A process for vacuum freeze-drying Tibetan medicinal materials

Abstract

This invention belongs to the field of Tibetan medicinal material processing technology, specifically a process for vacuum freeze-drying Tibetan medicinal materials, including the following steps: placing the loading tray into a freeze-drying chamber and cooling it at a rate of 1-2℃ / min; maintaining the temperature at the center of the material at -35℃ for 2-4 hours; activating the vacuum system to reduce the pressure inside the chamber to 10-30 Pa; setting the initial temperature of the heating plate to -10℃ to 10℃; real-time monitoring via an array of sensor networks; and vacuum breaking operation. This invention, through precise sensing, intelligent diagnosis, robust decision-making, and adaptive control, effectively prevents quality problems such as collapse, charring, and degradation of active ingredients during the drying process, ensuring the high quality and uniformity of the final product. Simultaneously, by optimizing the drying path, unnecessary energy consumption and time waste are avoided, improving the economic efficiency of the entire production process. It is particularly suitable for the modern processing of precious Tibetan medicinal materials with extremely high quality requirements.

Description

Technical Field

[0001] This invention belongs to the field of Tibetan medicine processing technology, specifically a process for vacuum freeze-drying Tibetan medicine. Background Technology

[0002] Tibetan medicinal materials, as an important part of my country's traditional medicine system, contain a variety of natural components with unique biological activities, such as rhodioloside and cordycepin. These active ingredients are sensitive to environmental factors such as heat, light, and oxygen, and are prone to degradation, oxidation, or isomerization during processing, leading to a reduction or loss of efficacy. Therefore, choosing a suitable drying process is crucial for preserving the medicinal value of Tibetan medicinal materials.

[0003] Currently, the drying methods for Tibetan medicinal materials mainly include traditional natural drying and modern mechanical drying. Natural drying methods (such as sun-drying and shade-drying) are simple to operate and inexpensive, but they have long drying cycles, are greatly affected by climate conditions, and are prone to mold and insect infestation, resulting in significant loss of active ingredients. Among modern mechanical drying methods, hot air drying is the most widely used. This method removes moisture through forced convection heating. Although it has high drying efficiency, the continuous high temperature (usually 50-80℃) easily damages heat-sensitive components and may harden the surface of the medicinal material, affecting subsequent extraction and utilization.

[0004] Vacuum freeze-drying technology, as an advanced dehydration method, can theoretically solve the aforementioned problems well. Its principle is to freeze the material and then remove moisture directly through sublimation in a vacuum environment. The entire process is carried out at low temperatures, which can maximize the preservation of the active ingredients, color, and morphology of the medicinal materials. However, existing vacuum freeze-drying processes still have significant limitations when applied to the processing of Tibetan medicinal materials:

[0005] First, existing processes mostly employ fixed drying curves and parameter settings, lacking the ability to dynamically adjust the drying process. Tibetan medicinal materials are diverse, with significant differences in key thermophysical properties such as eutectic points and disintegration temperatures. Even different parts and batches of the same material can exhibit variations. Fixed process parameters cannot adapt to these changes, easily leading to insufficient or excessive drying, thus affecting product quality.

[0006] Secondly, traditional freeze-drying processes lack effective online monitoring and quality control methods. Currently, they mainly rely on the operator's experience to judge the drying progress or monitor the process status through a limited number of temperature measurement points. This extensive monitoring method is unable to detect problems such as localized overheating and uneven moisture migration during the drying process in a timely manner, and cannot achieve precise process control. Summary of the Invention

[0007] In order to overcome the shortcomings of the prior art, at least one technical problem raised in the background art is solved.

[0008] The technical solution adopted by this invention to solve its technical problem is: a process for vacuum freeze-drying Tibetan medicinal materials, comprising the following steps:

[0009] S1. Raw material pretreatment

[0010] Select Tibetan medicinal materials that are free from mold and insect infestation, rinse them with pure water, and drain the surface moisture.

[0011] S2, Pre-freezing process

[0012] Send the loading pallet into the freeze-drying chamber and cool it at a rate of 1-2℃ / min; when the core temperature of the material reaches -35℃, maintain it for 2-4 hours.

[0013] S3, Sublimation Drying Stage

[0014] Activate the vacuum system to reduce the pressure inside the chamber to 10-30 Pa; set the initial temperature of the heating plate to -10℃ to 10℃; monitor in real time via an array of sensor networks.

[0015] S4, Breakthrough Operation

[0016] High-purity nitrogen gas is introduced at a venting rate of 0.1 MPa / min, and the material is vacuum-packed in aluminum foil bags. Discharge conditions: material temperature: 15-25℃, ambient humidity: ≤30%.

[0017] During the freeze-drying cycle, the core temperature and real-time moisture value of the medicinal materials in different sub-regions of the medicinal material tray are monitored in real time using array-type temperature sensors and moisture sensors. The medicinal material tray area is divided into several sub-regions, and the core temperature of the medicinal materials is compared with the preset ideal sublimation temperature range to identify sub-regions with abnormal temperatures. The real-time moisture value is analyzed and compared with the preset phased target moisture value to identify sub-regions with abnormal moisture content.

[0018] The temperature anomaly sub-region and the moisture anomaly sub-region are spatially overlapped and compared to filter out abnormally overlapping sub-regions; based on the number of abnormally overlapping sub-regions and their temperature / moisture deviation, the dryness state anomaly value is calculated; if the dryness state anomaly value is greater than the anomaly threshold, a dryness optimization signal is generated.

[0019] Identify historical drying cycles that are highly similar to the current anomaly pattern from historical data; calculate the improvement in finished product quality after process adjustments in highly similar historical cycles; and calculate the confidence level of the process adjustments based on the improvement and the sample size of historical data.

[0020] When a process adjustment execution signal is received, the heating plate temperature adjustment coefficient is calculated based on the current abnormal mode and historical adjustment experience.

[0021] As a further technical solution of the present invention: Pre-freezing endpoint determination: the medicinal material is completely frozen and the core temperature is ≤-35℃.

[0022] As a further technical solution of the present invention: the temperature monitoring point density is one sensor per 0.25m², and the moisture monitoring frequency is a full scan every 30 minutes.

[0023] As a further technical solution of the present invention: setting an ideal sublimation temperature range: lower limit temperature: based on the eutectic point of the medicinal material, upper limit temperature: based on the disintegration temperature of the medicinal material.

[0024] As a further technical solution of the present invention: set phased moisture target values: 0-10 hours: target moisture 30%-40%, 10-20 hours: target moisture 15%-25%, after 20 hours: target moisture 5%-10%; wherein, the drying endpoint is determined as follows: the moisture content of the medicinal material is ≤5%, and the temperature fluctuation range is within ±2℃.

[0025] As a further technical solution of the present invention: if the real-time core temperature of a sub-region is within the ideal sublimation temperature range, then the sub-region is marked as a temperature-normal sub-region;

[0026] If the real-time core temperature of a subregion is not within the ideal sublimation temperature range, then the subregion is marked as a temperature abnormality subregion.

[0027] If the real-time moisture deviation value is within the phased target moisture deviation range, then the sub-region is marked as a normal moisture sub-region.

[0028] If the real-time moisture deviation value is not within the phased target moisture deviation range, then the sub-region is marked as a moisture anomaly sub-region.

[0029] If a sub-region is marked as a temperature anomaly sub-region or a moisture anomaly sub-region, then that sub-region is marked as an anomalous overlapping sub-region.

[0030] As a further technical solution of the present invention, the calculation process for abnormal values ​​of the drying state is as follows:

[0031] Obtain the number of abnormally overlapping sub-regions, calculate its ratio to the total number of sub-regions, and obtain the region weighting factor;

[0032] Calculate the temperature deviation and moisture deviation of each anomalous overlapping sub-region, sum the temperature deviation and moisture deviation of each anomalous overlapping sub-region to obtain the total deviation of the region, and take the average of the total deviation of all anomalous overlapping sub-regions to obtain the comprehensive evaluation deviation.

[0033] The abnormal value of the dry state is obtained by multiplying the regional weight factor and the comprehensive evaluation deviation.

[0034] As a further technical solution of the present invention: the process of calculating the confidence level of process adjustment is as follows:

[0035] Construct the current anomaly pattern vector, which includes: spatial distribution characteristics: calculate the distribution dispersion of the anomaly overlapping sub-regions; deviation characteristics: calculate the average temperature deviation and average moisture deviation of all anomaly overlapping sub-regions; process stage characteristics: record the current drying stage.

[0036] Constructing historical anomaly pattern vectors: For each drying cycle in the historical database, if a drying optimization signal has been triggered, construct the same anomaly pattern vector based on the data at the time of triggering.

[0037] The pattern similarity between the current anomalous pattern vector and the historical anomalous pattern vector is calculated by weighted Euclidean distance or cosine similarity.

[0038] Set a similarity threshold. If the pattern similarity is greater than the similarity threshold, filter out all historical periods that meet the conditions to form a set of similar periods.

[0039] For each similar historical period, obtain the final product quality achieved after the actual adjustment in that period, as well as the baseline quality for that period;

[0040] Calculate the difference between the final product quality and the baseline quality, and then calculate the ratio of this difference to the baseline quality to obtain the improvement.

[0041] Obtain the number of cycles in the similar cycle set to get the sample size factor; calculate the standard deviation of all improvement magnitudes in the similar cycle set to get the effect consistency factor; calculate the evaluation value of all improvement magnitudes in the similar cycle set to get the average improvement magnitude.

[0042] The comprehensive confidence value is obtained by multiplying the sample size factor, the consistency factor of the effect, and the average improvement.

[0043] As a further technical solution of the present invention: if the comprehensive confidence value is greater than the set confidence threshold, a process adjustment execution signal is generated.

[0044] As a further technical solution of the present invention: the process of adjusting the temperature coefficient of the heating plate is as follows:

[0045] Obtain the current dryness anomalies, the average temperature deviation of the overlapping abnormal sub-regions, and the distribution dispersion of the abnormal regions. Calculate the basic adjustment coefficient using a multiple regression model.

[0046] Obtain the current actual output power of the heating plate, and process the ratio of the current actual output power of the heating plate to the rated power to obtain the power regulation ratio;

[0047] The heating plate temperature adjustment coefficient is obtained by multiplying the basic adjustment coefficient by the power control ratio.

[0048] The beneficial effects of this invention are as follows:

[0049] This invention utilizes an array-type sensor network to perform real-time, zoned monitoring of medicinal herb trays. This allows for precise detection of localized temperature and moisture anomalies. Combined with a preset ideal sublimation temperature range and phased target moisture values, the system can identify abnormal sub-regions early in the drying process, providing a data foundation for timely intervention and effectively avoiding the quality degradation caused by delayed detection in traditional methods. By calculating anomaly values ​​in the drying state, the complex drying conditions are transformed into a quantifiable comprehensive index. This index comprehensively reflects the quantity and scale of abnormal areas (regional weighting factor) and the severity of the anomalies (overall deviation in evaluation), achieving an accurate assessment of the health status of the drying process. The introduction of pattern similarity matching and confidence level calculation mechanisms enables the system to find the optimal solution from historical data. By comprehensively considering the improvement magnitude, sample size, and consistency of historical adjustment effects, the obtained process adjustment confidence value can effectively evaluate the reliability of the current adjustment strategy. Based on a multivariate regression model and combined with real-time power status calculations, the heating plate temperature adjustment coefficient achieves precise matching between the adjustment magnitude and the abnormal pattern. This coefficient not only responds to the current degree of anomaly but also considers the actual operating status of the equipment, effectively improving drying uniformity.

[0050] In summary, this invention, through precise sensing, intelligent diagnosis, robust decision-making, and adaptive control, effectively prevents quality problems such as collapse, charring, and degradation of active ingredients in medicinal materials during the drying process, ensuring the high quality and uniformity of the final product. Simultaneously, by optimizing the drying path, unnecessary energy consumption and time waste are avoided, improving the economic efficiency of the entire production process. It is particularly suitable for the modern processing of precious Tibetan medicinal materials with extremely high quality requirements. Attached Figure Description

[0051] The invention will now be further described with reference to the accompanying drawings.

[0052] Figure 1 This is a process flow diagram of a vacuum freeze-drying process for Tibetan medicinal materials according to the present invention;

[0053] Figure 2 This is a flowchart illustrating the monitoring and control process of a vacuum freeze-drying method for Tibetan medicinal materials according to the present invention. Detailed Implementation

[0054] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0055] Example 1

[0056] like Figure 1 As shown in the embodiment of the present invention, a process for vacuum freeze-drying Tibetan medicinal materials includes the following steps:

[0057] S1. Raw material pretreatment

[0058] Select Tibetan medicinal materials that are free from mold and insect infestation, rinse them with pure water, and drain the surface moisture.

[0059] Cut the medicinal materials into uniform thin slices of 3-5mm along the grain; lay them in a single layer on a drying tray, with the loading density controlled at 2-3kg / m², ensuring that there are appropriate gaps between the medicinal materials.

[0060] S2, Pre-freezing process

[0061] Send the loading tray into the freeze-drying chamber and cool it at a rate of 1-2℃ / min; when the core temperature of the material reaches -35℃, maintain it for 2-4 hours; the pre-freezing endpoint is determined when the medicinal material is completely frozen and the core temperature is ≤-35℃.

[0062] S3, Sublimation Drying Stage

[0063] Activate the vacuum system to reduce the pressure inside the chamber to 10-30 Pa; set the initial temperature of the heating plate to -10℃ to 10℃; monitor in real time via an array of sensor networks.

[0064] Among them, the temperature monitoring point density is one sensor per 0.25m², the moisture monitoring frequency is a full scan every 30 minutes, and the ideal sublimation temperature range is set as follows: lower limit temperature: -30℃ (based on the eutectic point of the medicinal material), upper limit temperature: -8℃ (based on the disintegration temperature of the medicinal material).

[0065] Set phased moisture target values: 0-10 hours: target moisture 30%-40%, 10-20 hours: target moisture 15%-25%, after 20 hours: target moisture 5%-10%; Among them, the drying endpoint is determined by the moisture content of the medicinal material ≤5% and the temperature fluctuation range within ±2℃.

[0066] S4, Breakthrough Operation

[0067] High-purity nitrogen gas is introduced, the venting rate is 0.1 MPa / min, and vacuum packaging is carried out using aluminum foil bags; discharge conditions: material temperature: 15-25℃, ambient humidity: ≤30%.

[0068] Example 2

[0069] Based on the above embodiment 1, as Figure 2 As shown in the embodiment of the present invention, a process for vacuum freeze-drying Tibetan medicinal materials further includes:

[0070] Step 1: Process Monitoring and Abnormal Area Identification: During the freeze-drying cycle, the core temperature and real-time moisture value of the medicinal materials in different sub-regions of the medicinal material tray are monitored in real time using array-type temperature sensors and moisture sensors; the medicinal material tray area is divided into several sub-regions, and the core temperature of the medicinal materials is compared with the preset ideal sublimation temperature range to identify sub-regions with abnormal temperatures; the real-time moisture value is analyzed and compared with the preset stage target moisture value to identify sub-regions with abnormal moisture content;

[0071] The medicinal herb tray area is divided into several sub-areas by dividing the medicinal herb tray area into several sub-areas with equal areas, and each sub-area has an equal area.

[0072] The temperature of the core of the medicinal material is compared with a preset ideal sublimation temperature range to identify temperature anomaly subregions; the real-time moisture value is compared with a preset phased target moisture value to identify moisture anomaly subregions. The specific process is as follows:

[0073] Obtain the real-time core temperature of all sub-regions and compare the real-time core temperature of each sub-region with the ideal sublimation temperature range;

[0074] If the real-time core temperature of a subregion is within the ideal sublimation temperature range, then the subregion is marked as a normal temperature subregion.

[0075] If the real-time core temperature of a subregion is not within the ideal sublimation temperature range, then the subregion is marked as a temperature abnormality subregion.

[0076] It should be noted that the ideal sublimation temperature range includes the lower ideal sublimation limit and the upper ideal sublimation limit. The lower ideal sublimation limit is mainly determined by the eutectic point temperature of the medicinal material. It is essential to ensure that the temperature of the frozen portion of the medicinal material remains below the eutectic point; otherwise, the ice crystals will melt, causing the medicinal material to collapse. The lower ideal sublimation limit is mainly determined by the disintegration temperature of the medicinal material. This is the highest temperature at which the structure of the dried layer of the medicinal material remains stable; exceeding this temperature will cause the dried layer to soften, collapse, or char.

[0077] Obtain the real-time moisture value and the phased target moisture value of the sub-region, and calculate the absolute value of their deviation to obtain the real-time moisture deviation value;

[0078] If the real-time moisture deviation value is within the phased target moisture deviation range, then the sub-region is marked as a normal moisture sub-region.

[0079] If the real-time moisture deviation value is not within the phased target moisture deviation range, then the sub-region is marked as a moisture anomaly sub-region.

[0080] If a sub-region is marked as a temperature anomaly sub-region or a moisture anomaly sub-region, then mark that sub-region as an anomalous overlapping sub-region.

[0081] If a sub-region is marked as a normal temperature sub-region and a normal moisture sub-region, then mark that sub-region as a normal overlapping sub-region.

[0082] Step 2: Drying State Anomaly Analysis and Signal Generation: Spatially overlap the temperature anomaly sub-region and the moisture anomaly sub-region, and filter out the abnormally overlapping sub-regions; based on the number of abnormally overlapping sub-regions and their temperature / moisture deviation, calculate the drying state anomaly value; if the drying state anomaly value is greater than the anomaly threshold, then generate a drying optimization signal.

[0083] In some embodiments, the calculation process for abnormal values ​​of drying status is as follows:

[0084] Obtain the number of abnormally overlapping sub-regions, calculate its ratio to the total number of sub-regions, and obtain the region weighting factor;

[0085] Calculate the temperature deviation and moisture deviation of each anomalous overlapping sub-region, sum the temperature deviation and moisture deviation of each anomalous overlapping sub-region to obtain the total deviation of the region, and take the average of the total deviation of all anomalous overlapping sub-regions to obtain the comprehensive evaluation deviation.

[0086] The dry state anomaly value is obtained by multiplying the regional weight factor with the comprehensive evaluation deviation; the larger the dry state anomaly value, the more serious the problem of uneven drying or heat and mass transfer obstruction.

[0087] Outliers in the drying state are compared with an anomaly threshold. The threshold is a key parameter used to distinguish between normal drying states and anomalies requiring optimization. Its determination is a data-driven and domain-knowledge-based process that ensures neither important anomalies are missed nor minor fluctuations are overreacted; the threshold is set to 0.45.

[0088] If the abnormal value of the drying state is greater than or equal to the abnormal value of the drying state threshold, a drying optimization signal is generated;

[0089] If the abnormal value of the drying state is less than the abnormal value of the drying state threshold, a continuous drying signal is generated;

[0090] Among them, the drying optimization signal indicates a significant anomaly in the drying process, and the problem may be serious. Specifically: a high abnormal value in the drying state usually means that there are multiple abnormal overlapping areas on the herb tray, and the temperature or moisture in these areas deviates significantly from the ideal value. This anomaly may lead to low drying efficiency, increased energy consumption, or a decline in the quality of the final product (such as herb collapse, degradation of active ingredients, uneven drying, etc.). System response: Once this signal is generated, the system will trigger the optimization program and initiate subsequent steps (such as retrieving historical data, calculating process adjustment confidence values, and dynamically adjusting the heating plate temperature or vacuum degree) to correct the current problem and ensure that the drying process returns to the optimal track. The continuous drying signal indicates that the drying process is in an acceptable state and no serious anomalies have been detected. Specifically:

[0091] A low abnormal value in the drying state indicates a small number of abnormal overlapping regions or a minor deviation, indicating a generally stable drying process with temperature and moisture distribution meeting expectations. Although minor fluctuations may exist, these issues are within a controllable range and will not significantly affect product quality. System response: The system will maintain current process parameters (such as heating plate temperature and vacuum level) and continue the drying procedure without intervention. Simultaneously, the system will continuously monitor to respond promptly if abnormal values ​​increase.

[0092] Step 3: Optimization verification and decision-making based on historical data: Identify historical drying cycles that are highly similar to the current abnormal pattern (similar abnormal area distribution, deviation) from historical data; calculate the improvement in finished product quality after process adjustment in highly similar historical cycles; calculate the confidence level of process adjustment based on the improvement and the sample size of historical data.

[0093] In some embodiments, a current anomaly pattern vector is constructed, which includes: spatial distribution features: calculating the distribution dispersion of the overlapping anomaly regions to determine whether the anomalies are concentrated in the center or edge of the tray, or distributed in a banded pattern; deviation features: calculating the average temperature deviation and average moisture deviation of all overlapping anomaly regions; process stage features: recording the current drying stage (e.g., divided by drying time or average moisture content).

[0094] Constructing historical anomaly pattern vectors: For each drying cycle in the historical database (assuming there are N cycles), if a drying optimization signal has been triggered, construct the same anomaly pattern vector based on the data at the time of triggering.

[0095] The pattern similarity between the current anomalous pattern vector and the historical anomalous pattern vector is calculated by weighted Euclidean distance or cosine similarity.

[0096] Set a similarity threshold (with a value of 0.8). If the pattern similarity is greater than the similarity threshold, filter out all historical periods that meet the conditions to form a set of similar periods.

[0097] For each similar historical cycle, obtain the final product quality achieved after actual adjustments for that cycle, as well as the baseline quality for that cycle; this is the quality predicted by the baseline model that could be obtained without adjustments. Baseline model construction: Using data from all normal drying cycles that did not trigger optimization signals, train a machine learning model (such as a random forest or gradient boosting tree), with inputs being the process parameters and material state in the early stages of drying, and outputting the predicted final quality.

[0098] Calculate the difference between the final product quality and the baseline quality, and then calculate the ratio of this difference to the baseline quality to obtain the improvement.

[0099] Obtain the number of cycles in the similar cycle set to get the sample size factor; calculate the standard deviation of all improvement magnitudes in the similar cycle set to get the effect consistency factor; calculate the evaluation value of all improvement magnitudes in the similar cycle set to get the average improvement magnitude.

[0100] The comprehensive confidence value is obtained by multiplying the sample size factor, the consistency factor of the effect, and the average improvement.

[0101] If the overall confidence value is greater than the set confidence threshold, a process adjustment execution signal will be generated, and the system will adopt an adjustment strategy similar to the most successful case in a similar historical period.

[0102] If the overall confidence value is less than or equal to the set confidence threshold, an expert intervention request signal is generated; this indicates that the system has encountered an unfamiliar or uncertain abnormal pattern. The system will pause automated adjustments, notify engineers with an alarm, and package the current pattern and data for engineers to analyze and make decisions.

[0103] Step 4: Dynamic adjustment of process parameters: When a process adjustment execution signal is received, the heating plate temperature adjustment coefficient is calculated based on the current abnormal mode and historical adjustment experience; the heating plate temperature of the current freeze-drying process is dynamically adjusted according to the coefficient.

[0104] In some embodiments, the current dryness state anomaly value, the average temperature deviation of the abnormal overlapping sub-region, and the distribution dispersion of the abnormal region are obtained, and the basic adjustment coefficient is calculated using a multiple regression model: where the coefficient of the current dryness state anomaly value is a1, the coefficient of the average temperature deviation of the abnormal overlapping sub-region is a2, and the coefficient of the distribution dispersion of the abnormal region is a3, and a1, a2, and a3 are regression coefficients obtained by training with historical data, and the specific values ​​are determined by least squares fitting.

[0105] Obtain the current actual output power of the heating plate, and process the ratio of the current actual output power of the heating plate to the rated power to obtain the power regulation ratio;

[0106] The heating plate temperature adjustment coefficient is obtained by multiplying the basic adjustment coefficient by the power control ratio.

[0107] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A process for vacuum freeze-drying Tibetan medicinal materials, characterized in that: Includes the following steps: S1. Raw material pretreatment Select Tibetan medicinal materials that are free from mold and insect infestation, rinse them with pure water, and drain the surface moisture. S2, Pre-freezing process Send the loading pallet into the freeze-drying chamber and cool it at a rate of 1-2℃ / min; when the core temperature of the material reaches -35℃, maintain it for 2-4 hours. S3, Sublimation Drying Stage Turn on the vacuum system to reduce the pressure inside the chamber to 10-30 Pa; set the initial temperature of the heating plate to -10℃ to 10℃; monitor in real time through an array of sensor networks; S4, Breakthrough Operation High-purity nitrogen gas is introduced at a venting rate of 0.1 MPa / min, and the material is vacuum-packed in aluminum foil bags. Discharge conditions: material temperature: 15-25℃, ambient humidity: ≤30%. During the freeze-drying cycle, the core temperature and real-time moisture value of the medicinal materials in different sub-regions of the medicinal material tray are monitored in real time using array-type temperature sensors and moisture sensors. The medicinal material tray area is divided into several sub-regions, and the core temperature of the medicinal materials is compared with the preset ideal sublimation temperature range to identify sub-regions with abnormal temperatures. The real-time moisture value is analyzed and compared with the preset phased target moisture value to identify sub-regions with abnormal moisture content. The temperature anomaly sub-region and the moisture anomaly sub-region are spatially overlapped and compared to filter out abnormally overlapping sub-regions; based on the number of abnormally overlapping sub-regions and their temperature / moisture deviation, the dryness state anomaly value is calculated; if the dryness state anomaly value is greater than the anomaly threshold, a dryness optimization signal is generated. Identify historical drying cycles that are highly similar to the current anomaly pattern from historical data; calculate the improvement in finished product quality after process adjustments in highly similar historical cycles; and calculate the confidence level of the process adjustments based on the improvement and the sample size of historical data. When a process adjustment execution signal is received, the heating plate temperature adjustment coefficient is calculated based on the current abnormal mode and historical adjustment experience.

2. The process for vacuum freeze-drying Tibetan medicinal materials according to claim 1, characterized in that: Pre-freezing endpoint determination: The medicinal material is completely frozen, and the core temperature is ≤-35℃.

3. The process for vacuum freeze-drying Tibetan medicinal materials according to claim 1, characterized in that: in, Temperature monitoring point density: one sensor is placed every 0.25m²; Moisture monitoring frequency: a full scan every 30 minutes.

4. The process for vacuum freeze-drying Tibetan medicinal materials according to claim 1, characterized in that: Set the ideal sublimation temperature range: lower limit temperature: based on the eutectic point of the herb, upper limit temperature: based on the disintegration temperature of the herb.

5. The process for vacuum freeze-drying Tibetan medicinal materials according to claim 1, characterized in that: Set phased moisture target values: 0-10 hours: target moisture 30%-40%, 10-20 hours: target moisture 15%-25%, after 20 hours: target moisture 5%-10%; Among them, the drying endpoint is determined by the moisture content of the medicinal material ≤5% and the temperature fluctuation range within ±2℃.

6. The process for vacuum freeze-drying Tibetan medicinal materials according to claim 1, characterized in that: If the real-time core temperature of a subregion is within the ideal sublimation temperature range, then the subregion is marked as a normal temperature subregion. If the real-time core temperature of a subregion is not within the ideal sublimation temperature range, then the subregion is marked as a temperature abnormality subregion. If the real-time moisture deviation value is within the phased target moisture deviation range, then the sub-region is marked as a normal moisture sub-region. If the real-time moisture deviation value is not within the phased target moisture deviation range, then the sub-region is marked as a moisture anomaly sub-region. If a sub-region is marked as a temperature anomaly sub-region or a moisture anomaly sub-region, then that sub-region is marked as an anomalous overlapping sub-region.

7. The process for vacuum freeze-drying Tibetan medicinal materials according to claim 6, characterized in that: The calculation process for abnormal values ​​in the drying state is as follows: Obtain the number of abnormally overlapping sub-regions, calculate its ratio to the total number of sub-regions, and obtain the region weighting factor; Calculate the temperature deviation and moisture deviation of each anomalous overlapping sub-region, sum the temperature deviation and moisture deviation of each anomalous overlapping sub-region to obtain the total deviation of the region, and take the average of the total deviation of all anomalous overlapping sub-regions to obtain the comprehensive evaluation deviation. The abnormal value of the dry state is obtained by multiplying the regional weight factor and the comprehensive evaluation deviation.

8. The process for vacuum freeze-drying Tibetan medicinal materials according to claim 1, characterized in that: The process of calculating the confidence level for process adjustments is as follows: Construct the current anomaly pattern vector, which includes: spatial distribution characteristics: calculate the distribution dispersion of the anomaly overlapping sub-regions; deviation characteristics: calculate the average temperature deviation and average moisture deviation of all anomaly overlapping sub-regions; process stage characteristics: record the current drying stage. Constructing historical anomaly pattern vectors: For each drying cycle in the historical database, if a drying optimization signal has been triggered, construct the same anomaly pattern vector based on the data at the time of triggering. The pattern similarity between the current anomalous pattern vector and the historical anomalous pattern vector is calculated by weighted Euclidean distance or cosine similarity. Set a similarity threshold. If the pattern similarity is greater than the similarity threshold, filter out all historical periods that meet the conditions to form a set of similar periods. For each similar historical period, obtain the final product quality achieved after the actual adjustment in that period, as well as the baseline quality for that period; Calculate the difference between the final product quality and the baseline quality, and then calculate the ratio of this difference to the baseline quality to obtain the improvement. Obtain the number of cycles in the similar cycle set to get the sample size factor; calculate the standard deviation of all improvement magnitudes in the similar cycle set to get the effect consistency factor; calculate the evaluation value of all improvement magnitudes in the similar cycle set to get the average improvement magnitude. The comprehensive confidence value is obtained by multiplying the sample size factor, the consistency factor of the effect, and the average improvement.

9. The process for vacuum freeze-drying Tibetan medicinal materials according to claim 8, characterized in that: If the overall confidence value is greater than the set confidence threshold, a process adjustment execution signal will be generated.

10. The process for vacuum freeze-drying Tibetan medicinal materials according to claim 1, characterized in that: The process of adjusting the heating plate temperature coefficient is as follows: Obtain the current dryness anomalies, the average temperature deviation of the overlapping abnormal sub-regions, and the distribution dispersion of the abnormal regions. Calculate the basic adjustment coefficient using a multiple regression model. Obtain the current actual output power of the heating plate, and process the ratio of the current actual output power of the heating plate to the rated power to obtain the power regulation ratio; The heating plate temperature adjustment coefficient is obtained by multiplying the basic adjustment coefficient by the power control ratio.

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