Intelligent oil supplementing control method and system for instant noodle frying machine

Abstract

The invention relates to the field of automatic control of frying equipment, and discloses an intelligent oil supplementing control method and system for an instant noodle frying machine. Oil level, temperature and feeding amount data in the frying process are collected in real time; a PID algorithm is utilized, and the basic oil supplementing demand quantity is calculated according to the oil level data and serves as a control reference; a feed-forward compensation mechanism is constructed based on the temperature data, the oil supplementing rate is preliminarily restrained, and the oil temperature is corrected; the frying time and the oil level state are associated, and the maximum allowable oil supplementing rate control amplitude is calculated; calculating a quality attenuation index according to the initial mass of grease and the like, and dynamically adjusting a control reference; predicting an oil level descending trend in combination with the feeding amount, generating a predicted oil supplementing component, and obtaining a comprehensive oil supplementing execution index; monitoring the oil cleanliness and the running state of the filtering system, calculating the filtering compensation flow and superposing the filtering compensation flow to an oil supplementing instruction, and outputting a final signal. According to the method, accurate automatic control and dynamic cooperative adjustment are realized, the problems of oil level fluctuation and the like are solved, the safety and the cake quality are improved, and the cost is reduced.

Description

Technical Field

[0001] This invention relates to the field of automated control technology for frying equipment, and more specifically to an intelligent oil replenishment control method and system for an instant noodle fryer. Background Technology

[0002] The instant noodle manufacturing industry has put forward increasingly refined management requirements for oil utilization efficiency, product quality stability, and energy consumption cost control in the frying process. Especially in continuous and large-scale production scenarios, it is necessary to use intelligent technology to achieve real-time response and precise control of complex dynamic parameters in the frying process, so as to optimize oil consumption to the greatest extent and improve the overall automation level of the production line while meeting food quality and safety standards, thereby adapting to the needs of modern, efficient and energy-saving manufacturing mode.

[0003] However, the existing intelligent oil replenishment control system for instant noodle fryers still has the following shortcomings: Firstly, existing technologies generally rely on manual experience to replenish palm oil. Due to the significant lag and subjective arbitrariness in manual observation and oil replenishment, it is difficult to match the actual oil consumption during frying in real time and accurately. This results in large fluctuations in oil level during frying, leading to poor stability in the quality of the pancakes and making it impossible to achieve precise automatic control of oil level. Secondly, the existing oil replenishment control method is not responsive enough to changes in operating conditions. It is easy to cause the oil level to be too low due to insufficient oil replenishment, which may lead to the product being undercooked, or the oil level to be too high due to excessive oil replenishment, which may cause the safety hazard of grease overflow, reduce the level of production safety and increase the probability of potential accidents. Thirdly, existing control methods are unable to balance the stability of oil level and frying temperature, and cannot be dynamically adjusted according to the real-time quality status of the oil. This results in large fluctuations in frying parameters, which not only leads to a decline in the quality of the dough but also accelerates the excessive oxidation and aging of the oil, increasing the waste oil rate and production costs. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, the present invention provides an intelligent oil replenishment control method and system for instant noodle fryers, so as to solve the problems existing in the background art.

[0005] This invention provides the following technical solution: an intelligent oil replenishment control method for an instant noodle fryer, comprising: S1: Real-time collection of oil level data, frying temperature data, and feed amount data during the frying process of instant noodle cakes; S2: Based on the collected oil level data, the basic oil replenishment requirement required to maintain the current target oil level is calculated using the PID algorithm, which serves as the benchmark for oil replenishment control. S3: Based on the collected frying temperature data, a feedforward compensation mechanism for oil replenishment operation is constructed. The oil replenishment rate is initially constrained in conjunction with the oil replenishment control benchmark, and the oil temperature is dynamically corrected. S4: Obtain the frying time, perform correlation analysis between the frying time and the oil level status, calculate the maximum allowable oil replenishment rate, and use the maximum allowable oil replenishment rate to control the magnitude of the oil replenishment rate; S5: Obtain the initial quality of the oil and the mixing ratio of new and old oils, calculate the oil quality decay index based on the dynamic dilution algorithm, use the oil quality decay index as a weighted correction parameter for the oil replenishment strategy, dynamically adjust the oil replenishment control benchmark, and obtain the adjusted oil replenishment control benchmark. S6: Based on the collected feed rate data and combined with the oil consumption rate statistical model to predict the oil level decline trend, generate the oil level prediction replenishment component, and analyze the replenishment execution process in combination with the adjusted replenishment control benchmark to obtain the comprehensive replenishment execution index. S7: Monitors the cleanliness of the oil and the operating status of the filtration system, calculates the flow loss caused by the filtration and oil discharge, generates the filtration compensation flow based on the flow loss, and adds the filtration compensation flow to the oil replenishment command generated based on the comprehensive oil replenishment execution index, and outputs the final oil replenishment control signal.

[0006] Preferably, S1 uses a multi-sensor array deployed at different spatial locations in the fryer to collect in real time signals of oil level changes, temperature distribution in multiple areas of the frying tank, and feed cake flow rate on the conveyor belt during the operation of the fryer. The collected oil level height change signal is filtered to eliminate the interference of liquid level fluctuation caused by frying bubbles, and the oil level data is obtained. The average value of the collected temperature distribution signals in multiple areas of the frying tank is calculated to obtain the frying temperature data; The collected feed cake flow rate signal is normalized to unify the unit of measurement, thereby obtaining the feed amount data.

[0007] Preferably, in step S2, the obtained oil level data is compared with the preset target oil level to calculate the deviation value, and a PID closed-loop control model with integral separation function is constructed based on the real-time deviation value. The basic oil replenishment requirement required to maintain the current target oil level is calculated using a PID closed-loop control model, which serves as the benchmark for oil replenishment control. When the real-time deviation exceeds the preset deviation threshold, the integral action is activated to eliminate steady-state error. Furthermore, the control gain parameter of the PID closed-loop control model is dynamically mapped to the frying temperature data. This is achieved by incorporating the influence of frying temperature changes on fluid viscosity characteristics to compensate for nonlinear adaptive adjustment of the oil replenishment control response speed.

[0008] Preferably, in step S3, the temperature deviation between the obtained frying temperature data and the target frying temperature is analyzed to obtain the temperature deviation change rate. Based on the temperature deviation change rate, a dynamic feedforward compensation model for oil replenishment operation is constructed to obtain the feedforward oil replenishment correction coefficient. The feedforward oil replenishment correction coefficient is used to weight and constrain the oil replenishment control benchmark to obtain the initial oil replenishment rate command. The fluid saturated heat capacity parameter at the current oil temperature is calculated based on the frying temperature data. The transient thermal shock effect caused by the mixing of cold fluid in the oil replenishment is analyzed to obtain the thermal shock attenuation factor. Then, the initial oil replenishment rate command is corrected by feedback compensation based on the thermal shock attenuation factor to eliminate the false oil temperature drop feedback caused by the oil replenishment operation, thereby realizing the nonlinear dynamic correction of the oil replenishment rate.

[0009] Preferably, step S4 acquires the current frying time data of the instant noodle cake, performs multi-dimensional correlation coupling analysis on the frying time data and the real-time oil level status, calculates the remaining safe volume in the frying tank that dynamically shrinks as the frying time progresses, and determines the liquid level safety threshold and the maximum allowable oil replenishment rate corresponding to the current moment based on the mapping of the remaining safe volume; calculates the rate deviation value between the real-time oil replenishment rate and the maximum allowable oil replenishment rate, and determines whether the rate deviation value is greater than zero. If the rate deviation value is greater than zero, it is determined that the oil replenishment rate exceeds the dynamic safety boundary, and a corresponding amplitude suppression command is generated according to the amplitude of the rate deviation value, thereby constraining the real-time oil level below the liquid level safety threshold to prevent excessive oil spillage caused by the instantaneous fluctuation of the oil replenishment amount exceeding the capacity of the frying tank.

[0010] Preferably, step S5 acquires the initial quality data of the oil in the current fryer and the mixing ratio of new and old oil. Based on the initial quality data and the mixing ratio of new and old oil, a correlation analysis is performed on the physicochemical properties of the oil to obtain the oil quality degradation index, which characterizes the degree of oil deterioration. By using the oil quality decay index as the key weighted correction parameter for the oil replenishment strategy, a nonlinear weighted correction calculation is performed on the original oil replenishment control benchmark to obtain the quality compensation correction amount. This quality compensation correction amount is then superimposed on the oil replenishment control benchmark to obtain the adjusted oil replenishment control benchmark, thereby achieving adaptive optimization of the oil replenishment strategy based on the dynamic changes in oil quality.

[0011] Preferably, step S6 calculates the predicted oil replenishment amount based on the collected instant noodle cake feed data and a preset oil consumption rate statistical model to predict the downward trend of oil level over time. The predicted oil replenishment amount is used to characterize the real-time oil replenishment flow rate required to offset the physical oil loss caused by the increase in feed. Based on the obtained oil level prediction replenishment component, liquid level safety threshold, and adjusted replenishment control benchmark, a multi-dimensional fusion analysis is performed to calculate the matching degree between replenishment demand and safety boundary and quality constraints. Based on the calculated matching degree, deviation judgment and correction calculation are performed. By allocating the proportion of replenishment safety weight and quality correction weight, the comprehensive replenishment execution index is calculated and determined.

[0012] Preferably, step S7 monitors the cleanliness index of the oil in the fryer and the operating status parameters of the filtration system in real time. Based on the cleanliness index and operating status parameters, it calculates the instantaneous flow loss caused by the oil filtration process and calculates the filtration compensation flow based on the instantaneous flow loss. At the same time, based on the comprehensive oil replenishment execution index obtained in step S6, it calculates the basic oil replenishment command in combination with the preset flow mapping coefficient. The basic oil replenishment command and the filtration compensation flow are superimposed and fused for analysis, and the final oil replenishment control signal is output.

[0013] To achieve the above objectives, the present invention provides the following technical solution: an intelligent oil replenishment control system for an instant noodle fryer, comprising the following steps: Data acquisition module: Real-time acquisition of oil level data, frying temperature data, and feed amount data during the frying process of instant noodle cakes; Basic oil replenishment calculation module: Based on the collected oil level data, it uses a PID algorithm to calculate the basic oil replenishment requirement required to maintain the current target oil level, which serves as the benchmark for oil replenishment control. Feedforward compensation and oil temperature correction module: Based on the collected frying temperature data, a feedforward compensation mechanism for oil replenishment operation is constructed. Combined with the oil replenishment control benchmark, the oil replenishment rate is initially constrained, and the oil temperature is dynamically corrected. Frying time-related oil replenishment control module: acquires frying time, performs correlation analysis between frying time and oil level status, calculates the maximum allowable oil replenishment rate, and controls the magnitude of the oil replenishment rate using the maximum allowable oil replenishment rate; Oil quality correction module: Obtain the initial quality of oil and the mixing ratio of new and old oil, calculate the oil quality decay index based on the dynamic dilution algorithm, use the oil quality decay index as the weighted correction parameter of the oil replenishment strategy, dynamically adjust the oil replenishment control benchmark, and obtain the adjusted oil replenishment control benchmark. Feed rate replenishment analysis module: Based on the collected feed rate data and combined with the oil consumption rate statistical model to predict the oil level decline trend, it generates an oil level prediction replenishment component, and analyzes the replenishment execution process in combination with the adjusted replenishment control benchmark to obtain a comprehensive replenishment execution index. Filtration compensation module: Monitors the cleanliness of the oil and the operating status of the filtration system, calculates the flow loss caused by the filtration and oil discharge, generates a filtration compensation flow based on the flow loss, and adds the filtration compensation flow to the oil replenishment command generated based on the comprehensive oil replenishment execution index, and outputs the final oil replenishment control signal.

[0014] The technical effects and advantages of this invention are as follows: (1) By collecting data on oil level, temperature and feed amount in real time, the basic oil replenishment requirement is automatically calculated using the PID algorithm. Combined with the prediction of oil level decline trend based on the oil consumption rate statistical model, the transformation from passive response to active prediction is realized, thereby accurately matching the actual oil consumption, eliminating the subjective arbitrariness of human operation, ensuring that the oil level remains constant, and significantly improving the stability of the dough quality.

[0015] (2) By constructing a feedforward compensation mechanism to initially constrain the oil replenishment rate, and by performing correlation analysis between frying time and oil level status to calculate the maximum allowable oil replenishment rate, the upper limit of the oil replenishment rate is set to prevent oil spillage. At the same time, forward prediction is used to prevent insufficient oil replenishment. Thus, the oil level is strictly locked within the safe range when the working conditions change, effectively eliminating the risk of undercooked products and oil spillage, and improving the level of production safety.

[0016] (3) By introducing the oil quality decay index as a weighted correction parameter, the oil replenishment strategy is dynamically adjusted according to the initial quality and the mixing ratio of new and old oils, the oil temperature is dynamically corrected, and the filter compensation flow is superimposed, thus realizing the coordinated control of oil level, temperature and oil quality. This not only optimizes the quality of the dough, but also reduces the generation of waste oil by delaying the excessive oxidation of oil, effectively reducing production costs. Attached Figure Description

[0017] Figure 1 This is a diagram illustrating the method steps of the present invention.

[0018] Figure 2 This is a system structure block diagram of the present invention. Detailed Implementation

[0019] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. In addition, the forms of the various structures described in the following embodiments are merely illustrative. The intelligent oil replenishment control method and system for instant noodle fryers involved in the present invention are not limited to the structures described in the following embodiments. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] like Figure 1 The embodiment shown provides an intelligent oil replenishment control method for an instant noodle fryer, including: S1: Real-time collection of oil level data, frying temperature data, and feed rate data during the frying process of instant noodle cakes.

[0021] In this embodiment, S1 uses a multi-sensor array deployed at different spatial locations in the fryer to collect in real time the oil level height change signal, the temperature distribution signal in multiple areas of the frying tank, and the feed cake flow rate signal on the conveyor belt during the operation of the fryer. The collected oil level height change signal is filtered to eliminate the interference of liquid level fluctuation caused by frying bubbles, and the oil level data is obtained. The average value of the collected temperature distribution signals in multiple areas of the frying tank is calculated to obtain the frying temperature data; The collected feed cake flow rate signal is normalized to unify the unit of measurement, thereby obtaining the feed amount data.

[0022] It should be specifically noted that for the oil level change signal, the median average filtering algorithm in digital signal processing is used to denoise the continuously acquired liquid level signal. The specific calculation process is as follows: oil level values ​​within a set sampling window (e.g., 10 consecutive sampling points) are collected, the maximum and minimum values ​​are removed to eliminate transient extreme value interference caused by the bursting of frying bubbles, and then the arithmetic mean of the remaining values ​​is calculated to obtain smooth and accurate oil level data. For the temperature distribution signal in multiple areas within the frying tank, weighting coefficients are set according to the distance of each sensor from the heating source and the heat exchange efficiency, and a weighted average is used. The average algorithm calculates the oil temperature by assigning a weight of 0.6 to the high-temperature area in the center of the frying tank and a weight of 0.4 to the low-temperature area at the edge, and then performing a weighted sum to accurately reflect the overall thermal field state. For the feed dough flow rate signal, the number and area parameters of dough per unit time are obtained through photoelectric sensors or a visual recognition system and converted into standard units of measurement (such as kg / min). By dividing by the preset maximum production capacity value, the feed rate is normalized and mapped to a dimensionless value range of 0 to 1, thereby eliminating the measurement differences caused by different production specifications and obtaining standardized feed rate data.

[0023] S2: Based on the collected oil level data, the basic oil replenishment requirement required to maintain the current target oil level is calculated using the PID algorithm, which serves as the benchmark for oil replenishment control.

[0024] In this embodiment, S2 calculates the deviation between the obtained oil level data and the preset target oil level to obtain a real-time deviation value. Based on the real-time deviation value, a PID closed-loop control model with integral separation function is constructed. The basic oil replenishment requirement required to maintain the current target oil level is calculated using a PID closed-loop control model, which serves as the benchmark for oil replenishment control. When the real-time deviation exceeds the preset deviation threshold, the integral action is activated to eliminate steady-state error. Furthermore, the control gain parameter of the PID closed-loop control model is dynamically mapped to the frying temperature data. This is achieved by incorporating the influence of frying temperature changes on fluid viscosity characteristics to compensate for nonlinear adaptive adjustment of the oil replenishment control response speed.

[0025] It needs to be explained in detail that, firstly, the difference between the real-time oil level data and the preset target oil level is calculated as the real-time deviation value. An integral separation PID model is then constructed. The specific process is as follows: a deviation threshold (e.g., 5mm) is set. The integral term is only introduced to eliminate steady-state error when the absolute value of the real-time deviation is less than this threshold. When the deviation is large, the integral action is cut off to prevent overshoot. At the same time, a dynamic mapping table between the PID control gain parameter and the frying temperature data is established to achieve adaptive adjustment. For example, based on the influence of oil temperature changes on palm oil viscosity, the control gain parameter is set to be positively correlated with oil temperature. That is, when an increase in oil temperature (e.g., from 140°C to 160°C) is detected, which leads to a decrease in oil viscosity and an increase in fluidity, the proportional gain and derivative gain are automatically increased by a preset proportional coefficient (e.g., 1.2 times). This improves the response speed of the oil replenishment control valve to offset the flow control delay caused by changes in fluid viscosity. Finally, an accurate basic oil replenishment demand is output as the oil replenishment control benchmark.

[0026] S3: Based on the collected frying temperature data, a feedforward compensation mechanism for oil replenishment operation is constructed. The oil replenishment rate is initially constrained in conjunction with the oil replenishment control benchmark, and the oil temperature is dynamically corrected.

[0027] In this embodiment, step S3 analyzes the temperature deviation between the obtained frying temperature data and the target frying temperature to obtain the temperature deviation change rate. Based on the temperature deviation change rate, a dynamic feedforward compensation model for oil replenishment operation is constructed to obtain the feedforward oil replenishment correction coefficient. The feedforward oil replenishment correction coefficient is used to weight and constrain the oil replenishment control benchmark to obtain the initial oil replenishment rate command. The fluid saturated heat capacity parameter at the current oil temperature is calculated based on the frying temperature data. The transient thermal shock effect caused by the mixing of cold fluid in the oil replenishment is analyzed to obtain the thermal shock attenuation factor. Then, the initial oil replenishment rate command is corrected by feedback compensation based on the thermal shock attenuation factor to eliminate the false oil temperature drop feedback caused by the oil replenishment operation, thereby realizing the nonlinear dynamic correction of the oil replenishment rate.

[0028] It needs to be explained in detail that, firstly, the difference between the frying temperature data and the target frying temperature is calculated, and the time derivative of this difference is performed to obtain the rate of change of temperature deviation. For example, the rate of temperature drop within a set sampling period is calculated. Based on this rate, a preset feedforward mapping table is consulted to obtain the feedforward oil replenishment correction coefficient. The faster the temperature drop rate, the larger the correction coefficient. This coefficient is then used to apply a weighted constraint to the oil replenishment control benchmark to obtain the initial oil replenishment rate command. Subsequently, based on the current frying temperature, the corresponding fluid saturated heat capacity parameter is obtained from the oil physical property database, and the transient heat exchange quantity Q generated by the mixing of the replenished cold fluid and hot oil is calculated in combination with the current real-time flow parameter of the oil replenishment pump. ),in This indicates the specific heat capacity of the added oil, obtained by querying a preset database of oil physical properties based on the current frying temperature. It reflects the heat absorption capacity of the oil at different temperatures. This indicates the temperature difference, which is the difference between the real-time oil temperature inside the fryer and the temperature of the oil to be added to the replenishment tank (usually room temperature). The instantaneous mass flow rate during the oil replenishment process is calculated using the current operating frequency of the oil replenishment pump or the flow meter reading, and is used to characterize the mass of cold oil replenished per unit time. This is used to quantitatively analyze the thermal shock effect and obtain the thermal shock attenuation factor K, where the formula for calculating K is set as follows: ,in The preset thermal shock compensation coefficient is experimentally calibrated to match the thermal inertia of the fryer, so that the value of the thermal shock attenuation factor K is linearly positively correlated with the heat exchange quantity Q. For example, when a sudden drop in the temperature sensor value is detected due to a large amount of oil replenishment, a large heat exchange quantity Q is calculated, and a thermal shock attenuation factor K greater than 1 is generated to dynamically amplify and correct the initial oil replenishment rate command, thereby offsetting the excessive reduction in oil replenishment caused by the false temperature drop detected by the temperature sensor, and realizing nonlinear dynamic correction of the oil replenishment rate.

[0029] To construct the feedforward mapping table, the first step is to establish a functional relationship between the temperature deviation change rate and the feedforward oil replenishment correction coefficient based on historical production data. Specifically, the temperature deviation change rate is divided into multiple intervals; for example, when the absolute value of the change rate is less than 0.5... When the condition is considered stable, the correction factor is set to 1.0, meaning the base fuel replenishment is maintained; when the absolute value of the rate of change is 0.5... Up to 2.0 When the change rate is between 1.0 and 1.2, the correction factor increases linearly from 1.0 to 1.2; when the absolute value of the rate of change is greater than 2.0... When the correction coefficient is set to the maximum value of 1.3, a discretized data mapping table is constructed. When performing weighted constraints, the feedforward replenishment correction coefficient obtained from the query is directly multiplied by the replenishment control benchmark obtained in S2. For example, if the basic replenishment demand is 10L / min and the correction coefficient corresponding to the current temperature drop rate is 1.2, then the weighted initial replenishment rate command is calculated to be 12L / min. Thus, the precise adjustment of the replenishment rate is achieved through this dynamic weighting mechanism.

[0030] S4: Obtain the frying time, perform correlation analysis between the frying time and the oil level, calculate the maximum allowable oil replenishment rate, and use the maximum allowable oil replenishment rate to control the magnitude of the oil replenishment rate.

[0031] In this embodiment, step S4 acquires the current frying time data of the instant noodle cake, performs multi-dimensional correlation coupling analysis on the frying time data and the real-time oil level status, calculates the remaining safe volume in the frying tank that dynamically shrinks as the frying time progresses, and determines the liquid level safety threshold and the maximum allowable oil replenishment rate corresponding to the current moment based on the mapping of the remaining safe volume; calculates the rate deviation value between the real-time oil replenishment rate and the maximum allowable oil replenishment rate, and determines whether the rate deviation value is greater than zero. If the rate deviation value is greater than zero, it is determined that the oil replenishment rate exceeds the dynamic safety boundary, and generates a corresponding amplitude suppression command based on the amplitude of the rate deviation value, thereby constraining the real-time oil level below the liquid level safety threshold to prevent excessive oil spillage caused by the instantaneous fluctuation of the oil replenishment amount exceeding the capacity of the frying tank.

[0032] It should be noted that, firstly, the current frying time data of the instant noodle cake is obtained. Combined with the total volume parameter of the frying tank, a preset function relating the emptying time and the feed volume is used. This function is specifically defined as follows: ,in This is a curve showing the volume expansion coefficient of the dough, calibrated based on frying experiments. This curve characterizes the volume percentage of the dough at a specific frying time point. Given the current feed rate, calculate the remaining safe volume of the dough as it dynamically shrinks due to oil absorption and volume displacement over frying time. The formula for calculating this remaining safe volume is as follows: ,in This is the total volume of the frying tank. This represents the current measured oil volume. To reserve a safety margin for spillage prevention, for example, in the early stage of frying (e.g., 0-30 seconds), when the dough is first put into the tank, the moisture content is the highest, causing the oil level to rise the fastest. At this time, the calculated remaining safety volume is the smallest. In the middle and later stages of frying, after the moisture has evaporated, the remaining safety volume increases linearly with time. Subsequently, based on the remaining safety volume, a preset volume-threshold mapping table is consulted to obtain the liquid level safety threshold corresponding to the current moment. This threshold is set as a dynamic height value of 5-10mm from the upper edge of the frying tank. During control execution, the difference between the initial oil replenishment rate and the maximum allowable oil replenishment rate is calculated in real time as the rate deviation value. If the rate deviation value is greater than zero, an amplitude suppression command is generated according to the proportional control algorithm. For example, the oil replenishment rate value is reduced by 20% to 50% of the excess or directly clamped to the maximum allowable oil replenishment rate value, thereby ensuring that the real-time oil level is always constrained below the liquid level safety threshold, effectively preventing oil spillage caused by instantaneous fluctuations in the oil replenishment amount.

[0033] S5: Obtain the initial quality of the oil and the mixing ratio of new and old oils, calculate the oil quality decay index based on the dynamic dilution algorithm, use the oil quality decay index as a weighted correction parameter for the oil replenishment strategy, dynamically adjust the oil replenishment control benchmark, and obtain the adjusted oil replenishment control benchmark.

[0034] In this embodiment, step S5 obtains the initial quality data of the oil in the current fryer and the mixing ratio of new and old oil. Based on the initial quality data and the mixing ratio of new and old oil, a correlation analysis is performed on the physicochemical properties of the oil to obtain the oil quality decay index, which characterizes the degree of oil deterioration. By using the oil quality decay index as the key weighted correction parameter for the oil replenishment strategy, a nonlinear weighted correction calculation is performed on the original oil replenishment control benchmark to obtain the quality compensation correction amount. This quality compensation correction amount is then superimposed on the oil replenishment control benchmark to obtain the adjusted oil replenishment control benchmark, thereby achieving adaptive optimization of the oil replenishment strategy based on the dynamic changes in oil quality.

[0035] It should be specifically explained that, firstly, the initial quality data of the oil in the current fryer (such as acid value AV, peroxide value POV) and the mixing ratio of new and old oil are obtained. Then, a correlation analysis is performed based on the change rate of polar component (TPM) in the physicochemical properties of the oil, and a weighted summation formula is used. ,in Indicates the oil quality degradation index. This indicates the measured acid value of the grease currently inside the machine. This indicates the enterprise standard limit for the acid value of oils. This indicates the measured peroxide value of the grease currently inside the machine. The standard limit for the peroxide value of oils and fats. The mixing ratio of new and old oil. The weighting coefficients are preset; subsequently, the oil quality degradation index is used as a key weighted correction parameter for the oil replenishment strategy, establishing a nonlinear mapping relationship between the quality degradation index and the oil replenishment increment, for example, by setting a quality compensation correction amount. Where K is the correction factor. The quality warning threshold is set; finally, the calculated quality compensation correction amount is superimposed on the oil replenishment control benchmark to obtain the adjusted oil replenishment control benchmark, thereby realizing the automatic increase of fresh oil replenishment when the quality of oil deteriorates rapidly, ensuring that the oil replenishment strategy is adaptively optimized according to the dynamic changes in oil quality.

[0036] S6: Based on the collected feed rate data and combined with the oil consumption rate statistical model to predict the oil level decline trend, generate the oil level prediction replenishment component, and analyze the replenishment execution process in conjunction with the adjusted replenishment control benchmark to obtain the comprehensive replenishment execution index.

[0037] In this embodiment, step S6 performs a forward-looking predictive analysis and calculation of the downward trend of oil level over time based on the collected data of instant noodle cake feed and a preset oil consumption rate statistical model, and obtains the oil level prediction replenishment amount; wherein, the oil level prediction replenishment amount is used to characterize the real-time replenishment flow rate required to offset the physical oil loss caused by the increase in feed. Based on the obtained oil level prediction replenishment component, liquid level safety threshold, and adjusted replenishment control benchmark, a multi-dimensional fusion analysis is performed to calculate the matching degree between replenishment demand and safety boundary and quality constraints. Based on the calculated matching degree, deviation judgment and correction calculation are performed. By allocating the proportion of replenishment safety weight and quality correction weight, the comprehensive replenishment execution index is calculated and determined.

[0038] It should be specifically explained that, firstly, based on the collected data on the amount of instant noodle cake fed into the machine, a pre-set statistical model for oil consumption rate is used for forward prediction. The specific calculation logic is as follows: multiply the current feeding rate by the standard oil content per unit of noodle cake and combine it with the real-time frying temperature correction coefficient to obtain the predicted value of physical oil loss over time, and then generate the predicted oil replenishment amount; subsequently, the predicted oil replenishment amount, the maximum allowable flow rate corresponding to the liquid level safety threshold, and the adjusted oil replenishment control benchmark are subjected to multi-dimensional fusion analysis to construct a matching degree calculation formula. ,in This is the upper limit of flow rate calculated based on the liquid level safety threshold. The adjusted oil replenishment control benchmark, The system predicts the amount of oil needed for replenishment based on the oil level. Then, based on the calculated matching degree, deviation judgment and correction calculations are performed. When the matching degree M is greater than a preset critical threshold (e.g., 0.95), the replenishment demand is determined to be close to the safety boundary. At this point, a dynamic weight allocation function is used to increase the safety weight of the replenishment. And reduce the weight of quality correction. For example, setting , The final calculation of the comprehensive fuel replenishment execution index This allows for precise control of the refueling process through indexed adjustments, ensuring no oil spillage.

[0039] S7: Monitors the cleanliness of the oil and the operating status of the filtration system, calculates the flow loss caused by the filtration and oil discharge, generates the filtration compensation flow based on the flow loss, and adds the filtration compensation flow to the oil replenishment command generated based on the comprehensive oil replenishment execution index, and outputs the final oil replenishment control signal.

[0040] In this embodiment, step S7 monitors the cleanliness index of the oil in the fryer and the operating status parameters of the filtration system in real time. Based on the cleanliness index and operating status parameters, it calculates the instantaneous flow loss caused by the oil filtration process and calculates the filtration compensation flow based on the instantaneous flow loss. At the same time, based on the comprehensive oil replenishment execution index obtained in step S6, it calculates the basic oil replenishment command in combination with the preset flow mapping coefficient. The basic oil replenishment command and the filtration compensation flow are superimposed and fused for analysis, and the final oil replenishment control signal is output.

[0041] It needs to be specifically explained that, firstly, the cleanliness index of the oil inside the fryer is monitored in real time. Specifically, the transmittance value is obtained through an oil turbidity sensor, and combined with the operating status parameters of the filtration system, including the duty cycle of the slag discharge valve or the real-time speed of the oil pump, the instantaneous flow loss caused by the filtration and oil discharge process is calculated. The calculation formula is set as follows: ,in The volumetric flow rate is calculated based on the current diameter and pressure of the waste discharge pipeline. Based on the loss coefficient determined by the filtration status (e.g., 0.9-1.0 in filtration mode and 0 in non-filtration mode), the filter compensation flow rate is obtained according to the instantaneous flow loss. Simultaneously, based on the comprehensive replenishment execution index obtained in S6, the basic replenishment command is calculated by querying the preset flow mapping coefficient table. The basic replenishment command and the filter compensation flow rate are then linearly superimposed and fused for analysis, thus setting the final replenishment control signal. ,in This indicates the basic oil replenishment command flow rate calculated based on the comprehensive oil replenishment execution index, thereby ensuring that when the oil level physically drops due to the discharge of waste oil or sludge from the filtration system, the oil replenishment system can compensate in real time with an equal amount, maintaining the dynamic constant oil level in the frying tank.

[0042] like Figure 2This embodiment provides an implementation system for an anti-counterfeiting method for items based on local feature visual information. The system includes a data acquisition module, a basic oil replenishment calculation module, a feedforward compensation and oil temperature correction module, a frying time-related oil replenishment control module, an oil quality correction module, a feed amount oil replenishment analysis module, and a filter compensation module. The data acquisition module is connected to the basic oil replenishment calculation module, which is also connected to the feedforward compensation and oil temperature correction module. The feedforward compensation and oil temperature correction module is connected to the frying time-related oil replenishment control module, and the basic oil replenishment calculation module is connected to the oil quality correction module. Both the frying time-related oil replenishment control module and the oil quality correction module are connected to the feed amount oil replenishment analysis module, which is also connected to the filter compensation module.

[0043] The data acquisition module collects real-time data on oil level, frying temperature, and feed amount during the frying process of the instant noodle cake; The basic oil replenishment calculation module uses the collected oil level data and a PID algorithm to calculate the basic oil replenishment requirement required to maintain the current target oil level, which serves as the oil replenishment control benchmark. The feedforward compensation and oil temperature correction module constructs a feedforward compensation mechanism for oil replenishment operation based on the collected frying temperature data, implements preliminary constraints on the oil replenishment rate in conjunction with the oil replenishment control benchmark, and dynamically corrects the oil temperature. The frying time-related oil replenishment control module acquires the frying time, performs correlation analysis between the frying time and the oil level status, calculates the maximum allowable oil replenishment rate, and uses the maximum allowable oil replenishment rate to control the magnitude of the oil replenishment rate. The oil quality correction module obtains the initial quality of the oil and the mixing ratio of new and old oil, calculates the oil quality decay index based on the dynamic dilution algorithm, and uses the oil quality decay index as a weighted correction parameter for the oil replenishment strategy to dynamically adjust the oil replenishment control benchmark and obtain the adjusted oil replenishment control benchmark. The feed rate replenishment analysis module generates an oil level prediction replenishment component based on the collected feed rate data and the oil consumption rate statistical model to predict the oil level decline trend. It then analyzes the replenishment execution process in conjunction with the adjusted replenishment control benchmark to obtain a comprehensive replenishment execution index. The filter compensation module monitors the cleanliness of the oil and the operating status of the filtration system, calculates the flow loss caused by the oil discharge, generates a filter compensation flow based on the flow loss, and adds the filter compensation flow to the oil replenishment command generated based on the comprehensive oil replenishment execution index, and outputs the final oil replenishment control signal.

[0044] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0045] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for intelligent oil replenishment control in a frying machine for instant noodles, characterized in that, include: S1: Real-time collection of oil level data, frying temperature data, and feed amount data during the frying process of instant noodle cakes; S2: Based on the collected oil level data, the basic oil replenishment requirement required to maintain the current target oil level is calculated using the PID algorithm, which serves as the benchmark for oil replenishment control. S3: Based on the collected frying temperature data, a feedforward compensation mechanism for oil replenishment operation is constructed. The oil replenishment rate is initially constrained in conjunction with the oil replenishment control benchmark, and the oil temperature is dynamically corrected. S4: Obtain the frying time, perform correlation analysis between the frying time and the oil level status, calculate the maximum allowable oil replenishment rate, and use the maximum allowable oil replenishment rate to control the magnitude of the oil replenishment rate; S5: Obtain the initial quality of the oil and the mixing ratio of new and old oils, calculate the oil quality decay index based on the dynamic dilution algorithm, use the oil quality decay index as a weighted correction parameter for the oil replenishment strategy, dynamically adjust the oil replenishment control benchmark, and obtain the adjusted oil replenishment control benchmark. S6: Based on the collected feed rate data and combined with the oil consumption rate statistical model to predict the oil level decline trend, generate the oil level prediction replenishment component, and analyze the replenishment execution process in combination with the adjusted replenishment control benchmark to obtain the comprehensive replenishment execution index. S7: Monitors the cleanliness of the oil and the operating status of the filtration system, calculates the flow loss caused by the filtration and oil discharge, generates the filtration compensation flow based on the flow loss, and adds the filtration compensation flow to the oil replenishment command generated based on the comprehensive oil replenishment execution index, and outputs the final oil replenishment control signal.

2. The intelligent oil replenishment control method for an instant noodle fryer according to claim 1, characterized in that, The S1 uses a multi-sensor array deployed at different spatial locations in the fryer to collect in real time signals of oil level changes, temperature distribution in multiple areas of the frying tank, and feed cake flow rate on the conveyor belt during the operation of the fryer. The collected oil level change signal is filtered to eliminate the interference of liquid level fluctuation caused by frying bubbles, and the oil level data is obtained. The average value of the collected temperature distribution signals in multiple areas of the frying tank is calculated to obtain the frying temperature data; The collected feed cake flow rate signal is normalized to unify the measurement unit and obtain the feed amount data.

3. The intelligent oil replenishment control method for a frying machine for instant noodles according to claim 2, characterized in that, S2 calculates the deviation between the obtained oil level data and the preset target oil level to obtain the real-time deviation value. Based on the real-time deviation value, a PID closed-loop control model with integral separation function is constructed. The basic oil replenishment requirement required to maintain the current target oil level is calculated using a PID closed-loop control model, which serves as the benchmark for oil replenishment control. When the real-time deviation exceeds the preset deviation threshold, the integral action is activated to eliminate steady-state error. Furthermore, the control gain parameter of the PID closed-loop control model is dynamically mapped to the frying temperature data. This is achieved by incorporating the influence of frying temperature changes on fluid viscosity characteristics to compensate for nonlinear adaptive adjustment of the oil replenishment control response speed.

4. The intelligent oil replenishment control method for a frying machine for instant noodles according to claim 3, characterized in that, S3 analyzes the temperature deviation between the obtained frying temperature data and the target frying temperature to obtain the temperature deviation change rate. Based on the temperature deviation change rate, a dynamic feedforward compensation model for oil replenishment operation is constructed to obtain the feedforward oil replenishment correction coefficient. The feedforward oil replenishment correction coefficient is used to weight and constrain the oil replenishment control benchmark to obtain the initial oil replenishment rate command. The fluid saturated heat capacity parameter at the current oil temperature is calculated based on the frying temperature data. The transient thermal shock effect caused by the mixing of cold fluid in the oil replenishment is analyzed to obtain the thermal shock attenuation factor. Then, the initial oil replenishment rate command is corrected by feedback compensation based on the thermal shock attenuation factor to eliminate the false oil temperature drop feedback caused by the oil replenishment operation, thereby realizing the nonlinear dynamic correction of the oil replenishment rate.

5. The intelligent oil replenishment control method for an instant noodle fryer according to claim 4, characterized in that, S4 acquires the current frying time data of the instant noodle cake, performs multi-dimensional correlation coupling analysis on the frying time data and the real-time oil level status, calculates the remaining safe volume in the frying tank that dynamically shrinks as the frying time progresses, and determines the liquid level safety threshold and the maximum allowable oil replenishment rate corresponding to the current moment based on the mapping of the remaining safe volume. The rate deviation between the real-time oil replenishment rate and the maximum allowable oil replenishment rate is calculated, and it is determined whether the rate deviation is greater than zero. If the rate deviation is greater than zero, it is determined that the oil replenishment rate exceeds the dynamic safety boundary, and a corresponding amplitude suppression command is generated according to the amplitude of the rate deviation, thereby constraining the real-time oil level below the liquid level safety threshold to prevent excessive oil spillage caused by instantaneous fluctuations in the oil replenishment amount exceeding the capacity of the frying tank.

6. The intelligent oil replenishment control method for a frying machine for instant noodles according to claim 5, characterized in that, S5 acquires the initial quality data of the oil in the current fryer and the mixing ratio of new and old oil. Based on the initial quality data and the mixing ratio of new and old oil, and combined with the physicochemical properties of the oil, a correlation analysis is performed to calculate the oil quality degradation index, which characterizes the degree of oil deterioration. By using the oil quality decay index as the key weighted correction parameter for the oil replenishment strategy, a nonlinear weighted correction calculation is performed on the original oil replenishment control benchmark to obtain the quality compensation correction amount. This quality compensation correction amount is then superimposed on the oil replenishment control benchmark to obtain the adjusted oil replenishment control benchmark, thereby achieving adaptive optimization of the oil replenishment strategy based on the dynamic changes in oil quality.

7. The intelligent oil replenishment control method for an instant noodle fryer according to claim 6, characterized in that, S6 calculates the predicted oil replenishment amount based on the collected instant noodle cake feed data and the preset oil consumption rate statistical model to predict the downward trend of oil level over time. The predicted oil replenishment amount is used to characterize the real-time oil replenishment flow rate required to offset the physical oil loss caused by the increase in feed. Based on the obtained oil level prediction replenishment component, liquid level safety threshold, and adjusted replenishment control benchmark, a multi-dimensional fusion analysis is performed to calculate the matching degree between replenishment demand and safety boundary and quality constraints. Based on the calculated matching degree, deviation judgment and correction calculation are performed. By allocating the proportion of replenishment safety weight and quality correction weight, the comprehensive replenishment execution index is calculated and determined.

8. The intelligent oil replenishment control method for a frying machine for instant noodles according to claim 7, characterized in that, The S7 monitors the cleanliness index of the oil in the fryer and the operating status parameters of the filtration system in real time. Based on the cleanliness index and operating status parameters, it calculates the instantaneous flow loss caused by the oil filtration process and calculates the filtration compensation flow based on the instantaneous flow loss. At the same time, based on the comprehensive oil replenishment execution index obtained by S6 and combined with the preset flow mapping coefficient, it calculates the basic oil replenishment command, and combines the basic oil replenishment command with the filtration compensation flow for analysis and outputs the final oil replenishment control signal.

9. An intelligent oil replenishment control system for an instant noodle fryer, implementing the intelligent oil replenishment control method for an instant noodle fryer as described in any one of claims 1-8, characterized in that, include: Data acquisition module: Real-time acquisition of oil level data, frying temperature data, and feed amount data during the frying process of instant noodle cakes; Basic oil replenishment calculation module: Based on the collected oil level data, it uses a PID algorithm to calculate the basic oil replenishment requirement required to maintain the current target oil level, which serves as the benchmark for oil replenishment control. Feedforward compensation and oil temperature correction module: Based on the collected frying temperature data, a feedforward compensation mechanism for oil replenishment operation is constructed. Combined with the oil replenishment control benchmark, the oil replenishment rate is initially constrained, and the oil temperature is dynamically corrected. Frying time-related oil replenishment control module: acquires frying time, performs correlation analysis between frying time and oil level status, calculates the maximum allowable oil replenishment rate, and controls the magnitude of the oil replenishment rate using the maximum allowable oil replenishment rate; Oil quality correction module: Obtain the initial quality of oil and the mixing ratio of new and old oil, calculate the oil quality decay index based on the dynamic dilution algorithm, use the oil quality decay index as the weighted correction parameter of the oil replenishment strategy, dynamically adjust the oil replenishment control benchmark, and obtain the adjusted oil replenishment control benchmark. Feed rate replenishment analysis module: Based on the collected feed rate data and combined with the oil consumption rate statistical model to predict the oil level decline trend, it generates an oil level prediction replenishment component, and analyzes the replenishment execution process in combination with the adjusted replenishment control benchmark to obtain a comprehensive replenishment execution index. Filtration compensation module: Monitors the cleanliness of the oil and the operating status of the filtration system, calculates the flow loss caused by the filtration and oil discharge, generates a filtration compensation flow based on the flow loss, and adds the filtration compensation flow to the oil replenishment command generated based on the comprehensive oil replenishment execution index, and outputs the final oil replenishment control signal.

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