Traditional Chinese medicine extraction process optimization method and system based on dynamic countercurrent extraction and medium

Abstract

The invention relates to the technical field of process data optimization, and discloses a traditional Chinese medicine extraction process optimization method and system based on dynamic countercurrent extraction and a medium. The method comprises the following steps: carrying out online monitoring data processing on a traditional Chinese medicinal material digital feature data set and a dynamic countercurrent extraction parameter initial data set to obtain a countercurrent extraction process real-time monitoring data stream and parameter associated data, and carrying out multi-dimensional parameter optimization processing; a dynamic countercurrent extraction process optimization parameter group is obtained and is analyzed and processed in real time, and an automatic parameter regulation and control instruction sequence and process abnormity early warning data are obtained; and performing quality characteristic data processing on the extract after execution of the automatic parameter regulation and control instruction sequence to obtain a process quality evaluation data set and a process tracing data set, and performing association analysis processing to obtain a dynamic countercurrent extraction process parameter optimization rule base. According to the invention, intelligent monitoring and automatic parameter optimization of the extraction process are realized, so that the accuracy and controllability of the traditional Chinese medicine extraction process are improved.

Description

Technical Field

[0001] The present application relates to the field of process data optimization, and in particular to a method, system and medium for optimizing a traditional Chinese medicine extraction process based on dynamic countercurrent extraction. Background Art

[0002] With the advancement of the modernization of traditional Chinese medicine, the efficiency and quality control of traditional Chinese medicine extraction technology are becoming increasingly important. At present, traditional Chinese medicine extraction mainly adopts the traditional tank extraction process, which usually requires a higher decoction temperature and a longer extraction time. Some improvement schemes have also appeared in the prior art, such as new extraction methods such as microwave extraction and ultrasonic extraction, and the use of automated control systems to monitor the extraction process. Among them, dynamic countercurrent extraction technology has shown good application prospects by maintaining a large concentration difference between the medicinal materials and the solvent through the reverse movement of the medicinal materials and the solvent in the leaching container.

[0003] However, the existing dynamic countercurrent extraction technology still has some shortcomings. First, the process parameter setting in the extraction process mainly relies on experience, lacking scientific data support and optimization methods; second, the real-time monitoring data of the extraction process has not been fully utilized, and the intelligent regulation of process parameters cannot be achieved; third, the quality evaluation and process traceability of the extract lack a systematic data analysis method, making it difficult to establish a complete quality control system. These problems lead to poor stability and repeatability of the extraction process, affecting the quality of Chinese medicine extraction products. Summary of the invention

[0004] The present application provides a method, system and medium for optimizing the extraction process of traditional Chinese medicine based on dynamic countercurrent extraction, which are used to establish a data-driven dynamic countercurrent extraction process optimization method to achieve intelligent monitoring of the extraction process and automatic optimization of parameters, thereby improving the accuracy and controllability of the traditional Chinese medicine extraction process.

[0005] In the first aspect, the present application provides a method for optimizing the extraction process of traditional Chinese medicine based on dynamic countercurrent extraction, and the method for optimizing the extraction process of traditional Chinese medicine based on dynamic countercurrent extraction includes: performing multi-dimensional feature data collection and processing on traditional Chinese medicine to obtain a digital feature data set of traditional Chinese medicine and an initial data set of dynamic countercurrent extraction parameters; performing online monitoring data processing on the digital feature data set of traditional Chinese medicine and the initial data set of dynamic countercurrent extraction parameters to obtain a real-time monitoring data stream and parameter association data of the countercurrent extraction process; performing multi-dimensional parameter optimization processing on the real-time monitoring data stream of the countercurrent extraction process and the parameter association data to obtain a dynamic countercurrent extraction process optimization parameter group; performing real-time analysis and processing on the dynamic countercurrent extraction process optimization parameter group to obtain a parameter automatic control instruction sequence and process abnormality warning data; based on the process abnormality warning data, performing quality feature data processing on the extract after the execution of the parameter automatic control instruction sequence to obtain a process quality assessment data set and a process traceability data set; performing correlation analysis and processing on the process quality assessment data set and the process traceability data set to obtain a dynamic countercurrent extraction process parameter optimization rule base.

[0006] In a second aspect, the present application provides a Chinese medicine extraction process optimization system based on dynamic countercurrent extraction, and the Chinese medicine extraction process optimization system based on dynamic countercurrent extraction comprises:

[0007] The acquisition module is used to collect and process multi-dimensional characteristic data of Chinese medicinal materials to obtain a digital characteristic data set of Chinese medicinal materials and an initial data set of dynamic countercurrent extraction parameters;

[0008] A monitoring module, used for performing online monitoring data processing on the digital characteristic data set of Chinese medicinal materials and the initial data set of dynamic countercurrent extraction parameters to obtain real-time monitoring data flow and parameter correlation data of the countercurrent extraction process;

[0009] An optimization module, used for performing multi-dimensional parameter optimization processing on the real-time monitoring data stream of the countercurrent extraction process and the parameter association data to obtain a dynamic countercurrent extraction process optimization parameter group;

[0010] An analysis module is used to perform real-time analysis and processing on the dynamic countercurrent extraction process optimization parameter group to obtain parameter automatic control instruction sequence and process abnormality warning data;

[0011] A processing module, for performing quality feature data processing on the extract after the execution of the parameter automatic control instruction sequence based on the process abnormality warning data, to obtain a process quality assessment data set and a process traceability data set;

[0012] The association module is used to perform association analysis on the process quality assessment data set and the process traceability data set to obtain a dynamic countercurrent extraction process parameter optimization rule base.

[0013] In a third aspect, the present application provides a computer-readable storage medium, which stores instructions, and when the computer-readable storage medium is run on a computer, it enables the computer to execute the above-mentioned method for optimizing the extraction process of traditional Chinese medicine based on dynamic countercurrent extraction.

[0014] In the technical solution provided by the present application, through multi-dimensional feature data collection and processing, a comprehensive characterization of the physical and chemical properties of Chinese medicinal materials is achieved, a reliable data basis is provided for process parameter optimization, and a real-time data collection and parameter correlation analysis system is established through online monitoring data processing, ensuring dynamic monitoring and timely adjustment of the extraction process. The method adopts a multi-dimensional parameter optimization processing strategy, effectively integrates key process parameters such as temperature, pressure, and flow, improves the accuracy and scientificity of parameter optimization, and establishes an automatic control and abnormal warning system for process parameters through a real-time analysis and processing mechanism, enhancing the stability and controllability of the extraction process. The quality feature data processing method based on process abnormality warning data realizes the full-process monitoring and evaluation of the extract quality, ensures the reliability and consistency of product quality, and the introduction of the correlation analysis processing mechanism promotes the in-depth understanding of the relationship between process parameters and quality indicators, and provides a scientific basis for process optimization. In addition, the dynamic countercurrent extraction process parameter optimization rule base constructed by the present invention not only realizes the systematic accumulation and inheritance of process knowledge, but also provides strong support for the continuous optimization and improvement of process parameters, and effectively improves the intelligent level and industrial application ability of Chinese medicine extraction technology. Through the organic combination of these technical features, the accuracy, stability and controllability of the dynamic countercurrent extraction process of traditional Chinese medicine are significantly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0016] Figure 1 A schematic diagram of an embodiment of a method for optimizing the extraction process of traditional Chinese medicine based on dynamic countercurrent extraction in an embodiment of the present application;

[0017] Figure 2 This is a schematic diagram of an embodiment of a Chinese medicine extraction process optimization system based on dynamic countercurrent extraction in the embodiments of the present application. DETAILED DESCRIPTION

[0018] The present application embodiment provides a kind of Chinese medicine extraction process optimization method, system and medium based on dynamic countercurrent extraction. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable in appropriate circumstances, so that the embodiments described here can be implemented in a sequence other than the content illustrated or described here. In addition, the term "including" or "having" and any variation thereof are intended to cover non-exclusive inclusions, for example, the process, method, system, product or equipment comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or equipment.

[0019] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 In the embodiments of the present application, an embodiment of the method for optimizing the extraction process of traditional Chinese medicine based on dynamic countercurrent extraction includes:

[0020] Step S101, performing multi-dimensional feature data collection and processing on Chinese medicinal materials to obtain a digital feature data set of Chinese medicinal materials and an initial data set of dynamic countercurrent extraction parameters;

[0021] Step S102, performing online monitoring data processing on the digital characteristic data set of Chinese medicinal materials and the initial data set of dynamic countercurrent extraction parameters to obtain real-time monitoring data flow and parameter correlation data of the countercurrent extraction process;

[0022] Step S103, performing multi-dimensional parameter optimization processing on the real-time monitoring data stream and parameter association data of the countercurrent extraction process to obtain a dynamic countercurrent extraction process optimization parameter group;

[0023] Step S104, performing real-time analysis and processing on the dynamic countercurrent extraction process optimization parameter group to obtain parameter automatic control instruction sequence and process abnormality warning data;

[0024] Step S105: Based on the process abnormality warning data, the quality characteristic data of the extract after the execution of the parameter automatic control instruction sequence is processed to obtain a process quality assessment data set and a process traceability data set;

[0025] Step S106: performing correlation analysis on the process quality assessment data set and the process traceability data set to obtain a dynamic countercurrent extraction process parameter optimization rule base.

[0026] It is understandable that the execution subject of the present application can be a Chinese medicine extraction process optimization system based on dynamic countercurrent extraction, or a terminal or a server, which is not specifically limited here. The present application embodiment is described by taking the server as the execution subject as an example.

[0027] Specifically, the multi-dimensional feature data of Chinese medicinal materials are collected and processed, including three dimensions: morphological features, physical properties and spectral features. In the process of morphological feature collection, the color, shape and texture information of Chinese medicinal materials are obtained through high-definition image acquisition equipment, and the collected image data are converted into numerical feature vectors; physical feature collection mainly quantifies the bulk density, water content and particle size distribution of Chinese medicinal materials; spectral feature collection uses a near-infrared spectrometer to scan Chinese medicinal materials in the wavelength range of 800-2500nm to obtain spectral curve data. These multi-dimensional feature data are processed by data fusion to form a digital feature data set of Chinese medicinal materials. At the same time, according to the characteristics of the dynamic countercurrent extraction equipment, the initial range of process parameters such as temperature, pressure and flow rate are set to construct the initial data set of dynamic countercurrent extraction parameters. After obtaining the initial data set, the online monitoring data processing link is entered. By arranging temperature sensors, pressure sensors and flow sensors at key positions of the dynamic countercurrent extraction equipment, a multi-point monitoring network is established. Temperature monitoring points are mainly distributed at different heights of the extraction tank, pressure monitoring points are set at locations with significant pressure changes such as the feed port and the discharge port, and flow monitoring points are arranged at key nodes of the material conveying pipeline. The sensor acquisition frequency is set to once per second, and the real-time data of each monitoring point is aggregated through the data acquisition module to form a real-time monitoring data stream for the countercurrent extraction process, and it is correlated and analyzed with the digital feature data set of Chinese medicinal materials to generate parameter correlation data.

[0028] When multi-dimensional parameter optimization processing is performed on the collected real-time monitoring data stream and parameter association data, the data of the three dimensions of temperature, pressure and flow are first preprocessed. The temperature data is processed by layering to identify the characteristics of different temperature ranges, the pressure data is processed by pulse elimination algorithm to remove interference signals, and the flow data is processed by fluctuation correction to obtain stable flow characteristics. The preprocessed data is subjected to parameter combination analysis by clustering algorithm to determine the mutual influence relationship between the parameters. Through the verification and analysis of different parameter combinations, the parameter combinations with excellent performance are screened out, and these parameter combinations are optimized by genetic algorithm, and finally the dynamic countercurrent extraction process optimization parameter group is obtained. Real-time analysis and processing are performed on the optimized process parameter group, focusing on the parameter change trend and abnormal fluctuation. By setting the normal fluctuation thresholds of temperature, pressure and flow, when the parameters change beyond the threshold, the corresponding control instructions are generated. These control instructions are arranged according to the time series relationship to form a parameter automatic control instruction sequence. At the same time, the degree of deviation of the parameters from the normal range is calculated and graded to generate process abnormality warning data.

[0029] After completing the automatic control of process parameters, the quality characteristic data of the extract is processed. First, the extract is comprehensively analyzed by near-infrared spectroscopy, ultraviolet spectrophotometry, and gas chromatography-mass spectrometry to obtain characteristic spectral data, active ingredient content data, and compound component data. These data are processed by quality index calculation to form a process quality assessment data set. At the same time, the process data is timestamped and stored in the blockchain to form a traceable process data set. Finally, through correlation analysis, the process quality assessment data and process traceability data are analyzed in multiple dimensions. First, the data is classified according to the type of process parameters, and the degree of influence of different parameters on the extraction quality is analyzed. Then, the batch quality characteristics are statistically analyzed to identify the quality fluctuation pattern. Finally, through cross-validation and case analysis, the process rules are summarized and a dynamic countercurrent extraction process parameter optimization rule base is established.

[0030] For example, taking the optimization of the dynamic countercurrent extraction process of a Chinese herbal medicine as an example, the morphological characteristic data of the Chinese herbal medicine was obtained through image acquisition, including color value RGB (124, 156, 89), ellipticity 0.85, surface texture index 0.67, etc.; the physical property measurement obtained a bulk density of 0.45 g / cm 3 , water content 8.5%, particle size distribution 80-100 mesh accounting for 65% and other data; near-infrared spectrum scanning shows characteristic absorption peaks at 1100nm, 1450nm and 1940nm. The initial process parameter range is set based on experience: temperature 45-55℃, pressure 0.15-0.25MPa, flow rate 2-4L / min. During the extraction process, the dynamic change data of temperature, pressure and flow rate are obtained through real-time monitoring by sensors. After parameter optimization, the optimal process parameter combination is determined: temperature 50℃, pressure 0.2MPa, flow rate 3L / min. The quality test data of the extract show that under this process parameter, the yield of the active ingredient is increased by 15%, the batch-to-batch difference is reduced to within ±3%, and the process repeatability is good.

[0031] In the embodiment of the present application, through multi-dimensional feature data collection and processing, a comprehensive characterization of the physical and chemical properties of Chinese medicinal materials is achieved, a reliable data basis is provided for process parameter optimization, and a real-time data collection and parameter association analysis system is established through online monitoring data processing, ensuring dynamic monitoring and timely adjustment of the extraction process. The method adopts a multi-dimensional parameter optimization processing strategy, effectively integrates key process parameters such as temperature, pressure, and flow, improves the accuracy and scientificity of parameter optimization, and establishes an automatic control and abnormal warning system for process parameters through a real-time analysis and processing mechanism, enhancing the stability and controllability of the extraction process. The quality feature data processing method based on process abnormality warning data realizes the full-process monitoring and evaluation of the extract quality, ensures the reliability and consistency of product quality, and the introduction of the association analysis processing mechanism promotes the in-depth understanding of the relationship between process parameters and quality indicators, and provides a scientific basis for process optimization. In addition, the dynamic countercurrent extraction process parameter optimization rule base constructed by the present invention not only realizes the systematic accumulation and inheritance of process knowledge, but also provides strong support for the continuous optimization and improvement of process parameters, and effectively improves the intelligent level and industrial application ability of Chinese medicine extraction technology. Through the organic combination of these technical features, the accuracy, stability and controllability of the dynamic countercurrent extraction process of traditional Chinese medicine are significantly improved.

[0032] In a specific embodiment, the process of executing step S101 may specifically include the following steps:

[0033] (1) performing image data acquisition and processing on the Chinese medicinal materials to obtain morphological feature data including color, shape, and texture, and performing numerical processing on the morphological feature data to obtain morphological parameter data;

[0034] (2) Performing physical property testing on the Chinese medicinal materials to obtain physical property data including bulk density, water content, and particle size, and quantifying the physical property data to obtain physical parameter data;

[0035] (3) performing near-infrared spectral scanning processing on the Chinese medicinal materials to obtain spectral characteristic data, and performing wavelength correction processing on the spectral characteristic data to obtain spectral parameter data;

[0036] (4) performing feature fusion processing on the morphological parameter data, physical parameter data, and spectral parameter data to obtain a digital feature data set of Chinese medicinal materials;

[0037] (5) Performing parameter setting processing on the dynamic countercurrent extraction equipment, including process parameters of temperature, pressure, and flow rate, obtaining a candidate parameter set, and performing boundary processing on the candidate parameter set to obtain an initial data set of dynamic countercurrent extraction parameters.

[0038] Specifically, images of Chinese medicinal materials are collected by high-definition industrial cameras. During the collection process, the light intensity is controlled to be 1000 lux, the camera resolution is 4096×3072 pixels, and the collection distance is fixed at 30 cm. The collected original image is converted into RGB color feature values ​​through color space conversion, the shape features are obtained by calculating geometric parameters such as perimeter and area, and the texture features are obtained through grayscale co-occurrence matrix analysis. Color features include the mean and standard deviation of the three RGB channels, shape features include parameters such as roundness, aspect ratio, and area, and texture features include statistics such as energy, moment of inertia, and entropy. Through the feature extraction algorithm, these morphological feature data are numerically processed and converted into standardized morphological parameter data. In the physical property detection link, professional testing instruments are used to analyze the physical properties of Chinese medicinal materials. The bulk density test is measured by a special measuring cylinder, where the Chinese medicinal materials are naturally dropped into a measuring cylinder of known volume, and the mass-to-volume ratio is recorded; the moisture content test adopts the drying method, which is dried to constant weight at 105°C; the particle size analysis adopts the standard screening method, using 20-mesh, 40-mesh, 60-mesh, 80-mesh, and 100-mesh standard sieves, and the mass distribution of each particle size range is obtained through oscillation screening. These physical property data are converted into physical parameter data of uniform dimension through standardization processing to construct physical feature vectors.

[0039] Near infrared spectroscopy was collected using a Fourier transform near infrared spectrometer with a scanning range of 800-2500nm and a resolution of 2nm. Each sample was scanned 32 times and the average value was taken as the original spectral data. The original spectral data was first scattering corrected to eliminate the baseline drift caused by the sample particle size and surface roughness, and then the spectral data was preprocessed by multivariate scattering correction to obtain the corrected spectral parameter data. The feature fusion of morphological parameter data, physical parameter data and spectral parameter data adopted a hierarchical processing strategy. First, the data of each dimension was normalized to unify the data range to the interval of 0-1. Then, the principal component analysis method was used to perform feature fusion, extract the main feature vectors, and construct a digital feature data set of Chinese medicinal materials. This data set contains comprehensive feature information of Chinese medicinal materials, providing a data basis for subsequent process parameter optimization.

[0040] In the parameter setting phase of dynamic countercurrent extraction equipment, the initial range of process parameters is set according to the characteristics of Chinese medicinal materials and the requirements of the extraction process. The temperature parameter range is set at 30-60°C, taking into account the thermosensitivity of the effective ingredients of Chinese medicinal materials; the pressure parameter range is 0.1-0.4MPa to ensure the safety of the extraction process; the flow parameter range is 1-5L / min to ensure sufficient mass transfer effect. Through the orthogonal experimental design method, these parameters are combined to form a candidate parameter set. The candidate parameter set is subjected to boundary analysis to eliminate parameter combinations that do not meet process safety and equipment limitations, and finally the initial data set of dynamic countercurrent extraction parameters is obtained.

[0041] For example, taking the optimization of the dynamic countercurrent extraction process of Astragalus as an example, the sample image is first obtained through image acquisition, and the morphological feature data is obtained after processing: RGB value is (186, 157, 92), roundness is 0.76, surface energy value is 0.45, and these data are normalized to obtain the morphological parameter data group (0.73, 0.62, 0.36, 0.76, 0.45). The physical property test results show that the bulk density is 0.42g / cm 3 , the water content is 8.9%, the particle size distribution is 40-60 mesh accounting for 62%, 60-80 mesh accounting for 25%, and the rest accounting for 13%. Near-infrared spectral scanning shows obvious absorption peaks at 1450nm and 1940nm. Stable spectral characteristic data are obtained by 32 repeated scans and data averaging. After data fusion processing, 5 main eigenvectors are extracted to construct a characteristic data set. Based on these data features, the initial process parameter range is set: temperature 45-55℃, pressure 0.15-0.25MPa, flow rate 2-4L / min. 27 groups of parameter combinations are obtained through orthogonal design. After boundary analysis, 18 groups are retained as the initial parameter data set. These parameter combinations cover the main areas of the process parameter space.

[0042] In a specific embodiment, the process of executing step S102 may specifically include the following steps:

[0043] (1) Performing sensor point processing on the temperature parameters in the initial data set of dynamic countercurrent extraction parameters to obtain temperature collection point data, and performing periodic sampling processing on the temperature collection point data to obtain temperature monitoring data;

[0044] (2) performing sensor point processing on the pressure parameters in the initial data set of dynamic countercurrent extraction parameters to obtain pressure collection point data, and performing periodic sampling processing on the pressure collection point data to obtain pressure monitoring data;

[0045] (3) Performing sensor point processing on the flow parameters in the initial data set of dynamic countercurrent extraction parameters to obtain flow collection point data, and performing periodic sampling processing on the flow collection point data to obtain flow monitoring data;

[0046] (4) The temperature monitoring data, pressure monitoring data and flow monitoring data are processed in time series combination to obtain the real-time monitoring data stream of the countercurrent extraction process, and the digital feature data set of Chinese medicinal materials and the real-time monitoring data stream of the countercurrent extraction process are matched and associated to obtain parameter association data.

[0047] Specifically, a multi-point layout strategy is adopted for the online monitoring of the initial data set of dynamic countercurrent extraction parameters. First, the sensor points are arranged for the temperature parameters. The temperature sensors are arranged at the key positions of the extraction equipment, including the feed port, the discharge port, the top, middle and bottom of the extraction tank, with a total of 5 temperature collection points. The temperature sensor uses a PT100 platinum resistor with a measurement range of 0-100℃ and an accuracy of ±0.1℃. Each collection point collects data at a sampling frequency of 1 second. After the sampled data is processed by the signal conditioning circuit, it is converted into a standard 4-20mA current signal, and then converted into a digital signal through the data acquisition module to obtain temperature monitoring data. The sensor layout of the pressure parameter mainly considers the key points of pressure change during the extraction process. Pressure sensors are arranged at the feed pump outlet, the extraction tank inlet, the extraction tank outlet, the circulation pump outlet and other positions, and a total of 4 pressure collection points are set. A piezoresistive pressure sensor is used with a range of 0-0.6MPa and an accuracy of 0.1%. The pressure data collection also uses a sampling frequency of 1 second, and the pressure monitoring data is obtained through signal conditioning and analog-to-digital conversion. In order to eliminate the interference caused by pressure pulsation, the digital filtering algorithm was used to smooth the raw data. The sliding window size was set to 5 data points, and the average value was taken as the effective pressure value.

[0048] The flow parameter monitoring adopts electromagnetic flowmeter, and flow sensors are arranged at the feed port, discharge port, circulation loop and other locations of the material conveying pipeline, with a total of 3 flow collection points. The electromagnetic flowmeter range is 0-10L / min, and the accuracy is ±0.5%. The flow data collection frequency is also set to 1 second. After the collected raw data is calibrated by zero point and range, standardized flow monitoring data is obtained. In order to ensure the accuracy of the data, the flow mutation detection threshold is set during the data processing process. When the change of two adjacent sampling data exceeds 30%, it is marked as an abnormal data point for special processing. The time series combination of the three types of monitoring data of temperature, pressure and flow adopts the data synchronization collection strategy. Through timestamp alignment, the temperature, pressure and flow data at the same time are combined into a data vector to form a complete monitoring data stream. The data vector contains 12 parameters: 5 temperature values, 4 pressure values ​​and 3 flow values. To ensure the real-time nature of the data, a cache queue is used to store the data of the last 60 seconds to form a sliding time window to realize the dynamic update of the data.

[0049] When performing data matching and association, the characteristic parameters in the digital characteristic data set of Chinese medicinal materials are time-correlated with the monitoring data stream to establish the correlation between parameter changes and the characteristics of Chinese medicinal materials. The data association process uses the time window matching method. For each monitoring data point, the data before and after 30 seconds are extracted for analysis, the correlation between the parameter change trend and the characteristics of Chinese medicinal materials is calculated, and parameter association data is generated.

[0050] For example, during the dynamic countercurrent extraction of a batch of astragalus, the temperature data collected by the temperature sensor at 5 collection points are: 45.2℃ at the feed port, 44.8℃ at the discharge port, 46.1℃ at the tank top, 45.5℃ in the tank, and 45.3℃ at the tank bottom; the pressure data collected by the pressure sensor at 4 collection points are: 0.22MPa at the feed pump outlet, 0.20MPa at the extraction tank inlet, 0.18MPa at the extraction tank outlet, and 0.21MPa at the circulation pump outlet; the flow rate data collected by the flow sensor at 3 collection points are: 2.8L / min at the feed port, 2.7L / min at the discharge port, and 2.9L / min at the circulation loop. These data are collected synchronously and combined in time series to form a monitoring data stream. These data were correlated with the digital characteristic data of Chinese medicinal materials (such as particle size distribution of 40-60 mesh accounting for 62%, water content of 8.9%, etc.), and it was found that within the process parameter range of temperature 45-46°C, pressure 0.18-0.22MPa, and flow rate 2.7-2.9L / min, the extraction effect showed a significant correlation with the physical properties of Chinese medicinal materials.

[0051] In a specific embodiment, the process of executing step S103 may specifically include the following steps:

[0052] (1) performing data stratification processing on the temperature monitoring data in the real-time monitoring data stream of the countercurrent extraction process to obtain a stratified temperature data group, and performing parameter range division processing on the stratified temperature data group to obtain temperature control interval data;

[0053] (2) performing pulse elimination processing on the pressure monitoring data in the real-time monitoring data stream of the countercurrent extraction process to obtain a smoothed pressure data group, and performing parameter range division processing on the smoothed pressure data group to obtain pressure control interval data;

[0054] (3) performing fluctuation correction processing on the flow monitoring data in the real-time monitoring data stream of the countercurrent extraction process to obtain a steady-state flow data group, and performing parameter range division processing on the steady-state flow data group to obtain flow control interval data;

[0055] (4) combining the temperature control interval data, the pressure control interval data, and the flow control interval data by a clustering algorithm to obtain parameter combination data, and performing difference analysis on the parameter combination data to obtain parameter group difference data;

[0056] (5) cross-validating the parameter group difference data and the parameter association data to obtain parameter verification data, and screening the parameter verification data to obtain process parameter target data;

[0057] (6) The process parameter target data is iteratively optimized through a genetic algorithm to obtain an optimized parameter group for the dynamic countercurrent extraction process.

[0058] Specifically, multi-dimensional parameter optimization processing of real-time monitoring data streams involves multiple links. First, the temperature monitoring data is layered and processed, and the data is divided into three levels: high temperature layer, medium temperature layer and low temperature layer according to the temperature gradient. Based on the spatial distribution characteristics of the extracted tank, the top temperature is set as the high temperature layer (55-60℃), the middle temperature is set as the medium temperature layer (45-55℃), and the bottom temperature is set as the low temperature layer (35-45℃). Statistical analysis is performed on the temperature data of each layer, and the mean, standard deviation and coefficient of variation of the temperature of each layer are calculated to obtain the layered temperature data group. By setting the temperature control threshold, the layered temperature data is divided into different control intervals, including the optimal interval, acceptable interval and alarm interval, to form temperature control interval data. The processing of pressure monitoring data first requires eliminating the interference caused by pressure pulsation. The sliding average method is used to smooth the original pressure data, and the data is smoothed with a window size of 10 data points to eliminate short-term pressure fluctuations. The smoothed pressure data is then divided into high pressure range (0.3-0.4MPa), medium pressure range (0.2-0.3MPa) and low pressure range (0.1-0.2MPa) according to process requirements. Corresponding control limits are set for each range to obtain pressure control range data.

[0059] The fluctuation correction of flow monitoring data adopts digital filtering technology, and filters small fluctuations by setting the dead zone value ±0.2L / min, and corrects the data beyond the dead zone. The corrected flow data constitutes a steady-state flow data group, and then the flow range is divided into a large flow interval (4-5L / min), a medium flow interval (2-4L / min) and a small flow interval (1-2L / min) according to process requirements to form flow control interval data.

[0060] The K-means clustering algorithm is used to combine the control interval data of temperature, pressure and flow. The number of cluster centers is set to 5, and all parameter combinations are divided into 5 categories, each of which represents a process parameter combination mode. The clustering results are analyzed for differences, the inter-class distance and intra-class distance are calculated, the discrimination of different parameter combinations is evaluated, and the parameter group difference data is obtained.

[0061] The parameter group difference data was cross-validated with the parameter association data obtained in the early stage, and the grid search method was used to find the optimal solution in different parameter combinations. The grid search step size was set to: temperature 0.5℃, pressure 0.02MPa, flow rate 0.1L / min. By calculating the process indicator score corresponding to each parameter combination, the parameter combination with the top 20% score was screened out as the process parameter target data. Finally, the genetic algorithm was used to optimize the process parameter target data. The population size was set to 50, the chromosome coding length was 18 bits (6 bits each for temperature, pressure, and flow), the crossover probability was 0.8, the mutation probability was 0.1, and the number of iterations was 100 generations. The fitness function was used to evaluate the quality of each generation of individuals, and high-quality individuals were selected for crossover and mutation operations, and finally the dynamic countercurrent extraction process optimization parameter group was obtained.

[0062] For example, taking the optimization of the dynamic countercurrent extraction process of Astragalus as an example, the original temperature data fluctuated in the range of 45-55°C. Through stratification processing, the data distribution of the high temperature layer (53-55°C), the medium temperature layer (48-52°C), and the low temperature layer (45-47°C) was obtained. The original value of the pressure data was between 0.15-0.25MPa, and a smooth pressure curve was obtained after sliding average processing. The flow data was in the range of 2.5-3.5L / min, and a stable flow value was obtained through fluctuation correction. Cluster analysis combined these data into 5 types of process parameter combinations. After cross-validation and screening, the optimized parameter group was finally obtained: temperature 50°C, pressure 0.2MPa, flow 3.0L / min. This parameter combination showed good extraction effect and process stability in multiple verifications.

[0063] In a specific embodiment, the process of executing step S104 may specifically include the following steps:

[0064] (1) performing a change trend analysis on the temperature parameters in the dynamic countercurrent extraction process optimization parameter group to obtain temperature trend data, and performing a threshold comparison process on the temperature trend data to obtain a temperature control instruction;

[0065] (2) performing fluctuation amplitude analysis on the pressure parameters in the dynamic countercurrent extraction process optimization parameter group to obtain pressure fluctuation data, and performing threshold comparison processing on the pressure fluctuation data to obtain pressure control instructions;

[0066] (3) performing stability analysis on the flow parameters in the dynamic countercurrent extraction process optimization parameter group to obtain flow characteristic data, and performing threshold comparison processing on the flow characteristic data to obtain flow control instructions;

[0067] (4) performing time sequence processing on the temperature control instructions, the pressure control instructions, and the flow control instructions to obtain a parameter automatic control instruction sequence;

[0068] (5) The temperature trend data, pressure fluctuation data and flow characteristic data are processed by deviation calculation to obtain parameter deviation data, and the parameter deviation data are processed by abnormal classification to obtain process abnormality warning data.

[0069] Specifically, the temperature parameters are analyzed and processed for change trends, and the temperature change rate is calculated based on the sliding time window. The window size is set to 60 seconds and updated every 10 seconds. By comparing the temperature averages of adjacent time windows, the temperature change slope is calculated to determine the temperature change trend. When the temperature change slope exceeds the preset threshold of ±0.5℃ / min, the corresponding heating or cooling control instructions are generated. The temperature threshold comparison adopts three-level settings: normal range (target temperature ±1℃), warning range (target temperature ±2℃) and alarm range (target temperature ±3℃). The fluctuation amplitude analysis of the pressure parameter adopts the standard deviation method to calculate the standard deviation of each pressure sampling point and the previous 10 sampling points. When the standard deviation exceeds 0.02MPa, it is judged as an abnormal fluctuation. The pressure fluctuation data is threshold-compared, and the pressure fluctuation threshold is set to ±10% of the target pressure. When the pressure value exceeds the threshold range, a boost or decompression control instruction is generated. At the same time, different levels of control intensity are set for different pressure deviation ranges: fine-tuning instructions are used for slight deviations (±5%), moderate adjustment instructions are used for medium deviations (±10%), and strong adjustment instructions are used for severe deviations (>±10%).

[0070] The stability analysis of flow parameters is based on the calculation of the coefficient of variation. The coefficient of variation (the ratio of the standard deviation to the mean value) of the flow data is calculated with a statistical period of 30 seconds. When the coefficient of variation exceeds 5%, the flow is judged to be unstable. The threshold comparison of flow characteristic data adopts a two-way restriction method. The upper threshold is set to 110% of the target flow, and the lower threshold is set to 90% of the target flow. When the range is exceeded, the corresponding control instructions are generated. When the three types of control instructions of temperature, pressure, and flow are arranged in time sequence, the priority sorting method is adopted considering the mutual influence between the parameters. The temperature control instruction has the highest priority, followed by the pressure control instruction, and the flow control instruction has the lowest priority. Within the same priority, the instructions are arranged in the order of timestamps. Each control instruction contains four elements: parameter type, control direction, control amplitude, and execution time.

[0071] The calculation of parameter deviation adopts the weighted comprehensive scoring method, and its mathematical expression is:

[0072]

[0073] Among them, D is the comprehensive deviation, w i is the weight coefficient of each parameter, x i is the actual parameter value, x tiis the target parameter value, and n is the number of parameters. The weight coefficients of temperature, pressure, and flow rate are set to 0.4, 0.3, and 0.3 respectively. According to the calculated deviation value, the abnormal state is divided into three levels: slight abnormality (D ≤ 0.1), moderate abnormality (0.1 < D ≤ 0.2), and severe abnormality (D > 0.2).

[0074] For example: During the dynamic countercurrent extraction process of a certain batch of Astragalus membranaceus, the temperature parameter is set to 50 °C. During continuous monitoring, it is found that the temperature shows an upward trend, rising from 50.2 °C to 51.5 °C within 60 seconds. The calculated temperature change slope is 1.3 °C / min, exceeding the preset threshold of 0.5 °C / min, and a cooling control instruction is immediately generated. At the same time, the pressure monitoring shows a fluctuation from 0.20 MPa to 0.23 MPa, and the calculated standard deviation is 0.025 MPa, exceeding the fluctuation threshold of 0.02 MPa, generating a pressure reduction instruction. The average value of the flow rate data within a 30-second statistical period is 3.0 L / min, and the standard deviation is 0.18 L / min. The calculated coefficient of variation is 6%, exceeding the stability requirement, generating a flow rate adjustment instruction. After sorting these three instructions by priority, a control instruction sequence is generated. According to the real-time parameter value, the calculated comprehensive deviation D = 0.15, belonging to the moderate abnormal state, generating the corresponding warning information.

[0075] In a specific embodiment, the process of executing step S105 may specifically include the following steps:

[0076] (1) Perform near-infrared spectroscopy scanning on the extract to obtain spectral data in the wavelength range of 800 - 2500 nm, and perform baseline correction on the spectral data to obtain characteristic spectral data;

[0077] (2) Perform component content determination on the extract by ultraviolet spectrophotometry to obtain active ingredient content data, and perform comparison on the active ingredient content data to obtain component characteristic data;

[0078] (3) Perform gas chromatography - mass spectrometry analysis on the extract to obtain compound component data, and perform spectral analysis on the compound component data to obtain component characteristic data;

[0079] (4) Perform quality index calculation on the characteristic spectral data, component characteristic data, and component characteristic data to obtain a process quality evaluation data set, and perform correlation analysis on the process quality evaluation data set to obtain quality correlation data;

[0080] (5) Perform feature extraction on the process abnormal warning data and quality correlation data to obtain process feature data, and perform coding on the process feature data to obtain process coding data;

[0081] (6) The process coding data is timestamped to obtain time-series process data, and the time-series process data is stored in the blockchain to obtain a process traceability data set.

[0082] Specifically, multiple analytical methods are used to process the quality characteristic data of the extract. First, the extract is scanned using a Fourier transform near-infrared spectrometer, with the scanning range set at 800-2500nm and a resolution of 2nm. Each sample is scanned 32 times and the average value is taken. When collecting spectral data, the extract is placed in a 1mm quartz cuvette and measured at room temperature. The original spectral data is corrected for scattering and baseline drift to eliminate the interference caused by sample turbidity and particle size, and standardized characteristic spectral data is obtained. The position and intensity of the key absorption peaks in the characteristic spectral data reflect the information of the main components in the extract. In the determination of the active ingredient content, the extract is quantitatively analyzed by ultraviolet spectrophotometry. According to the characteristics of Chinese medicinal materials, an appropriate detection wavelength is selected, such as flavonoids have characteristic absorption at 280nm. Quantification is performed by establishing a standard curve, and each sample is measured 3 times, and the average value is taken as the final result. The active ingredient content data is compared with the standard requirements, the relative content percentage is calculated, and the content fluctuation range between batches is recorded to form complete ingredient characteristic data.

[0083] Gas chromatography-mass spectrometry analysis uses a capillary column, the temperature program is set to 50-280℃, and the heating rate is 10℃ / min. The mass spectrometer uses an electron bombardment ion source with a scanning range of m / z 50-500. By comparing with the standard spectral library, the compound components in the extract are identified, the retention time, relative content and mass spectrum characteristic peaks of each component are recorded, and a compound component database is constructed. The component data are analyzed for spectral similarity, and the common peaks and characteristic peaks of each batch of extracts are calculated to obtain component characteristic data. The quality index calculation and processing comprehensively evaluates the characteristic spectral data, component characteristic data and component characteristic data. A multidimensional quality evaluation system is established, including active ingredient content indicators, impurity control indicators and stability indicators. After standardized processing, the results of each indicator form a process quality assessment data set. The data set is analyzed for correlation, the correlation coefficients between the quality parameters are calculated, the key quality control points are identified, and the quality correlation data are obtained.

[0084] The extraction of process characteristic data is based on process abnormality warning data and quality-related data, and characteristic information including process parameter fluctuation range, quality indicator change trend, abnormality frequency, etc. is extracted. These characteristic information are converted into binary codes to construct standardized process coding data. Each code contains three parts: process parameter code, quality indicator code and time information code. A timestamp accurate to seconds is added to the process coding data to record the specific time point of each process operation and quality inspection to form time-series process data. Blockchain technology is used to store time-series process data. Each data block contains process code, timestamp, previous block hash value and digital signature to ensure that the data is not tampered with, achieve traceability of the entire process, and form a process traceability data set.

[0085] For example: During the dynamic countercurrent extraction of a batch of astragalus, near-infrared spectroscopy detected significant absorption peaks at 1450nm and 1940nm, with absorbances of 0.876 and 1.234, respectively. The content of astragaloside IV was determined by ultraviolet spectrophotometry to be 2.45mg / mL, which was compared with the standard requirement of 2.40mg / mL, and the relative content reached 102%. Gas chromatography-mass spectrometry detected 20 main compound peaks, 15 of which were characteristic components, with a relative content between 0.5% and 15%. The quality assessment results showed that the yield of active ingredients was 95.8%, the impurity content was less than 0.2%, and the content of three consecutive batches of samples fluctuated within ±3%. The process parameter records showed that the temperature was maintained at 50±1℃, the pressure was 0.20±0.02MPa, and the flow rate was 3.0±0.2L / min. These data are encoded and timestamps are added, such as "2024032115301500" represents the record at 15:30:15 on March 21, 2024, forming a complete traceability chain.

[0086] In a specific embodiment, the process of executing step S106 may specifically include the following steps:

[0087] (1) Perform multi-dimensional classification processing on the process quality assessment data set, and divide it into temperature assessment data, pressure assessment data, and flow assessment data according to the process parameter type, and perform parameter correlation analysis on the temperature assessment data, pressure assessment data, and flow assessment data to obtain parameter impact data;

[0088] (2) performing statistical distribution processing on the batch quality characteristic data in the process quality assessment data set to obtain quality distribution data, and performing fluctuation feature extraction processing on the quality distribution data to obtain quality characteristic regularity data;

[0089] (3) The process traceability data set is segmented according to the time series to obtain the time series process data, and the time series process data is matched with the process parameters to obtain the process law data;

[0090] (4) Cross-validate the parameter influence data and quality characteristic law data to obtain process quality association rules, and conduct case analysis on the process quality association rules to obtain process experience data;

[0091] (5) The process law data and process experience data are hierarchically summarized to obtain process rule data, and the process rule data are hierarchically stored to obtain a dynamic countercurrent extraction process parameter optimization rule base.

[0092] Specifically, the data is classified. According to the type of process parameters, the data is divided into three categories: temperature evaluation data contains information such as temperature change curve, temperature control accuracy, and temperature gradient distribution during the extraction process; pressure evaluation data contains parameters such as pressure fluctuation range, pressure uniformity, and pressure response time; flow evaluation data contains flow stability, flow distribution uniformity, and liquid circulation efficiency. The three types of data are subjected to correlation analysis, and the Pearson correlation coefficient is used to calculate the degree of correlation between parameters, and a parameter influence matrix is ​​established to identify the degree of influence of each parameter on the extraction effect. The statistical distribution processing of batch quality characteristic data adopts the normal distribution test method to calculate the mean, standard deviation, and coefficient of variation of each batch. The normality of data distribution is judged by the chi-square test to determine the fluctuation range of quality indicators. Feature extraction is performed on quality distribution data, including the central tendency, discrete degree, and distribution morphology characteristics of the data, so as to obtain quality characteristic regularity data. The quality fluctuation characteristics mainly examine the variation between consecutive batches, including batch differences, trend changes, and periodic fluctuations.

[0093] The sliding window method is used to process the time series of the process traceability data set, with the window size set to 30 minutes and the step length set to 5 minutes. In each time window, the statistical characteristics of the process parameters are extracted, including the mean, maximum, minimum, variance, etc. The extracted features are arranged in chronological order to form time series process data. The process parameters are matched for the time series process data, the corresponding relationship between parameter changes and quality indicators is established, and the influence of process parameter changes on the extraction effect is analyzed. The cross-validation of parameter influence data and quality characteristic law data uses a grid search method to find the optimal combination in the parameter space. During the verification process, the quality indicator scores under different parameter combinations are calculated, and process quality association rules are established. Case analysis is performed on these rules, and the change rules of quality indicators under different process conditions are summarized to form a process experience database.

[0094] The hierarchical induction of process regularity data and process experience data adopts the decision tree method to classify process rules according to importance and frequency of use. The first-level rules are the basic principles that must be followed, the second-level rules are references for optimization and adjustment, and the third-level rules are suggestions for handling special situations. The process rule data is stored in a hierarchical manner to build a complete dynamic countercurrent extraction process parameter optimization rule library.

[0095] For example, in the optimization of the dynamic countercurrent extraction process of a batch of Astragalus, the process quality evaluation data was classified and analyzed. The temperature evaluation data showed that the average extraction temperature was 50.2℃, the standard deviation was ±0.3℃, and the temperature control accuracy was 99%; the pressure evaluation data showed that the average pressure was 0.20MPa, the pressure fluctuation range was ±0.02MPa, and the pressure response time was less than 5 seconds; the flow evaluation data showed that the average flow rate was 3.0L / min, and the flow stability was 98%. Through correlation analysis, it was found that the correlation coefficient between temperature and effective ingredient extraction rate was 0.85, the correlation coefficient between pressure and extraction time was 0.72, and the correlation coefficient between flow rate and extraction uniformity was 0.68. The statistics of quality characteristic data showed that the average effective ingredient content of 10 consecutive batches of samples was 2.45mg / mL, the standard deviation was 0.07mg / mL, and the coefficient of variation was 2.8%. The time series analysis showed that the process parameters tended to be stable after 30 minutes of extraction, and the fluctuation of quality indicators decreased. The process rules established based on these data are divided into three levels: the first-level rule stipulates that the temperature must be controlled within the range of 48-52°C, the second-level rule recommends that the pressure fluctuation should not exceed ±0.02MPa, and the third-level rule provides specific suggestions for flow regulation. After verification, these rules are stored in the optimization rule library to provide a reference for subsequent process optimization.

[0096] The above describes the method for optimizing the extraction process of traditional Chinese medicine based on dynamic countercurrent extraction in the embodiment of the present application. The following describes the optimization system for the extraction process of traditional Chinese medicine based on dynamic countercurrent extraction in the embodiment of the present application. Figure 2 In the embodiment of the present application, an embodiment of a Chinese medicine extraction process optimization system based on dynamic countercurrent extraction includes:

[0097] The acquisition module is used to collect and process multi-dimensional characteristic data of Chinese medicinal materials to obtain a digital characteristic data set of Chinese medicinal materials and an initial data set of dynamic countercurrent extraction parameters;

[0098] A monitoring module, used for performing online monitoring data processing on the digital characteristic data set of Chinese medicinal materials and the initial data set of dynamic countercurrent extraction parameters to obtain real-time monitoring data flow and parameter correlation data of the countercurrent extraction process;

[0099] An optimization module, used for performing multi-dimensional parameter optimization processing on the real-time monitoring data stream of the countercurrent extraction process and the parameter association data to obtain a dynamic countercurrent extraction process optimization parameter group;

[0100] An analysis module is used to perform real-time analysis and processing on the dynamic countercurrent extraction process optimization parameter group to obtain parameter automatic control instruction sequence and process abnormality warning data;

[0101] A processing module, for performing quality feature data processing on the extract after the execution of the parameter automatic control instruction sequence based on the process abnormality warning data, to obtain a process quality assessment data set and a process traceability data set;

[0102] The association module is used to perform association analysis on the process quality assessment data set and the process traceability data set to obtain a dynamic countercurrent extraction process parameter optimization rule base.

[0103] Through the synergy of the above-mentioned components, through multi-dimensional feature data collection and processing, a comprehensive characterization of the physical and chemical properties of Chinese medicinal materials is achieved, providing a reliable data basis for process parameter optimization, and establishing a real-time data collection and parameter correlation analysis system through online monitoring data processing, ensuring dynamic monitoring and timely adjustment of the extraction process. The method adopts a multi-dimensional parameter optimization processing strategy, effectively integrates key process parameters such as temperature, pressure, and flow, improves the accuracy and scientificity of parameter optimization, and establishes an automatic control and abnormal warning system for process parameters through a real-time analysis and processing mechanism, enhancing the stability and controllability of the extraction process. The quality feature data processing method based on process abnormality warning data realizes the full-process monitoring and evaluation of the extract quality, ensures the reliability and consistency of product quality, and the introduction of the correlation analysis processing mechanism promotes the in-depth understanding of the relationship between process parameters and quality indicators, providing a scientific basis for process optimization. In addition, the dynamic countercurrent extraction process parameter optimization rule base constructed by the present invention not only realizes the systematic accumulation and inheritance of process knowledge, but also provides strong support for the continuous optimization and improvement of process parameters, and effectively improves the intelligent level and industrial application ability of Chinese medicine extraction technology. Through the organic combination of these technical features, the accuracy, stability and controllability of the dynamic countercurrent extraction process of traditional Chinese medicine are significantly improved.

[0104] The present application also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium, wherein instructions are stored in the computer, and when the instructions are executed on a computer, the computer executes the steps of the method for optimizing the extraction process of traditional Chinese medicine based on dynamic countercurrent extraction.

[0105] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0106] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program codes.

[0107] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for optimizing the extraction process of traditional Chinese medicine based on dynamic countercurrent extraction, characterized in that: The traditional Chinese medicine extraction process optimization method based on dynamic countercurrent extraction comprises: Collect and process multi-dimensional characteristic data of Chinese medicinal materials to obtain a digital characteristic data set of Chinese medicinal materials and an initial data set of dynamic countercurrent extraction parameters; Performing online monitoring data processing on the digital characteristic data set of Chinese medicinal materials and the initial data set of dynamic countercurrent extraction parameters to obtain real-time monitoring data flow and parameter correlation data of the countercurrent extraction process; Performing multi-dimensional parameter optimization processing on the real-time monitoring data stream of the countercurrent extraction process and the parameter association data to obtain a dynamic countercurrent extraction process optimization parameter group; Performing real-time analysis and processing on the dynamic countercurrent extraction process optimization parameter group to obtain parameter automatic control instruction sequence and process abnormality warning data; Based on the process abnormality warning data, quality feature data processing is performed on the extract after the execution of the parameter automatic control instruction sequence to obtain a process quality assessment data set and a process traceability data set; The process quality assessment data set and the process traceability data set are subjected to correlation analysis to obtain a dynamic countercurrent extraction process parameter optimization rule base.

2. The method for optimizing the extraction process of traditional Chinese medicine based on dynamic countercurrent extraction according to claim 1, characterized in that: The multi-dimensional feature data collection and processing of the Chinese medicinal materials is performed to obtain a digital feature data set of the Chinese medicinal materials and an initial data set of dynamic countercurrent extraction parameters, including: Performing image data acquisition and processing on the Chinese medicinal materials to obtain morphological feature data including color, shape, and texture, and performing numerical processing on the morphological feature data to obtain morphological parameter data; Performing physical property detection on the Chinese medicinal materials to obtain physical property data including bulk density, water content, and particle size, and quantifying the physical property data to obtain physical parameter data; Performing near-infrared spectral scanning processing on the Chinese medicinal materials to obtain spectral characteristic data, and performing wavelength correction processing on the spectral characteristic data to obtain spectral parameter data; Performing feature fusion processing on the morphological parameter data, physical parameter data and spectral parameter data to obtain a digital feature data set of Chinese medicinal materials; The dynamic countercurrent extraction equipment is subjected to parameter setting processing, including process parameters of temperature, pressure and flow rate, to obtain a candidate parameter set, and the candidate parameter set is subjected to boundary processing to obtain an initial data set of the dynamic countercurrent extraction parameters.

3. The method for optimizing the extraction process of traditional Chinese medicine based on dynamic countercurrent extraction according to claim 1, characterized in that: The online monitoring data processing of the digital characteristic data set of Chinese medicinal materials and the initial data set of dynamic countercurrent extraction parameters is performed to obtain real-time monitoring data flow and parameter association data of the countercurrent extraction process, including: Performing sensor point processing on the temperature parameters in the initial data set of the dynamic countercurrent extraction parameters to obtain temperature collection point data, and performing periodic sampling processing on the temperature collection point data to obtain temperature monitoring data; Performing sensor point processing on the pressure parameters in the initial data set of the dynamic countercurrent extraction parameters to obtain pressure collection point data, and performing periodic sampling processing on the pressure collection point data to obtain pressure monitoring data; Performing sensor point processing on the flow parameters in the initial data set of the dynamic countercurrent extraction parameters to obtain flow collection point data, and performing periodic sampling processing on the flow collection point data to obtain flow monitoring data; The temperature monitoring data, pressure monitoring data and flow monitoring data are processed in time series combination to obtain a real-time monitoring data stream of the countercurrent extraction process, and the digital characteristic data set of Chinese medicinal materials and the real-time monitoring data stream of the countercurrent extraction process are matched and associated to obtain parameter association data.

4. The method for optimizing the extraction process of traditional Chinese medicine based on dynamic countercurrent extraction according to claim 1, characterized in that: The real-time monitoring data stream of the countercurrent extraction process and the parameter association data are subjected to multi-dimensional parameter optimization processing to obtain a dynamic countercurrent extraction process optimization parameter group, including: Performing data stratification processing on the temperature monitoring data in the real-time monitoring data stream of the countercurrent extraction process to obtain a stratified temperature data group, and performing parameter range division processing on the stratified temperature data group to obtain temperature control interval data; Performing pulse elimination processing on the pressure monitoring data in the real-time monitoring data stream of the countercurrent extraction process to obtain a smoothed pressure data group, and performing parameter range division processing on the smoothed pressure data group to obtain pressure control interval data; Performing fluctuation correction processing on the flow monitoring data in the real-time monitoring data stream of the countercurrent extraction process to obtain a steady-state flow data group, and performing parameter range division processing on the steady-state flow data group to obtain flow control interval data; Combining the temperature control interval data, the pressure control interval data and the flow control interval data by a clustering algorithm to obtain parameter combination data, and performing difference analysis on the parameter combination data to obtain parameter group difference data; Cross-validating the parameter group difference data and the parameter association data to obtain parameter verification data, and screening the parameter verification data to obtain process parameter target data; The process parameter target data is iteratively optimized through a genetic algorithm to obtain a dynamic countercurrent extraction process optimization parameter group.

5. The method for optimizing the extraction process of traditional Chinese medicine based on dynamic countercurrent extraction according to claim 1, characterized in that: The real-time analysis and processing of the dynamic countercurrent extraction process optimization parameter group to obtain the parameter automatic control instruction sequence and process abnormality warning data includes: Performing a change trend analysis on the temperature parameters in the dynamic countercurrent extraction process optimization parameter group to obtain temperature trend data, and performing a threshold comparison on the temperature trend data to obtain a temperature control instruction; Performing fluctuation amplitude analysis on the pressure parameters in the dynamic countercurrent extraction process optimization parameter group to obtain pressure fluctuation data, and performing threshold comparison processing on the pressure fluctuation data to obtain pressure control instructions; Performing stability analysis on the flow parameters in the dynamic countercurrent extraction process optimization parameter group to obtain flow characteristic data, and performing threshold comparison on the flow characteristic data to obtain flow control instructions; Performing time sequence processing on the temperature control instructions, the pressure control instructions and the flow control instructions to obtain a parameter automatic control instruction sequence; Deviation calculation processing is performed on the temperature trend data, pressure fluctuation data and flow characteristic data to obtain parameter deviation data, and abnormality classification processing is performed on the parameter deviation data to obtain process abnormality warning data.

6. The method for optimizing the extraction process of traditional Chinese medicine based on dynamic countercurrent extraction according to claim 1, characterized in that: Based on the process abnormality warning data, the extract after the execution of the parameter automatic control instruction sequence is processed with quality characteristic data to obtain a process quality assessment data set and a process traceability data set, including: Performing near-infrared spectral scanning on the extract to obtain spectral data in the wavelength range of 800-2500 nm, and performing baseline correction on the spectral data to obtain characteristic spectral data; Performing component content determination processing on the extract by ultraviolet spectrophotometry to obtain active component content data, and performing comparison processing on the active component content data to obtain component characteristic data; Performing gas chromatography-mass spectrometry analysis on the extract to obtain compound component data, and performing spectrum analysis on the compound component data to obtain component characteristic data; Performing quality index calculation processing on the characteristic spectrum data, the composition characteristic data and the component characteristic data to obtain a process quality assessment data set, and performing correlation analysis processing on the process quality assessment data set to obtain quality correlation data; Performing feature extraction processing on the process abnormality warning data and the quality-related data to obtain process feature data, and performing encoding processing on the process feature data to obtain process encoding data; The process coding data is timestamped to obtain time-series process data, and the time-series process data is stored in a blockchain to obtain a process traceability data set.

7. The method for optimizing the extraction process of traditional Chinese medicine based on dynamic countercurrent extraction according to claim 1, characterized in that: The process quality assessment data set and the process traceability data set are subjected to correlation analysis to obtain a dynamic countercurrent extraction process parameter optimization rule base, including: Performing multi-dimensional classification processing on the process quality assessment data set, dividing it into temperature assessment data, pressure assessment data and flow assessment data according to the process parameter type, and performing parameter correlation analysis processing on the temperature assessment data, pressure assessment data and flow assessment data to obtain parameter impact data; Performing statistical distribution processing on the batch quality feature data in the process quality assessment data set to obtain quality distribution data, and performing fluctuation feature extraction processing on the quality distribution data to obtain quality feature regularity data; Performing data segmentation processing on the process traceability data set according to the time series to obtain time series process data, and performing process parameter matching processing on the time series process data to obtain process regularity data; Cross-validation processing is performed on the parameter influence data and the quality characteristic law data to obtain process quality association rules, and case analysis processing is performed on the process quality association rules to obtain process experience data; The process regularity data and the process experience data are hierarchically summarized to obtain process rule data, and the process rule data are hierarchically stored to obtain a dynamic countercurrent extraction process parameter optimization rule base.

8. A Chinese medicine extraction process optimization system based on dynamic countercurrent extraction, used to implement the Chinese medicine extraction process optimization method based on dynamic countercurrent extraction as described in any one of claims 1 to 7, characterized in that: The traditional Chinese medicine extraction process optimization system based on dynamic countercurrent extraction comprises: The acquisition module is used to collect and process multi-dimensional characteristic data of Chinese medicinal materials to obtain a digital characteristic data set of Chinese medicinal materials and an initial data set of dynamic countercurrent extraction parameters; A monitoring module, used for performing online monitoring data processing on the digital characteristic data set of Chinese medicinal materials and the initial data set of dynamic countercurrent extraction parameters to obtain real-time monitoring data flow and parameter correlation data of the countercurrent extraction process; An optimization module, used for performing multi-dimensional parameter optimization processing on the real-time monitoring data stream of the countercurrent extraction process and the parameter association data to obtain a dynamic countercurrent extraction process optimization parameter group; An analysis module is used to perform real-time analysis and processing on the dynamic countercurrent extraction process optimization parameter group to obtain parameter automatic control instruction sequence and process abnormality warning data; A processing module, for performing quality feature data processing on the extract after the execution of the parameter automatic control instruction sequence based on the process abnormality warning data, to obtain a process quality assessment data set and a process traceability data set; The association module is used to perform association analysis on the process quality assessment data set and the process traceability data set to obtain a dynamic countercurrent extraction process parameter optimization rule base.

9. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by the processor, the method for optimizing the extraction process of traditional Chinese medicine based on dynamic countercurrent extraction as described in any one of claims 1 to 7 is implemented.

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