Unsteady-state intelligent control method for hot air temperature of feeding equipment of cut stem production line

Abstract

The invention discloses an unsteady-state intelligent control method for the hot air temperature of feeding equipment of a cut stem production line. The unsteady-state intelligent control method comprises the steps of establishing a hot air temperature prediction model in a stirring drum and regulating and controlling the hot air temperature in the stirring drum based on the hot air temperature prediction model in the stirring drum. According to the invention, the multiple linear regression algorithm is applied to the intelligent control system of the feeding equipment of the cut stem production line. Through a machine learning mode, long-time production data accumulation is utilized to predict the temperature of hot air in the stirring roller, a steam valve and a water supply valve are adjusted in combination with a prediction result, and accurate regulation and control of the temperature of the hot air in the stirring roller are achieved.

Description

Technical Field

[0001] The invention relates to the technical field of tobacco processing, and in particular to a non-steady-state intelligent control method for hot air temperature of feeding equipment in a cut stem production line. Background Art

[0002] In the tobacco industry's tobacco production workshop, the stability and accuracy of the feeding equipment of the stem production line has an important impact on the quality of the final product. Traditional control methods are often unable to cope with non-steady-state changes in the production process, resulting in large fluctuations in hot air temperature, affecting product quality.

[0003] At present, the feeding equipment of the stem production line in the tobacco industry's silk-making workshop mainly adopts the traditional PID (proportional-integral-differential) control method. The PID controller adjusts the valve opening through a feedback mechanism to maintain the set hot air temperature. However, when facing non-steady-state changes in the production process, PID control often shows large hysteresis and instability, making it difficult to achieve precise control of the hot air temperature. Summary of the invention

[0004] The purpose of the present invention is to provide a non-steady-state intelligent control method for the hot air temperature of a feeding device in a shredded stem production line. By machine learning and utilizing long-term production data accumulation, the hot air temperature in a stirring drum is predicted, and the water supply valve is controlled in combination with the prediction result, and the steam valve and the water supply valve are adjusted to achieve regulation of the hot air temperature in the stirring drum.

[0005] In order to achieve the above object, the scheme of the present invention is: A non-steady-state intelligent control method for hot air temperature of feeding equipment of a shredded stem production line comprises a stirring drum, shredded stem raw materials are fed into the stirring drum from an inlet of the stirring drum through a conveyor belt, a steam pipe and a water pipe are connected to the stirring drum through a steam valve and a water supply valve, the shredded stem raw materials are stirred by the stirring drum and then sent out from an outlet of the stirring drum to the next process, a metering scale is arranged on the conveyor belt, a shredded stem moisture content tester is respectively arranged at the inlet and outlet of the stirring drum, a temperature / humidity sensor is arranged in the stirring drum, and the hot air temperature in the stirring drum is adjusted by controlling the steam valve and the water supply valve, wherein: the method comprises: establishing a hot air temperature prediction model in the stirring drum and regulating the hot air temperature in the stirring drum based on the hot air temperature prediction model in the stirring drum: The establishment of the hot air temperature prediction model in the stirring drum comprises: obtaining past production data of the shredded stem production line, sending the past production data into a neural network model for training, wherein the training is for the purpose of obtaining the delayed temperature of the hot air in the stirring drum, and obtaining the hot air temperature prediction model in the stirring drum after training, wherein the production data comprises: steam valve opening, water supply valve opening, feeding unit batch, feeding unit cigarette brand, moisture content of shredded stems at the inlet and outlet of the stirring drum, hot air temperature at the inlet of the stirring drum, mixed air temperature, return air temperature of shredded stems, ambient temperature, ambient humidity, hot air temperature in the stirring drum, and scale accumulation; The hot air temperature control in the stirring drum based on the hot air temperature prediction model in the stirring drum includes: Step 1: Calculate and determine the initial openings of the steam valve and the water supply valve based on the current preheated hot air temperature, start the conveyor belt to feed the shredded stems into the mixing drum, and gradually adjust the steam supply valve and the water supply valve to the initial openings; Step 2: Acquire production data in real time, input the real-time acquired production data into the hot air temperature prediction model in the mixing drum to obtain the predicted hot air temperature in the mixing drum, compare the predicted hot air temperature in the mixing drum with the actual hot air temperature in the mixing drum, and adjust the steam valve and the water supply valve according to the comparison result to realize the regulation of the hot air temperature in the mixing drum.

[0006] The scheme is further that: the method further includes a stage of slowly adjusting the temperature of the hot air in the stirring drum, a stage of quickly adjusting the temperature of the hot air in the stirring drum, and a stage of steadily adjusting the temperature of the hot air in the stirring drum: The stage of slowly adjusting the hot air temperature in the stirring drum is the material head stage, which is the stage where the hot air temperature drops by a slope of >-0.02 to a scale cumulative amount of ≤170KG. Slowly adjusting the hot air temperature in the stirring drum includes: (1) In step 1, when the temperature drop slope of the hot air in the mixing drum during the feeding process is greater than -0.02, the initial openings of the steam supply valve and the water supply valve are switched to the average valve openings in the past production data, and then: (2) continuously inputting the real-time acquired production data into the hot air temperature prediction model in the mixing drum at first intervals to obtain the predicted value of the hot air temperature in the mixing drum after delay, comparing the predicted value of the hot air temperature in the mixing drum after delay with the actually measured hot air temperature in the mixing drum, and adjusting the steam valve and the water supply valve according to the comparison result; The stage of quickly adjusting the temperature of the hot air in the stirring drum is when the accumulated weight of the scale is 170 kg or less and the temperature of the hot air in the stirring drum is 300 kg or less. The stage of quickly adjusting the temperature of the hot air in the stirring drum includes: Input the real-time acquired production data into the hot air temperature prediction model in the mixing drum at every second interval to obtain the predicted value of the hot air temperature in the mixing drum after a delay of 35 seconds, compare the predicted value of the hot air temperature in the mixing drum after a delay of 35 seconds with the actually measured hot air temperature in the mixing drum, and adjust the steam valve and the water supply valve according to the comparison result; The stage of steadily adjusting the temperature of the hot air in the stirring drum is when the accumulated amount of the scale is ≥300KG, and the stage of steadily adjusting the temperature of the hot air in the stirring drum includes: Continuously input the real-time acquired production data into the hot air temperature prediction model in the mixing drum every third interval to obtain the predicted value of the hot air temperature in the mixing drum after a delay of 35 seconds, compare the predicted value of the hot air temperature in the mixing drum after the delay with the actually measured hot air temperature value in the mixing drum, and adjust the steam valve and the water supply valve according to the comparison result; wherein: the third interval time is greater than the second interval time, and the second interval time is greater than the first interval time.

[0007] The solution is further: when the number of intervals is equal to or greater than 2, the measured hot air temperature value in the stirring drum for comparison is the average value of the sum of the measured hot air temperature value in the stirring drum in the current interval and the measured hot air temperature value in the stirring drum in each previous interval.

[0008] The solution is further that: the first interval time is 5 seconds, the second interval time is 10 seconds, and the third interval time is 15 seconds.

[0009] The solution is further that: the neural network model is a multivariate linear regression algorithm model.

[0010] The scheme is further as follows: the predicted value of the hot air temperature in the delayed stirring drum is compared with the actual value of the hot air temperature in the stirring drum: In the stage of slowly adjusting the hot air temperature in the stirring drum, if the difference does not exceed 0.5, no adjustment is made, the single valve adjustment range does not exceed 1, and the valve opening does not exceed the historical batch average value ±6; When quickly adjusting the hot air temperature in the mixing drum, if the difference does not exceed 0.6, no adjustment will be made, the single valve adjustment range will not exceed 1, and the valve opening will not exceed the historical batch average value ±4; In the stage of steadily adjusting the hot air temperature in the mixing drum, when the difference does not exceed 1.0, no adjustment is made, the single valve adjustment range does not exceed 1, and the valve opening does not exceed the historical batch average value ±4.

[0011] The beneficial effects of the present invention are: The present invention applies a multivariate linear regression algorithm to the intelligent control system of the feeding equipment of the shredded stem production line. Through machine learning, the hot air temperature in the stirring drum is predicted by utilizing the long-term accumulation of production data, and the steam valve and the water supply valve are adjusted in combination with the prediction results to achieve precise control of the hot air temperature in the stirring drum. The present invention can effectively cope with the non-steady-state changes in the production process and provide a more stable control effect. The prediction and control by the multivariate linear regression algorithm can significantly improve the control accuracy of the hot air temperature in the stirring drum. The model training and prediction are performed by utilizing the long-term accumulation of production data, and the invention has strong robustness and adaptability.

[0012] The invention is further explained in detail below with reference to the accompanying drawings and specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 It is a schematic diagram of the structure of the feeding equipment of the shredded stem production line of the present invention; Figure 2 It is a hot air temperature waveform diagram in the stirring drum according to the present invention following the predicted value. DETAILED DESCRIPTION

[0014] A non-steady-state intelligent control method for hot air temperature of feeding equipment in a shredded stem production line, such as Figure 1 As shown, it includes a stirring drum 1, and the shredded stem raw material 2 is sent into the stirring drum 1 from the inlet 101 of the stirring drum through a conveyor belt 3. The shredded stem raw material is stirred by the stirring drum 1 and sent out from the outlet 102 of the stirring drum to the next process. A weighing scale 4 is arranged on the conveyor belt, and a shredded stem moisture content tester 5 is respectively arranged at the inlet 101 and the outlet 102 of the stirring drum 1. A temperature / humidity sensor 6 is arranged in the stirring drum. The stirring drum 1 adjusts the temperature of the hot air from the stirring drum 1 by controlling a steam valve 7 and a water supply valve 8 by a controller (not shown). The steam valve 7 and the water supply valve 8 are connected to a steam pipe 9 and a water supply pipe 10 respectively, wherein: the method includes: establishing a hot air temperature prediction model in the stirring drum and regulating the hot air temperature in the stirring drum based on the hot air temperature prediction model in the stirring drum: The establishment of the hot air temperature prediction model in the stirring drum comprises: obtaining past production data of the shredded stem production line, sending the past production data into a neural network model for training, wherein the training is for the purpose of obtaining the delayed temperature of the hot air in the stirring drum, and obtaining the hot air temperature prediction model in the stirring drum after training, wherein the production data comprises: steam valve opening, water supply valve opening, feeding unit batch, feeding unit cigarette brand, moisture content of shredded stems at the inlet and outlet of the stirring drum, hot air temperature at the inlet of the stirring drum, mixed air temperature, return air temperature of shredded stems, ambient temperature, ambient humidity, hot air temperature in the stirring drum, and scale accumulation; The hot air temperature control in the stirring drum based on the hot air temperature prediction model in the stirring drum includes: Step 1: Calculate and determine the initial openings of the steam valve and the water supply valve according to the current preheated hot air temperature, start the conveyor belt to feed the shredded stems into the mixing drum, and gradually adjust the steam supply valve and the water supply valve to the initial openings; Step 2, real-time acquisition of production data, input of the real-time acquisition of production data into the hot air temperature prediction model in the mixing drum to obtain the predicted hot air temperature in the mixing drum, the predicted hot air temperature in the mixing drum is compared with the measured hot air temperature in the mixing drum, and the steam valve and the water supply valve are adjusted according to the comparison result to realize the regulation of the hot air temperature in the mixing drum; Wherein: the initial opening is the initial valve opening calculated based on the current preheating hot air temperature: through historical data analysis, it is concluded that when the hot air temperature is 61 degrees Celsius when the material is started to be fed, as the shredded stems enter the feeder, the hot air temperature drops to the value closest to the standard value and stops, because in actual production the preheating temperature is difficult to ensure at 61 degrees Celsius, that is, the initial valve opening setting value = the historical non-first batch valve opening average value + (61 degrees Celsius - preheating temperature) * 1.4 corresponding opening value; the non-first batch opening average value refers to the average value of the valve opening during the period that is not the first batch of steady-state data of each day.

[0015] Figure 2 In the figure, a shows the predicted waveform of the hot air temperature in the mixing drum, and b shows the actual measured waveform of the hot air temperature in the mixing drum. The vertical Y coordinate in the figure is the temperature value, and the horizontal X coordinate is the accumulated amount of the scale.

[0016] Wherein: the method further comprises a stage of slowly adjusting the temperature of the hot air in the stirring drum, a stage of quickly adjusting the temperature of the hot air in the stirring drum, and a stage of steadily adjusting the temperature of the hot air in the stirring drum: The stage of slowly adjusting the temperature of the hot air in the stirring drum is the head stage, which is the stage where the hot air temperature drops by a slope of >-0.02 to a cumulative amount of ≤170KG continuously weighed by the metering scale 4. The slow adjustment of the temperature of the hot air in the stirring drum includes: (1) In step 1, when the temperature drop slope of the hot air in the mixing drum during the feeding process is greater than -0.02, the initial openings of the steam supply valve and the water supply valve are switched to the average valve openings in the past production data, and then: (2) continuously inputting the real-time acquired production data into the hot air temperature prediction model in the mixing drum at first intervals to obtain the predicted value of the hot air temperature in the mixing drum after delay, comparing the predicted value of the hot air temperature in the mixing drum after delay with the actually measured hot air temperature in the mixing drum, and adjusting the steam valve and the water supply valve according to the comparison result; The stage of quickly adjusting the temperature of the hot air in the stirring drum is when the cumulative amount of the continuous weighing by the metering scale 4 is 170KG≤≤300KG, and the stage of quickly adjusting the temperature of the hot air in the stirring drum includes: Input the real-time acquired production data into the hot air temperature prediction model in the mixing drum at every second interval to obtain the predicted value of the hot air temperature in the mixing drum after a delay of 35 seconds, compare the predicted value of the hot air temperature in the mixing drum after a delay of 35 seconds with the actually measured hot air temperature in the mixing drum, and adjust the steam valve and the water supply valve according to the comparison result; The stage of steadily adjusting the temperature of the hot air in the stirring drum is a stage in which the cumulative amount of the continuous weighing by the metering scale 4 is ≥ 300KG. The stage of steadily adjusting the temperature of the hot air in the stirring drum includes: Continuously input the real-time acquired production data into the hot air temperature prediction model in the mixing drum every third interval to obtain the predicted value of the hot air temperature in the mixing drum after a delay of 35 seconds, compare the predicted value of the hot air temperature in the mixing drum after the delay with the actually measured hot air temperature value in the mixing drum, and adjust the steam valve and the water supply valve according to the comparison result; wherein: the third interval time is greater than the second interval time, and the second interval time is greater than the first interval time.

[0017] In the embodiment: when the number of intervals is equal to or greater than 2, the measured hot air temperature value in the stirring drum for comparison is the average value of the hot air temperature value in the stirring drum measured in the current interval and the sum of the hot air temperature values ​​in the stirring drum measured in each previous interval.

[0018] Wherein: the first interval time is 5 seconds, the second interval time is 10 seconds, and the third interval time is 15 seconds.

[0019] The predicted value of the hot air temperature in the delayed stirring drum is compared with the actual measured hot air temperature value in the stirring drum: In the stage of slowly adjusting the hot air temperature in the stirring drum, if the difference does not exceed 0.5, no adjustment is made, the single valve adjustment range does not exceed 1, and the valve opening does not exceed the historical batch average value ±6; When quickly adjusting the hot air temperature in the mixing drum, if the difference does not exceed 0.6, no adjustment will be made, the single valve adjustment range will not exceed 1, and the valve opening will not exceed the historical batch average value ±4; In the stage of steadily adjusting the hot air temperature in the mixing drum, when the difference does not exceed 1.0, no adjustment is made, the single valve adjustment range does not exceed 1, and the valve opening does not exceed the historical batch average value ±4.

[0020] In the embodiment: about establishing a hot air temperature prediction model: The neural network model is a multivariate linear regression algorithm model, which is the same as the traditional neural network model and is a well-known technology. Its formula is expressed as: Y = β0 + β1X1 + β2X2 + ... + βkXk + ε Where: Y is the dependent variable, which represents the outcome we wish to explain or predict.

[0021] X1, X2, ..., Xk are independent variables, representing the factors that affect the dependent variable.

[0022] β0 is the intercept term.

[0023] β1,β2,...,βk are the regression coefficients of each independent variable.

[0024] ε is the random error term, which represents the part that cannot be explained by the independent variables.

[0025] The relationship between data (including independent variables and dependent variables) and the multiple linear regression model. In practical applications, the data set usually contains multiple independent variables and one dependent variable. These data are used to train the multiple linear regression model, and the model parameters β0, β1, ..., βk are estimated by the least squares method and other methods. The training goal of the model is to predict the hot air temperature. The multiple linear regression model is a statistical method used to analyze the relationship between multiple independent variables and one dependent variable. In this method, the independent variables include steam valve opening, water supply valve opening, feeding unit batch, feeding unit cigarette brand, stem moisture content at the inlet and outlet of the mixing drum, mixing drum inlet hot air temperature, mixed air temperature, stem feeding return air temperature, ambient temperature, ambient humidity, hot air temperature in the mixing drum, and scale accumulation.

[0026] The specific steps of obtaining prediction data through the hot air temperature prediction model in the mixing drum are as follows: (1) Data collection: During the production process, data are collected in real time on batches of feeding units, tobacco brands of feeding units, steam valve opening, water supply valve opening, batches of feeding units, tobacco brands of feeding units, moisture content of shredded stems at the inlet and outlet of the mixing drum, hot air temperature at the inlet of the mixing drum, mixed air temperature, return air temperature of shredded stems, ambient temperature, ambient humidity, hot air temperature in the mixing drum, and accumulated volume on the scale.

[0027] (2) Data preprocessing: Standardize the collected data to eliminate the influence of different magnitudes and dimensions between data. Data centralization and data dimensionless processing. Data centralization refers to the translation transformation of data so that the mean of the data and sample point set in the new coordinate system coincide. Dimensionless processing refers to normalizing the variance of different variables to achieve dimensionless processing in order to eliminate false variation and make the data model of each variable have equal weight, so as to improve the accuracy of model construction.

[0028] (3) Feature selection: With hot air temperature as the output variable, the independent variables are the compensation steam opening, the humidification water supply valve opening, the preheating temperature, the scale accumulation, the historical valve opening, the ambient humidity, the wind speed, the unit batch, the brand of the unit cigarette, the moisture content of the cut stems after adding, the stem water flow rate, the moisture content of the cut stems before adding, the material flow rate of the cut stems, the drum inlet hot air temperature, the mixed air temperature, the stem return air temperature, the ambient temperature, and the ambient humidity. In order to effectively analyze the correlation between the data, the Pearson correlation coefficient is used to measure the relevant data. The Pearson correlation coefficient is defined as the quotient of the covariance and the standard deviation between two variables. By calculating the correlation coefficient between the independent variables, highly correlated variables are identified and eliminated.

[0029] To establish a prediction model for the hot air temperature in the mixing drum: first collect past production process data, use the above steps (2) and (3) to perform data preprocessing and feature selection, and organize the collected historical data according to input variables and output variables to form a training data set, a test data set, and a validation data set. Use the multivariate linear regression algorithm model to establish a prediction model for the hot air temperature in the mixing drum using the training data; then: (1) Model evaluation: Use the test data set and validation data set to introduce the mean square error loss function (MSE) to the established model, and evaluate the model obtained by offline training. The smaller the MSE, the more accurate the model. Confirm the MSE evaluation standard according to the control index requirements and start the model evaluation.

[0030] (2) Model optimization: Distinguish between the first batch of production and non-first batch of production, and adopt stage division for optimization control, which is divided into the raw material stage, the stable initial stage, and the stable stage, and establish algorithm control respectively to ensure better optimization control effect and model generalization ability.

[0031] (3) Model application: In the actual production process, the predicted value is calculated in real time, and the water supply valve opening is adjusted according to the predicted results. Through the feedback mechanism, the control strategy is continuously optimized to improve the stability and accuracy of the system and ensure that the prediction accuracy of the model is within ±1°C.

[0032] Through the above specific implementation, it is possible to achieve precise control of the hot air temperature of the feeding equipment of the stem production line under the first batch and non-first batch non-steady-state conditions. In practical applications, this method significantly improves production efficiency and product quality, and the key assessment index CPK of hot air temperature is increased from 4 to 10 under manual control to above 11 to reach the process standard of CPK greater than 5, reducing the scrap rate caused by temperature fluctuations, which has important economic and social benefits.

Claims

1. A non-steady-state intelligent control method for hot air temperature of a feeding device of a shredded stem production line, comprising a stirring drum, wherein the shredded stem raw material is fed into the stirring drum from an inlet of the stirring drum through a conveyor belt, a steam pipe and a water pipe are connected to the stirring drum through a steam valve and a water supply valve, and the shredded stem raw material is stirred by the stirring drum and then sent out from an outlet of the stirring drum to the next process, a metering scale is arranged on the conveyor belt, a shredded stem moisture content tester is arranged at the inlet and outlet of the stirring drum respectively, a temperature / humidity sensor is arranged in the stirring drum, and the hot air temperature in the stirring drum is adjusted by controlling the steam valve and the water supply valve, wherein the method comprises: The method comprises: establishing a hot air temperature prediction model in the stirring drum and controlling the hot air temperature in the stirring drum based on the hot air temperature prediction model in the stirring drum: The establishment of the hot air temperature prediction model in the stirring drum comprises: obtaining past production data of the shredded stem production line, sending the past production data into a neural network model for training, wherein the training is for the purpose of obtaining the delayed temperature of the hot air in the stirring drum, and obtaining the hot air temperature prediction model in the stirring drum after training, wherein the production data comprises: steam valve opening, water supply valve opening, feeding unit batch, feeding unit cigarette brand, moisture content of shredded stems at the inlet and outlet of the stirring drum, hot air temperature at the inlet of the stirring drum, mixed air temperature, return air temperature of shredded stems, ambient temperature, ambient humidity, hot air temperature in the stirring drum, and scale accumulation; The hot air temperature control in the stirring drum based on the hot air temperature prediction model in the stirring drum includes: Step 1: Calculate and determine the initial openings of the steam valve and the water supply valve based on the current preheated hot air temperature, start the conveyor belt to feed the shredded stems into the mixing drum, and gradually adjust the steam supply valve and the water supply valve to the initial openings; Step 2: Acquire production data in real time, input the real-time acquired production data into the hot air temperature prediction model in the mixing drum to obtain the predicted hot air temperature in the mixing drum, compare the predicted hot air temperature in the mixing drum with the actual hot air temperature in the mixing drum, and adjust the steam valve and the water supply valve according to the comparison result to realize the regulation of the hot air temperature in the mixing drum.

2. The non-steady-state intelligent control method for hot air temperature of feeding equipment of stem shreds production line according to claim 1, characterized in that: The method further comprises a stage of slowly adjusting the temperature of the hot air in the stirring drum, a stage of quickly adjusting the temperature of the hot air in the stirring drum, and a stage of steadily adjusting the temperature of the hot air in the stirring drum: The stage of slowly adjusting the hot air temperature in the stirring drum is the material head stage, which is the stage where the hot air temperature drops by a slope of >-0.02 to a scale cumulative amount of ≤170KG. Slowly adjusting the hot air temperature in the stirring drum includes: (1) In step 1, when the temperature drop slope of the hot air in the stirring drum during the feeding process is greater than -0.02, the initial openings of the steam supply valve and the water supply valve are switched to the average valve openings in the past production data, and then: (2) continuously inputting the real-time acquired production data into the hot air temperature prediction model in the mixing drum at first intervals to obtain the predicted value of the hot air temperature in the mixing drum after delay, comparing the predicted value of the hot air temperature in the mixing drum after delay with the actually measured hot air temperature in the mixing drum, and adjusting the steam valve and the water supply valve according to the comparison result; The stage of quickly adjusting the temperature of the hot air in the stirring drum is when the accumulated weight of the scale is 170 kg or less and the temperature of the hot air in the stirring drum is 300 kg or less. The stage of quickly adjusting the temperature of the hot air in the stirring drum includes: Input the real-time acquired production data into the hot air temperature prediction model in the mixing drum at every second interval to obtain the predicted value of the hot air temperature in the mixing drum after a delay of 35 seconds, compare the predicted value of the hot air temperature in the mixing drum after a delay of 35 seconds with the actually measured hot air temperature in the mixing drum, and adjust the steam valve and the water supply valve according to the comparison result; The stage of steadily adjusting the temperature of the hot air in the stirring drum is when the accumulated amount of the scale is ≥300KG, and the stage of steadily adjusting the temperature of the hot air in the stirring drum includes: Continuously input the real-time acquired production data into the hot air temperature prediction model in the mixing drum every third interval to obtain the predicted value of the hot air temperature in the mixing drum after a delay of 35 seconds, compare the predicted value of the hot air temperature in the mixing drum after the delay with the actually measured hot air temperature value in the mixing drum, and adjust the steam valve and the water supply valve according to the comparison result; wherein: the third interval time is greater than the second interval time, and the second interval time is greater than the first interval time.

3. The non-steady-state intelligent control method for hot air temperature of feeding equipment of stem shreds production line according to claim 2, characterized in that: When the number of intervals is equal to or greater than 2, the measured hot air temperature value in the stirring drum for comparison is the average value of the hot air temperature value in the stirring drum measured in the current interval and the sum of the hot air temperature values ​​in the stirring drum measured in each previous interval.

4. The non-steady-state intelligent control method for hot air temperature of feeding equipment of stem shreds production line according to claim 2, characterized in that: The first interval time is 5 seconds, the second interval time is 10 seconds, and the third interval time is 15 seconds.

5. The non-steady-state intelligent control method for hot air temperature of feeding equipment of shredded stem production line according to claim 2, characterized in that: The neural network model is a multiple linear regression algorithm model.

6. The non-steady-state intelligent control method for hot air temperature of feeding equipment of a stem production line according to claim 2, characterized in that: The predicted value of the hot air temperature in the delayed stirring drum is compared with the actual measured hot air temperature value in the stirring drum: In the stage of slowly adjusting the hot air temperature in the stirring drum, if the difference does not exceed 0.5, no adjustment is made, the single valve adjustment range does not exceed 1, and the valve opening does not exceed the historical batch average value ±6; When quickly adjusting the hot air temperature in the mixing drum, if the difference does not exceed 0.6, no adjustment will be made, the single valve adjustment range will not exceed 1, and the valve opening will not exceed the historical batch average value ±4; In the stage of steadily adjusting the hot air temperature in the mixing drum, when the difference does not exceed 1.0, no adjustment is made, the single valve adjustment range does not exceed 1, and the valve opening does not exceed the historical batch average value ±4.

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