Intelligent plant extraction control method and system

Abstract

The invention relates to the technical field of plant extraction, in particular to an intelligent plant extraction control method and system, and the method comprises the following steps: based on historic plant extraction data, including temperature, pressure and pH value during extraction, analyzing the long-term trend of parameters, predicting the requirements of plant extraction conditions by simulating parameter reactions under various conditions, and obtaining a plant extraction result; and obtaining a parameter prediction result. According to the method, long-term trend prediction of parameters such as temperature, pressure and pH value is realized through analysis of historically recorded plant extraction data, so that the parameters of extraction equipment are adjusted in advance, changes of different plant materials and environmental conditions are effectively adapted, the extraction efficiency is optimized, and by dynamically simulating the expression of the extraction efficiency in various time periods, the extraction efficiency is improved. According to the innovative scheme, an optimal extraction time window is mined, resource consumption and extraction time in the extraction process are reduced, and meanwhile the quality and consistency of extracts are kept.

Description

Technical Field

[0001] The present invention relates to the technical field of plant extraction, and in particular to an intelligent plant extraction control method and system. Background Art

[0002] The field of plant extraction technology involves methods and systems for extracting valuable compounds or components from plant materials. Components include pharmaceutically active molecules, flavors, pigments, and bioactive chemicals. The plant extraction process uses different extraction techniques, such as solvent extraction, ultrasonic extraction, supercritical fluid extraction, and enzyme-assisted extraction, to improve extraction efficiency, maintain the activity of the extract, and reduce energy consumption and solvent usage. This technical field continues to explore more environmentally friendly and economical extraction methods to meet the needs of the industrial, pharmaceutical, and food industries for high-quality plant extracts.

[0003] Among them, intelligent plant extraction control method refers to a technology that uses advanced control systems and intelligent technologies to optimize the plant extraction process. The core is to improve extraction efficiency, ensure the consistency and quality of extracted substances, and reduce human operation errors through automatic control and precise adjustment of extraction parameters. Intelligent plant extraction control methods are widely used and can be used in drug manufacturing, food additive production, and cosmetics, where there are strict requirements on the accuracy and efficiency of ingredient extraction. With the help of real-time monitoring and data analysis, the extraction process is more controllable, efficient, and sustainable.

[0004] Existing plant extraction technologies use a variety of extraction techniques such as solvent extraction, ultrasonic extraction, supercritical fluid extraction, and enzyme-assisted extraction, but they generally have limitations, rely on fixed operating parameters, and lack adaptability to real-time data and environmental changes, resulting in fluctuations in extraction efficiency and the quality of extracted substances. Especially when it is necessary to process a variety of plant materials and a wide range of environmental conditions, fixed parameter settings are difficult to guarantee optimal extraction efficiency and quality. Parameter adjustment relies on experience or post-analysis and lacks the ability to adjust in real time. The static processing logic results in the extraction process not being able to respond immediately when the characteristics of the plant materials or environmental conditions change in actual operations, affecting the extraction efficiency and product quality. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose an intelligent plant extraction control method and system.

[0006] In order to achieve the above object, the present invention adopts the following technical scheme, an intelligent plant extraction control method, comprising the following steps: S1: Based on the historical plant extraction data, including the temperature, pressure, and pH value during extraction, the long-term trend of the parameters is analyzed. By simulating the parameter reactions under various conditions, the plant extraction condition requirements are predicted to obtain the parameter prediction results; S2: Based on the parameter prediction results, adjust the extraction equipment parameters, optimize the extraction rate and temperature control, evaluate the impact of various configurations on resource consumption, and obtain the optimal resource configuration; S3: Based on the resource optimization configuration, dynamic monitoring of key parameters is performed, and parameter adjustment results are obtained by adjusting the pressure threshold and pH fluctuation range; S4: Based on the parameter adjustment results, the extraction cycle is calculated and set, and the optimal time window is explored to obtain the cycle adjustment strategy by dynamically simulating the performance of the extraction efficiency in various time periods and referring to the physiological cycle and seasonal changes of the plant material; S5: Based on the cycle adjustment strategy, real-time efficiency monitoring is performed, and the impact of the cycle adjustment on the extraction efficiency is evaluated through data analysis and efficiency testing to obtain efficiency comparison results; S6: Based on the efficiency comparison results, adjust the key parameters for extracting the plant materials, optimize the extraction efficiency according to the changes in various plant materials and environmental conditions, and generate optimized extraction parameters.

[0007] As a further scheme of the present invention, the parameter prediction results include recommended temperature range, pressure setting value and pH adjustment range, the resource optimization configuration includes extraction rate parameters, temperature control and energy consumption reduction targets, the parameter adjustment results include set pressure upper limit, pressure lower limit and pH fluctuation range, the cycle adjustment strategy includes optimal extraction cycle, predicted seasonal change response time and physiological cycle synchronous adjustment, the efficiency comparison results include energy consumption data, raw material consumption ratio and output quality indicators, and the optimized extraction parameters include extraction temperature setting, pressure adjustment plan and pH management strategy.

[0008] As a further solution of the present invention, based on the historical plant extraction data, including the temperature, pressure, and pH value during extraction, the long-term trend of the parameters is analyzed, and the plant extraction condition requirements are predicted by simulating the parameter reactions under various conditions. The steps of obtaining the parameter prediction results are specifically as follows: S101: based on the plant extraction data recorded in the history, record the change range and frequency of the data, and statistically analyze the long-term trend of the plant extraction data to obtain a parameter statistical table; S102: Based on the parameter statistics table, design and execute a simulation experiment, set the experimental parameter range, simulate the plant extraction effects under various temperatures, pressures, and pH values, analyze the impact of parameter changes on the extraction efficiency, and generate an experimental result record; S103: Based on the experimental result records, the correlation between parameter changes and extraction efficiency is explored, the optimal parameter combination under various conditions is analyzed, parameters of the extraction equipment are adjusted, the extraction conditions are optimized according to the parameter combination, and parameter prediction results are obtained.

[0009] As a further solution of the present invention, based on the parameter prediction results, the extraction equipment parameters are adjusted, and the effects of various configurations on resource consumption are evaluated by optimizing the extraction rate and temperature control. The steps of obtaining the optimal resource configuration are specifically as follows: S201: Based on the parameter prediction results, the rate parameters in the extraction equipment are set, the plant extraction rate readings are monitored in real time, and the optimal rate is adjusted to capture the deviation beyond the predetermined range to obtain a stable rate record; S202: Based on the rate stabilization record, adjusting the temperature change, matching the optimal temperature for plant extraction according to the extraction efficiency at various temperatures, comparing the impact of historical temperature differences on the extraction rate, and generating a temperature adjustment record; S203: Based on the rate stabilization record and the temperature adjustment record, evaluate the impact of various configurations on energy and material consumption, compare energy consumption data and raw material utilization rate, optimize resource utilization efficiency, and generate resource optimization configuration.

[0010] As a further solution of the present invention, based on the resource optimization configuration, dynamic monitoring of key parameters is performed, and the optimal extraction efficiency is maintained by adjusting the pressure threshold and pH fluctuation range. The steps of obtaining the parameter adjustment results are specifically as follows: S301: Based on the resource optimization configuration, the correlation between the energy absorption characteristics of the raw materials during extraction and the product output is analyzed, and the power output and cooling requirements of the extraction equipment are adjusted to optimize the balance between energy consumption and efficiency, and generate an energy configuration optimization record; S302: Based on the energy configuration optimization record, according to the extraction requirements of various raw materials, dynamically adjust the extraction pressure value, analyze the extraction state of the plant under the corresponding pressure, determine the optimal extraction pressure range, and obtain a stable pressure configuration; S303: Based on the pressure stabilization configuration, the pH value changes during plant extraction are monitored in real time, the influence on plant extraction is analyzed by adjusting the pH value, the chemical environment during plant extraction is stabilized, the reaction time between plants and chemical components is optimized, and parameter adjustment results are constructed.

[0011] As a further solution of the present invention, based on the parameter adjustment results, the extraction cycle is calculated and set, and the performance of the extraction efficiency in multiple time periods is dynamically simulated, and the optimal time window is mined with reference to the physiological cycle and seasonal changes of the plant material. The steps of obtaining the cycle adjustment strategy are specifically as follows: S401: Based on the parameter adjustment result, recording the physiological data of the plant material, including the maturity cycle and seasonal growth changes, analyzing the correlation between the extraction cycle and the physiological cycle of the plant material, calculating the physiological active period of the plant material, and generating an initialization cycle parameter table; S402: Based on the initialized cycle parameter table, simulate the growth environment of the plant, monitor the change of extraction efficiency under various cycle settings, analyze and adjust the key time period affecting the extraction efficiency, and generate an extraction efficiency monitoring log; S403: Based on the extraction efficiency monitoring log, identify the optimal extraction time window, refer to the seasonal changes of plants, predict the plant extraction cycle in the future time period, optimize the plant extraction efficiency, and obtain a cycle adjustment strategy.

[0012] As a further solution of the present invention, based on the cycle adjustment strategy, real-time efficiency monitoring is performed, and the impact of the cycle adjustment on the extraction efficiency is evaluated through data analysis and efficiency testing. The steps for obtaining the efficiency comparison results are specifically as follows: S501: Based on the cycle adjustment strategy, set monitoring parameters, including cycle length and adjustment frequency, record plant extraction energy efficiency, operation time and output frequency data, capture the baseline efficiency level before cycle adjustment, and generate an initialization efficiency record; S502: Based on the initialization efficiency record, evaluate and calibrate the periodic adjustment result, extract the magnitude and direction of the efficiency change according to the result analysis, and compare the outputs of multiple adjustment schemes to generate a performance parameter record; S503: Based on the performance parameter records, a support vector machine regression method is used to set an extraction cycle according to the adjusted key operating parameters, the influence of the measurement parameters on the equipment operating efficiency is measured, the performance difference of the equipment under various extraction cycle settings is analyzed, and an efficiency comparison result is generated; The support vector machine regression algorithm is based on the formula: ; in, To predict the extraction efficiency, is the support vector, is the Lagrange multiplier, is the kernel function, is the extraction temperature, is the pressure during extraction, is the pH value during extraction, For the extraction cycle, is the deviation term.

[0013] As a further solution of the present invention, based on the efficiency comparison results, the key parameters for extracting plant materials are adjusted, and the extraction efficiency is optimized according to the changes in various plant materials and environmental conditions. The steps of generating optimized extraction parameters are specifically as follows: S601: Based on the efficiency comparison results, identify key parameters that affect the extraction efficiency, record the values ​​of temperature, pressure, and pH value during extraction in real time, detect the effects of differences in environment and plant materials on the parameters, and generate parameter benchmark data; S602: Based on the parameter benchmark data, simulate the plant extraction requirements under various environmental changes, adjust the environmental parameters of the target plant material, determine the range of temperature and pressure changes, and generate an environmental adaptability record; S603: Based on the environmental adaptability record and according to the simulation results, the optimal environmental condition values ​​are mined, key parameters affecting plant extraction efficiency are adjusted, the target plant material characteristics are matched, the adjusted parameters are standardized, and optimized extraction parameters are generated.

[0014] An intelligent plant extraction control system, the intelligent plant extraction control system is used to execute the above intelligent plant extraction control method, the system comprises: The parameter trend analysis module analyzes the long-term trend of temperature, pressure, and pH parameters based on historical plant extraction data, simulates parameter responses under various conditions, predicts plant extraction condition requirements, and generates parameter prediction results; The equipment parameter optimization module adjusts the parameters of the extraction equipment based on the parameter prediction results, including extraction rate and temperature control, optimizes equipment performance and resource consumption, evaluates resource efficiency of various configurations, and generates resource optimization configurations; The dynamic monitoring module implements dynamic monitoring of key parameters based on the resource optimization configuration, including adjustment of pressure threshold and pH fluctuation, optimizes extraction conditions according to the adjustment results, and generates parameter adjustment results; The cycle adjustment module calculates and sets the extraction cycle based on the parameter adjustment results, simulates the physiological cycle and seasonal changes of the plant material, explores the optimal extraction time window, and generates a cycle adjustment strategy; The real-time efficiency monitoring module monitors the efficiency in real time based on the cycle adjustment strategy, compares the extraction efficiency before and after the cycle adjustment through data analysis and efficiency testing, and adjusts key parameters according to plant materials and environmental changes to generate optimized extraction parameters.

[0015] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, through the analysis of plant extraction data recorded in history, long-term trend prediction of parameters such as temperature, pressure, pH value, etc. is realized, so as to adjust the extraction equipment parameters in advance. It effectively adapts to the changes of different plant materials and environmental conditions and optimizes the extraction efficiency. By dynamically simulating the performance of extraction efficiency in various time periods, the innovative solution digs out the optimal extraction time window, reduces resource consumption and extraction time in the extraction process, and maintains the quality and consistency of the extract. Real-time efficiency monitoring of the cycle adjustment strategy further evaluates the impact of cycle adjustment on the extraction process and enhances the accuracy of the operation. By dynamically monitoring key parameters and adjusting the pressure threshold and pH fluctuation range, the stability and controllability of the extraction process are enhanced. Real-time monitoring and adjustment not only reduce human operation errors, but also improve the automation level of the extraction process, making the entire extraction process more efficient and sustainable. While ensuring the consistency and quality of the extracted material, the extraction process improves operating efficiency and reduces energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic diagram of the workflow of the present invention; Figure 2 This is a detailed flow chart of S1 of the present invention; Figure 3 This is a detailed flow chart of S2 of the present invention; Figure 4 This is a detailed flow chart of S3 of the present invention; Figure 5 This is a detailed flow chart of S4 of the present invention; Figure 6 This is a detailed flow chart of S5 of the present invention; Figure 7 This is a detailed flow chart of S6 of the present invention; Figure 8 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0018] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0019] Embodiment 1 See also Figure 1 The present invention provides a technical solution, an intelligent plant extraction control method, comprising the following steps: S1: Based on the historical plant extraction data, including the temperature, pressure, and pH value during extraction, the long-term change trend of the extracted plant parameters is analyzed, the parameter response under various conditions is simulated, the plant extraction condition requirements in the future time period are predicted, and the parameter prediction results are obtained; S2: Based on the parameter prediction results, analyze the key bottlenecks of resource consumption, including the utilization efficiency of water and energy, adjust the extraction equipment parameters, optimize the extraction rate and temperature control, evaluate the impact of various configurations on resource consumption, and obtain the optimal resource configuration; S3: Based on resource optimization configuration, key parameters are dynamically monitored, and extraction conditions are adjusted according to real-time data, including pressure threshold and pH fluctuation range, to maintain optimal extraction efficiency and obtain parameter adjustment results; S4: Based on the parameter adjustment results, the extraction cycle is calculated and set. By dynamically simulating the performance of the extraction efficiency in various time periods, referring to the physiological cycle and seasonal changes of the plant materials, the optimal time window is explored to obtain the cycle adjustment strategy; S5: Based on the cycle adjustment strategy, real-time efficiency monitoring is performed to compare the extraction efficiency before and after the adjustment. Through data analysis and efficiency testing, the impact of the cycle adjustment on the efficiency of the extraction process is evaluated to obtain efficiency comparison results; S6: Based on the efficiency comparison results, adjust the key parameters for extracting plant materials, match the optimal environmental conditions according to the changes in various plant materials and environmental conditions, optimize the extraction efficiency, and generate optimized extraction parameters.

[0020] Parameter prediction results include recommended temperature range, pressure setting value and pH adjustment range; resource optimization configuration includes extraction rate parameters, temperature control and energy consumption reduction targets; parameter adjustment results include set upper and lower pressure limits and pH fluctuation range; cycle adjustment strategies include optimal extraction cycle, predicted seasonal change response time and synchronous adjustment of physiological cycle; efficiency comparison results include energy consumption data, raw material consumption ratio and output quality indicators; optimized extraction parameters include extraction temperature setting, pressure adjustment plan and pH management strategy.

[0021] See also Figure 2 Based on the historical plant extraction data, including the temperature, pressure, and pH value during extraction, the long-term trend of the parameters is analyzed. By simulating the parameter reactions under various conditions, the plant extraction condition requirements are predicted. The specific steps for obtaining the parameter prediction results are as follows: S101: Based on the plant extraction data recorded in the history, the change interval and frequency of the data are recorded, and the long-term trend of the plant extraction data is statistically analyzed to obtain the parameter statistics table. The execution process is as follows; S101: Based on the plant extraction data recorded in history, record the data change range and frequency, collect the plant extraction data within the target period, set the observation window time period, record the data volume of each extraction, and summarize the data set of each week's extraction volume. Divide the data range, determine the frequency distribution of different extraction volumes, divide the extraction volume into four intervals: 0-25%, 25%-50%, 50%-75% and 75%-100%, and calculate the frequency of occurrence of each interval to obtain a parameter statistics table.

[0022] S102: Based on the parameter statistics table, design and execute simulation experiments, set the experimental parameter range, simulate the plant extraction effects under various temperatures, pressures, and pH values, analyze the impact of parameter changes on the extraction efficiency, and generate experimental result records. The execution process is as follows; S102: Based on the parameter statistics table, design and execute simulation experiments, construct evaluation criteria for plant extraction effects, use temperature, pressure and pH values ​​in the parameter statistics table as the main variables, and set the experimental parameter range, such as temperature from 20°C to 100°C, pressure from 1atmos to 10atmos, and pH value from 4 to 9. Adjust and record the experimental environment through sensors and automated control systems to ensure accurate implementation of parameters. Before implementing the experiment, preset the number of experimental repetitions for each parameter combination to ensure data reliability. For specific operations during the experiment, prepare standardized plant raw materials to ensure consistent plant quality and status for each experiment. Use automated extraction to extract according to preset parameters, perform at least three repeated experiments for each parameter combination, and collect the extract. Use high performance liquid chromatography (HPLC) to analyze the concentration of active ingredients in the extract, input the measured data into the data analysis software, and generate a record of experimental results. The formula is: ,in, represents the extraction efficiency, , and represent temperature, pressure and pH value respectively. , , and are the coefficients obtained by fitting the data.

[0023] S103: Based on the experimental result records, the correlation between parameter changes and extraction efficiency is explored, the optimal parameter combination under various conditions is analyzed, the parameters of the extraction equipment are adjusted, the extraction conditions are optimized according to the parameter combination, and the parameter prediction results are obtained. The execution process is as follows; S103: Based on the experimental result records, explore the correlation between parameter changes and extraction efficiency, and collect experimental data involving different parameter settings, such as temperature, pressure, solvent ratio, etc., as well as the corresponding plant extraction efficiency. Record the data and establish a database containing experimental parameters and extraction efficiency. Use data mining technology to analyze the statistical correlation between parameters and extraction efficiency, such as calculating the mean and variance of extraction efficiency under different parameter configurations, and the interaction effect between parameters. Analyze the optimal parameter combination under various conditions, and determine the parameters that affect the extraction efficiency through combined analysis methods. Optimize the extraction conditions according to the parameter combination, and verify the accuracy of the model by simulating the effects of different parameter combinations on the extraction efficiency, and gradually refine the parameter settings to obtain parameter prediction results. The formula is: ,in, is the extraction efficiency, It is Experimental parameters, is the influence coefficient of the parameter, is the constant term of the model.

[0024] See also Figure 3 Based on the parameter prediction results, the extraction equipment parameters are adjusted. By optimizing the extraction rate and temperature control, the impact of various configurations on resource consumption is evaluated. The steps to obtain the optimal resource configuration are as follows: S201: Based on the parameter prediction results, the rate parameters in the extraction equipment are set, the plant extraction rate readings are monitored in real time, and the optimal rate is adjusted to capture the deviation beyond the predetermined range, and the rate stability record is obtained. The execution process is as follows; S201: Based on the parameter prediction results, set the rate parameters in the extraction equipment. According to historical data, integrate the data of the rate in the equipment in the past 30 days to form a data set. Standardize the data in the data set to eliminate the dimension effect. Subtract the mean of the data set from each data point and divide it by the standard deviation. Use regression analysis method, time as the independent variable, rate as the dependent variable, and establish a rate prediction model. Through the model, calculate the predicted rate, which is the rate parameter that the extraction equipment should set. Monitor the plant extraction rate readings in real time, adjust the optimal rate, capture deviations beyond the predetermined range, and obtain a stable rate record. The formula is: ,in, represents the i-th time point, Indicates the rate reading, and are the average values ​​of time and rate, respectively.

[0025] S202: Based on the rate stability record, adjust the temperature change, match the optimal temperature for plant extraction according to the extraction efficiency at various temperatures, compare the impact of historical temperature differences on the extraction rate, and generate a temperature adjustment record. The execution process is as follows; S202: Based on the rate stability record, the efficiency of plant extracts at different temperatures is determined by setting multiple temperature points for experiments. The temperature range is set to 20°C to 100°C, and 8 temperature points are set at intervals of 10°C. For each temperature point, a plant extraction experiment is performed, the extraction rate is recorded, and the data is input into the data set. The deviation of the data point from the average extraction rate is calculated, and the standard deviation is calculated using the deviation data to evaluate the stability of the extraction efficiency at each temperature point. The temperature point with the smallest deviation and the lowest standard deviation is selected as the optimal temperature, and a temperature adjustment record is generated.

[0026] S203: Based on the rate stability record and temperature adjustment record, evaluate the impact of various configurations on energy and material consumption, compare energy consumption data and raw material utilization rate, optimize resource utilization efficiency, and generate resource optimization configuration execution process as follows; S203: Based on the rate stability record and temperature adjustment record, conduct a quantitative analysis of the energy consumption and material usage of each configuration, collect energy consumption data E and material consumption data, conduct statistical analysis on the data under different configurations, calculate the energy consumption data and raw material usage rate of each configuration under different temperature and rate conditions, set parameters to represent different configurations, and sort each configuration according to the efficiency of energy consumption and material consumption through a comprehensive scoring method for energy consumption data and raw material usage rate. Introduce a scoring function to adjust the coefficients of energy and material weights, which are determined by actual application requirements. By comparing the values ​​of each configuration, select the configuration that optimizes resource utilization efficiency. And compare energy consumption data and raw material usage rate, optimize resource utilization efficiency, and generate resource optimization configuration. The formula is: ,in, Indicates the resource optimization score of each configuration, and Represent the inverse efficiency of unit energy and unit material, respectively. and is the weight coefficient.

[0027] See also Figure 4 Based on resource optimization configuration, dynamic monitoring of key parameters is performed to maintain optimal extraction efficiency by adjusting the pressure threshold and pH fluctuation range. The specific steps to obtain parameter adjustment results are as follows: S301: Based on resource optimization configuration, the correlation between the energy absorption characteristics of the raw materials during extraction and the product output is analyzed, and the power output and cooling requirements of the extraction equipment are adjusted to optimize the balance between energy consumption and efficiency, and the energy configuration optimization record is generated. The execution process is as follows; S301: Based on the optimal resource allocation, the energy absorption characteristics of raw materials are quantitatively analyzed. By establishing a database of the energy absorption characteristics of raw materials, the energy absorption data of different raw materials under different conditions are recorded. Including the type of raw materials, energy absorption rate and its corresponding environmental factors, such as temperature, humidity, etc. Use the correlation between data and product output for analysis. The analysis is performed through a regression model, which takes the energy absorption characteristics of raw materials as independent variables and product output as dependent variables to determine the strength of the relationship between the two. Based on the obtained relationship strength, the power output of the extraction equipment is adjusted. The specific operation of the power output adjustment includes calculating the required optimal power output value and setting the equipment parameters to match this value. Ensure that the equipment operates at the optimal temperature, thereby reducing energy waste and generating energy configuration optimization records. The formula is: ,in, Represents product output, represents the energy absorption rate of the raw material, represents the ambient temperature, , , represents the regression coefficient, represents the error term.

[0028] S302: Based on the energy configuration optimization record, according to the extraction requirements of various raw materials, the extraction pressure value is dynamically adjusted, the extraction state of the plant under the corresponding pressure is analyzed, the optimal extraction pressure range is determined, and the pressure stable configuration execution process is obtained as follows; S302: Based on the energy configuration optimization record, according to the extraction requirements of various raw materials, dynamically adjust the extraction pressure value, set the preliminary extraction pressure value, and collect the extraction efficiency data of plant raw materials under pressure. For each raw material, collect five consecutive extraction data and calculate the average efficiency of each extraction. Construct a relationship model between pressure and extraction efficiency, and introduce pressure P and material characteristic parameters (such as material density and particle size) as variables in the model. According to the experimental data, adjust the pressure value, and re-evaluate the extraction efficiency in steps of 0.1MPa. After each adjustment, calculate the new average extraction efficiency and compare it with the previous average. If the new average efficiency is significantly improved compared to the previous one, continue to increase the pressure, otherwise, reduce the pressure. The process continues until the extraction efficiency no longer changes significantly or reaches the upper limit of the safe pressure. It is a specific value within the optimal extraction pressure range. Monitor the impact of each pressure adjustment on energy consumption to ensure that the entire extraction process achieves the best balance between energy consumption and extraction efficiency and obtains a stable pressure configuration. The formula is: ,in, Represents the comprehensive extraction efficiency, is the number of data points, , , Respectively Pressure, material property parameters and extraction efficiency in the experiments.

[0029] S303: Based on the pressure stabilization configuration, the pH value change during plant extraction is monitored in real time, the influence on plant extraction is analyzed by adjusting the pH value, the chemical environment during plant extraction is stabilized, the reaction time between plants and chemical components is optimized, and the parameter adjustment result execution process is constructed as follows; S303: Based on the pressure stabilization configuration, real-time monitoring and tracking of pH changes during the plant extraction process, installing a pH sensor in the extraction equipment, real-time collection of pH data of the extract, transmitting the data to the central control unit, and instantly analyzing the pH change trend according to the preset parameters. Adjust and analyze the impact on plant extraction based on the monitored pH data. Adjusting the pH value includes adding an appropriate amount of acid or alkali to the extraction solution, and calculating the amount of acid or alkali required to be added based on the difference between the current pH value and the target pH value. Optimize the adjustment of the pH value based on the reaction rate and the required reaction time, stabilize the chemical environment during plant extraction to maintain a stable reaction state of the chemical components, record the initial value of the pH value, the adjusted value, the amount of acid and alkali added, the change in reaction time, and the specific impact of the change on the plant extraction efficiency, and generate the parameter adjustment result. The formula is: ,in, represents the extraction efficiency, Indicates the change in pH value. Represents the reaction time, , , is the regression coefficient, is the error term.

[0030] See also Figure 5 Based on the parameter adjustment results, the extraction cycle is calculated and set. By dynamically simulating the performance of the extraction efficiency in multiple time periods, referring to the physiological cycle and seasonal changes of plant materials, the optimal time window is explored and the steps for obtaining the cycle adjustment strategy are as follows: S401: Based on the parameter adjustment results, the physiological data of the plant material is recorded, including the maturity cycle and seasonal growth changes, the correlation between the extraction cycle and the physiological cycle of the plant material is analyzed, the physiological active period of the plant material is calculated, and the initialization cycle parameter table is generated. The execution process is as follows; S401: Based on the parameter adjustment results, record the physiological data of the plant material, including the maturity cycle and seasonal growth changes, and collect corresponding physiological data in different seasons, including the growth height, number of leaves and root development of the plant. Each type of data is collected three times, and the average value is taken as the representative. Through data analysis, a correlation model between seasonal changes and physiological data is constructed, and the model is used to predict the optimal physiological active period of the plant material. The extraction cycle is compared and analyzed with the physiological cycle to find the correlation between the two. It is found that there is a high correlation between the extraction cycle and the physiological active period of the plant. The extraction cycle is further adjusted to adapt to the optimal period of plant growth, and an initialization cycle parameter table is generated. The formula is: ,in, Represents the comprehensive index in the periodic parameter table, is the number of data points, , , Respectively Environmental parameters, seasonal indicators and physiological response data of the observations.

[0031] S402: Based on the initialization cycle parameter table, simulate the growth environment of the plant, monitor the changes in extraction efficiency under various cycle settings, analyze and adjust the key time periods that affect the extraction efficiency, and generate the extraction efficiency monitoring log. The execution process is as follows; S402: Based on the initialized cycle parameter table, simulate the growth environment of the plant, and simulate the corresponding environmental conditions according to the optimal growth environment parameters recorded in the cycle parameter table, such as light intensity, temperature, humidity, etc., to monitor the plant growth under various cycle settings, and evaluate the specific impact of different growth environments on the extraction efficiency. Use sensors to collect plant growth data, such as leaf area, root length, and plant height, and correlate the data with the extraction efficiency for analysis. Identify the key time periods that affect the extraction efficiency, that is, the key stages of plant growth, such as before flowering, flowering, and maturity. Adjust the extraction cycle according to the time window of the key stage, optimize the extraction efficiency, record the environmental parameters, plant physiological responses, and their corresponding extraction efficiencies within each cycle, and generate an extraction efficiency monitoring log. The formula is: ,in, Represents the comprehensive evaluation index of extraction efficiency, is the number of observation periods, , , Respectively represent Plant physiological data, environmental parameters and extraction efficiency of the period.

[0032] S403: Based on the extraction efficiency monitoring log, identify the optimal extraction time window, refer to the seasonal changes of plants, predict the plant extraction cycle in the future time period, optimize the plant extraction efficiency, and obtain the cycle adjustment strategy execution process as follows; S403: Based on the extraction efficiency monitoring log, identify the optimal extraction time window and establish a database to store detailed logs of each extraction experiment, including the extraction date, time, environmental conditions (temperature, humidity, light) and the efficiency of each extraction. By analyzing the data in the database, use statistical analysis methods to determine the time window associated with high extraction efficiency. Note that seasonal changes in plants, such as seasonal fluctuations in temperature and daylight hours, have a significant impact on the extraction cycle. Cross-analyze seasonal variation parameters with extraction efficiency data to identify the most efficient extraction time window in a specific season. Based on the analysis results, predict the optimal extraction cycle of plants in future time periods and optimize the extraction efficiency accordingly. This includes adjusting the extraction time window to match the seasonal physiological active period to ensure that each extraction is performed when the plant's physiological conditions are optimal. Develop a cycle adjustment strategy based on the analysis and prediction results. The formula is: ,in, represents the optimized parameters of the extraction cycle, is the number of observation periods, , , Respectively represent Extraction efficiency, environmental variables and seasonal variation parameters of the cycle.

[0033] See also Figure 6 Based on the cycle adjustment strategy, real-time efficiency monitoring is performed. Through data analysis and efficiency testing, the impact of cycle adjustment on extraction efficiency is evaluated. The specific steps for obtaining efficiency comparison results are as follows: S501: Based on the cycle adjustment strategy, set monitoring parameters, including cycle length and adjustment frequency, record plant extraction energy efficiency, operation time and output frequency data, capture the baseline efficiency level before cycle adjustment, and generate initialization efficiency records. The execution process is as follows; S501: Based on the cycle adjustment strategy, set monitoring parameters, including cycle length and adjustment frequency, determine the key monitoring parameters, cycle length and adjustment frequency required to monitor plant extraction efficiency. The cycle length is set according to the plant's growth cycle and seasonal changes, while the adjustment frequency is determined based on the rate of change of plant physiological data and the data analysis results of previous extraction efficiency. Deploy sensors and data recording equipment to collect energy efficiency, operating time and output frequency data during the extraction process, and record the initial efficiency level of plant extraction, which will serve as a comparison benchmark for subsequent efficiency improvements. The baseline test includes multiple extractions in the cycle before adjustment to generate an initial efficiency record table. The formula is: ,in, Represents the average efficiency before and after adjustment, is the number of observation periods, , , Represent the Energy efficiency, operating time and output frequency of the cycle.

[0034] S502: Based on the initialization efficiency record, the evaluation and calibration period adjustment results are analyzed, the magnitude and direction of the efficiency change are extracted, and the outputs of various adjustment schemes are compared to generate the performance parameter record. The execution process is as follows; S502: Based on the initialization efficiency record, evaluate and calibrate the cycle adjustment results, and extract the energy efficiency, operating time and output frequency data of each test cycle from the initialization efficiency record table. Construct an efficiency analysis model, which evaluates the effectiveness of the adjustment plan by comparing the data of each cycle. For each adjustment plan, analyze and extract the amplitude and direction of the efficiency change to determine whether the parameter adjustment is effective for improving efficiency. Including calculating the percentage change in efficiency after each adjustment and drawing a trend chart of efficiency changes. Systematically compare the outputs of multiple adjustment plans, including comparing the performance parameters of different adjustment plans under the same conditions, such as energy efficiency maximization, operating time optimization and output frequency stability. Identify the optimal cycle adjustment plan and generate a performance parameter record. The formula is: ,in, Represents comprehensive evaluation index, is the number of adjustment options to be compared, and Respectively represent The range of variation in the efficiency of the scheme and the weight of the scheme in actual operation.

[0035] S503: Based on the performance parameter records, a support vector machine regression method is used to set the extraction cycle according to the adjusted key operating parameters, the influence of the measurement parameters on the equipment operating efficiency is analyzed, the performance difference of the equipment under various extraction cycle settings is analyzed, and the efficiency comparison results are generated; The support vector machine regression algorithm follows the formula: ; in, To predict the extraction efficiency, is the support vector, is the Lagrange multiplier, is the kernel function, is the extraction temperature, is the pressure during extraction, is the pH value during extraction, For the extraction cycle, is the deviation term.

[0036] The execution flow is as follows: According to the historical extraction conditional feature vector , the temperature when adding the extraction , extraction pressure , pH value during extraction , as the target extraction cycle The environmental parameters are trained by the support vector machine regression model, and the Lagrange multipliers corresponding to each parameter are , and deviation , using the improved kernel function , calculate the extraction efficiency predicted in the target environment , optimizing the performance and efficiency of plant extraction processes.

[0037] See also Figure 7 Based on the efficiency comparison results, the key parameters of plant material extraction are adjusted, and the extraction efficiency is optimized according to the changes of various plant materials and environmental conditions. The specific steps for generating optimized extraction parameters are as follows: S601: Based on the efficiency comparison results, identify the key parameters that affect the extraction efficiency, record the values ​​of temperature, pressure, and pH value during extraction in real time, detect the impact of environmental and plant material differences on the parameters, and generate parameter benchmark data. The execution process is as follows; S601: Based on the efficiency comparison results, identify the key parameters that affect the extraction efficiency through the collected extraction efficiency data. Analyze the correlation between parameters such as temperature, pressure, and pH value and the extraction efficiency under different environmental conditions, and set up monitoring and recording of real-time data of key parameters such as temperature, pressure, and pH value during the extraction process. By installing sensors such as temperature sensors, pressure gauges, and pH meters in the extraction device, continuously monitor and record relevant data to ensure the accuracy and real-time nature of the data during the entire extraction process. It is also necessary to detect the impact of environmental and plant material differences on key parameters. By setting up different environmental test groups and changing the type of plant material, observe how the changes affect the performance of key parameters in the extraction process. For example, under different humidity or temperature conditions, plant materials may exhibit different chemical reaction rates, thereby affecting changes in pH and pressure, and generating parameter baseline data. The formula is: ,in, represents the extraction efficiency, , , represent temperature, pressure and pH value respectively. , , , Respectively represent the specific influence of temperature, pressure and pH value on the extraction efficiency, is the error term.

[0038] S602: Based on the parameter benchmark data, simulate the plant extraction requirements under various environmental changes, adjust the environmental parameters of the target plant material, determine the range of temperature and pressure changes, and generate environmental adaptability records. The execution process is as follows; S602: Based on the parameter benchmark data, simulate the plant extraction requirements under various environmental changes. According to the ideal parameter range defined in the benchmark data, set the simulation environment, be able to adjust and simulate different environmental conditions, such as temperature and pressure, to evaluate the impact on plant extraction efficiency. The controller automatically adjusts the temperature and pressure to simulate the range of changes from the lowest to the highest, so as to observe the performance of plant extraction efficiency under extreme and normal conditions. Through experimental simulation, determine the response of the target plant material under different environmental parameters. Record and analyze the extraction efficiency under each condition, including identifying the parameter settings that have the greatest impact on the extraction efficiency within the adjusted temperature and pressure range. Based on the experimental results, determine the optimal range of temperature and pressure changes. The parameter settings within the range can maximize the extraction efficiency while maintaining the stability and quality of the plant material, and generate environmental adaptability records. The formula is: ,in, represents the environmental adaptability index, is the number of test environments, , , Respectively represent Environmental temperature, pressure and corresponding extraction efficiency.

[0039] S603: Based on the environmental adaptability record and the simulation results, the optimal environmental condition values ​​are mined, the key parameters affecting the plant extraction efficiency are adjusted, the target plant material characteristics are matched, the adjusted parameters are standardized, and the optimized extraction parameters are generated. The execution process is as follows; S603: Based on the environmental adaptability records and simulation results, use data analysis tools to mine the optimal environmental condition values. Analyze key parameters including temperature and pressure, identify the degree of influence of parameters on plant extraction efficiency through statistical models, and determine the optimal value of each parameter. The optimal value is to select environmental settings that can maximize the extraction of plant active ingredients while ensuring the highest extraction efficiency, taking into account the characteristics of different plant materials, such as thermal sensitivity or pressure sensitivity. Adjust key parameters to ensure a perfect match with the characteristics of the target plant material. For example, if the target plant is more sensitive to temperature changes, adjust the temperature to a narrower optimal range to avoid damage to the plant. For pressure, adjust the pressure setting according to the physical structure of the plant to maximize the rupture of the cell wall, thereby increasing the release rate of the active ingredients. After the optimization and adjustment of the parameters are completed, standardization is performed, including the formulation of standard operating procedures and parameter setting guidelines to ensure consistency and repeatability during the production process. Generate an optimized extraction parameter table. The formula is: ,in, represents the optimization efficiency index, is the number of parameters, , , Respectively represent The adjusted conditions of the parameters, the variable values ​​of the conditions and the corresponding extraction efficiency.

[0040] An intelligent plant extraction control system, which is used to execute the above-mentioned intelligent plant extraction control method, and the system includes: The parameter trend analysis module is based on the historical plant extraction data, including temperature, pressure, and pH records. It uses data sorting tools to analyze the long-term change trend of parameters, simulate the response of parameters under differentiated environmental conditions, and predict the demand for plant extraction conditions by comparing the difference between simulated data and real-time data, generating parameter prediction results. The equipment parameter optimization module adjusts the key parameters of the extraction equipment based on the parameter prediction results, including the extraction rate and temperature control settings. By analyzing the equipment operation data and resource consumption records, it adjusts the equipment configuration, optimizes the resource utilization efficiency, and generates the resource optimization configuration; The dynamic monitoring module dynamically monitors the key parameters pressure threshold and pH value based on resource optimization configuration, adjusts the parameters according to the monitoring data, matches the changes in extraction conditions, and generates parameter adjustment results; The cycle adjustment module calculates and sets a reasonable extraction cycle based on the parameter adjustment results, simulates the physiological cycle and seasonal changes of plant materials, analyzes data and determines the optimal extraction time window, and generates a cycle adjustment strategy; The real-time efficiency monitoring module implements real-time monitoring of extraction efficiency based on a periodic adjustment strategy. By collecting and analyzing efficiency test data, it adjusts key parameters in response to changes in plant materials and the environment to generate optimized extraction parameters.

[0041] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. An intelligent plant extraction control method, characterized in that: The following steps are involved: Based on historical plant extraction data, including temperature, pressure, and pH value during extraction, the long-term trend of parameters is analyzed. By simulating parameter responses under various conditions, the plant extraction condition requirements are predicted to obtain parameter prediction results. Based on the parameter prediction results, the extraction equipment parameters are adjusted, and the effects of various configurations on resource consumption are evaluated by optimizing the extraction rate and temperature control to obtain the optimal resource configuration; Based on the resource optimization configuration, dynamic monitoring of key parameters is performed, and parameter adjustment results are obtained by adjusting the pressure threshold and pH fluctuation range; Based on the parameter adjustment results, the extraction cycle is calculated and set, and the optimal time window is explored to obtain the cycle adjustment strategy by dynamically simulating the performance of the extraction efficiency in various time periods and referring to the physiological cycle and seasonal changes of the plant materials; Based on the cycle adjustment strategy, real-time efficiency monitoring is performed, and the impact of the cycle adjustment on the extraction efficiency is evaluated through data analysis and efficiency testing to obtain efficiency comparison results; Based on the efficiency comparison results, key parameters for extracting plant materials are adjusted, and the extraction efficiency is optimized according to changes in various plant materials and environmental conditions to generate optimized extraction parameters.

2. The intelligent plant extraction control method according to claim 1, characterized in that: The parameter prediction results include recommended temperature range, pressure setting value and pH adjustment range, the resource optimization configuration includes extraction rate parameters, temperature control and energy consumption reduction targets, the parameter adjustment results include set pressure upper limit, pressure lower limit and pH fluctuation range, the cycle adjustment strategy includes optimal extraction cycle, predicted seasonal change response time and physiological cycle synchronous adjustment, the efficiency comparison results include energy consumption data, raw material consumption ratio and output quality indicators, and the optimized extraction parameters include extraction temperature setting, pressure adjustment plan and pH management strategy.

3. The intelligent plant extraction control method according to claim 1, characterized in that: Based on the historical plant extraction data, including the temperature, pressure, and pH value during extraction, the long-term trend of the parameters is analyzed. By simulating the parameter reactions under various conditions, the plant extraction condition requirements are predicted. The specific steps to obtain the parameter prediction results are as follows: Based on the historical plant extraction data, record the data change range and frequency, and statistically analyze the long-term trend of the plant extraction data to obtain a parameter statistical table; Based on the parameter statistics table, design and execute simulation experiments, set the experimental parameter range, simulate the plant extraction effects under various temperatures, pressures, and pH values, analyze the impact of parameter changes on extraction efficiency, and generate experimental result records; Based on the experimental result records, the correlation between parameter changes and extraction efficiency is explored, the optimal parameter combination under various conditions is analyzed, the parameters of the extraction equipment are adjusted, the extraction conditions are optimized according to the parameter combination, and the parameter prediction results are obtained.

4. The intelligent plant extraction control method according to claim 1, characterized in that: Based on the parameter prediction results, the extraction equipment parameters are adjusted, and the effects of various configurations on resource consumption are evaluated by optimizing the extraction rate and temperature control. The steps for obtaining the optimal resource configuration are as follows: Based on the parameter prediction results, the rate parameters in the extraction equipment are set, the plant extraction rate readings are monitored in real time, and the optimal rate is adjusted to capture deviations beyond a predetermined range to obtain a stable rate record; Based on the rate stability record, adjusting the temperature change, matching the optimal temperature for plant extraction according to the extraction efficiency at various temperatures, comparing the effect of historical temperature differences on the extraction rate, and generating a temperature adjustment record; Based on the rate stabilization record and temperature adjustment record, the impact of various configurations on energy and material consumption is evaluated, and the energy consumption data and raw material utilization rate are compared to optimize resource utilization efficiency and generate resource optimization configuration.

5. The intelligent plant extraction control method according to claim 1, characterized in that: Based on the resource optimization configuration, dynamic monitoring of key parameters is performed, and the optimal extraction efficiency is maintained by adjusting the pressure threshold and pH fluctuation range. The steps for obtaining parameter adjustment results are as follows: Based on the resource optimization configuration, the correlation between the energy absorption characteristics of the raw materials during extraction and the product output is analyzed, and the power output and cooling requirements of the extraction equipment are adjusted to optimize the balance between energy consumption and efficiency, and generate energy configuration optimization records; Based on the energy configuration optimization record, according to the extraction requirements of various raw materials, the extraction pressure value is dynamically adjusted, the extraction state of the plant under the corresponding pressure is analyzed, the optimal extraction pressure range is determined, and the pressure stable configuration is obtained; Based on the pressure stabilization configuration, the pH value changes during plant extraction are monitored in real time, the influence on plant extraction is analyzed by adjusting the pH value, the chemical environment during plant extraction is stabilized, the reaction time between plants and chemical components is optimized, and parameter adjustment results are constructed.

6. The intelligent plant extraction control method according to claim 1, characterized in that: Based on the parameter adjustment results, the extraction cycle is calculated and set, and the extraction efficiency is dynamically simulated in multiple time periods. Referring to the physiological cycle and seasonal changes of plant materials, the optimal time window is explored and the steps of obtaining the cycle adjustment strategy are as follows: Based on the parameter adjustment results, recording physiological data of the plant material, including maturity cycle and seasonal growth changes, analyzing the correlation between the extraction cycle and the physiological cycle of the plant material, calculating the physiological active period of the plant material, and generating an initialization cycle parameter table; Based on the initialization cycle parameter table, the growth environment of the plant is simulated, the changes in extraction efficiency under various cycle settings are monitored, the key time periods affecting the extraction efficiency are analyzed and adjusted, and an extraction efficiency monitoring log is generated; Based on the extraction efficiency monitoring log, the optimal extraction time window is identified, and with reference to the seasonal changes of plants, the plant extraction cycle in the future time period is predicted, the plant extraction efficiency is optimized, and a cycle adjustment strategy is obtained.

7. The intelligent plant extraction control method according to claim 1, characterized in that: Based on the cycle adjustment strategy, real-time efficiency monitoring is performed, and the impact of cycle adjustment on extraction efficiency is evaluated through data analysis and efficiency testing. The specific steps for obtaining efficiency comparison results are as follows: Based on the cycle adjustment strategy, setting monitoring parameters, including cycle length and adjustment frequency, recording plant extraction energy efficiency, operation time and output frequency data, capturing baseline efficiency levels before cycle adjustment, and generating an initialization efficiency record; Based on the initialization efficiency record, evaluate and calibrate the periodic adjustment results, extract the magnitude and direction of the efficiency change according to the result analysis, and compare the outputs of multiple adjustment schemes to generate a performance parameter record; Based on the performance parameter records, a support vector machine regression method is used to set the extraction cycle according to the adjusted key operating parameters, measure the impact of the parameters on the equipment operating efficiency, analyze the performance differences of the equipment under various extraction cycle settings, and generate efficiency comparison results; The support vector machine regression algorithm is based on the formula: ； in, To predict the extraction efficiency, is the support vector, is the Lagrange multiplier, is the kernel function, is the extraction temperature, is the pressure during extraction, is the pH value during extraction, For the extraction cycle, is the deviation term.

8. The intelligent plant extraction control method according to claim 1, characterized in that: Based on the efficiency comparison results, the key parameters for extracting plant materials are adjusted, and the extraction efficiency is optimized according to the changes in various plant materials and environmental conditions. The steps for generating optimized extraction parameters are specifically as follows: Based on the efficiency comparison results, identify key parameters that affect the extraction efficiency, record the values ​​of temperature, pressure, and pH value during extraction in real time, detect the effects of differences in environment and plant materials on the parameters, and generate parameter benchmark data; Based on the parameter benchmark data, simulate the plant extraction requirements under various environmental changes, adjust the environmental parameters of the target plant material, determine the range of temperature and pressure changes, and generate environmental adaptability records; Based on the environmental adaptability records and according to the simulation results, the optimal environmental condition values ​​are mined, the key parameters affecting the plant extraction efficiency are adjusted, the target plant material characteristics are matched, the adjusted parameters are standardized, and the optimized extraction parameters are generated.

9. An intelligent plant extraction control system, characterized in that: According to any one of claims 1 to 8, the intelligent plant extraction control method comprises: The parameter trend analysis module analyzes the long-term trend of temperature, pressure, and pH parameters based on historical plant extraction data, simulates parameter responses under various conditions, predicts plant extraction condition requirements, and generates parameter prediction results; The equipment parameter optimization module adjusts the parameters of the extraction equipment based on the parameter prediction results, including extraction rate and temperature control, optimizes equipment performance and resource consumption, evaluates resource efficiency of various configurations, and generates resource optimization configurations; The dynamic monitoring module implements dynamic monitoring of key parameters based on the resource optimization configuration, including adjustment of pressure threshold and pH fluctuation, optimizes extraction conditions according to the adjustment results, and generates parameter adjustment results; The cycle adjustment module calculates and sets the extraction cycle based on the parameter adjustment results, simulates the physiological cycle and seasonal changes of the plant material, explores the optimal extraction time window, and generates a cycle adjustment strategy; The real-time efficiency monitoring module monitors the efficiency in real time based on the cycle adjustment strategy, compares the extraction efficiency before and after the cycle adjustment through data analysis and efficiency testing, and adjusts key parameters according to plant materials and environmental changes to generate optimized extraction parameters.

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