real time with the updated pre-baking parameter set until the optimal pre-baking parameter set is iteratively updated. The hot air circulation oven is then adjusted with the optimal pre-baking parameter set, and the pre-baked crust is obtained after the pre-baking operation is completed. The pre-baked dough is removed from the hot air circulating oven and transferred to a preset cooling device to perform a cooling operation, thereby obtaining the target Danish croissant pizza base.

5. The method for making Danish croissant pizza crust based on variable temperature proofing control as described in claim 4, characterized in that, The step of iteratively updating the pre-baking parameter set, obtaining an updated pre-baking parameter set after each iteration, and immediately adjusting the hot air circulation oven in real time based on the updated pre-baking parameter set until the optimal pre-baking parameter set is obtained through iterative updates, includes: Confirm the pre-baking parameter set, wherein the pre-baking parameter set consists of multiple parameters, and sequentially set the adjustment step size and value range of each parameter; The temperature standard deviation and temperature non-uniformity are weighted and summed to obtain a comprehensive evaluation index. The comprehensive evaluation index is marked as the current index, and a convergence threshold for the comprehensive evaluation index is set. Select individual parameters from the pre-baking parameter set in sequence, mark them as the current parameters, and then perform the following parameter tuning operations in sequence: Increase the current parameter by one adjustment step and calculate the corresponding comprehensive evaluation index to obtain the positive index; Reduce the current parameter by one adjustment step and calculate the corresponding comprehensive evaluation index to obtain the negative index. The positive and negative indicators are compared numerically. If the positive indicator is smaller, the current parameter is increased by one step. If the negative indicator is smaller, the current parameter is decreased by one step. If the positive and negative indicators are equal, the current parameter remains unchanged. After adjusting the current parameter, continue to make a judgment: if the value of the current parameter is less than the lower limit of the corresponding value range, then assign the current parameter the lower limit of the value range. If the value of the current parameter is greater than the upper limit of its value range, then the current parameter is set to the upper limit of its value range; After traversing and performing parameter tuning operations on all parameters in the pre-baking parameter set, an updated pre-baking parameter set is obtained. The comprehensive evaluation index corresponding to the updated pre-baking parameter set is calculated and marked as the updated index. The absolute value of the difference between the updated index and the current index is calculated. If the absolute value of the difference is less than the set convergence threshold, the updated index is used as the current index for the new round of iteration. The parameter adjustment process is repeated until the absolute value of the difference between the updated index and the current index is less than the convergence threshold, and the optimal pre-baking parameter set is generated.

6. The method for making Danish croissant pizza crust based on variable temperature proofing control as described in claim 4, characterized in that, The process of removing the pre-baked dough from the hot air circulating oven and transferring it to a preset cooling device to perform a cooling operation on the pre-baked dough to obtain the target Danish croissant pizza dough includes: The pre-baked dough is removed from the hot air circulating oven and transferred to a preset cooling device, wherein the preset cooling device has a built-in circulating fan and a temperature sensor; Set a first wind speed value and a second wind speed value for the circulating fan, wherein the first wind speed value is greater than the second wind speed value; A first temperature threshold and a second temperature threshold are set, wherein the first temperature threshold is greater than the second temperature threshold; The circulating fan is started at the first wind speed value to perform forced convection cooling, and the temperature sensor is used to continuously monitor the center temperature of the pre-baked dough. When the temperature at the center of the pie crust drops to the first temperature threshold, the fan speed is reduced to the second speed, and forced convection cooling continues until the difference between the temperature at the center of the pie crust and the current ambient temperature is less than the second temperature threshold, thus completing the cooling operation. Let the pre-baked dough rest at room temperature for 15 to 30 minutes to obtain the desired Danish croissant pizza base.

7. The method for making Danish croissant pizza crust based on variable temperature proofing control as described in claim 1, characterized in that, The variable temperature control model constructed based on the awakening time, real-time output power, target temperature, and actual temperature includes: Collect multiple time-by-time running data during the wake-up process. The time-by-time running data consists of the wake-up time, target temperature, actual temperature and real-time output power at each time. Each of the time-by-time running data is used to form a sample, and the real-time output power in the sample is used as the sample real-time output power. A variable temperature control model is constructed based on a neural network algorithm, wherein the variable temperature control model includes an input layer, a processing layer, and an output layer; Each sample is selected sequentially, and the input layer is used to construct an input parameter sequence from the wake-up time, target temperature, and the calculated temperature difference between the target temperature and the actual temperature in the training sample. The input parameter sequence is then input into the variable temperature control model. By setting processing weights, the processing layer sequentially performs weighted operations, feature extraction, and summation on the input parameter sequence based on the processing weights to obtain the original output value. By setting output weights and output bias values, the original output values ​​are weighted based on the output weights and then superimposed with the output bias values ​​to obtain the predicted real-time output power. The difference between the predicted real-time output power and the sample real-time output power is calculated to obtain the power deviation value. The processing weight and output weight are iteratively adjusted based on the power deviation value until the predicted real-time output power is equal to the sample real-time output power. The variable temperature control model is constructed when the predicted real-time output power of all samples is equal to the real-time output power of the samples.

8. The method for making Danish croissant pizza crust based on variable temperature proofing control as described in claim 7, characterized in that, The set processing weights are used by the processing layer to sequentially perform weighted operations, feature extraction, and summation operations on the input parameter sequence based on the processing weights to obtain the original output value, including: The processing layer is confirmed and the processing weights are set. The processing layer includes a preprocessing layer, an intermediate processing layer and a postprocessing layer. The processing weights include preprocessing weights, intermediate processing weights and postprocessing weights. The preprocessing layer receives the input parameter sequence, and performs a weighted operation on the wake-up time, target temperature, and calculated temperature difference between the target temperature and the actual temperature in the input parameter sequence according to the preprocessing weight to obtain multiple preprocessing values. The multiple preprocessing values ​​are then combined to generate a preprocessing sequence. The intermediate processing layer receives the preprocessed sequence, calculates the difference between each group of adjacent preprocessed values ​​in the preprocessed sequence as the trend feature value, and calculates the difference between the maximum and minimum values ​​of the preprocessed values ​​in the preprocessed sequence to obtain the sequence deviation. The trend feature value and the sequence deviation are concatenated in the order of generation to obtain the trend sequence. The trend sequence is weighted according to the intermediate processing weight to obtain multiple intermediate processing values. The intermediate processing values ​​are then arranged according to the original splicing order of the trend sequence to obtain the intermediate processing sequence. The intermediate processing sequence is received by the post-processing layer, and the intermediate processing values ​​are weighted by matching them with post-processing weights to obtain multiple post-processing values. The original output value is obtained by summing all the post-processing values.

9. The method for making Danish croissant pizza crust based on variable temperature proofing control as described in claim 7, characterized in that, The iterative adjustment of the processing weights and output weights based on the power deviation value includes: If the predicted real-time output power is less than the sample real-time output power, based on the power deviation value, the output weight, output bias value, post-processing weight, intermediate processing weight and pre-processing weight are adjusted downward in sequence. If the predicted real-time output power is greater than the sample real-time output power, based on the power deviation value, the output weight, output bias value, post-processing weight, intermediate processing weight and pre-processing weight are adjusted upward in sequence. The input parameter sequence of the corresponding sample is repeatedly input into the temperature control model, and the predicted real-time output power is recalculated. The numerical adjustment process of the above input parameter sequence is iterated repeatedly until the predicted real-time output power is equal to the sample real-time output power.

10. A Danish croissant pizza crust making system based on variable temperature proofing control, characterized in that, The system includes: The instruction receiving and material preparation module is used to receive pizza base making instructions and start the pizza base preparation device according to the pizza base making instructions. The pizza base preparation device includes a conveyor, a mixing bin, a mixer, and a variable temperature proofing box. The material weighing and proportioning module is used to obtain basic materials, including flour, butter, eggs and drinking water. The flour is transported to the mixing bin by the conveyor and the flour mass is weighed after the conveying is completed. The mass ratio of flour to butter, eggs and drinking water is set respectively. Based on the flour mass and the mass ratio, the target mass of butter, eggs and drinking water is calculated respectively. The mixing and forming module is used to weigh out the target mass of butter, eggs and drinking water from the base materials, and transport the weighed butter, eggs and drinking water to the mixing bin via a conveyor. Based on the target mass, the mixing frequency is set, and the stirrer is commanded to stir at the set mixing frequency to obtain the preform to be prepared. A variable-temperature proofing and forming module is used to feed the blank to be proofed into a variable-temperature proofing chamber, which is equipped with a semiconductor temperature controller and a digital sensor. Multiple proofing periods are set, and each proofing period has a pre-set target temperature and target duration. The digital sensor collects the actual temperature inside the variable-temperature proofing chamber and the proofing time of the blank. Based on the proofing time, target temperature, target duration, and actual temperature, the real-time output power of the semiconductor temperature controller is calculated, and the semiconductor temperature controller is commanded to run synchronously at the real-time output power. After all proofing periods, the proofed blank is obtained. The proofed blank is then subjected to pre-baking and cooling operations in sequence to obtain the target Danish croissant pizza base. A variable-temperature control model is constructed based on the proofing time, real-time output power, target temperature, and actual temperature. The variable-temperature control model is used to continue calculating the real-time output power to complete the production of the Danish croissant pizza base based on variable-temperature proofing control.

Images