Intelligent production process of thousand-layer toast

Abstract

The present application belongs to the field of intelligent production process technology of toast, in particular to a kind of intelligent production process of thousand-layer toast, build intelligent control platform based on industrial internet, integrate raw material detection module, intelligent proportioning module, accurate crumb stacking module, dynamic proofing module, intelligent baking module, cooling sorting module, traceability packaging module and data traceability module, each module is real-time acquisition production process data by sensor, output accurate control instruction after AI algorithm analysis, realize whole process parameter dynamic optimization;Process steps include raw material intelligent pretreatment, dough intelligent modulation, accurate crumb stacking processing, dynamic proofing control, intelligent baking optimization, cooling sorting classification, traceability packaging warehousing and whole-process data traceability.

Description

Technical Field

[0001] This invention belongs to the field of intelligent production technology for toast, and particularly relates to an intelligent production process for layered toast. Background Technology

[0002] Traditional mille-feuille toast production relies heavily on manual experience to control key processes. Raw material testing lacks precise quantitative methods, and core indicators such as flour gluten content and shortening particle size are difficult to monitor in real time, resulting in large fluctuations in raw material ratio parameters. During the folding process, the control of rolling thickness, oiling uniformity, and the number of folds is entirely manual, which easily leads to problems such as layer breakage and uneven flaking. Furthermore, the proofing and baking stages generally use fixed parameter modes, which cannot be dynamically adjusted according to the real-time state of the dough and the ambient temperature and humidity. Ultimately, this results in poor product consistency, insufficient clarity of flaky layers, and difficulty in consistently improving the pass rate, making it difficult to meet the quality control requirements of large-scale production.

[0003] While some existing toast production equipment has incorporated basic intelligent modules, it generally suffers from the drawbacks of fragmented modules and lack of data interoperability, lacking a unified collaborative control platform to achieve full-process parameter linkage optimization. Furthermore, existing processes cannot flexibly adapt to different raw material types, product specifications, and complex environmental conditions, and lack a full lifecycle data traceability mechanism, making it difficult to pinpoint quality issues and hindering continuous process iteration through data accumulation. This results in room for improvement in production efficiency, product flexibility, and quality stability, failing to meet market demands for diversified, high-quality, and intelligent production of crepe toast.

[0004] Therefore, a technical solution is designed to address the aforementioned technical problems. Summary of the Invention

[0005] The purpose of this invention is to address the aforementioned technical problems by providing an intelligent production process for layered toast.

[0006] In view of this, the present invention provides an intelligent production process for layered toast, comprising the following steps: An intelligent control platform based on the Industrial Internet is built, integrating modules such as raw material detection, intelligent proportioning, precise layering, dynamic proofing, intelligent baking, cooling and sorting, traceability packaging, and data traceability. Each module collects production process data in real time through sensors, and outputs precise control commands after analysis by AI algorithms, realizing dynamic optimization of parameters throughout the entire process. The process steps include intelligent pretreatment of raw materials, intelligent dough preparation, precise layering of pastry, dynamic proofing control, intelligent baking optimization, cooling, sorting and grading, traceable packaging and warehousing, and full data traceability.

[0007] Preferably, the intelligent pretreatment of raw materials involves detecting the gluten content and moisture content of flour using a near-infrared spectroscopy sensor, and detecting the particle size of shortening using a laser particle size analyzer. The detection data is uploaded to an intelligent control platform, which optimizes the proportions of flour, shortening, water, yeast, sugar, salt, and optional additives using an AI algorithm based on preset toast quality standards, and outputs the results to an automatic batching scale to complete precise ingredient mixing.

[0008] Preferably, the intelligent dough modulation involves feeding the prepared ingredients into an intelligent dough mixer with built-in torque and temperature sensors. The intelligent control platform dynamically adjusts the kneading speed to 50-120 r / min and the kneading time to 8-15 min based on the flour gluten data, controlling the dough temperature to 26-28℃.

[0009] Preferably, the precise folding process employs an integrated intelligent rolling and automatic folding device, and the process includes: Initial rolling: Roll the dough to a thickness of 5-8mm; Automatic oiling: Shortening is evenly applied by an automatic oiling device. The oiling thickness is dynamically adjusted by AI according to the particle size of the shortening and the thickness of the dough to 0.3-0.5mm. Automatic folding: Automatically folds to a preset 16-32 layers, with the folding force controlled by a pressure sensor to be 0.2-0.3 MPa during the folding process; Secondary rolling: After folding, roll to a thickness of 2-3mm, with the rolling speed matched in real time to the dough's extensibility.

[0010] Preferably, the dynamic proofing control sends the folded dough into an intelligent proofing box equipped with temperature and humidity sensors and CO2 concentration sensors. The intelligent control platform dynamically adjusts the proofing parameters based on the initial temperature of the dough, ambient temperature and humidity, and the yeast content of the flour: initial temperature 36-38℃, humidity 85-90%, when the dough volume expands to 1.8-2 times, the temperature is adjusted to 34-35℃ and the humidity is maintained at 80-85%, and the total proofing time is determined by AI based on real-time fermentation data to be 40-60 minutes.

[0011] Preferably, the intelligent baking optimization adopts a segmented baking strategy. The intelligent oven has a built-in infrared temperature sensor and camera, and the AI ​​algorithm dynamically optimizes parameters based on the weight and thickness of the toast. First stage 0-8min: Top heat 180-190℃, bottom heat 160-170℃; Second stage 8-18min: top heat 165-175℃, bottom heat 175-185℃; The third stage, 18-25 minutes: top heat 150-160℃, bottom heat 160-170℃; baking will automatically end when the camera detects that the surface of the toast has reached the preset color difference and the center temperature has reached 92-95℃.

[0012] Preferably, the cooling and sorting process involves sending the baked toast into an intelligent cooling tunnel. Multiple temperature sensors are installed inside the tunnel to dynamically adjust the cooling airflow speed to 1.5-3 m / s and the cooling time to 20-30 min based on the initial temperature of the toast, ensuring that the center temperature of the toast drops to 30-35℃. After discharge, the appearance of the toast is inspected by a visual inspection system, and the weight is detected by a weight sensor. Qualified products with uniform color and a weight error within ±5g are automatically sorted out, while unqualified products are automatically rejected.

[0013] Preferably, during the traceability packaging and warehousing process, qualified toast is sliced ​​by an automatic slicer, and the slice thickness can be preset through the intelligent control platform with an error of ±0.2mm. During the packaging process, each packaging unit is printed with a unique traceability QR code, which includes the raw material batch, production time, equipment number, and test data information. After packaging is completed, the intelligent warehousing system automatically allocates storage locations according to the product batch to complete the warehousing.

[0014] Preferably, the full-process data traceability is achieved by recording raw material testing data, proportioning parameters, kneading parameters, layering parameters, proofing parameters, baking parameters, and testing data in real time through an intelligent control platform. The data is stored on a cloud server and can be queried via traceability QR codes or the platform backend.

[0015] Preferably, the process parameters of each stage can be adaptively adjusted by AI algorithms according to the type of raw materials, product specifications, nutritional requirements or environmental conditions, so as to achieve production adaptation under different scenarios. Furthermore, the intelligent control platform can accumulate production data through machine learning, enabling iterative optimization of process parameters.

[0016] The beneficial effects of this invention are: This invention establishes a unified industrial internet intelligent control platform to achieve data interoperability and collaborative control among various production modules. It utilizes multiple types of sensors to capture key production information in real time and outputs precise control commands via AI algorithms, completely transforming the traditional process that relies on manual experience and fixed, rigid parameters. From intelligent detection and proportioning of raw materials to dynamic adaptation of dough preparation and puff pastry processing, and then to precise segmented control of proofing and baking, the entire process avoids problems such as imbalanced raw material proportions, broken puff pastry layers, and improper proofing and baking. This results in toast with clear, evenly distributed puff pastry layers, a soft, fluffy, and tender texture, and a uniform color, significantly improving the stability and standardization of product quality.

[0017] Meanwhile, this process boasts exceptional flexibility and continuous optimization capabilities. Through AI algorithms, it can rapidly respond to changes in raw material types, product specifications, nutritional requirements, and complex environmental conditions, flexibly adjusting process parameters at each stage to meet diverse market production demands. A comprehensive data traceability mechanism enables end-to-end control from raw materials to warehousing, facilitating rapid identification of quality issues. Furthermore, the machine learning capabilities of the intelligent control platform continuously iterate and optimize process parameters by accumulating production practice data, reducing manual intervention while improving production efficiency. This provides reliable support for the large-scale, intelligent, and high-quality production of layered toast. Attached Figure Description

[0018] Figure 1 This is a flowchart of an intelligent production process for layered toast according to the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0020] The core of this process lies in building an intelligent control platform based on the Industrial Internet, integrating modules for raw material detection, intelligent proportioning, precise layering, dynamic proofing, intelligent baking, cooling and sorting, traceability packaging, and data tracing. Each module collects data in real time through sensors, and after analysis by AI algorithms, outputs precise control commands to achieve dynamic optimization of parameters throughout the entire process. The specific process steps are as follows: Intelligent pre-processing of raw materials: The gluten content and moisture content of flour are detected by a near-infrared spectroscopy sensor, and the particle size of shortening is detected by a laser particle size analyzer. The detection data is uploaded to the intelligent control platform. The platform optimizes the proportion parameters of raw materials such as flour, shortening, water, yeast, sugar, and salt through AI algorithms according to the preset toast quality standards (such as softness, density, and tenderness), and outputs the results to the automatic ingredient scale to complete the precise ingredient mixing.

[0021] Intelligent dough preparation: The prepared ingredients are fed into an intelligent dough kneading machine. The dough kneading machine has built-in torque and temperature sensors to collect the torque and temperature values ​​of the dough in real time during the kneading process. The control platform dynamically adjusts the kneading speed (50-120 r / min) and time (8-15 min) based on the flour gluten data to ensure that the dough gluten network is fully formed and the temperature is controlled at 26-28℃ (to avoid over-activation or deactivation of gluten).

[0022] Precise dough layering: The system employs an integrated "intelligent rolling + automatic folding" device. After the initial rolling (thickness controlled at 5-8mm), the dough is evenly coated with shortening by an automatic oiling device (the oiling thickness is dynamically adjusted by AI based on the shortening particle size and dough thickness, ranging from 0.3-0.5mm). Subsequently, the device automatically folds the dough according to a preset number of layers (16-32 layers). During the folding process, the folding force is controlled by a pressure sensor (0.2-0.3MPa) to prevent the dough from breaking. After folding, a second rolling is performed, with the thickness controlled at 2-3mm. The rolling speed is matched with the dough's extensibility in real time.

[0023] Dynamic proofing control: The folded dough is placed into an intelligent proofing box, which is equipped with temperature and humidity sensors and CO2 concentration sensors (to monitor the fermentation degree of the dough in real time). The control platform dynamically adjusts the proofing parameters based on the initial temperature of the dough, the ambient temperature and humidity, and the yeast content of the flour: the initial temperature is 36-38℃ and the humidity is 85-90%. When the dough volume expands to 1.8-2 times, the temperature is finely adjusted to 34-35℃ and the humidity is maintained at 80-85%. The total proofing time is determined by AI based on real-time fermentation data (usually 40-60 minutes) to avoid under-proofing or over-proofing.

[0024] Intelligent Baking Optimization: After proofing, the dough is placed in an intelligent oven equipped with an infrared temperature sensor and camera to collect real-time data on the surface temperature and color changes of the toast. A "segmented baking" strategy is employed, with AI algorithms dynamically optimizing parameters based on the toast's weight and thickness: First segment (0-8 min): Top heat 180-190℃, bottom heat 160-170℃, for rapid shaping; Second segment (8-18 min): Top heat 165-175℃, bottom heat 175-185℃, to promote the formation of a crispy layer; Third segment (18-25 min): Top heat 150-160℃, bottom heat 160-170℃, for even browning; When the camera detects that the toast surface reaches the preset color difference (Lab value: L=70-75, a=2-4, b=15-18) and the center temperature reaches 92-95℃, baking automatically ends.

[0025] Cooling, sorting, and grading: The baked toast is sent into an intelligent cooling tunnel. Multiple temperature sensors are installed in the tunnel to dynamically adjust the cooling air speed (1.5-3m / s) and cooling time (20-30min) according to the initial temperature of the toast, ensuring that the center temperature of the toast drops to 30-35℃ before being discharged. After discharge, the appearance of the toast is detected by a visual inspection system (camera + AI recognition) (whether there are cracks or deformations), and the weight is detected by a weight sensor. Products with uniform color and qualified weight (error ±5g) are automatically sorted out, and unqualified products are automatically rejected.

[0026] Traceable Packaging and Warehousing: Qualified toast is sliced ​​by an automatic slicer (slice thickness can be preset through the intelligent control platform with an error of ±0.2mm) and then sent to an automatic packaging machine; during the packaging process, each packaging unit is printed with a unique traceability QR code (containing information such as raw material batch, production time, equipment number, and test data); after packaging is completed, the intelligent warehousing system automatically allocates storage locations according to the product batch and completes the warehousing.

[0027] Full data traceability: The intelligent control platform records parameters of each stage in real time (raw material testing data, proportioning parameters, kneading / stacking / proofing / baking parameters, testing data, etc.), and the data is stored on the cloud server. It supports querying via traceability QR code or platform backend, which facilitates the location of quality problems and process optimization.

[0028] Example 1: Production of crepe bread using high-gluten flour (30-32% gluten content); Raw material testing: Near-infrared spectroscopy showed that the flour had a gluten content of 31% and a moisture content of 13.5%, while the shortening had a particle size of 20μm. Intelligent proportioning: AI-optimized proportions (based on 10kg of flour): 2.5kg shortening, 5.8kg water, 0.12kg yeast, 0.8kg sugar, 0.2kg salt; Dough preparation: Knead at 100 rpm for 12 minutes, and control the dough temperature at 27℃; Folding parameters: initial rolling thickness 6mm, oil coating thickness 0.4mm, number of folds 32, second rolling thickness 2.5mm; Dynamic proofing: Initial temperature and humidity 37℃ / 88%, adjusted to 34℃ / 82% when fermentation reaches 2 times the volume, total proofing time 52min; Intelligent baking: The segmented parameters are adjusted to 185℃ / 165℃ for the first segment (0-8min), 170℃ / 180℃ for the second segment (8-18min), and 155℃ / 165℃ for the third segment (18-25min). The end conditions are surface color difference L=72, a=3, b=16, and center temperature 93℃. Cooling and sorting: cooling air velocity 2.2m / s, time 25min, standard weight 120g / piece, error ±5g; Results: The toast has 32 distinct flaky layers, a soft and fluffy texture, and a tender core, with a pass rate of 99.2%.

[0029] Example 2: Production of crepe bread using high-gluten flour (20-22% gluten content); Raw material testing: flour gluten content 21%, moisture content 13.2%, shortening particle size 15μm; Intelligent proportioning: AI-optimized proportions (10kg flour): 2.8kg shortening, 6.0kg water, 0.15kg yeast, 1.0kg sugar, 0.18kg salt (increasing the shortening and sugar content to compensate for the insufficient gluten in high-gluten flour). Dough preparation: Knead at 80 rpm for 10 minutes, and control the dough temperature at 26℃ (to avoid over-kneading and breaking the low-gluten dough). Folding parameters: initial rolling thickness 1mm, oil coating thickness 0.5mm, number of folds 24, second rolling thickness 2mm; Dynamic proofing: Initial temperature and humidity 38℃ / 90%, adjust to 35℃ / 85% when fermentation reaches 1.9 times the volume, total proofing time 48min; Intelligent baking: Segment parameters: first segment (0-8min) 180℃ / 160℃, second segment (8-18min) 165℃ / 175℃, third segment (18-24min) 150℃ / 160℃, ending conditions are surface color difference L=73, a=2.5, b=17, and center temperature 92℃; Results: The toast has 24 evenly distributed flaky layers, a soft and fluffy texture, no cracks, and a pass rate of 98.8%.

[0030] Example 3: Production of large-format layered toast (250g / piece); Ingredient ratio: Based on 10kg of flour, the ratio is adjusted to 2.6kg of shortening, 5.9kg of water, 0.13kg of yeast, 0.9kg of sugar, and 0.2kg of salt; Folding parameters: initial rolling thickness 8mm, oil coating thickness 0.45mm, number of folds 32, second rolling thickness 3mm; Proofing optimization: Due to the increased weight of the dough, the initial temperature and humidity were 36℃ / 87%, and when the dough had fermented to 1.8 times its original volume, the temperature and humidity were adjusted to 34℃ / 83%, with a total proofing time of 60 minutes. Baking adjustments: Extend the time in segments. First segment (0-10 min): 185℃ / 165℃; second segment (10-22 min): 170℃ / 180℃; third segment (22-32 min): 155℃ / 165℃. End conditions: surface color difference L=71, a=3.5, b=16, center temperature 95℃. Cooling and sorting: cooling air velocity 2.8m / s, time 30min, standard weight 250g / piece, error ±8g; Results: The toast has distinct layers, is fully baked in the center without any undercooked parts, and has a consistent texture, making it suitable for family sharing.

[0031] Example 4: Production of low-sugar, low-fat crepe toast (sugar content ≤2%, fat content ≤15%). Raw material testing: flour gluten content 26%, moisture content 13.0%, low-fat shortening (80% fat content) selected. Intelligent proportioning: AI-optimized proportions (10kg flour): 1.8kg low-fat shortening, 6.2kg water, 0.14kg yeast, 0.2kg sugar (≤2%), 0.22kg salt, and 0.05kg dietary fiber powder (to improve taste). Dough preparation: Knead at 110 r / min for 13 min to ensure full gluten development, at a temperature of 28℃; Puff pastry parameters: initial rolling thickness 7mm, oil coating thickness 0.3mm, 28 folds, second rolling thickness 2.2mm (to reduce shortening usage and ensure basic layers); Proofing and baking: Proofing time 55 min, baking segment parameters: first segment (0-8 min) 182℃ / 162℃, second segment (8-19 min) 168℃ / 178℃, third segment (19-26 min) 152℃ / 162℃; Results: The toast has 28 layers of puff pastry, a soft and fluffy texture with moderate density, and meets the standards for sugar and fat content, making it suitable for health-conscious consumers. The pass rate is 98.5%.

[0032] Example 5: Production adaptation under high temperature and high humidity environment (ambient temperature 32℃, humidity 75%); Raw material pretreatment: Flour is sent to a constant temperature warehouse (25℃) in advance, and shortening is refrigerated to 10℃ (to avoid softening at high temperature). Dough preparation: Turn on the cooling device of the dough mixer to control the dough temperature at 26℃, knead at 90r / min for 11min; Folding control: The folding equipment has a built-in cooling system to ensure that the temperature of the shortening is ≤15℃ when it is applied, the thickness of the shortening is 0.35mm, and the number of folds is 32. Dynamic proofing: Due to the high ambient temperature and humidity, the initial temperature was adjusted to 34℃ / 82%, and when the volume increased to 1.9 times, it was adjusted to 33℃ / 80%, and the total proofing time was shortened to 42 minutes (to avoid over-fermentation). Baking optimization: A dehumidifier was added to the oven air inlet. The temperature parameters for each stage are as follows: Stage 1 (0-8min) 188℃ / 168℃, Stage 2 (8-18min) 172℃ / 182℃, Stage 3 (18-24min) 158℃ / 168℃. Results: Effectively avoids the problems of dough fermentation being too fast and shortening melting caused by high temperature and high humidity environments, with a product qualification rate of 99.0%.

[0033] Example 6: Production adaptation in low temperature and low humidity environment (ambient temperature 10℃, humidity 30%); Raw material pretreatment: Preheat flour and water to 20°C, and shortening to 12°C (to improve extensibility). Dough preparation: Turn on the heating device of the dough mixer, control the dough temperature at 27℃, knead at 100r / min for 14min (extend the kneading time to ensure gluten formation); Folding parameters: initial rolling thickness 6mm, oil coating thickness 0.4mm, 32 folds, second rolling speed reduced by 10% (to adapt to the dough extensibility at low temperatures). Dynamic proofing: Initial temperature and humidity 39℃ / 92% (increase initial temperature and humidity), adjust to 36℃ / 88% when fermentation reaches 1.8 times the volume, and extend the total proofing time to 58 minutes; Baking adjustments: Extend oven preheating time by 5 minutes. Segment parameters: first segment (0-9 min) 185℃ / 165℃, second segment (9-20 min) 170℃ / 180℃, third segment (20-27 min) 155℃ / 165℃. Effect: Solves the problem of insufficient kneading and slow proofing of dough under low temperature and low humidity conditions, and ensures that the product quality is consistent with that under normal temperature conditions.

[0034] Example 7: Production of colored crepe toast with added fruit and vegetable powder (spinach powder); Raw material testing: flour gluten content 28%, moisture content 13.3%, spinach powder moisture content 8%, color value (L=55, a=-10, b=12); Intelligent proportioning: AI-optimized proportions (10kg flour): 2.4kg shortening, 5.7kg water, 0.12kg yeast, 0.7kg sugar, 0.2kg salt, 0.5kg spinach powder (replacing part of the flour to ensure color and nutrition); Dough preparation: Knead at 95 rpm for 13 minutes, control the dough temperature at 27℃, and ensure that the spinach powder is evenly dispersed; Baking optimization: Due to the moisture content of spinach powder, the baking time is extended by 2 minutes. The segmented parameters are 170℃ / 180℃ for the second segment (8-20min) and 155℃ / 165℃ for the third segment (20-27min). The ending conditions are adjusted to surface color difference L=65, a=-8, b=14 (matching spinach color) and center temperature 94℃. Results: The toast is a uniform light green color with a clear flaky layer, and has both spinach aroma and toast flavor. The pass rate is 98.6%.

[0035] Example 8: Production of ultra-thin sliced ​​mille-feuille toast (2mm slice thickness) (suitable for sandwich making); Folding parameters: initial rolling thickness 5mm, oil coating thickness 0.3mm, number of folds 24, second rolling thickness 2mm; Proofing control: shorten the proofing time to 45 minutes to ensure that the dough volume expands to 1.8 times (avoid over-expansion, which will make the slices brittle). Baking adjustments: shorten the segmented time. First segment (0-7min) 185℃ / 165℃, second segment (7-15min) 170℃ / 180℃, third segment (15-22min) 155℃ / 165℃, and control the center temperature at 92℃ (to ensure that it is not easily broken when slicing). Cooling slices: cooling air velocity 2.0m / s, time 22min, automatic slicer adjusted slice thickness 2mm, error ±0.2mm; Sorting and Packaging: Visual inspection focuses on checking for broken slices. Packaging uses individual small packages (5 slices / pack), and the traceability QR code indicates the slice thickness. Results: The slices are thin and intact, with clear puff pastry layers, making them suitable for sandwich fillings and preventing them from crumbling.

[0036] Example 9: AI-based self-learning process optimization production (100th batch of mass production). Data foundation: The intelligent control platform has accumulated production data from the first 99 batches (raw material parameters, process parameters, product qualification rate, taste score, etc.). AI self-learning: The platform analyzes data through machine learning and finds that the product tastes best when "the gluten content of the flour increases by 1%, the kneading time is extended by 0.5 minutes, and the proofing time is shortened by 2 minutes". Process optimization: The gluten content of the flour in the 100th batch of raw materials was 30%. The AI ​​automatically output optimized parameters: kneading time 11.5 min, proofing time 48 min, oil coating thickness for layering and pastry 0.42 mm, and the temperature of the third baking stage reduced by 2℃. Production implementation: Production is carried out according to optimized parameters, with real-time data monitoring throughout the entire process; Results: The product qualification rate increased to 99.5%, and the taste score (softness, density, and tenderness) improved by 8% compared to the previous batch, achieving self-iterative optimization of the process.

[0037] Example 10: Production of layered toast in high-altitude areas (2000m altitude, 80kPa air pressure); Ingredient ratio: AI-optimized ratio (10kg flour): 2.7kg shortening, 6.1kg water, 0.16kg yeast (high altitude and low air pressure, increase yeast usage to improve fermentation efficiency), 0.9kg sugar, 0.22kg salt; Proofing optimization: The proofing box adopts a sealed pressurized design (pressure increased to 101kPa), with an initial temperature and humidity of 38℃ / 90%, which is adjusted to 35℃ / 85% when fermentation reaches 2.0 times the volume, and a total proofing time of 55min; Baking adjustments: Due to the lower boiling point at high altitudes, the oven adopts a high-pressure baking mode (120kPa), with segmented temperature increases of 5-8℃: first segment (0-8min) 190℃ / 170℃, second segment (8-18min) 175℃ / 185℃, third segment (18-26min) 160℃ / 170℃, with the center temperature controlled at 95℃; Cooling and sorting: The cooling tunnel adopts a gradient pressure reduction design (to prevent the toast from cracking due to changes in air pressure), with a cooling air velocity of 2.5 m / s and a cooling time of 28 min; Results: Effectively solves the problems of slow fermentation, incomplete baking, and easy cracking in high-altitude areas, ensuring product quality is consistent with that in plain areas.

[0038] The embodiments of this application have been described above with reference to the accompanying drawings. Unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other. This application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. An intelligent production process for layered toast, characterized in that: Includes the following steps: An intelligent control platform based on the Industrial Internet is built, integrating modules such as raw material detection, intelligent proportioning, precise layering, dynamic proofing, intelligent baking, cooling and sorting, traceability packaging, and data traceability. Each module collects production process data in real time through sensors, and outputs precise control commands after analysis by AI algorithms, realizing dynamic optimization of parameters throughout the entire process. The process steps include intelligent pretreatment of raw materials, intelligent dough preparation, precise layering of pastry, dynamic proofing control, intelligent baking optimization, cooling, sorting and grading, traceable packaging and warehousing, and full data traceability.

2. The intelligent production process for layered toast according to claim 1, characterized in that: The intelligent pretreatment of raw materials uses a near-infrared spectroscopy sensor to detect the gluten content and moisture content of flour, and a laser particle size analyzer to detect the particle size of shortening. The detection data is uploaded to the intelligent control platform. Based on the preset toast quality standards, the platform uses AI algorithms to optimize the proportions of flour, shortening, water, yeast, sugar, salt, and optional additives, and outputs the data to an automatic batching scale to complete the precise batching.

3. The intelligent production process for layered toast according to claim 2, characterized in that: The intelligent dough modulation process involves feeding the prepared ingredients into an intelligent dough mixer with built-in torque and temperature sensors. The intelligent control platform dynamically adjusts the kneading speed to 50-120 r / min and the kneading time to 8-15 min based on the flour gluten data, while controlling the dough temperature to 26-28℃.

4. The intelligent production process for layered toast according to claim 3, characterized in that: The precise folding process employs an integrated intelligent rolling and automatic folding equipment, and the process includes: Initial rolling: Roll the dough to a thickness of 5-8mm; Automatic oiling: Shortening is evenly applied by an automatic oiling device. The oiling thickness is dynamically adjusted by AI according to the particle size of the shortening and the thickness of the dough to 0.3-0.5mm. Automatic folding: It automatically folds into 16-32 layers according to a preset schedule. During the folding process, the folding force is controlled by a pressure sensor to be 0.2-0.3MPa. Secondary rolling: After folding, roll to a thickness of 2-3mm, with the rolling speed matched in real time to the dough's extensibility.

5. The intelligent production process for layered toast according to claim 4, characterized in that: The dynamic proofing control sends the folded dough into an intelligent proofing box equipped with temperature and humidity sensors and CO2 concentration sensors. The intelligent control platform dynamically adjusts the proofing parameters based on the initial temperature of the dough, the ambient temperature and humidity, and the yeast content of the flour: the initial temperature is 36-38℃ and the humidity is 85-90%. When the dough volume expands to 1.8-2 times, the temperature is adjusted to 34-35℃ and the humidity is maintained at 80-85%. The total proofing time is determined by AI based on real-time fermentation data and is 40-60 minutes.

6. The intelligent production process for layered toast according to claim 5, characterized in that: The intelligent baking optimization adopts a segmented baking strategy. The intelligent oven has a built-in infrared temperature sensor and camera, and the AI ​​algorithm dynamically optimizes parameters based on the weight and thickness of the toast. First stage 0-8min: Top heat 180-190℃, bottom heat 160-170℃; Second stage 8-18min: top heat 165-175℃, bottom heat 175-185℃; The third stage, 18-25 minutes: top heat 150-160℃, bottom heat 160-170℃; baking will automatically end when the camera detects that the surface of the toast has reached the preset color difference and the center temperature has reached 92-95℃.

7. The intelligent production process for layered toast according to claim 6, characterized in that: The cooling and sorting process involves sending the baked toast into an intelligent cooling tunnel. Multiple temperature sensors are installed inside the tunnel to dynamically adjust the cooling airflow speed to 1.5-3 m / s and the cooling time to 20-30 min based on the initial temperature of the toast, ensuring that the center temperature of the toast drops to 30-35℃. After discharge, the appearance of the toast is inspected by a vision inspection system, and the weight is detected by a weight sensor. Qualified products with uniform color and a weight error within ±5g are automatically sorted out, while unqualified products are automatically rejected.

8. The intelligent production process for layered toast according to claim 7, characterized in that: During the traceability packaging and warehousing process, qualified toast is sliced ​​by an automatic slicer. The slice thickness can be preset through the intelligent control platform with an error of ±0.2mm. During the packaging process, each packaging unit is printed with a unique traceability QR code, which includes the raw material batch, production time, equipment number, and test data information. After packaging, the intelligent warehousing system automatically allocates storage locations according to product batches to complete the warehousing process.

9. The intelligent production process for layered toast according to claim 1, characterized in that: The entire data traceability system records raw material testing data, proportioning parameters, kneading parameters, layering parameters, proofing parameters, baking parameters, and testing data in real time through an intelligent control platform. The data is stored on a cloud server and can be queried via traceability QR codes or the platform backend.

10. The intelligent production process for layered toast according to any one of claims 1-9, characterized in that: Based on the type of raw materials, product specifications, nutritional requirements, or environmental conditions, AI algorithms can adaptively adjust the process parameters of each stage to achieve production adaptation in different scenarios. Furthermore, the intelligent control platform can accumulate production data through machine learning, enabling iterative optimization of process parameters.

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