A fine temperature control system and method for a pancake oven

Abstract

The application discloses a fine temperature control system and method for a pancake oven, and relates to the technical field of pancake ovens.The application solves the problem of the lack of adaptive capacity of the temperature control system of the existing oven to the dynamic changes of the characteristics of the powder.The application constructs a whole-process closed-loop temperature control system of "monitoring-judging-adjusting-analyzing-executing": the density data is obtained by real-time monitoring of the discharging flow rate to form the basic input of the temperature control judgment;the correlation rule of the density and the duty cycle is mined by linear or nonlinear analysis to output the accurate execution duty cycle;finally, the execution duty cycle is used to guide the subsequent baking process, and the new baking data can be used to optimize the next round of analysis;the closed-loop mechanism enables the temperature control system to have self-learning and iteration capabilities, can continuously adapt to the characteristics of different batches and different states of the powder, effectively resists the interference of external environmental fluctuations on the baking process, and long-term guarantees the stability and reliability of the baking effect of the pancake.

Description

Technical Field

[0001] This invention relates to the field of baking oven technology, specifically to a refined temperature control system and method for baking ovens. Background Technology

[0002] As a traditional staple food, the quality and taste of baked flatbread depend heavily on the precision of the oven's temperature control. Most flatbread ovens on the market currently use a preset heating level temperature control mode, which adjusts the oven temperature by setting a fixed heating power and duration. This method is easy to operate and can meet basic baking needs in an ideal scenario where the powder characteristics are stable.

[0003] However, in actual production, the viscosity of the flour used to make sesame seed cakes is easily affected by various factors, such as the moisture content of the flour, the difference in gluten strength between different batches of flour, the amount of water added during kneading and the precision of water temperature control, as well as changes in ambient temperature and humidity. All of these factors can lead to significant differences in the viscosity of the flour. Fluctuations in viscosity directly affect the solidification rate and moisture evaporation efficiency of the flour during baking: when the flour viscosity is low (high internal moisture content), its solidification speed is slow at the preset temperature, which can easily lead to a sesame seed cake with low density, a soft and chewy texture, or even undercooked parts; when the flour viscosity is high, the solidification rate is too fast, which will result in a sesame seed cake with high density and a hard crust, affecting the eating experience.

[0004] Existing oven temperature control systems lack the ability to adapt to dynamic changes in powder properties. Their fixed heating power and duty cycle parameters cannot respond to the differentiated heat requirements of powders with different viscosities, resulting in significant differences in the shaping and quality fluctuations of baked flatbreads under the same temperature control parameters. At the same time, traditional temperature control methods rely on operators to manually adjust heating parameters based on experience, which is not only inefficient but also makes it difficult to guarantee the accuracy of parameter adjustments. Human judgment errors can further exacerbate the instability of baking quality. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a refined temperature control system and method for a sesame seed cake oven, which solves the problem that existing oven temperature control systems lack the ability to adapt to dynamic changes in powder characteristics.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a refined temperature control method for a sesame seed cake oven, comprising the following steps: Step 1: Real-time monitoring of the feeding flow rate of the sesame seed cake oven during a single feeding, and based on the real-time monitored feeding flow rate, confirming the single feeding amount, and simultaneously confirming the density data of the sesame seed cake according to the preset volume built into the oven. Specifically, the feeding flow rate monitored at different times is marked as V. iWhere i represents different times within a single instance, and i = 1, 2, ..., n, where n represents the total number of times associated with a single instance; ZL = (V1 + V2 + ... + V...) n Step 1: Calculate the unit time (ZL) of the single feeding quantity ZL, where the unit time is the preset time. Then, use ZL ÷ Rz = MD to confirm the density data MD associated with this single feeding, where Rz is the preset oven cavity volume. Step 2: Perform feature comparison on the density data confirmed during the single feeding process to identify whether the density data meets the standard. Based on the identification result, confirm whether a temperature control signal is generated. Then, execute the temperature control process based on the temperature control signal, adjust the duty cycle associated with the heating process, and record the adjustment data. Compare the confirmed density data MD with the preset interval: If MD ∈ the preset interval, it means the temperature of this feeding is within the preset range. If the density data during the control process meets the standard, no processing is required. The endpoints of the preset range are all preset values, which are generally determined in advance by the operator based on experience. If MD < preset range, resulting in low density, the duty cycle needs to be appropriately increased, generating a duty cycle increase signal. If MD > preset range, resulting in high density, the duty cycle needs to be appropriately decreased, generating a duty cycle decrease signal. The specific method for recording the adjustment data associated with the heating adjustment process is as follows: if the generated temperature control signal is a duty cycle increase signal, the duty cycle ZB associated with the corresponding heating current is confirmed in subsequent single feeding processes. k Its: ZB k =ZB 初 -(k×unit duty cycle), where the unit duty cycle is the preset duty cycle, and ZB 初 The initial duty cycle is before adjustment, and k represents different subsequent cycles, where 1 ≤ k ≤ 5; if the generated temperature control signal is a duty cycle reduction signal, the duty cycle ZB associated with the corresponding heating current is confirmed during different subsequent feeding processes. k Its: ZB k =ZB 初+ (k × unit duty cycle); Record the density data and associated duty cycle generated for each single instance, and label them as adjustment data. Step 3: Based on the confirmed adjustment data, perform linear analysis on the density data and duty cycle, and based on the analysis results, confirm the associated execution duty cycle and execute it. The method for confirming the execution duty cycle is as follows: construct a two-dimensional coordinate system with the associated duty cycle as the horizontal coordinate axis and the density data as the vertical coordinate axis, and confirm the positions of the density data and duty cycle associated with different instances in the two-dimensional coordinate system according to the time sequence, and lock the single instance target point; confirm whether the change characteristics associated with adjacent single instance target points are consistent: the first group of single instance target points of adjacent single instance target points The first point is designated as the "previous point," and the second point is designated as the "next point." The associated coordinates of the previous point are (A1, M1), and the associated coordinates of the next point are (A2, M2). The change characteristics associated with adjacent single target points are confirmed using the formula BH = (M2 - M1) ÷ (A2 - A1). Then, it is determined whether the confirmed change characteristics are all the same. The variance of the change characteristics is processed to confirm the characteristic variance. If the characteristic variance ≤ Y1, it means that there is a linear characteristic. The mean of the change characteristics is then processed to confirm the undetermined characteristics. The execution duty cycle is locked by combining the undetermined characteristics with the linear processing method. If the characteristic variance > Y1, it means that there is no linear characteristic. The execution duty cycle is then confirmed by the non-linear processing method.

[0007] The specific method for determining the execution duty cycle using linear processing is as follows: First, confirm the density data MD associated with the temperature control signal before its generation and the associated preset interval. Then, confirm the median value of the preset interval. Use: |MD - median value| = the feature value to be adjusted, and confirm the feature value to be changed. Then, use: the feature value to be adjusted ÷ the feature value to be determined = the correction value. If the determined temperature control signal is a duty cycle reduction signal, then use: (ZB) 初 - Correction value) = Execution duty cycle. If the determined temperature control signal is a duty cycle increase signal, then use: (ZB) 初 +correction value) = execution duty cycle, and directly adjust the duty cycle of the subsequent feeding process to the execution duty cycle.

[0008] The specific method for determining the execution duty cycle using non-linear processing is as follows: From k single operations, determine if there is a single operation in the corresponding material feeding process where the density data belongs to a preset range. If so, directly determine the duty cycle associated with that single operation and use this duty cycle as the execution duty cycle. If not, determine the last duty cycle among the k single operations and record it as the first duty cycle. If the temperature control signal is a duty cycle reduction signal, select the minimum duty cycle as the second duty cycle. Determine the midpoint between the first and second duty cycles as the process duty cycle, and execute this process duty cycle through the execution terminal to complete a set of material feeding and baking processes, and determine the associated... The density data is used to determine the minimum duty cycle of the process. If the density data is still greater than the preset range, the current process duty cycle is taken as the first duty cycle. The midpoint between the first and second duty cycles is then determined as the process duty cycle, and the density data is checked again for the next set of feeding processes. This process continues until the density data falls within the preset range, and the confirmed process duty cycle under the condition of meeting the target is taken as the execution duty cycle. If the density data is less than the preset range, the current process duty cycle is taken as the second duty cycle. The midpoint between the second and first duty cycles is then determined as the execution duty cycle, and the density data is checked again for the next set of feeding processes. Confirmation is complete, stopping when the density data falls within the preset range. The confirmed process duty cycle under the condition of meeting the standard is used as the execution duty cycle. If the density data falls within the preset range, the determined duty cycle is directly used as the execution duty cycle. If the temperature control signal is a duty cycle increase signal, the maximum duty cycle is selected as the second duty cycle. The minimum and maximum duty cycles are the minimum and maximum duty cycles achievable at the corresponding heating level, both of which are preset values. The midpoint between the first and second duty cycles is determined as the process duty cycle, and a set of feeding and baking processes is executed through the execution terminal. The density data associated with this process is determined: if the density data is greater than... If the preset interval is reached, the current process duty cycle is used as the second duty cycle. The midpoint between the second and first duty cycles is then determined as the process duty cycle, and the density data for the next set of material feeding processes is checked again until the density data falls within the preset interval. If the density data is less than the preset interval, the current duty cycle is used as the first duty cycle, and the midpoint between the first and second duty cycles is determined as the process duty cycle. The density data for the next set of material feeding processes is checked again until the density data falls within the preset interval. If the density data falls within the preset interval, the determined process duty cycle is directly used as the execution duty cycle.

[0009] Preferably, a refined temperature control system for a sesame seed cake oven includes: a density feature confirmation end, which monitors the feeding flow rate of the sesame seed cake oven in real time during a single feeding, and confirms the feeding amount per feeding based on the real-time monitored feeding flow rate, and simultaneously confirms the density data of the sesame seed cake based on the preset volume built into the oven; an adjustment data recording end, which performs feature comparison on the density data confirmed during a single feeding process, identifies whether the density data meets the standard, and confirms whether to generate a temperature control signal based on the identification result, then executes the temperature control process based on the temperature control signal, adjusts the duty cycle associated with the heating process, and records the adjustment data; and an execution feature confirmation end, which performs linear analysis on the density data and duty cycle based on the confirmed adjustment data, and confirms and executes the associated execution duty cycle based on the analysis result.

[0010] This invention provides a refined temperature control system and method for a sesame seed cake oven. Compared with existing technologies, it has the following advantages: This invention accurately generates a duty cycle adjustment signal based on the deviation between density data and a preset range. By adjusting the initial duty cycle in a stepwise manner, it achieves dynamic matching between "material state and temperature control parameters"—when the powder has low viscosity and high moisture content, resulting in a lower density, the duty cycle is increased to extend the heating time to accelerate solidification; when the viscosity is high and solidification is too fast, resulting in a higher density, the duty cycle is decreased to shorten the heating time to avoid over-solidification. This "on-demand adjustment" model effectively avoids the differences in sesame seed cake forming caused by powders of different viscosities under the same temperature control parameters, significantly improving the consistency of the appearance, taste, and internal structure of sesame seed cakes in mass production. In scenarios with linear characteristics (characteristic variance ≤ Y1), the execution duty cycle can be directly locked based on the quantitative calculation of the feature values ​​to be adjusted and the features to be determined, achieving "one-time analysis, precise positioning." In nonlinear scenarios, the process duty cycle is iteratively adjusted using a bisection approach, quickly approaching the target parameters with the fewest trial and error attempts, especially for multiple consecutive sets of density variations. When the target conditions are met, the optimal duty cycle can be efficiently locked through boundary contraction. This method constructs a closed-loop temperature control system covering the entire process of "monitoring-judgment-adjustment-analysis-execution": Density data is obtained by real-time monitoring of the material flow rate, forming the basic input for temperature control judgment; adjustment signals are generated based on density deviations and data is recorded to build a sample library for parameter optimization; the correlation between density and duty cycle is explored through linear or nonlinear analysis to output a precise execution duty cycle; finally, the execution duty cycle guides the subsequent baking process, while new baking data can feed back into the next round of analysis and optimization. This closed-loop mechanism enables the temperature control system to have self-learning and iterative capabilities, continuously adapting to the characteristics of different batches and states of powder, effectively resisting the interference of external environmental fluctuations on the baking process, and ensuring the long-term stability and reliability of the baked flatbread's baking effect. Attached Figure Description

[0011] Figure 1 This is a schematic diagram of the method flow of the present invention;

[0012] Figure 2 This is a schematic diagram of the principle framework of the present invention. Detailed Implementation

[0013] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0014] Example 1: Please refer to Figure 1 This application provides a refined temperature control method for a sesame seed cake oven. During the temperature control process, the oven has preset heating levels, each associated with a different heating power. However, in the actual feeding process, the consistency of the powder varies under different conditions, leading to differences in the sesame seed cake's shape even at the same temperature. To achieve the best shaping effect, the heating duty cycle needs to be adjusted synchronously according to the actual feeding process to achieve refined temperature control and improve the overall baking effect of the sesame seed cake. The method includes the following steps: Step 1: Real-time monitoring of the feeding flow rate of the sesame seed cake oven in a single feeding cycle (a designated feeding port allows control of the powder feeding process, meaning each sesame seed cake is associated with a specific powder, and the feeding time is consistent each time). Based on the real-time monitored feeding flow rate, the single feeding quantity is confirmed, and simultaneously, the density data of the sesame seed cake is confirmed according to the oven's preset volume. Specifically, the density data of the sesame seed cake is confirmed by marking the feeding flow rate monitored at different times as V. i (Because of different viscosities, the specific duty cycle monitored at different times will be different), where i represents different times within a single instance, and i = 1, 2, ..., n, where n represents the total number of times associated with a single instance; using: ZL = (V1 + V2 + ... + V n() × unit time, confirm the single feeding amount ZL, where the unit time is a preset time, determined in advance by relevant personnel based on experience, generally taken as 1 second; and use: ZL ÷ Rz = MD to confirm the density data MD associated with this single feeding, where Rz is the preset oven cavity volume. In each feeding process, it will stop when the corresponding cavity is full, so here the preset oven cavity volume Rz and the specific feeding amount are used to confirm the density data associated with the corresponding temperature; Step 2, perform feature comparison on the density data confirmed in the single feeding process to identify whether the density data meets the standard, and confirm whether to generate a temperature control signal based on the identification result, and then execute the temperature control process according to the temperature control signal, adjust the duty cycle associated with the heating process, and record the adjustment data; First, the specific method to confirm whether the density data meets the standard is: compare the confirmed density data MD with the preset interval: if MD∈ preset interval, it means that the density data in this feeding temperature control process meets the standard, and no processing is required, and the endpoint values ​​of its preset interval are all This is a preset value, and the specific value is usually determined in advance by the operator based on experience. If MD < preset range, it means that the temperature inside the oven is too low, the sesame seed cake solidifies too slowly, resulting in low density. Therefore, the duty cycle needs to be appropriately increased to generate a duty cycle increase signal (the higher the duty cycle, the longer the associated conduction time, and thus the longer the heating time). This also means that the viscosity of the corresponding powder liquid is too low, with more internal moisture and more heat absorption. Therefore, heating this part requires increasing the temperature to ensure the baking effect. If MD > preset range, it means the temperature inside the oven is too low, the sesame seed cake solidifies too slowly, resulting in low density. Therefore, the temperature inside the oven needs to be increased to ensure the baking effect. The interval indicates that the temperature inside the oven is too high, causing the flatbread to solidify too quickly and resulting in excessive density. Therefore, the duty cycle needs to be appropriately reduced, generating a duty cycle reduction signal (when the duty cycle is reduced, the conduction time will be shortened; the duty cycle is the ratio of the current conduction time to the total time within a frequency cycle). The specific method for recording the adjustment data associated with the heating adjustment process is as follows: if the generated temperature control signal is a duty cycle increase signal, confirm the corresponding duty cycle ZB associated with the heating current in subsequent single feeding processes. k Its: ZB k =ZB 初 - (k × unit duty cycle), where the unit duty cycle is a preset duty cycle, determined in advance by the operator based on experience, generally taken as 0.01, and ZB 初 The initial duty cycle is before adjustment, and k represents different subsequent cycles, where 1 ≤ k ≤ 5; if the generated temperature control signal is a duty cycle reduction signal, the duty cycle ZB associated with the corresponding heating current is confirmed during different subsequent feeding processes. k Its: ZB k =ZB 初+ (k × unit duty cycle); Record the density data and associated duty cycle generated for each single instance, and label them as adjustment data; Step 3: Based on the confirmed adjustment data, perform linear analysis on the density data and duty cycle, and based on the analysis results, confirm the associated execution duty cycle and execute it. The method for confirming the execution duty cycle is as follows: construct a two-dimensional coordinate system with the associated duty cycle as the horizontal coordinate axis and the density data as the vertical coordinate axis, and confirm the points of the density data and duty cycle associated with different instances in the two-dimensional coordinate system according to the time sequence, and lock the single instance target point; confirm whether the change characteristics associated with adjacent single instance target points are consistent: record the first set of single instance target points of adjacent single instance target points as the previous point, and the second set of single instance target points as the next point, and determine the association of the previous point. The coordinates are (A1, M1), and the associated coordinates of the proposed point are (A2, M2). The formula BH = (M2-M1) ÷ (A2-A1) is used to determine the change characteristics associated with adjacent single target points. Then, it is identified whether the confirmed sets of change characteristics are all the same feature. The variance of the set of change characteristics is processed to confirm the feature variance. If the feature variance ≤ Y1, it means that there is a linear feature. The mean of the set of change characteristics is then processed to confirm the undetermined feature. The linear processing method is used in combination with the undetermined feature to lock the execution duty cycle. If the feature variance > Y1, it means that there is no linear feature. The non-linear processing method is used to confirm the execution duty cycle. The variance processing method is common in the existing technology, so it will not be elaborated on here. Y1 is a preset value, and its specific value is determined by the operator based on experience.

[0015] The specific method for determining the execution duty cycle using linear processing is as follows: First, confirm the density data MD associated with the temperature control signal before its generation and the associated preset interval. Then, confirm the median value of the preset interval. Use: |MD - median value| = the feature value to be adjusted, and confirm the feature value to be changed. Then, use: the feature value to be adjusted ÷ the feature value to be determined = the correction value. If the determined temperature control signal is a duty cycle reduction signal, then use: (ZB) 初 - Correction value) = Execution duty cycle. If the determined temperature control signal is a duty cycle increase signal, then use: (ZB) 初 +correction value) = execution duty cycle, and directly adjust the duty cycle of the subsequent material feeding process to the execution duty cycle, so as to ensure that the density of the subsequent material feeding meets the standard and quickly lock the corresponding execution duty cycle.

[0016] The specific method for determining the execution duty cycle using non-linear processing is as follows: From k single operations, determine if there is a single operation in the corresponding material feeding process where the density data belongs to a preset range. If so, directly determine the duty cycle associated with that single operation and use this duty cycle as the execution duty cycle. If not, determine the last duty cycle among the k single operations and record it as the first duty cycle. If the temperature control signal is a duty cycle reduction signal, select the minimum duty cycle as the second duty cycle. Determine the midpoint between the first and second duty cycles as the process duty cycle, and execute this process duty cycle through the execution terminal to complete a set of material feeding and baking processes. The process involves determining the density data associated with this process, with its minimum duty cycle set as a preset value. If the density data is still greater than the preset range, the current process duty cycle is taken as the first duty cycle. The midpoint between the first and second duty cycles is then determined as the new process duty cycle, and the density data for the next material feeding process is checked again until the density data falls within the preset range. The confirmed process duty cycle under these conditions is then used as the execution duty cycle. If the density data is less than the preset range, the current process duty cycle is taken as the second duty cycle. The midpoint between the second and first duty cycles is then determined as the execution duty cycle. The density data for the next batch of material feeding is then confirmed again, stopping when the density data falls within the preset range. The duty cycle confirmed under the condition of meeting the standard is used as the execution duty cycle. If the density data falls within the preset range, the determined duty cycle is directly used as the execution duty cycle. For the example of the temperature control signal being the duty cycle adjustment signal, the last duty cycle is designated as the first duty cycle L1, and its minimum duty cycle is L2. The intermediate value between L1 and L2 is denoted as Lz. When the duty cycle is adjusted to Lz, the original density data is greater than the preset range; when adjusted to Lz, it is greater than the preset range. If both L1 and Lz are greater than the specified value, the accurate value should be between Lz and L2. Further adjustment is needed. Therefore, Lz is re-established as the first duty cycle L1, and the intermediate value is reconfirmed to lock in the corresponding accurate duty cycle. Similarly, when adjusted to Lz, the density data is greater than the preset range, meaning L1 is less than the specified value and Lz is greater than the specified value. The accurate value should be between L1 and Lz. Therefore, Lz is re-established as the second duty cycle, and the intermediate value is reconfirmed. This process continues until the closest standard duty cycle is locked in, allowing for rapid locking.If the temperature control signal is a duty cycle increase signal, then the maximum duty cycle is selected as the second duty cycle. The minimum and maximum duty cycles are the minimum and maximum duty cycles achievable at the corresponding heating level, both preset values. The midpoint between the first and second duty cycles is determined as the process duty cycle. A set of feeding and baking processes is executed through the execution terminal, and the density data associated with this process is determined: If the density data is greater than the preset range, the current process duty cycle is used as the second duty cycle, and the midpoint between the second and first duty cycles is redefined as the process duty cycle. The density data for the next feeding process is then checked again until the density data falls within the preset range. If the density data is less than the preset range, the current duty cycle is used as the first duty cycle, and the midpoint between the first and second duty cycles is redefined as the process duty cycle. The density data for the next feeding process is then checked again until the density data falls within the preset range. If the density data is less than the preset range, the current duty cycle is used as the first duty cycle, and the midpoint between the first and second duty cycles is redefined as the process duty cycle. The density data for the next feeding process is then checked again until the density data falls within the preset range. If the data falls within a preset range, the determined process duty cycle is directly used as the execution duty cycle. For example, considering the temperature control signal as a duty cycle adjustment signal, the last duty cycle is designated as the first duty cycle L1, and the selected maximum duty cycle is designated as the second duty cycle L2. The intermediate value Lz between L1 and L2 is determined first. After specific adjustments are made, the corresponding feeding and baking process is executed, and the comparison between the corresponding density data and the preset range is evaluated: If the density data > the preset range, and L1 is less than the preset range, and the determined Lz is greater than the preset range, then the accurate value is between Lz and L1. In this case, Lz is used as the second duty cycle, and the intermediate value is reconfirmed, and so on. If the density data < the preset range, and L1 is less than the preset range, and Lz is less than the preset range, then the accurate value is between Lz and L2. In this case, Lz is used as the first duty cycle, and the intermediate value is reconfirmed, and so on, thus quickly locking in the corresponding accurate value.

[0017] Subsequently, the execution end controls the duty cycle of the subsequent material feeding process to be the standard duty cycle based on the determined standard duty cycle.

[0018] Example 2: Combination Figure 2A refined temperature control system for a flatbread oven includes: a density feature confirmation end, which monitors the feeding flow rate of the flatbread oven in real time during a single feeding cycle, confirms the feeding amount based on the real-time monitored feeding flow rate, and simultaneously confirms the density data of the flatbread based on the preset volume built into the oven; an adjustment data recording end, which performs feature comparison on the density data confirmed during a single feeding cycle, identifies whether the density data meets the standard, confirms whether to generate a temperature control signal based on the identification result, executes the temperature control process based on the temperature control signal, adjusts the duty cycle associated with the heating process, and records the adjustment data; and an execution feature confirmation end, which performs linear analysis on the density data and duty cycle based on the confirmed adjustment data, and confirms and executes the associated execution duty cycle based on the analysis result.

[0019] Some of the data in the above formulas are numerical calculations with dimensions removed, and the contents not described in detail in this specification are all prior art known to those skilled in the art.

[0020] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A method for precise temperature control in a sesame seed cake oven, characterized in that, Includes the following steps: Step 1: Monitor the feeding flow rate of the sesame seed cake oven in real time, and based on the real-time monitoring of the feeding flow rate, confirm the feeding amount per batch, and simultaneously confirm the density data of the sesame seed cake according to the preset volume built into the oven. Step 2: Perform feature comparison on the density data confirmed during a single feeding process to identify whether the density data meets the standard. Based on the identification result, confirm whether to generate a temperature control signal, then execute the temperature control process based on the temperature control signal, adjust the duty cycle associated with the heating process, and record the adjustment data. The specific method for confirming whether the density data meets the standard is as follows: Compare the confirmed density data MD with the preset range: if MD∈Preset range, it means that the density data in this feeding temperature control process meets the standard and no processing is required. The endpoint values ​​of the preset range are all preset values. If MD < preset interval, generate duty cycle up adjustment signal; If MD > preset interval, generate a duty cycle reduction signal; The specific method for recording the adjusted data is as follows: If the generated temperature control signal is a duty cycle increase signal, confirm the duty cycle ZB associated with the corresponding heating current in subsequent single feeding processes. k Its: ZB k =ZB 初 +(k×unit duty cycle), where the unit duty cycle is the preset duty cycle, and ZB 初 The initial duty cycle before adjustment is given, and k represents different subsequent cycles, where 1 ≤ k ≤ 5. If the generated temperature control signal is a duty cycle reduction signal, confirm the duty cycle ZB associated with the corresponding heating current in subsequent single feeding processes. k Its: ZB k =ZB 初 -(k×unit duty cycle); Record the density data and associated duty cycle generated for each individual iteration, and label them as adjustment data; Step 3: Based on the confirmed adjustment data, perform linear analysis on the density data and duty cycle, and based on the analysis results, confirm the associated execution duty cycle and execute it.

2. The method for refined temperature control of a sesame seed cake oven according to claim 1, characterized in that, In step one, the specific method for confirming the density data of the sesame seed cake is as follows: The feed flow rate monitored at different times is labeled as V. i , where i represents different times within a single instance, and i = 1, 2, ..., n, where n represents the total number of times associated with a single instance; Using: ZL = (V1 + V2 + ... + V) n () × unit time, confirm the single feeding quantity ZL, the unit time is the preset time; The density data MD associated with this single transaction is confirmed using the formula ZL÷Rz=MD, where Rz is the preset oven cavity volume.

3. The method for refined temperature control of a sesame seed cake oven according to claim 1, characterized in that, In step three, the methods for confirming the duty cycle include: A two-dimensional coordinate system is constructed with the associated duty cycle as the horizontal coordinate axis and the density data as the vertical coordinate axis. Based on the chronological order, the density data and duty cycle associated with different transactions are located in the two-dimensional coordinate system to confirm the position and lock the target point for each transaction. Confirm whether the change features associated with adjacent single target points are consistent: Record the first set of single target points of adjacent single target points as the front point, and the second set of single target points as the back point. Set the associated coordinates of the front point as (A1, M1) and the associated coordinates of the back point as (A2, M2). Use BH = (M2-M1) ÷ (A2-A1) to confirm the change features associated with adjacent single target points, and then identify whether the confirmed sets of change features are all the same feature. Several sets of changing features are subjected to variance processing to confirm the feature variance. If the feature variance ≤ Y1, it means that there is a linear feature. Then, several sets of changing features are subjected to mean processing to confirm the undetermined feature. The execution duty cycle is locked by combining the undetermined feature with the linear processing method. If the feature variance > Y1, it means that there is no linear feature. Then, the execution duty cycle is confirmed by the non-linear processing method.

4. The refined temperature control method for a sesame seed cake oven according to claim 3, characterized in that, The specific method of using linear processing combined with locking the duty cycle based on undetermined features is as follows: Confirm the density data MD associated with the temperature control signal and the associated preset range before the signal is generated. Then confirm the median value of the preset range. Use the formula: |MD - median value| = the feature value to be adjusted to confirm the feature value to be adjusted that needs to be changed. Then, the following formula is used: Variable value to be adjusted ÷ Variable value to be determined = Correction value. If the determined temperature control signal is a duty cycle reduction signal, then the following formula is used: (ZB) 初 - Correction value) = Execution duty cycle. If the determined temperature control signal is a duty cycle increase signal, then use: (ZB) 初 +correction value) = execution duty cycle, and directly adjust the duty cycle of the subsequent feeding process to the execution duty cycle.

5. The refined temperature control method for a sesame seed cake oven according to claim 3, characterized in that, The specific method for locking the execution duty cycle using non-linear processing is as follows: From k single-time events, determine whether there are any single-time events during the material feeding process where the density data belongs to a preset range: If it exists, directly confirm the duty cycle associated with the corresponding single transaction and use this duty cycle as the execution duty cycle; If it does not exist, then determine the duty cycle of the last of the k single cycles and denote it as the first duty cycle: If the temperature control signal is a duty cycle reduction signal, then the minimum duty cycle is selected as the second duty cycle, the midpoint between the first duty cycle and the second duty cycle is determined as the process duty cycle, and this process duty cycle is executed by the execution terminal to complete a set of feeding and baking processes, and the density data associated with this process is determined, with its minimum duty cycle being a preset value. If the density data is still greater than the preset range, the current process duty cycle is taken as the first duty cycle. The midpoint between the first duty cycle and the second duty cycle is then redefined as the process duty cycle. The density data of the next set of feeding processes is then confirmed again until the density data falls within the preset range. The process duty cycle confirmed under the condition of meeting the standard is taken as the execution duty cycle. If the density data is less than the preset range, the current process duty cycle is used as the second duty cycle. The midpoint between the second duty cycle and the first duty cycle is then redefined as the execution duty cycle. The density data of the next set of feeding processes is then confirmed again until the density data falls within the preset range. The process duty cycle confirmed under the condition of meeting the target is then used as the execution duty cycle. If the density data falls within the preset range, the determined duty cycle will be used directly as the execution duty cycle.

6. The method for refined temperature control of a sesame seed cake oven according to claim 5, characterized in that, Specific methods for locking the execution duty cycle using non-linear processing also include: If the temperature control signal is a duty cycle increase signal, then the maximum duty cycle is selected as the second duty cycle. The minimum and maximum duty cycles are the minimum and maximum duty cycles achievable at the corresponding heating level, and are preset values. The midpoint between the first and second duty cycles is determined as the process duty cycle. A set of feeding and baking processes is executed through the execution terminal, and the density data associated with this process is determined. If the density data is greater than the preset range, the current process duty cycle is taken as the second duty cycle. The midpoint between the second duty cycle and the first duty cycle is then redefined as the process duty cycle. The density data of the next set of feeding processes is then confirmed again until the density data falls within the preset range. The process duty cycle confirmed under the condition of meeting the standard is taken as the execution duty cycle. If the density data is less than the preset range, the current duty cycle is taken as the first duty cycle, and the midpoint between the first duty cycle and the second duty cycle is re-determined as the process duty cycle. The density data of the next set of feeding processes is confirmed again until the density data is within the preset range. The process duty cycle confirmed under the condition of meeting the standard is taken as the execution duty cycle. If the density data falls within a preset range, the determined process duty cycle is directly used as the execution duty cycle.

7. A refined temperature control system for a sesame seed cake oven, the system operating according to any one of claims 1-6, characterized in that, include: The density characteristic confirmation end monitors the feeding flow rate of the sesame seed cake oven in real time during a single feeding, and confirms the feeding amount per feeding based on the real-time monitored feeding flow rate. Simultaneously, it confirms the density data of the sesame seed cake based on the preset volume built into the oven. Adjust the data recording end, perform feature comparison on the density data confirmed during a single feeding process, identify whether the density data meets the standard, and confirm whether to generate a temperature control signal based on the identification result. Then, execute the temperature control process based on the temperature control signal, adjust the duty cycle associated with the heating process, and record the adjustment data. The execution feature confirmation end performs linear analysis on density data and duty cycle based on the confirmed adjustment data, and confirms and executes the associated execution duty cycle based on the analysis results.

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