Tunnel baking kiln of multi-channel series-connection branch baking corridor system and control method of tunnel baking kiln

Abstract

The invention relates to a tunnel baking kiln of a multi-channel series-connection branch baking corridor system and a control method of the tunnel baking kiln, and aims to optimize the baking process of eels and improve the consistency and quality of products through accurate temperature and time control. The tunnel baking kiln comprises a plurality of baking galleries connected in series, different temperatures and time are set for each baking gallery, and the baking galleries are divided into a low-temperature preheating stage, a medium-temperature heating stage, a high-temperature heating stage and a high-temperature air drying stage. The third baking corridor is provided with a plurality of sub-baking corridors which are connected in series, and whether the eels meet the preset high-temperature baking requirement or not is judged through the bypass conveying mechanism. The system monitors the surface temperature and color of eels in real time through an infrared temperature sensor and color monitoring equipment, analyzes data by using a deep learning algorithm, automatically adjusts baking parameters, and optimizes the baking effect. Meanwhile, the system has an automatic feedback mechanism, the baking time and temperature can be adjusted according to production requirements, and the system is suitable for baking eels of different specifications.

Description

Technical Field

[0001] The present invention belongs to the field of baking tunnel kilns, and specifically relates to a tunnel baking kiln with a multi-channel series baking gallery system and its control method. Background Art

[0002] As a food deeply loved by consumers, the unique taste and flavor of eels are often affected by the baking process. Traditional eel baking methods generally use unified temperature control and time settings to process eels of different specifications. However, due to differences in the size, thickness, surface and internal structure of eels, a single baking method often fails to meet the best baking requirements of all specifications of eels, resulting in inconsistent baking quality of products, and even seriously affecting the taste and quality of eels in severe cases.

[0003] Problems of traditional baking methods: During traditional baking, the temperature and time of the baking tunnel kiln are usually set to fixed values, which are suitable for most eels. However, due to factors such as eel specifications, thickness, and surface water content, some problems of uneven baking occur. Smaller eels are overcooked on the outside due to longer baking time, while larger eels are not fully cooked inside due to shorter baking time. In addition, the traditional method cannot fully adjust the temperature of different parts, resulting in the surface of the eel being too dry or the inside being tough, and unable to achieve the ideal "crispy on the outside and tender on the inside" taste.

[0004] Challenges of eel size and thickness differences:

[0005] Small eels, due to their small size and thin surface, are more likely to be overcooked by high temperature, resulting in charring on the outer layer while the inside is still not fully cooked. To avoid this problem, a lower temperature preheating and a shorter high-temperature air-drying stage are required during the baking process.

[0006] Larger eels require longer baking time and higher temperature to ensure that the heat during the baking process can penetrate into their thick interior. Too short heating time not only makes it difficult to fully cook the inside, but also causes the outer layer to fail to achieve the ideal crispy effect.

[0007] Eels with different thicknesses also need to adjust the baking time and temperature settings according to their thickness changes. Thinner eels are easy to be quickly cooked, while thicker eels need longer time and moderate temperature to avoid overcooking or undercooking.

[0008] Deficiencies of existing temperature control systems: Existing temperature control systems usually cannot dynamically adjust the state of each eel during baking, and it is difficult to make real-time adjustments according to the specifications and baking progress of different eels. Existing equipment generally uses a single temperature control system, which makes it difficult to achieve "precision control" when facing eels of different sizes and thicknesses, thus affecting the quality and taste of the finished product.

[0009] Production efficiency and resource waste issues: Since traditional baking equipment usually cannot adjust its working state according to real-time feedback during the baking process, phenomena such as overheating, energy waste, or uneven baking occur in actual production. This not only leads to unstable quality of the eel finished products but also increases energy consumption and reduces the efficiency of the production line.

[0010] Lack of automation and intelligence: Most of the existing baking systems lack intelligent control and automatic adjustment functions, and the baking process mainly relies on manual adjustment and experience. Manual intervention introduces errors, resulting in unstable production processes and making it difficult to achieve precise control over large-scale production. Summary of the Invention

[0011] To solve the problems raised in the above background art, the present invention provides a tunnel baking kiln with a multi-channel series sub-baking gallery system and its control method, which not only ensures the ideal taste of the eel with a crispy exterior and tender interior but also ensures the balance of the entire baking process, avoids the poor taste caused by over-baking, and improves the quality and consistency of the products.

[0012] A tunnel baking kiln with a multi-channel series sub-baking gallery system includes a first baking gallery (low-temperature preheating stage), a second baking gallery (medium-temperature heating stage), a third baking gallery (high-temperature heating stage), and a fourth baking gallery (high-temperature air-drying stage); in the third baking gallery, multiple series-connected sub-baking galleries are provided, and at the outlet of each sub-baking gallery, an infrared temperature sensor, thermal imaging technology, or color monitoring device is provided to monitor the surface of the eel in real time, obtain the temperature and color data of the eel surface, and be used to judge whether the set high-temperature baking requirements are met according to the baking situation of the eel in the sub-baking gallery; a bypass conveying mechanism is also provided at the outlet of each sub-baking gallery; the bypass conveying mechanisms are respectively connected to the fourth baking gallery.

[0013] Among them, the temperature monitoring uses an infrared sensor, thermal imaging technology, or an infrared thermal imager to obtain the temperature data of the eel surface in a non-contact manner in real time; the color monitoring uses a high-resolution camera, a spectral sensor, or a color analysis system to judge the baking state by capturing the color change of the eel surface.

[0014] A control method for a tunnel baking kiln with a multi-channel series sub-baking gallery system, the method bakes through multiple series-connected baking galleries, the baking process is divided into multiple stages, and combines intelligent monitoring technology to monitor the surface temperature and color of the eel in real time to judge whether the eel meets the set baking requirements. The method includes the following steps:

[0015] Step 1: Roast the eels through a multi-stage series of roasting galleries, where each roasting gallery is set according to different temperatures and times to achieve different roasting effects. The roasting galleries include: the first roasting gallery (low-temperature preheating stage), the second roasting gallery (medium-temperature heating stage), the third roasting gallery (high-temperature heating stage), and the fourth roasting gallery (high-temperature air-drying stage).

[0016] Step 2: In the third roasting gallery, set up multiple series-connected sub-roasting galleries. A bypass transfer mechanism is provided at the outlet of each sub-roasting gallery to determine whether the set high-temperature roasting requirements are met based on the roasting situation of the eels in the sub-roasting gallery.

[0017] Step 3: Real-time monitor the surface of the eels through an infrared temperature sensor, thermal imaging technology, or color monitoring equipment to obtain the temperature and color data of the eel surface.

[0018] Step 4: Use a data processing unit to analyze the monitoring data to determine whether the eels have reached the high-temperature roasting requirements. If the requirements are met, send the eels to the fourth roasting gallery through the bypass transfer mechanism; if not, send them to the next sub-roasting gallery for continued heating.

[0019] Among them, the method uses a deep learning algorithm or a machine learning model to analyze the real-time collected temperature and color data, and automatically adjusts the temperature and residence time of each sub-roasting gallery, thereby optimizing the roasting effect of the eels.

[0020] Among them, the system has an automatic feedback mechanism that can analyze and statistically monitor the data, automatically adjust the roasting time and temperature of each sub-roasting gallery, maintain a reasonable quantity distribution of each sub-roasting gallery, and at the same time avoid over-roasting or under-roasting.

[0021] Among them, the fourth roasting gallery adopts a high-temperature air-drying treatment method. Through the air-drying equipment, the surface of the eels becomes crispy and the internal moisture is retained, so as to achieve the best edible effect.

[0022] Among them, the first roasting gallery (low-temperature preheating stage) adopts a low-temperature heating process, with the temperature controlled between 30°C and 80°C. Through a long preheating process, the surface and interior of the eels are gradually heated up to reach a suitable base temperature, laying a good foundation for the subsequent high-temperature roasting stage.

[0023] Among them, the second roasting gallery (medium-temperature heating stage) adopts a medium-temperature heating process, with the temperature controlled between 80°C and 160°C. Through a short heating time, the eel meat gradually becomes tender, a crispy outer layer begins to form on the surface, and the internal moisture is gradually and evenly distributed, preparing for high-temperature roasting.

[0024] Among them, the fourth roasting gallery (high-temperature air-drying stage) adopts a high-temperature air-drying process, with the temperature controlled between 160°C and 220°C. Through short-time high-temperature air-drying, the surface of the eel reaches an ideal golden and crispy effect, while remaining appropriately moist inside, ensuring that the eel is crispy on the outside and tender on the inside.

[0025] Among them, the third roasting gallery (high-temperature heating stage) consists of three series-connected sub-roasting galleries. The specific roasting process is as follows:

[0026] The first sub-roasting gallery: Adopts a high-temperature heating process with the temperature controlled between 220°C and 240°C. Through 6 to 8 minutes of high-temperature heating, the surface temperature of the eel is rapidly increased, causing its surface to quickly caramelize, forming a preliminary golden and crispy outer layer, and promoting the evaporation and distribution of internal moisture; if the eel roasted in this stage does not meet the preset high-temperature roasting requirements, it will be conveyed to the second sub-roasting gallery for continued roasting;

[0027] The second sub-roasting gallery: Adopts a high-temperature heating process with the temperature controlled between 200°C and 220°C, and the duration is 4 to 6 minutes, which is used to further roast the eel to ensure that the temperature of its surface and inside uniformly reaches the preset requirements; if the eel still does not meet the high-temperature roasting requirements at this stage, it will be conveyed to the third sub-roasting gallery for final roasting;

[0028] The third sub-roasting gallery: Adopts a high-temperature heating process with the temperature controlled between 180°C and 200°C, and the duration is 3 to 5 minutes, ensuring that the eel fully meets the preset high-temperature roasting requirements.

[0029] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0030] 1. Improve the consistency and accuracy of roasting:

[0031] Through multiple series-connected roasting galleries, and with different temperature and time settings for each roasting gallery, it is possible to achieve step-by-step heating and staged roasting, making the roasting effect of the eel more uniform and stable. The different temperature ranges of each roasting gallery ensure that the functions of each stage are maximally exerted, avoiding problems such as excessive surface caramelization or under-roasting inside caused by traditional single-temperature settings. Especially through the low-temperature preheating stage, the medium-temperature heating stage with a moderate temperature, and the high-temperature air-drying stage, a crispy-on-the-outside-and-tender-on-the-inside taste is formed.

[0032] 2. Efficient bypass transfer mechanism:

[0033] The bypass transfer mechanism is an innovative design of the present invention. Through a reasonable feedback mechanism, the eels that are not fully baked enter the next sub-baking corridor, while the fully baked eels are directly bypassed and conveyed to the fourth baking corridor. This design greatly improves the baking efficiency, avoids defective products caused by uneven baking or over-baking, and can also meet the baking requirements of eels of different sizes. There is no need to set up multiple baking lines according to the size of the eels, ensuring uniform baking of eels of different sizes.

[0034] 3. Ensure the crispy exterior and tender interior texture of the eels:

[0035] In each baking stage, especially in the high-temperature air-drying stage, the present invention emphasizes the crispy feeling on the outer layer and the retention of internal moisture. Through high-temperature air-drying treatment, the surface of the eel can achieve an ideal golden and crispy effect, while the inside retains abundant moisture, making the final product have a better taste and higher market competitiveness. The low-temperature preheating and medium-temperature heating stages ensure uniform heating of the surface and inside of the eel, avoiding problems such as over-drying of the outer layer or toughness of the inside in traditional baking, thus making the final texture of the eel more delicate and balanced.

[0036] 4. Enhancement of environmental protection and energy efficiency:

[0037] Through the design of multiple series-connected baking corridors, the gradient utilization of heat energy can be realized, making the temperature control in each stage more efficient. This can reduce energy waste and improve the energy utilization rate of production. At the same time, through precise temperature control, the heat energy waste caused by overheating is avoided, and the overall energy efficiency is improved.

[0038] 5. Intelligent temperature control and optimization of baking stages:

[0039] Optimization of stage switching: Through a prediction model based on deep learning, the system can judge whether it is necessary to enter the next baking stage according to the actual state of the eel (such as temperature, color, etc.). The algorithm analyzes the baking progress of the eel and realizes a smooth transition between multiple stages, avoiding over-baking or under-baking situations in traditional baking methods. This intelligent optimization of stage switching can ensure that each eel obtains ideal baking conditions at the appropriate time.

[0040] Precise adjustment of stage parameters: Through continuous optimization of the temperature and time allocation in each stage by the algorithm, the baking process can not only meet general baking standards but also be adjusted individually according to the characteristics of different eels (such as size, thickness, etc.), providing a more precise baking plan.

[0041] 6. Intelligent judgment in the bypass transfer mechanism:

[0042] Bypass Judgment Algorithm: In the sub-baking gallery stage of the third baking gallery, the algorithm determines whether each eel meets the set baking standard based on real-time temperature and color data. If the baking is incomplete, the algorithm will automatically instruct the bypass conveyor mechanism to send the eel back to the previous baking gallery for re-baking until the desired baking effect is achieved. This process is controlled by the algorithm, ensuring the quality and efficiency of eel baking through intelligent decision-making.

[0043] Dynamic Adjustment of Baking Path: In addition to determining whether to return to the previous stage, the system can also automatically adjust the baking path according to the analysis results, that is, it can decide whether to continue staying in the current sub-baking gallery or skip certain stages based on the feedback data of different baking stages, maximizing the shortening of the baking time and ensuring that the effect is not discounted.

[0044] 7. Optimization of Production Line Efficiency and Resource Allocation:

[0045] Resource Optimization Algorithm: The algorithm of this system can optimize the energy distribution and temperature control strategy of each baking gallery based on real-time data analysis, reducing energy waste. For example, based on the prediction model of baking time and temperature, the system can adjust the workload of each baking gallery in real time to make full use of the equipment's capacity within the same baking time, reduce idle time, and improve production efficiency.

[0046] Multi-objective Optimization: The algorithm can perform multi-objective optimization based on multiple factors (such as baking effect, baking time, energy consumption, etc.). In some cases, when the system detects that the baking effect of the eel is close to the set standard, the algorithm can adjust the baking time and temperature in a timely manner to reduce energy consumption and improve overall efficiency.

[0047] 8. Adaptive Ability to Intelligently Adjust Temperature and Time:

[0048] Adaptive Control Based on Machine Learning: Using machine learning algorithms, the system can continuously optimize the baking model by learning historical baking data (such as temperature, time, color, etc.), so as to provide a more accurate temperature control solution for future production batches. The system predicts the baking requirements of different specifications of eels based on historical data and automatically adjusts the settings of temperature and time to achieve the optimal baking effect.

[0049] Prediction and Early Warning Mechanism: Through continuous learning and analysis of historical data, the system can predict potential problems (such as abnormal temperature, uneven baking, etc.) during the baking process and issue early warnings in a timely manner. The algorithm can make timely adjustments according to the current situation to avoid poor baking effects caused by sudden temperature control problems. Brief Description of the Drawings

[0050] Figure 1 It is a schematic flowchart of the method of the present invention. Detailed Embodiment

[0051] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0052] See Figure 1 , a tunnel baking kiln of a multi-channel series baking corridor system and its control method. The method is used for baking through multiple series-connected baking corridors. The baking process is divided into multiple stages, and intelligent monitoring technology is combined to monitor the surface temperature and color of eels in real time to determine whether the eels meet the set baking requirements. The method includes the following steps:

[0053] Step 1: Bake the eels through a multi-channel series baking corridor. Each baking corridor is set according to different temperatures and times to achieve different baking effects. The baking corridors include: the first baking corridor (low-temperature preheating stage), the second baking corridor (medium-temperature heating stage), the third baking corridor (high-temperature heating stage), and the fourth baking corridor (high-temperature air-drying stage); specifically:

[0054] 1.1 Distribution and stage setting of the baking corridors

[0055] The baking process of this patent solution includes four main baking corridors. Each baking corridor has a different function, and the temperature and time settings increase gradually. Each baking corridor undertakes different tasks in the whole baking process, which are specifically divided into the following four stages:

[0056] The first baking corridor (low-temperature preheating stage):

[0057] Temperature range: 30°C to 80°C

[0058] Function: Slowly raise the surface and internal temperatures of the eels through low-temperature heating. The purpose of this stage is to make the eels reach a suitable basic temperature and prepare for the subsequent high-temperature heating stage.

[0059] Time control: The time of this stage is relatively long. Usually, a certain amount of time is required for preheating to ensure that the internal moisture of the eels can be evenly distributed and gradually heated up.

[0060] The second baking corridor (medium-temperature heating stage):

[0061] Temperature range: 80°C to 160°C

[0062] Function: Heat the eels at medium temperature to make their meat gradually tender, a certain crispy outer layer begins to form on the surface, and the internal moisture is further evenly distributed. The heating in this stage ensures that the internal and external temperatures of the eels are gradually balanced and lays the foundation for the subsequent high-temperature heating.

[0063] Time control: The time for this stage is short. Through reasonable time control, it can effectively avoid overheating or drying of the eels.

[0064] The third baking gallery (high-temperature heating stage):

[0065] Temperature range: This stage consists of multiple sub-galleries. The main temperature range is from 220°C to 240°C. Through the gradual heating of multiple sub-galleries, the temperature gradually decreases from high to low to ensure that a golden and crispy outer layer is formed on the surface of the eels and to promote the evaporation and uniform distribution of internal moisture.

[0066] Function: In this stage, the surface temperature of the eels is rapidly increased through high-temperature heating to ensure that a crispy outer layer is formed on its skin and the internal moisture is effectively distributed.

[0067] Time control: The time for this stage varies according to the settings of specific sub-galleries and the baking degree of the eels. Generally, it takes 3 to 10 minutes for high-temperature heating.

[0068] The fourth baking gallery (high-temperature air-drying stage):

[0069] Temperature range: 160°C to 220°C

[0070] Function: Through high-temperature air-drying treatment, it ensures that the surface of the eels reaches a golden and crispy effect while maintaining the internal moisture, giving the eels an ideal "crispy on the outside and tender on the inside" taste.

[0071] Time control: The time for this stage is short. Usually, through short-time high-temperature air-drying, the surface of the eels can reach the ideal baking effect.

[0072] 1.2 Temperature and time control of the baking galleries

[0073] The temperature and time control for each baking gallery are set according to the baking requirements of the eels and are monitored and adjusted in real time through an accurate temperature control system and sensors. The specific temperature control settings are as follows:

[0074] Temperature control system: Each baking gallery is equipped with a precise temperature control device that can maintain a stable heating effect according to the set temperature. The temperature can be adjusted within each stage to meet different baking requirements.

[0075] Real-time monitoring: By installing infrared temperature sensors and other monitoring devices, the temperature changes during the baking process are tracked in real time. If it is found that the temperature of a certain baking gallery deviates or the temperature of the eels fails to reach the set requirements, the system can automatically adjust the baking time or temperature.

[0076] 1.3 Baking principle and effect of each baking gallery

[0077] The first roasting gallery (low-temperature preheating): In this stage, through long-term low-temperature heating, the large temperature difference between the surface and the interior of the eel during the roasting process is avoided. The low-temperature preheating ensures that the surface and the interior of the eel are heated evenly, preparing for the subsequent high-temperature heating.

[0078] The second roasting gallery (medium-temperature heating): The medium-temperature heating helps to gradually form a crispy crust on the surface of the eel, while the meat becomes tender, providing a basis for the subsequent high-temperature heating. The temperature in this stage is relatively moderate, effectively ensuring the even distribution of internal moisture.

[0079] The third roasting gallery (high-temperature heating): The high-temperature heating causes the outer layer of the eel to quickly caramelize, forming a golden and crispy crust, and promoting the evaporation and even distribution of internal moisture. By gradually adjusting the temperature from 220°C to 240°C, the surface and internal temperatures are ensured to reach the optimal state.

[0080] The fourth roasting gallery (high-temperature air-drying): Through the air-drying technology, it is ensured that the surface of the eel reaches the ideal crispy effect, while the interior remains appropriately moist, avoiding over-drying, so that the final product has a crispy exterior and tender interior texture.

[0081] 1.4 Coordination of temperature and time

[0082] During the roasting process in each roasting gallery, the temperature and time are coordinated with each other, and the balance between temperature and time needs to be maintained to ensure that every part of the eel is heated evenly, avoiding over-roasting or under-roasting. The system adjusts the temperature and time of each roasting gallery according to the real-time monitoring data to ensure the optimal overall roasting effect.

[0083] 1.5 Flexibility and adaptability of the system

[0084] The temperature and time settings of each roasting gallery are not fixed, but are appropriately adjusted according to eels of different specifications and sizes. Through the feedback mechanism of the automatic adjustment system, eels of different specifications can obtain appropriate roasting time and temperature, meeting various production requirements.

[0085] Step 2: In the third roasting gallery, multiple series-connected sub-roasting galleries are set up, and each sub-roasting gallery is provided with a bypass transfer mechanism at the outlet for judging whether the set high-temperature roasting requirements are met according to the roasting situation of the eel in the sub-roasting gallery; the system can adjust the number of sub-roasting galleries for roasting, temperature and time settings according to different production requirements, so as to adapt to the roasting requirements of eels of various specifications and sizes. Specifically, the third roasting gallery (high-temperature heating stage) consists of three series-connected sub-roasting galleries, specifically:

[0086] 2.1 Sub-roasting gallery

[0087] The first sub-roasting gallery:

[0088] Temperature range: 220°C to 240°C

[0089] Function: Through high-temperature heating, the surface of the eel is quickly caramelized to form a preliminary golden and crispy outer layer, promoting the evaporation and distribution of internal moisture. The baking at this stage is mainly to increase the surface temperature of the eel and give it a preliminary crispy effect on the surface.

[0090] Time control: A high-temperature heating time of 6 to 8 minutes causes the surface of the eel to quickly heat up and form a preliminary outer layer caramelization. This time length ensures that the degree of surface caramelization is moderate while still maintaining an appropriate amount of internal moisture.

[0091] Judgment condition: If the baking process in the first sub-baking gallery fails to make the eel reach the set high-temperature baking requirements, the eel will enter the second sub-baking gallery through the bypass transfer mechanism for continued baking.

[0092] Second sub-baking gallery:

[0093] Temperature range: 200°C to 220°C

[0094] Function: Further high-temperature heating is carried out to ensure that the surface and internal temperatures of the eel uniformly reach the preset high-temperature requirements. The main purpose of this stage is to form a tighter outer shell on the surface of the eel and ensure the uniform distribution of internal moisture.

[0095] Time control: A heating time of 4 to 6 minutes, through a longer baking time, ensures that the eel reaches a higher internal temperature and continues to deepen the crispiness of the outer layer.

[0096] Judgment condition: If the set baking effect is still not achieved at this stage, the eel will be sent to the third sub-baking gallery for final baking.

[0097] Third sub-baking gallery:

[0098] Temperature range: 180°C to 200°C

[0099] Function: The high-temperature heating at this stage ensures that the eel fully reaches the set high-temperature baking requirements, and ensures that its surface is completely caramelized, presenting an ideal golden and crispy effect. The internal moisture is effectively evaporated, making the eel have a crispy exterior and tender interior taste.

[0100] Time control: A high-temperature heating time of 3 to 5 minutes. This stage has a relatively long time to ensure that the eel fully meets the predetermined temperature and taste requirements.

[0101] Judgment condition: If the eel has reached the preset high-temperature baking requirements after being baked in the third sub-baking gallery, it will enter the fourth baking gallery through the bypass transfer mechanism for final high-temperature air drying; if it has not reached the requirements, it can return to the previous sub-baking gallery through the bypass mechanism for continued baking until the set baking effect is achieved.

[0102] 2.2 Bypass Transfer Mechanism

[0103] The bypass transfer mechanism is an important part of this step. Its function is to ensure that eels can be adjusted in a timely manner according to the baking status in each sub-baking gallery. The specific functions are as follows:

[0104] Monitor the baking progress: The bypass transfer mechanism at the exit of each sub-baking gallery can monitor the baking status of eels in real time (mainly through data such as temperature and color). Once the eel reaches the set temperature requirement, it can directly skip the subsequent sub-baking galleries and enter the next stage.

[0105] Adjust the baking time: If the eel does not reach the preset baking effect, the bypass transfer mechanism can send it back to the previous sub-baking gallery for continued heating to ensure that the expected effect can be achieved in each stage.

[0106] 2.3 Dynamic Adjustment and Adaptability

[0107] Intelligent system adjustment: By monitoring the temperature and color of eels in real time, the system can dynamically adjust the temperature and time settings of each sub-baking gallery according to the baking progress and actual effect of each eel. This enables the system to adapt to eels of different specifications and sizes and optimize the baking effect of each stage based on real-time data.

[0108] Flexibility: Through this multi-sub-baking gallery system, not only can it adapt to different batches of eels, but also the number of sub-baking galleries, temperature, and baking time settings can be adjusted according to production requirements to ensure flexible response to various situations.

[0109] 2.4 Principle of High-Temperature Heating

[0110] The purpose of the high-temperature heating stage is to quickly increase the surface temperature of the eel, ensure that its surface can be quickly caramelized to form a crispy outer layer, while maintaining an appropriate amount of internal moisture. Through precise temperature control and time settings, the surface and interior of the eel can be evenly heated to achieve an ideal baking effect:

[0111] Crispy outside and tender inside: High-temperature heating promotes the caramelization process of the outer layer, enabling it to quickly form a golden and crispy shell. At the same time, the internal moisture gradually evaporates and is evenly distributed, thus maintaining the tenderness and moisture inside.

[0112] Evaporation and distribution: High temperature causes the surface moisture of the eel to evaporate rapidly, while the internal moisture is evenly distributed through high-temperature transfer, so that the eel will not be affected by uneven moisture during the baking process and affect the taste.

[0113] Summary: In Step 2, by setting up multiple series-connected sub-roasting galleries, the high-temperature heating process of eels is precisely controlled. The temperature and roasting time of each sub-roasting gallery are gradually adjusted according to the state of the eels to ensure that the surface and interior of the eels achieve the ideal roasting effect. The addition of the bypass transfer mechanism makes the roasting process more flexible and intelligent, enabling it to judge whether the eels have met the preset requirements based on real-time data, thereby optimizing the entire roasting process. This process not only improves production efficiency but also ensures the taste consistency of each batch of eels.

[0114] Step 3: Real-time monitor the surface of the eels through an infrared temperature sensor, thermal imaging technology, or color monitoring device to obtain the temperature and color data of the eel surface; among them, for the temperature monitoring, an infrared sensor, thermal imaging technology, or infrared thermal imager is used to obtain the temperature data of the eel surface in a non-contact manner in real time; for the color monitoring, a high-resolution camera, spectral sensor, or color analysis system is used to judge the roasting state of the eels by capturing the color changes on the eel surface. Specifically:

[0115] 3.1 Temperature Monitoring

[0116] The main function of the temperature monitoring technology is to obtain the temperature data of the eel surface in real time to ensure that it does not overheat or fail to reach the set temperature requirements during the roasting process. Different technical means can be used to capture the temperature data, including infrared sensors, thermal imaging technology, and infrared thermal imagers.

[0117] Infrared Sensor:

[0118] The infrared sensor measures the temperature by detecting the infrared radiation emitted from the surface of the object. Since the eel surface emits infrared rays, the sensor can calculate the surface temperature by receiving these radiations.

[0119] The infrared sensor features a fast response and can accurately measure the temperature of each part of the eel surface in a short time.

[0120] The non-contact measurement reduces the physical contact with the eels and avoids the heat interference caused by contact.

[0121] Thermal Imaging Technology:

[0122] Through thermal imaging technology, the temperature distribution of the entire surface of the eels can be obtained in real time during the roasting process. The thermal imaging device converts the temperature data of the eel surface into a thermal image through imaging for technicians or the system to automatically analyze.

[0123] Through thermal imaging, it is possible to precisely understand whether the surface of the eels is evenly heated and whether there are phenomena of local overheating or cooling. For example, by observing the color changes on the eel surface, it can be judged whether it has reached the state of being crispy on the outside and tender on the inside.

[0124] Infrared thermal imager:

[0125] The infrared thermal imager combines an infrared sensor and imaging technology. It captures thermal radiation through the sensor and presents a high - resolution thermal image on the display screen. This technology can accurately display the temperature changes on the surface of the eel, helping to judge its baking state.

[0126] It can display the temperature distribution map, track the temperature changes in real - time, and judge whether the preset baking temperature is reached.

[0127] Through the application of the above - mentioned technology, real - time temperature data of the eel surface can be obtained, and it can accurately judge whether the eel meets the set baking requirements, especially in the high - temperature stage, ensuring that the surface temperature of each piece of eel is uniform and meets the standards.

[0128] 3.2 Color monitoring

[0129] Color monitoring technology mainly captures the color changes on the surface of the eel in real - time through devices such as high - resolution cameras, spectral sensors, and color analysis systems. These technologies can identify the color changes on the surface of the eel and help judge the baking state of the eel to confirm whether it has formed an ideal golden and crispy outer layer.

[0130] High - resolution camera:

[0131] The high - resolution camera can capture the subtle color changes on the surface of the eel. By continuously taking pictures of the eel surface during the baking process, the color changes at each stage can be compared to ensure that they meet the expected baking effect.

[0132] The images of this camera can be used to judge the color changes through subsequent image - processing technologies (such as edge detection, color correction, etc.).

[0133] Spectral sensor:

[0134] The spectral sensor can monitor the spectral reflection characteristics of the eel surface within a specific wavelength range. Different colors are related to spectral reflection characteristics. The sensor analyzes the data of these reflected light wavelengths to determine whether the color of the eel surface has reached the preset golden and crispy effect.

[0135] By accurately analyzing the spectral data, the system can judge whether the eel has reached the ideal baking state and provide automatic feedback to the baking control system.

[0136] Color analysis system:

[0137] The color analysis system judges whether the eel has been baked to the ideal color by analyzing the surface color of the eel in real - time and combining parameters such as RGB values and color differences. The system automatically evaluates the surface color of the eel according to the preset standard color values and feeds back to the subsequent control system.

[0138] This system usually obtains more accurate color data through multi-light-source and multi-angle shooting methods to avoid misjudgment caused by the influence of external light.

[0139] 3.3 Combined analysis of temperature and color data

[0140] Temperature and color are two key factors for judging the roasting effect of eels. In step 3, the temperature and color data monitored in real time will be combined for comprehensive analysis:

[0141] Association between temperature and color:

[0142] If the surface temperature of the eel is too low, it usually leads to insufficiently golden color and a non-crispy outer layer. At this time, even if the time is sufficient, the surface of the eel remains moist and fails to achieve the ideal roasting effect.

[0143] If the surface temperature is too high, it will cause the surface to be overcooked, over-dried or too hard. Therefore, both too high and too low temperatures need to be precisely controlled. And the change in color can reflect the influence of the surface temperature. Especially when the temperature approaches or exceeds a certain threshold, the color will change rapidly.

[0144] Data fusion and analysis:

[0145] The temperature and color monitoring data are fused and analyzed by the data processing unit. If the temperature has reached the set standard but the color has not reached the ideal state, it indicates that the roasting time is insufficient or the roasting is uneven, and the roasting time or temperature needs to be adjusted.

[0146] The system judges whether the eel meets the high-temperature roasting requirements through the comprehensive analysis of the real-time monitoring data, providing a basis for the next step.

[0147] 3.4 System feedback and decision-making

[0148] Based on the real-time monitored data, the system can judge whether to adjust the roasting progress or continue roasting according to the comprehensive information of temperature and color:

[0149] Automated decision-making:

[0150] If the system detects that the eel has reached the set temperature and color requirements, it automatically judges that the roasting is completed and transfers the eel to the fourth roasting gallery (drying stage) of the next stage.

[0151] If the temperature or color does not meet the requirements, the system will automatically adjust the roasting process and send the eel to a sub-roasting gallery with a higher temperature through the bypass transfer mechanism for further heating until the standard is met.

[0152] Summary: In Step 3, through real-time monitoring of temperature and color, it is ensured that the eels during the baking process reach the preset optimal effect. With advanced devices such as infrared sensors, thermal imaging technology, high-resolution cameras, and spectral sensors, the system can accurately monitor the state of the eels during the baking process. Through real-time data analysis, the system can make timely decisions to ensure that each eel achieves the baked effect of being crispy on the outside and tender on the inside, golden and crispy, ultimately improving product quality and production efficiency.

[0153] Step 4: Use the data processing unit to analyze the monitored data to determine whether the eels have met the requirements of high-temperature baking. If the requirements are met, the eels will be sent to the fourth baking corridor through the bypass transfer mechanism. If the requirements are not met, they will enter the next sub-baking corridor for further heating. The fourth baking corridor adopts a high-temperature air-drying treatment method. Through the air-drying equipment, the surface of the eels becomes crispy while maintaining the internal moisture, thus achieving the best edible effect. The temperature is controlled between 160°C and 220°C. Through short-time high-temperature air-drying, the surface of the eels reaches the ideal golden and crispy effect, while the inside remains appropriately moist, ensuring that the eels are crispy on the outside and tender on the inside. Specifically:

[0154] 4.1 Data Collection and Processing

[0155] In Step 3, the temperature and color data of the eel surface have been collected in real time through means such as infrared sensors, thermal imaging technology, and color monitoring equipment. Next, these data need to enter the data processing unit for analysis. The data processing unit combines the temperature data, color data, and baking information such as time and temperature for comprehensive analysis and decision-making.

[0156] The temperature and color data are respectively represented as T and C, where:

[0157] T represents the actual temperature of the eel surface (unit: °C)

[0158] C represents the color parameter of the eel surface (such as color difference value, unit: dimensionless)

[0159] The temperature and color will be combined into a comprehensive evaluation index E:

[0160] E = f(T, C);

[0161] Among them, f(T, C) is the comprehensive function of temperature and color, representing the comprehensive evaluation of the eel baking effect. The function f(T, C) has different specific definitions according to different application scenarios. The comprehensive function will be described in detail below.

[0162] 4.2 Comprehensive Evaluation Function f(T, C)

[0163] In order to achieve accurate judgment of the baking effect, the comprehensive evaluation function f(T, C) considers the following two main factors:

[0164] The influence of temperature on the roasting effect: The surface of the roasted eel needs to reach a certain temperature to form a golden and crispy outer layer.

[0165] The preset temperature requirement is T target . When the surface temperature T of the eel reaches this temperature, it is considered that the roasting effect of the eel is close to the best.

[0166]

[0167] where: f T (T) is the influence coefficient of temperature on the roasting effect. When the value is 1, it means the temperature meets the expectation; when the value is less than 1, it means the temperature is insufficient. T target is the target roasting temperature.

[0168] The influence of color on the roasting effect: During the roasting process, the surface of the eel should present a golden and crispy color. The measurement of color is usually represented by the color difference ΔE. The smaller the color difference, the closer the color is to the target color. The target color is set as C target . When the actual color C is close to the target color, it means the roasting effect meets the requirements. The color difference metric ΔE is used to quantify the color difference:

[0169]

[0170] where: L, a, b represent the brightness, red - green component, and yellow - blue component in the color space of the eel surface respectively. L target , a target , b target are the brightness and color difference parameters of the target color. The influence coefficient f C (C) of color on the roasting effect can be determined by the color difference value:

[0171]

[0172] where: f C (C) is the influence coefficient of color on the roasting effect. When the value is 1, it means the color meets the expectation; when the value is greater than 1, it means the color deviates from the target. ΔE target is the threshold of the target color difference, representing the ideal color range.

[0173] 4.3 Comprehensive Roasting Effect Score

[0174] The influence coefficients of temperature and color are weighted and combined to obtain a comprehensive roasting effect score E. This score will reflect whether the eel has met the predetermined roasting requirements. Generally, the weights of temperature and color can be set based on actual production experience. The weights of temperature and color are w T and w C respectively. Then the comprehensive score E can be expressed as:

[0175] E = w T ·f T (T) + w C ·f C (C);

[0176] Where: w T and w C are the weight coefficients of temperature and color, satisfying w T + w C = 1, and adjusted according to the actual situation (for example, if color is more important than temperature, then w C can be set to a larger value). f T (T) and f C (C) are the influence coefficients of temperature and color on the baking effect respectively.

[0177] 4.4 Judging the baking state

[0178] Through the above comprehensive score E, the system can judge whether the eel has reached the preset baking requirements. Specifically, when the score E reaches a certain threshold E threshold , it means that the baking is completed, the eel has reached the ideal baking effect, and the system transfers the eel to the fourth baking corridor for air-drying treatment. The judgment process can be expressed as:

[0179]

[0180] 4.5 Automatic adjustment control strategy

[0181] To ensure the optimization of the baking effect, the system will also adjust the baking conditions of each sub-baking corridor according to the real-time change of the score E. The specific adjustment contents include:

[0182] Adjusting the temperature: If the score is lower than the target value, the system can automatically increase the baking temperature or extend the baking time to improve the temperature and color of the eel.

[0183] Adjusting the time: If the score is low, the system will extend the residence time of the eel in a certain baking corridor to ensure that the eel is fully heated and reaches the ideal effect.

[0184] Summary: In step 4, through the real-time monitoring data of temperature and color, combined with the comprehensive scoring function E, a comprehensive analysis of the eel baking effect is carried out. Through this score, the system can judge whether the baking is completed and automatically adjust the baking parameters to ensure that each eel can reach the ideal state of being crispy on the outside and tender on the inside.

[0185] Furthermore, in step 4, the system analyzes the real-time collected temperature and color data, and uses deep learning algorithms or machine learning models to optimize the baking effect of each sub-baking gallery. This part utilizes the capabilities of machine learning, not only relying on preset rules and thresholds, but automatically adjusting parameters such as temperature and time during the baking process through learning from a large amount of data, thereby improving the baking effect and efficiency.

[0186] 1. Problem Background

[0187] Traditional baking control methods rely on manually set rules (such as temperature and time). Although they can control the baking effect to a certain extent, it is difficult to cope with complex production environments and changes during the baking process (such as the size, shape, thickness, etc. of different eels). Deep learning and machine learning can automatically analyze multiple parameters such as temperature and color through training on a large amount of historical baking data, optimize the baking process, and thus obtain more accurate baking results.

[0188] 2. Data Collection and Input

[0189] To train and use deep learning or machine learning models, the system first needs to continuously collect a large amount of temperature and color data. The input of this data includes:

[0190] Temperature data: Obtain the temperature on the surface of the eel at each stage through infrared sensors, thermal imaging devices, etc.

[0191] Color data: Capture the color changes on the surface of the eel through color difference sensors, cameras or spectral sensors, and convert them into coordinates in color difference or color space.

[0192] Time data: The time of each baking stage (including the baking time of each sub-baking gallery).

[0193] Other relevant data: Such as information on the humidity and wind speed of the baking environment (supported by sensors).

[0194] All these data will be used as input features of the model to predict the optimal temperature and residence time settings.

[0195] 3. Deep Learning Algorithms / Machine Learning Models

[0196] According to actual requirements, the system can choose to use different types of deep learning or machine learning algorithms. Common algorithms include:

[0197] Regression model: Used to predict continuous variables of temperature and time, such as predicting the temperature and time required for a certain sub-baking gallery through a regression model.

[0198] For example, methods such as linear regression and support vector regression (SVR) are used to fit the relationship between temperature, time, and baking effects (such as color and tenderness).

[0199] Neural networks: Particularly suitable for dealing with complex non-linear relationships. Convolutional neural networks (CNNs) or fully connected neural networks (ANNs) can input temperature and color data and output the optimal baking parameters for each sub-baking gallery.

[0200] CNNs are suitable for processing image data (such as color image data), and ANNs are suitable for processing numerical data such as temperature and time.

[0201] Reinforcement learning: In this baking system, reinforcement learning is particularly suitable for the self-adjustment process. The system can be regarded as an agent that makes actions (such as adjusting temperature, extending time, etc.) relying on state information such as temperature and color. The goal of reinforcement learning is to ultimately find the best baking strategy through continuous trial and error.

[0202] Reinforcement learning algorithms (such as Q-learning and deep Q-network DQN) will adjust the parameters of each sub-baking gallery according to real-time feedback (such as temperature and color data).

[0203] 4. Model Training and Optimization

[0204] To enable the machine learning model to operate effectively, the system needs to perform the following steps:

[0205] Data preparation: Collect and organize a large amount of historical baking data, covering different eel sizes, baking stages, and different environmental conditions. This data will be used to train the model.

[0206] Data labeling: Each baking data point (including temperature, color, time, etc.) needs to have a corresponding "target" label. For example, the target label can be whether the eel has achieved the ideal baking effect, or whether it meets the color and temperature requirements.

[0207] Training process:

[0208] Supervised learning: If there is enough historical data, use supervised learning algorithms to train the baking process. The input of the model is temperature, time, color, etc., and the output is the corresponding optimal baking parameters.

[0209] Reinforcement learning: If the system allows self-optimization, reinforcement learning can be used. The model will continuously adjust baking parameters (such as temperature and time), experiment in a simulated environment, and obtain feedback.

[0210] During the training process, common optimization goals include:

[0211] Minimize error: Make the parameters such as temperature and color predicted by the model close to the actual target values.

[0212] Maximize the baking effect: Optimize the baking time and temperature to achieve the best crispy outside and tender inside effect for the eel.

[0213] Model evaluation and verification: After training, use new baking data to verify the model and evaluate its performance in real-time applications. Common evaluation methods include:

[0214] Cross-validation: Divide the data into a training set and a validation set to improve the generalization ability of the model through cross-validation.

[0215] Error analysis: Analyze the source of errors by comparing the model prediction results with the actual results and fine-tune the model.

[0216] 5. Real-time adjustment and feedback

[0217] After obtaining the optimal baking parameters through deep learning or machine learning model analysis, the system will make real-time adjustments to the temperature and residence time of each sub-baking gallery. The specific adjustment mechanism is as follows:

[0218] Temperature adjustment: Automatically adjust the temperature control device of each sub-baking gallery according to the optimal temperature output by the model. For example, if the model predicts that a certain sub-baking gallery needs a slightly higher temperature to accelerate the outer layer coking, the system will increase the temperature of that sub-baking gallery.

[0219] Time adjustment: The system will automatically adjust the baking time of each sub-baking gallery according to the optimal residence time predicted by the model. For example, if a certain baking stage requires a longer time to achieve the best effect, the system will extend the residence time of that stage.

[0220] Feedback mechanism: After each adjustment, the system will obtain new data again through real-time monitoring devices (such as infrared temperature sensors and color difference sensors) and input it into the model for update, thus forming a closed loop to continuously optimize the baking effect.

[0221] 6. Advantages of deep learning / machine learning optimization

[0222] Strong adaptability: Deep learning models can adapt to different eel species, sizes, and baking conditions, eliminating the need for manual setting of each variable and reducing manual intervention.

[0223] Dynamic adjustment: Machine learning models not only make decisions based on current baking data but also can predict optimal parameters according to historical data, thereby dynamically adjusting the temperature and time at each stage.

[0224] Continuous optimization: As the system continuously collects new data, the machine learning model will be continuously updated and optimized. Over time, the baking effect will be more precise and meet the production requirements.

[0225] Summary: Through the combination of deep learning and machine learning, the system can analyze the surface temperature and color data of eels in real time and automatically adjust the temperature and time of each sub-baking gallery, thereby optimizing the baking effect of eels. This method not only improves the baking accuracy and consistency, but also can adapt to different production environments and eel specifications, realizing an intelligent and automated production process.

[0226] Furthermore, the system has an automatic feedback mechanism that can analyze and statistically monitor data, automatically adjust the baking time and temperature of each sub-baking gallery, maintain a reasonable quantity distribution in each sub-baking gallery, and avoid over-baking or under-baking at the same time.

[0227] This part describes how the system dynamically adjusts the baking process based on monitoring data through a real-time feedback mechanism to ensure the best effect during the baking process of eels in each sub-baking gallery. This automatic feedback mechanism not only helps optimize the baking effect at each baking stage, but also effectively avoids over-baking or under-baking of eels, ensuring product consistency and quality.

[0228] 1. Basic principle of the feedback mechanism

[0229] The automatic feedback mechanism obtains real-time data by monitoring the surface temperature, color change of eels in each sub-baking gallery, and other relevant parameters (such as humidity, wind speed, etc.). The system performs real-time analysis while collecting data and automatically adjusts the temperature and baking time of each sub-baking gallery according to the analysis results. The basic principle of the feedback mechanism can be summarized as:

[0230] Input: Temperature data, color data, time data of each baking gallery, and other sensor data (such as humidity, wind speed, etc.).

[0231] Feedback: Based on the difference between the real-time monitoring data and the preset targets (such as temperature, color standards, etc.), the system automatically adjusts the baking parameters (temperature, time, humidity, etc.) through the control system.

[0232] Output: The automatically adjusted baking parameters (such as temperature, time, etc.) will be fed back to the control unit of each sub-baking gallery to complete the real-time adjustment of the baking process.

[0233] 2. Workflow of the feedback mechanism

[0234] The workflow of the automatic feedback mechanism can be divided into the following steps:

[0235] Step 1: Data collection

[0236] The system continuously monitors the eel roasting status in each sub-roasting chamber through various sensors:

[0237] Temperature monitoring: The surface temperature of the eel is monitored in real time through infrared sensors, thermal imaging technology, etc., to ensure that the temperature at each stage meets the predetermined standards.

[0238] Color monitoring: The color change on the surface of the eel is monitored through high-resolution cameras, spectral sensors, etc. The color change is an important indicator for judging the roasting effect of the eel.

[0239] Other parameters: Environmental parameters such as humidity, wind speed, etc. These will also affect the roasting effect and need to be monitored synchronously.

[0240] Step 2: Data analysis and judgment

[0241] The system analyzes the monitored data collected in real time to determine whether the current roasting process meets the set goals:

[0242] Temperature comparison: Compare the actual temperature of each sub-roasting chamber with the preset target temperature to judge whether the temperature is too high or too low.

[0243] Color comparison: Make a judgment by comparing the real-time obtained color data with the ideal color (such as golden yellow, crispy surface, etc.).

[0244] Time analysis: Analyze whether the optimal roasting time has passed based on the roasting time of each sub-roasting chamber.

[0245] This analysis can be based on simple threshold comparison or machine learning algorithms can be used to automatically distinguish the gap between the roasting effect and the expected goal.

[0246] Step 3: Adjust control parameters

[0247] Once the deviation between the roasting status and the goal is determined, the system will automatically adjust the roasting parameters according to the feedback data. The adjusted parameters include:

[0248] Temperature adjustment: Automatically adjust the temperature of each sub-roasting chamber according to the feedback data. For example, if it is found that the temperature of a certain roasting chamber is too low, the system will automatically increase the temperature of that sub-roasting chamber; if the temperature is too high, it will be automatically decreased.

[0249] Time adjustment: If the roasting time of a certain stage is insufficient, the system will extend the roasting time of that stage; on the contrary, if a certain stage is roasted for too long, the system will shorten the time. The system can predict the roasting completion time based on the real-time monitored color change or temperature data, so as to adjust the residence time of each sub-roasting chamber.

[0250] Quantity Allocation: For the use of multiple sub-broiling galleries, the system automatically adjusts the quantity allocation of each sub-broiling gallery according to the working status of each sub-broiling gallery and the current production demand. If a particular sub-broiling gallery has a heavy workload, the system will increase the quantity of that sub-broiling gallery or allocate more eels to that sub-broiling gallery to balance the loads of all sub-broiling galleries.

[0251] Step 4: Real-time Feedback and Adjustment

[0252] The system can make dynamic adjustments based on real-time data during the broiling process. The following are several scenarios for avoiding over-broiling or under-broiling:

[0253] Avoiding Over-broiling: If the eels in a particular sub-broiling gallery have reached the ideal color or surface temperature, but continued heating will cause over-broiling or drying, the system will stop heating or transfer them to the next stage based on real-time monitoring to avoid over-broiling.

[0254] Avoiding Under-broiling: If the eels in a particular sub-broiling gallery have not reached the predetermined color or temperature standard, the system will extend the broiling time for this stage to ensure that the eels achieve the ideal effect.

[0255] Optimized Allocation: Based on the specifications, sizes, shapes, etc. of the eels, the system automatically adjusts the broiling time, temperature, and quantity allocation of each sub-broiling gallery to ensure the best broiling effect for the eels. For example, larger eels require a longer heating time, and the system will automatically adjust the heating time and the number of broiling galleries to ensure that the large eels can also be properly broiled.

[0256] 3. Specific Implementations for Avoiding Over-broiling and Under-broiling

[0257] To avoid over-broiling or under-broiling, the system usually needs to set an "alarm" mechanism or judgment criteria:

[0258] Alarm Criteria for Over-broiling: During the broiling process, when the surface temperature of the eels exceeds the set range or the color is too charred, the system triggers an over-broiling alarm and automatically reduces the temperature or advances to the next stage ahead of time.

[0259] Alarm Criteria for Under-broiling: When the surface temperature of the eels does not reach the set standard or the color is too dark, the system triggers an under-broiling alarm, automatically extends the broiling time, or continues to broil at an appropriate temperature until the expected effect is achieved.

[0260] 4. Real-time Optimization and Adaptive Adjustment

[0261] The automatic feedback mechanism not only adjusts the broiling parameters in real time but also continuously optimizes the working efficiency of each sub-broiling gallery by analyzing the data from multiple broilings. Through machine learning algorithms, the system can learn the broiling requirements of each sub-broiling gallery for different specifications of eels and make automatic adjustments:

[0262] Temperature setting for each roasting chamber: Adjust the temperature setting for each stage according to the type, size, and shape of the eel.

[0263] Time allocation for each roasting chamber: Optimize the roasting time for each sub-roasting chamber based on historical data and real-time data analysis to ensure the best effect for each eel.

[0264] 5. Collaboration between System and Manual Intervention

[0265] Although the automatic feedback mechanism can greatly reduce manual intervention, in actual operation, the collaborative effect of manual intervention and the system is still important. Based on the system's automatic feedback, the operator can fine-tune certain roasting processes according to production requirements or special product requirements to further enhance the flexibility of roasting and product quality.

[0266] Summary: The automatic feedback mechanism continuously monitors data such as temperature and color, analyzes and feeds back to the system in real time, helps automatically adjust the temperature, time, and load distribution of each sub-roasting chamber, and avoids over-roasting or under-roasting. The system not only judges the roasting effect by setting thresholds but also can be optimized in real time through intelligent algorithms to ensure the best roasting effect for each eel and improve production efficiency and product consistency.

[0267] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A tunnel baking kiln with a multi-channel series baking gallery system, characterized in that: It includes a first baking gallery (low-temperature preheating stage), a second baking gallery (medium-temperature heating stage), a third baking gallery (high-temperature heating stage), and a fourth baking gallery (high-temperature air-drying stage); in the third baking gallery, a plurality of sub-baking galleries connected in series are arranged, and the outlet of each sub-baking gallery is provided with an infrared temperature sensor, thermal imaging technology or color monitoring equipment to monitor the surface of the eels in real time and obtain the temperature and color data of the surface of the eels, which is used to judge whether the set high-temperature baking requirements are met according to the baking conditions of the eels in the sub-baking gallery; the outlet of each sub-baking gallery is also provided with a bypass transmission mechanism; the bypass transmission mechanism is connected to the fourth baking gallery respectively.

2. The baking kiln according to claim 1, characterized in that: The temperature monitoring uses infrared sensors, thermal imaging technology or infrared thermal imagers to obtain temperature data of the eel surface in real time in a non-contact manner; the color monitoring uses high-resolution cameras, spectral sensors or color analysis systems to determine the roasting status of the eel by capturing the color changes on the eel surface.

3. A control method for a tunnel baking kiln with a multi-channel serial baking gallery system, characterized in that: The method uses multiple baking galleries connected in series to bake the eels. The baking process is divided into multiple stages. The surface temperature and color of the eels are monitored in real time in combination with intelligent monitoring technology to determine whether the eels meet the set baking requirements. The method includes the following steps: Step 1: grilling the eel through a plurality of grilling galleries connected in series, wherein each grilling galleries is used to achieve different grilling effects according to different settings of temperature and time, and the grilling galleries include: a first grilling galleries (low temperature preheating stage), a second grilling galleries (medium temperature heating stage), a third grilling galleries (high temperature heating stage), and a fourth grilling galleries (high temperature air drying stage); Step 2: In the third roasting gallery, a plurality of sub-roasting galleries are arranged in series, and a bypass conveying mechanism is arranged at the exit of each sub-roasting gallery, which is used to judge whether the roasting condition of the eels in the sub-roasting gallery has reached the set high temperature roasting requirement according to the roasting condition of the eels in the sub-roasting gallery; Step 3: Monitor the surface of the eel in real time through infrared temperature sensors, thermal imaging technology or color monitoring equipment to obtain temperature and color data of the eel surface; Step 4: Analyze the monitoring data using the data processing unit to determine whether the eel has met the high-temperature roasting requirements. If so, send the eel to the fourth roasting gallery through the bypass transmission mechanism; if not, send the eel to the next sub-roasting gallery for further heating.

4. The control method according to claim 3, characterized in that: The method uses a deep learning algorithm or a machine learning model to analyze the temperature and color data collected in real time, and automatically adjusts the temperature and residence time of each sub-baking gallery, thereby optimizing the baking effect of the eel.

5. The control method according to claim 3, characterized in that: The system has an automatic feedback mechanism, which can analyze and statistically monitor data, automatically adjust the baking time and temperature of each sub-baking gallery, maintain a reasonable quantity distribution of each sub-baking gallery, and avoid over-baking or under-baking.

6. The control method according to claim 3, characterized in that: The fourth grill adopts a high-temperature air-drying treatment method, and the air-drying equipment makes the surface of the eel crispy and retains the internal moisture, thereby achieving the best eating effect.

7. The control method according to claim 3, characterized in that: The first baking gallery (low-temperature preheating stage) adopts a low-temperature heating process, and the temperature is controlled between 30°C and 80°C. Through a relatively long preheating process, the surface and interior of the eel are gradually heated up to reach a suitable basic temperature, laying a good foundation for the subsequent high-temperature baking stage.

8. The control method according to claim 3, characterized in that: The second grill (medium-temperature heating stage) adopts a medium-temperature heating process, with the temperature controlled between 80°C and 160°C. Through a shorter heating time, the eel meat gradually becomes tender, a crispy outer layer begins to form on the surface, and the internal moisture is gradually evenly distributed, preparing for high-temperature baking.

9. The control method according to claim 3, characterized in that: The fourth baking gallery (high temperature air drying stage) adopts a high temperature air drying process, and the temperature is controlled between 160°C and 220°C. Through a short period of high temperature air drying, the surface of the eel achieves an ideal golden and crispy effect, while the inside remains properly moist, ensuring that the eel is crispy on the outside and tender on the inside.

10. The control method according to claim 3, characterized in that: The third baking gallery (high temperature heating stage) is composed of three sub-baking galleries connected in series, and the specific baking process is as follows: The first sub-baking gallery: adopts a high-temperature heating process with a temperature controlled between 220℃ and 240℃. Through 6 to 8 minutes of high-temperature heating, the surface temperature of the eel is quickly raised, and the surface is quickly carbonized to form a preliminary golden and crispy outer layer, which promotes the evaporation and distribution of internal moisture. If the eel baked at this stage does not meet the preset high-temperature baking requirements, it will be transferred to the second sub-baking gallery for further baking. Second sub-baking gallery: uses a high-temperature heating process with a temperature controlled between 200℃ and 220℃ for 4 to 6 minutes to further bake the eels and ensure that the temperature of the surface and the inside of the eels evenly reaches the preset requirements; if the eels still do not meet the high-temperature baking requirements at this stage, they will be transferred to the third sub-baking gallery for the final baking; The third grilling area uses a high-temperature heating process with a temperature controlled between 180°C and 200°C, lasting for 3 to 5 minutes, to ensure that the eel fully meets the preset high-temperature grilling requirements.

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