Traditional Chinese medicine composition for preventing and treating cardiovascular and cerebrovascular diseases and traditional Chinese medicine detection system

Abstract

The invention discloses a traditional Chinese medicine composition for preventing and treating cardiovascular and cerebrovascular diseases and a traditional Chinese medicine detection system, and relates to the technical field of traditional Chinese medicine detection. Five functional modules of raw material component identification, component uniformity detection, stability prediction, data processing feedback and visual monitoring are integrated; accurate identification of raw material components of the traditional Chinese medicine extract, real-time evaluation of mixing uniformity and predictive analysis of component stability under a high-temperature and high-pressure process are realized; the system carries out normalization and trend modeling on multi-batch data, automatically identifies abnormal fluctuation and feeds back a regulation and control signal to a production control system, and assists an operator in fast decision making in combination with a visual interface, so that the problems of component interference, poor uniformity, insufficient stability, large quality fluctuation and the like in traditional Chinese medicine tablet production are effectively solved, and the production efficiency is improved. The consistency, the safety and the intelligent level of the preparation are obviously improved.

Description

Technical Field

[0001] The present invention relates to the technical field of traditional Chinese medicine detection, and particularly relates to a traditional Chinese medicine composition for preventing and treating cardiovascular and cerebrovascular diseases and a traditional Chinese medicine detection system. Background Art

[0002] Cardiovascular and cerebrovascular diseases have long ranked first in the global disease mortality rate, and are characterized by high disability rate and high recurrence rate, and have become a major public health problem seriously threatening human life and health. Existing clinical treatment methods mostly rely on long-term intervention with western medicines, such as antihypertensive drugs, statin drugs, etc. Although they can control blood pressure and blood lipids in the short term, they often have obvious side effects and strong long-term dependence. Under the guidance of traditional Chinese medicine theory, the concept of homology of food and medicine has gradually been taken seriously, emphasizing the activation of the body's own regulatory mechanism to achieve the purposes of "preventing diseases before they occur" and "treating both the symptoms and the root causes".

[0003] The existing technology has the following deficiencies:

[0004] In the production of traditional Chinese medicine preparations for preventing and treating cardiovascular and cerebrovascular diseases, multiple high-concentration plant extracts may interfere with each other during the tablet pressing process of the preparation, resulting in uneven distribution of the effective components in the tablets, and ultimately causing fluctuations in the curative effect. For example, Cordyceps cicadae and Monascus purpureus extract are prone to chemical instability reactions under high temperature and high pressure, which in turn affects the activity retention of lovastatin and cordycepin. At the same time, due to the fact that traditional Chinese medicine production mainly relies on manual experience and lacks a real-time component detection system, it is extremely easy to cause inconsistent curative effects between different batches, and even lead to enhanced or weakened individual reactions after patients take them. Summary of the Invention

[0005] The purpose of the present invention is to provide a traditional Chinese medicine composition for preventing and treating cardiovascular and cerebrovascular diseases and a traditional Chinese medicine detection system to solve the deficiencies in the background art.

[0006] To achieve the above purpose, the present invention provides the following technical solution: A traditional Chinese medicine detection system for preventing and treating cardiovascular and cerebrovascular diseases, including a raw material component identification module, a component uniformity detection module, a stability prediction module, a data processing and feedback module, and a visualization monitoring module;

[0007] The raw material component identification module is used to perform near-infrared spectroscopy scanning on the traditional Chinese medicine extract raw materials in the preparation and extract characteristic wavelength information;

[0008] The component uniformity detection module is used to perform multi-point spectral sampling on the mixed materials before and after tablet pressing, and judge the distribution uniformity of each component in the preparation by constructing a multivariate regression model;

[0009] The stability prediction module is used to simulate the high temperature and high pressure preparation process conditions, analyze the chemical compatibility between plant extracts in real time, and predict the activity retention rate of different components during the preparation process;

[0010] A data processing and feedback module that normalizes and trend-analyzes the detection data of different batches. If the component fluctuation exceeds the set threshold, it automatically feeds back a control signal to the production control system;

[0011] A visualization monitoring module that is used to display the detection data, risk warning information, and recommended production process adjustment parameters, assisting operators in achieving intelligent production management.

[0012] Preferably, the specific steps of multi-point spectral sampling include:

[0013] Spread the mixed traditional Chinese medicine raw material on the optical scanning tray, with the thickness controlled at 3 - 5 mm;

[0014] Divide the sample area into several equal grid units in the software, numbered P1 - Pn, to form a multi-point sampling matrix;

[0015] Control the near-infrared scanning probe to sequentially collect spectra at the center of each grid, and collect reflectance data within the complete wavelength range at each point;

[0016] Repeat the scan 2 - 3 times at the same position and take the average;

[0017] Match the original spectral data of each sampling point with the corresponding coordinate number one by one, and store it as a sampling point matrix.

[0018] Preferably, the specific steps of judging the component distribution uniformity based on a multi-variable regression model include:

[0019] Use the preset component characteristic wavelength range to extract features from the spectra of all sampling points;

[0020] Introduce known standard samples, and establish a mathematical model between component concentration and spectral characteristics using partial least squares regression or support vector regression to calibrate the component prediction accuracy;

[0021] Input the spectra of each sampling point into the trained regression model, output the concentration estimate of the corresponding pharmacodynamic component, and form a full-region concentration distribution map;

[0022] Calculate the ratio of the average value and standard deviation of all point concentrations to obtain the coefficient of variation CV.

[0023] Preferably, the judgment criteria are set as follows: when CV ≤ 5%, it is judged as highly uniform; 5% < CV ≤ 10%, it is judged as basically uniform; CV > 10%, it is judged as non-uniform, triggering a system alarm or prompting re-mixing.

[0024] Preferably, the initial experimental concentration C0, the degradation rate constant s and the degradation time t are obtained; the component concentration change is calculated by a zero-order reaction model or a first-order reaction model, and the activity retention rate is calculated based on the calculated component concentration change.

[0025] Preferably, in the data processing and feedback module, the coefficient of variation and activity retention rate of each type of component in different batches are normalized so that they are all between [0,1], the normalized coefficient of variation and activity retention rate are sorted by time, a time series is constructed, and abnormal trends are identified through principal component analysis.

[0026] Preferably, the component detection results of nearly N batches are collected, including the normalized coefficient of variation of each component and the normalized activity retention rate of each component, and a time series matrix is constructed to center the time series data matrix D; the covariance matrix Σ is calculated; the eigenvalues and corresponding eigenvectors of the covariance matrix are solved to extract the principal components; the first k principal components are selected according to the cumulative variance contribution rate, the principal component space is constructed, and the component detection data of each batch is projected into the principal component space to obtain the score of each batch in the principal component direction; the principal component score time series is constructed, and the mean and standard deviation of the principal component score series are calculated; if the score of a batch satisfies: the absolute value of the batch score minus the mean is greater than twice the standard deviation, it is determined that the batch has abnormal quality fluctuations or a sudden change in component stability, the abnormal batch is marked, and the original data is associated, and if the detection value of a component exceeds the preset range, the feedback mechanism is triggered.

[0027] The present invention also provides a Chinese medicine composition for preventing and treating cardiovascular and cerebrovascular diseases. The composition is in the form of tablets and is composed of the following ingredients of the same origin as medicine and food by weight:

[0028] American ginseng water extract 7%-10%;

[0029] Panax notoginseng water extract 7%-10%;

[0030] Salvia miltiorrhiza aqueous extract 7%-10%;

[0031] Ginkgo biloba leaf water extract 9%-13%;

[0032] Cordyceps sinensis water extract 18%-23%;

[0033] Bamboo leaf flavonoids 10%-15%;

[0034] Lecithin 10%-15%;

[0035] Red yeast rice extract 4%-6%;

[0036] Excipients 20%-28%.

[0037] Preferably, the preparation steps of the composition include: weighing and pulverizing the traditional Chinese medicine extract into fine powder of 80 mesh; putting each component into a high-speed mixer and mixing for 15 minutes; adding excipients and mixing again for 10 minutes; granulating by wet method and drying to a water content not higher than 5%; using a rotary tablet press to obtain tablets with a mass of 0.5 g per tablet; packaging the tablets with aluminum-plastic blister and storing them in the dark at room temperature.

[0038] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0039] Based on the concept of medicine and food homology, the traditional Chinese medicine composition provided by the present invention selects natural ingredients such as American ginseng, notoginseng, salvia miltiorrhiza, ginkgo biloba leaf, cicada flower cordyceps, etc. with cardiovascular regulation effects, and is scientifically proportioned with bamboo leaf flavonoids, lecithin and red yeast rice extract, and has the comprehensive effects of synergistically improving blood lipid, reducing blood pressure and promoting blood circulation, antioxidation and enhancing blood vessel elasticity. The tablet dosage form is convenient to take and the formula is stable. The preliminary clinical application results show that it has a significant blood pressure regulation and blood lipid control effect on patients with hypertension and hyperlipidemia, and at the same time, no obvious adverse reactions are seen, and the patient compliance is high, which is suitable for long-term use as adjuvant treatment and preventive health care.

[0040] Aiming at the problems existing in the production process of traditional Chinese medicine preparations, such as uneven components, thermosensitive degradation, and quality fluctuation between batches, the present invention also constructs an intelligent traditional Chinese medicine detection system, which integrates raw material identification, multi-point spectral sampling, multi-variable modeling, stability prediction and data feedback mechanism. Through near-infrared spectroscopy technology and principal component analysis method, it can identify the component concentration fluctuation in real time and automatically generate process adjustment suggestions, significantly improving the quality consistency and production intelligent level in the preparation process, and providing a reliable quality control means for the modernization of traditional Chinese medicine. Description of the Drawings

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required to be used in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained according to these drawings.

[0042] Figure 1 It is the system module diagram of the present invention. Detailed Embodiments

[0043] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0044] Example 1. Refer to Figure 1 As shown, a traditional Chinese medicine detection system for preventing and treating cardiovascular and cerebrovascular diseases in this embodiment includes a raw material component identification module, a component uniformity detection module, a stability prediction module, a data processing and feedback module, and a visualization monitoring module.

[0045] The raw material component identification module is used to perform near-infrared spectroscopy scanning on the raw materials of traditional Chinese medicine extracts in the preparation and extract characteristic wavelength information.

[0046] The component uniformity detection module is used to perform multi-point spectral sampling on the mixed materials before and after tabletting, and judge the distribution uniformity of each component in the preparation by constructing a multivariate regression model.

[0047] The stability prediction module is used to simulate the process conditions of high-temperature and high-pressure preparations, analyze the chemical compatibility between plant extracts in real time, and predict the activity retention rate of different components during the preparation process.

[0048] The data processing and feedback module performs normalization processing and trend analysis on the detection data of different batches. If the component fluctuation exceeds the set threshold, an automatic feedback control signal is sent to the production control system.

[0049] The visualization monitoring module is used to display the detection data, risk warning information, and recommended production process adjustment parameters to assist the operator in realizing intelligent production management.

[0050] The raw material component identification module is one of the core components of this traditional Chinese medicine detection system. Its main function is to quickly and non-destructively identify the types of plant extracts in the traditional Chinese medicine composition qualitatively and preliminarily quantitatively to ensure the quality consistency and component controllability of the preparation raw materials. This module adopts near-infrared spectroscopy technology, combines characteristic wavelength extraction and chemometric modeling methods, and adapts to the multi-component detection requirements in the complex matrix (basic substance content) environment of traditional Chinese medicine.

[0051] In the prior art, near-infrared spectroscopy has been widely used in the compositional analysis of fields such as food, medicine, and agricultural products. Its principle is based on the vibration absorption characteristics of groups such as –CH, –OH, and –NH in molecules, and it can achieve rapid scanning of various organic components within the wavelength range of 780–2500 nm. In the detection of traditional Chinese medicine raw materials, NIR can capture their unique spectral fingerprint maps by scanning extract powders or solution samples.

[0052] In the present invention, the raw material component identification module first loads the traditional Chinese medicine extract sample into a special colorimetric cuvette or coats it on an optical platform, and collects full-spectrum data through a high-resolution NIR probe. For the preset characteristic wavelength intervals of the system, such as tanshinones (980 nm - 1020 nm), total flavonoids (1150 nm - 1230 nm), lovastatin (1360 nm - 1450 nm), cordycepin (1580 nm - 1650 nm), etc., the original spectrum is subjected to feature extraction and noise reduction processing through algorithms such as partial least squares (PLS) and principal component analysis (PCA).

[0053] To improve the recognition accuracy, this module pre-sets a standard spectral library of traditional Chinese medicine components trained based on historical data, covering spectral changes under different variables such as extraction processes, origins, and batches. The system realizes qualitative judgment of components by comparing the target sample spectrum with the standard spectral curve in real time; on this basis, semi-quantitative estimation of the main components is realized through an inversion model to ensure that each traditional Chinese medicine extract meets the formula requirements.

[0054] In addition, this module can also be linked with batch two-dimensional codes or raw material source databases to realize double tracing of raw material batch information and spectral fingerprints, ensuring the management of raw material consistency. Compared with traditional high-performance liquid chromatography (HPLC) or manual sensory judgment, this module has the advantages of being fast, non-destructive, automated, and deployable on-site, significantly improving the efficiency of raw material quality control and the intelligent level in the production process of traditional Chinese medicine preparations.

[0055] The specific steps of multi-point spectral sampling include:

[0056] Spread the mixed traditional Chinese medicine raw material on the optical scanning tray, with the thickness controlled at 3 - 5 mm to ensure uniform light penetration or reflection.

[0057] Divide the sample area into several equal grid units (such as 4×4, 5×5) in the software, numbered P1 - Pn, to form a multi-point sampling matrix.

[0058] Control the near-infrared scanning probe or area array NIR imaging device to sequentially collect spectra at the center of each grid, and collect reflectance data within the complete wavelength range (such as 780–2500 nm) for each point.

[0059] Scan 2 - 3 times at the same position and take the average value to exclude accidental errors and improve the stability of spectral data.

[0060] Match the original spectral data of each sampling point with the corresponding coordinate number one by one and store it as a sampling point matrix for subsequent analysis.

[0061] It should be noted here that the existing technology mostly uses single - point sampling inspection, which has poor representativeness and cannot reflect the overall mixing uniformity; this method enhances representativeness and improves the detection accuracy by constructing a grid - like full - coverage multi - point sampling.

[0062] The specific steps for judging the component distribution uniformity based on the multi - variable regression model include:

[0063] Use the preset component characteristic wavelength range to extract features from the spectra of all sampling points, such as the main absorption bands of components like cordycepin, total flavonoids, lovastatin, etc.

[0064] Introduce known standard samples and establish a mathematical model between component concentration and spectral features using partial least squares regression or support vector regression to calibrate the component prediction accuracy.

[0065] Input the spectrum of each sampling point into the trained regression model, output the concentration estimate of the corresponding pharmacodynamic component, and form a full - area concentration distribution map (such as a heat map).

[0066] Calculate statistical parameters, including: mean μ: the average value of the concentrations of all points; standard deviation σ: reflecting the fluctuation of component concentration; calculate the coefficient of variation CV: CV = σ / μ: a statistical index to quantify uniformity. The judgment criteria are set as follows: when CV ≤ 5%, it is judged as highly uniform; 5% < CV ≤ 10%, it is judged as basically uniform; CV > 10%, it is judged as non - uniform, triggering a system alarm or prompting re - mixing.

[0067] The stability prediction module is used to evaluate the stability and activity retention rate of each component (such as lovastatin, cordycepin, total flavonoids, etc.) in the traditional Chinese medicine composition during the production process under simulated preparation process conditions (such as high temperature, high pressure, shear force, etc. during the tabletting process). This module realizes real - time prediction and process feedback control of the potential degradation trend and interaction risk of pharmacodynamic components by establishing a component thermal degradation kinetic model and a compatibility analysis algorithm.

[0068] Collect and input the key process parameters in the preparation link: tabletting temperature (such as 80 - 120°C), pressure (such as 20 - 60 MPa), humidity and heat conditions, drying time, etc.;

[0069] Construct a simulation environment and form a set of preparation working condition parameters through preset standards in the laboratory or database.

[0070] Thermal degradation curves and critical temperature thresholds of key components of the built-in traditional Chinese medicine extracts; for example, lovastatin begins to degrade at >90°C, cordycepin is easily inactivated under strong acid and humid heat, and tanshinone is stable in a dry heat environment, etc.; the system matches the current input process parameters with the component response data in the database to identify potential degradation risk points.

[0071] Introduce a multi-component compatibility map, combined with molecular docking simulation and spectral overlap analysis algorithms (such as NIR / UV-Vis co-variation map); judge whether there are adverse interactions such as thermal shock reaction, cross-degradation or component mutual inhibition between extracts; for example, there is a risk of degradation coupling reaction between lovastatin and cordycepin in the red yeast rice extract under high pressure.

[0072] For thermosensitive pharmacodynamic components, select an appropriate chemical degradation kinetic model: zero-order reaction model (n = 0): applicable to the case where the degradation rate is independent of the concentration; first-order reaction model (n = 1): applicable to the degradation process of most natural products, and the degradation rate is proportional to the current concentration.

[0073] Obtain the initial concentration C0 of the experiment: the initial mass concentration (mg / g or %) of this component in the extract; degradation rate constant s: can be obtained by fitting at different temperatures through an accelerated stability experiment, and the unit varies according to the model (mg / (g·min) or 1 / min); degradation time t: under the process conditions, the time (unit: min) that this component is exposed to high temperature / high pressure.

[0074] Calculate the change in component concentration: For the zero-order reaction model: C t = C0 - s·t; for the first-order reaction model: C t = C0·e -s·t ; where: C t is the concentration of the active ingredient at time t.

[0075] Calculate the activity retention rate A, and the expression is:

[0076] Classify the retention rates of each component: ≥90%: safe and stable; 80% - 90%: acceptable, it is recommended to optimize; <80%: high risk, indicating that the process needs to be adjusted; if adverse interactions or high degradation risks are detected among multiple components, the system can issue a warning and recommend adjusting parameters (such as reducing the temperature, improving the drying method, adjusting the mixing order).

[0077] Data processing and feedback module, perform normalization processing and trend analysis on the detection data of different batches. If the component fluctuation exceeds the set threshold, an automatic feedback control signal will be sent to the production control system.

[0078] Normalize the coefficient of variation and activity retention rate of each type of ingredient in different batches so that they are all between [0, 1]. Sort the normalized coefficient of variation and activity retention rate by time to construct a time series, and identify abnormal trends through principal component analysis.

[0079] Collect the ingredient detection results of the last N batches (such as the last 30 batches), including: the normalized coefficient of variation of each ingredient and the normalized activity retention rate of each ingredient, and construct a time series data matrix D. Centralize the time series data matrix D (subtract the mean of each column); calculate the covariance matrix Σ; solve the eigenvalues and corresponding eigenvectors of the covariance matrix, and extract the principal components; select the first k principal components according to the cumulative variance contribution rate (such as more than 85%) to construct the principal component space. Project the ingredient detection data of each batch into the principal component space to obtain the scores of each batch in the principal component direction; construct a time series of principal component scores, and calculate the mean and standard deviation of the principal component score series; if the score of a certain batch satisfies: the absolute value of the batch score minus the mean is greater than twice the standard deviation, it is determined that there is a quality abnormal fluctuation or a sudden change in ingredient stability in this batch.

[0080] The system marks the abnormal batches and associates the original data; it can be given by process validation data or enterprise quality standards, for example: the total flavonoid content fluctuation is controlled within ±5%; the lovastatin content ≥ 4.75%, and it is regarded as out of control if it is lower.

[0081] If the detected value of a certain ingredient exceeds the preset range, a feedback mechanism is triggered; the system packs the fluctuation results into alarm information and calls the API interface to transmit it to the production control system (such as PLC, MES system); the control system automatically adjusts accordingly: stirring time / speed; raw material feeding amount; tabletting temperature / pressure; ingredient premixing order, etc.

[0082] The visualization monitoring module, as the human-computer interaction terminal of this detection system, is mainly used to display the key data from each sub-module (detection, analysis, feedback) to the operator in a visual form. This module not only provides real-time curves of detection indicators, batch comparison, and trend charts, but also has functions of risk warning prompts and process adjustment suggestion pushing, assisting the operator to make quick judgments and decisions, so as to realize the digital and intelligent management of the whole process of traditional Chinese medicine preparations.

[0083] It includes real-time display of the detection concentrations, coefficients of variation (CV), activity retention rates, etc. of each key ingredient (such as total flavonoids, cordycepin, lovastatin); supports multi-batch comparative analysis (line charts, bar charts, radar charts, etc.); can customize the time period (such as the last 7 batches, 30 batches) to view the ingredient change trends; provides an interface for exporting original data (CSV, Excel) for further analysis.

[0084] Abnormal detection values are highlighted (red warning, flashing icon); the main component abnormal trend chart shows the fluctuation range, abnormal batch number and specific value; a pop-up window prompts "This component has a downward trend, it is recommended to adjust the process parameters"; warnings are displayed by severity level (general, heavy, severe).

[0085] Automatically generate optimization suggestions and visualize them based on feedback from the detection and analysis modules;

[0086] Example: "Ginkgo biloba extract is unevenly distributed → it is recommended to extend the mixing time to +2min"; "Lovastatin activity retention rate is less than 90% → it is recommended to reduce the tableting temperature from 100℃ to 95℃". Display the comparison between the current process parameters and the recommended adjustment values (table or graphic)

[0087] The operator can click a button to confirm whether to accept the system's suggestion, or select "manual adjustment"; it can be linked with the host computer system or PLC production control system (API / OPC communication interface) to implement parameter distribution; all operations are recorded in a log to facilitate quality traceability and audit management.

[0088] It supports display on multiple terminals such as industrial touch screens, desktop computers, and tablets. It can achieve remote access through the Web interface and supports LAN or private cloud deployment. The UI design adopts a responsive layout and adapts to different screen resolutions.

[0089] Example 2. This example provides a Chinese medicine composition for preventing and treating cardiovascular and cerebrovascular diseases. The composition is prepared in tablet form and is mainly made of the following medicinal and edible ingredients (by mass percentage):

[0090] American ginseng water extract 7%-10%;

[0091] Panax notoginseng water extract 7%-10%;

[0092] Salvia miltiorrhiza aqueous extract 7%-10%;

[0093] Ginkgo biloba leaf water extract 9%-13%;

[0094] Cordyceps sinensis water extract 18%-23%;

[0095] Bamboo leaf flavonoids 10%-15%;

[0096] Lecithin 10%-15%;

[0097] Red yeast rice extract 4%-6%;

[0098] Excipients 20%-28%.

[0099] The preparation process steps are as follows:

[0100] Raw material pretreatment: Accurately weigh each traditional Chinese medicine extract according to the above ratios and crush them into fine powder of 80 mesh;

[0101] Uniform mixing: Put all the powder components into a high-speed mixer and mix evenly for 15 minutes to ensure uniform distribution of the components;

[0102] Adding excipients: Add excipients such as microcrystalline cellulose and talc powder and mix again for 10 minutes;

[0103] Granulation and drying: Use wet granulation, and after obtaining the granules, dry them at 60 °C until the moisture content ≤ 5%;

[0104] Tablet pressing and forming: Use a rotary tablet press to press the dried granules into tablets of 0.5 g per tablet;

[0105] Packaging and storage: Use aluminum-plastic blister packaging and store in the dark at room temperature.

[0106] Dosage method: Adults take 3 tablets orally each time, 2 times a day (once in the morning and once in the evening), and take them with warm water. It is recommended to take continuously for 1 month as a course of treatment, and it can be adjusted according to individual physique as appropriate.

[0107] Clinical application effect: In the preliminary clinical observation of 60 patients with hypertension combined with hyperlipidemia, this composition showed good antihypertensive and lipid-lowering effects. The improvement rate of the main indicators reached more than 90%, and no obvious adverse reactions were seen. The compliance of patients was good.

[0108] Example 3

[0109] The difference between this example and Example 2 is as follows:

[0110] Accurately weigh: 7 kg (7%) of American ginseng water extract, 7 kg (7%) of notoginseng water extract, 7 kg (7%) of salvia miltiorrhiza water extract, 10 kg (10%) of bamboo leaf flavonoids, 10 kg (10%) of lecithin, 9 kg (9%) of ginkgo biloba water extract, 18 kg (18%) of cicada flower cordyceps water extract, 5 kg (5%) of monascus extract, and 27 kg (27%) of other excipients. Stir all the above components evenly and produce tablets by conventional process, 0.5 g per tablet.

[0111] Example 4

[0112] The difference between this example and Example 2 is as follows:

[0113] Accurately weigh: 8 kg (8%) of American ginseng water extract, 8 kg (8%) of notoginseng water extract, 8 kg (8%) of salvia miltiorrhiza water extract, 11 g (11%) of bamboo leaf flavonoids, 11 kg (11%) of lecithin, 9 kg (9%) of ginkgo biloba water extract, 19 kg (19%) of cicada flower cordyceps water extract, 4 kg (4%) of monascus extract, and 22 kg (22%) of other excipients. Stir all the above components evenly and produce tablets by conventional technology, with each tablet weighing 0.5 g.

[0114] As described above, it is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed by this application can easily think of changes or substitutions, which should all be covered within the protection scope of this application.

Claims

1. A traditional Chinese medicine detection system for preventing and treating cardiovascular and cerebrovascular diseases, characterized in that: It includes a raw material component identification module, a component uniformity detection module, a stability prediction module, a data processing and feedback module, and a visualization monitoring module; The raw material component identification module is used to perform near-infrared spectroscopy scanning on the traditional Chinese medicine extract raw materials in the preparation and extract characteristic wavelength information; The component uniformity detection module is used to perform multi-point spectral sampling on the mixed materials before and after tabletting, and judge the distribution uniformity of each component in the preparation by constructing a multivariate regression model; The stability prediction module is used to simulate the high-temperature and high-pressure preparation process conditions, analyze the chemical compatibility between plant extracts in real time, and predict the activity retention rate of different components during the preparation process; The data processing and feedback module normalizes and trend-analyzes the detection data of different batches. If the component fluctuation exceeds the set threshold, it automatically feeds back a control signal to the production control system; The visualization monitoring module is used to display the detection data, risk warning information, and recommended production process adjustment parameters to assist operators in realizing intelligent production management.

2. The traditional Chinese medicine detection system for preventing and treating cardiovascular and cerebrovascular diseases according to claim 1, wherein: The specific steps of multi-point spectral sampling include: Spread the mixed traditional Chinese medicine raw materials on the optical scanning tray, and control the thickness to be 3-5 mm; Divide the sample area into several equal grid units in the software, numbered P1-Pn, to form a multi-point sampling matrix; Control the near-infrared scanning probe to sequentially collect spectra at the center of each grid, and collect reflectance data in the complete wavelength range at each point; Repeat the scan 2-3 times at the same position and take the average value; Match the original spectral data of each sampling point with the corresponding coordinate number one by one, and store it as a sampling point matrix.

3. The traditional Chinese medicine detection system for preventing and treating cardiovascular and cerebrovascular diseases according to claim 2, wherein: The specific steps of judging the component distribution uniformity based on the multivariate regression model include: Use the preset component characteristic wavelength range to extract the characteristics of the spectra of all sampling points; Introduce known standard samples, and use partial least squares regression or support vector regression to establish a mathematical model between the component concentration and spectral characteristics to calibrate the component prediction accuracy; Input the spectra of each sampling point into the trained regression model, output the concentration estimate of the corresponding pharmacodynamic component, and form a full-region concentration distribution map; Calculate the ratio of the average value and standard deviation of all point concentrations to obtain the coefficient of variation CV.

4. The Chinese medicine detection system for preventing and treating cardio-cerebrovascular diseases according to claim 3, wherein: Judgment criterion setting: When CV≤5%, it is judged as highly uniform; 5%<CV≤10%, it is judged as basically uniform; CV>10%, it is judged as non-uniform, trigger system alarm or prompt to remix.

5. The traditional Chinese medicine detection system for preventing and treating cardiovascular and cerebrovascular diseases according to claim 4, characterized in that: Obtain the initial concentration C0, degradation rate constant s, and degradation time t of the experiment; calculate the component concentration change through the zero-order reaction model or the first-order reaction model, and calculate the activity retention rate according to the calculated component concentration change.

6. The traditional Chinese medicine detection system for preventing and treating cardiovascular and cerebrovascular diseases according to claim 5, characterized in that: In the data processing and feedback module, normalize the coefficient of variation and activity retention rate of each type of component in different batches so that they are all between [0,1], sort the normalized coefficient of variation and activity retention rate by time, construct a time series, and identify abnormal trends through principal component analysis.

7. A traditional Chinese medicine detection system for preventing and treating cardiovascular and cerebrovascular diseases according to claim 6, characterized in that: Collect the component detection results of the recent N batches, including the normalized coefficient of variation of each component and the normalized activity retention rate of each component, construct a time series matrix, and centralize the time series data matrix D; Calculate the covariance matrix Σ; solve the eigenvalues and corresponding eigenvectors of the covariance matrix, and extract the principal components; select the first k principal components according to the cumulative variance contribution rate, construct the principal component space, project the component detection data of each batch into the principal component space, and obtain the scores of each batch in the principal component direction; construct a time series of principal component scores, and calculate the mean and standard deviation of the principal component score series; If the score of a certain batch meets the condition that the absolute value of the difference between the batch score and the mean is greater than twice the standard deviation, it is determined that there is an abnormal quality fluctuation or a mutation in component stability in this batch, mark the abnormal batch, and associate the original data. If the detected value of a certain component exceeds the preset range, trigger the feedback mechanism.

8. A traditional Chinese medicine composition for preventing and treating cardiovascular and cerebrovascular diseases, which adopts a traditional Chinese medicine detection system for preventing and treating cardiovascular and cerebrovascular diseases as described in any one of claims 1-7, characterized in that, The composition is in tablet dosage form and is composed of medicine and food homologous components by mass percentage: 7%-10% of the water extract of American ginseng; 7%-10% of the water extract of Panax notoginseng; 7%-10% of the water extract of Salvia miltiorrhiza; 9%-13% of the water extract of Ginkgo biloba; 18%-23% of the water extract of Cordyceps cicadae; 10%-15% of bamboo leaf flavonoids; 10%-15% of lecithin; 4%-6% of red yeast extract; 20%-28% of excipients.

9. The traditional Chinese medicine composition for preventing and treating cardiovascular and cerebrovascular diseases according to claim 8, wherein: The preparation steps of the composition include: weighing and pulverizing the traditional Chinese medicine extracts into 80-mesh fine powder respectively; putting each component into a high-speed mixer and mixing for 15 minutes; adding excipients and mixing again for 10 minutes; granulating by wet method and drying to a moisture content not higher than 5%; using a rotary tablet press to make tablets with a mass of 0.5 g per tablet; packaging the tablets with aluminum-plastic blister and storing them in the dark at room temperature.

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