A real-time monitoring method and system for the processing of granular drugs

Abstract

The present invention relates to the technical field of real-time monitoring, and specifically to a real-time monitoring method and system for the processing of granular drugs, including the following steps: Based on the photoacoustic signal data during the processing of granular drugs, a fixed time interval is set to capture the photoacoustic signals of granular drugs under the conditions of thermally induced photoacoustic excitation and pressure-induced photoacoustic excitation, record the amplitudes of the photoacoustic signals, and obtain a photoacoustic signal feature data set. In the present invention, through the capture of photoacoustic signals at fixed time intervals, continuous monitoring of the formation process of granular drugs is realized. By combining thermally induced and pressure-induced photoacoustic excitation, the signal acquisition coverage is improved, the contrast of photoacoustic holographic images is adjusted, the recognition accuracy of trace components is enhanced, the peak shift rate is analyzed, the noise baseline fluctuation is compared, abnormal time points are accurately screened, the accuracy of interface change detection is improved, the particle trajectory data is obtained by combining high-frame-rate imaging, the flow field disturbance distribution is analyzed, the precise control of the processing process is realized, and the stability of the particle quality is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of real-time monitoring, and particularly to a real-time monitoring method and system for the processing of granular drugs. Background Art

[0002] The technical field of real-time monitoring includes a technical system for real-time data acquisition, analysis, and feedback of various processes or states such as physical, chemical, and biological processes. Its core contents include sensor data acquisition, signal transmission and analysis, computer data processing, and feedback control. This technical field is widely used in industrial manufacturing, environmental monitoring, medical diagnosis, etc. Through high-precision and fast-response data acquisition means, continuous tracking and analysis of the target process can be achieved. In the process of granular drug processing, real-time monitoring can accurately obtain dynamic data on the formation, growth, and distribution of granules, providing data support for optimizing process parameters and ensuring the consistency and stability of processing quality.

[0003] Among them, the real-time monitoring method for the granular drug processing process refers to a technical method for real-time data acquisition and analysis of the formation, change, and distribution of granules during the production of granular drugs. This patent theme covers specific technical matters such as particle size detection based on optical measurement, particle charge state monitoring based on charge induction, and component detection based on online spectral analysis. It combines non-contact sensors to obtain particle state information and uses computer data processing means to analyze particle physical parameters to achieve real-time monitoring and data acquisition of the granular drug processing process.

[0004] In the prior art, the method for obtaining the physical state of granular drugs during real-time monitoring is relatively single, relying on optical measurement, charge state monitoring, or spectral analysis, resulting in the difficulty of fully presenting the characteristics of granular drugs in a complex environment. Especially for the identification accuracy of trace components, it is limited, and it is difficult to achieve high-resolution component analysis. Traditional methods mainly perform real-time acquisition based on a single sensor or non-multi-dimensional data fusion method, making it difficult to effectively distinguish the response characteristics of granules under different excitation conditions, resulting in problems such as local loss or low sensitivity of particle state information. The existing monitoring methods have a low time resolution for particle interface changes and are difficult to accurately capture the dynamic changes of the interface during rapid changes, resulting in the loss of some key data and affecting the precise adjustment of the processing process. The analysis of flow field disturbances relies on the indirect speculation of particle states rather than direct measurement, resulting in limited accuracy in obtaining particle movement trajectories and being unable to accurately reflect the movement trend of particles in the flow field. Since the evaluation of processing state deviations mainly relies on a single monitoring variable and does not fully consider the combined effects of various factors such as particle interface changes and flow field disturbances, the response speed of granular drug processing control is slow and it is difficult to meet the requirements of high-precision manufacturing. Summary of the Invention

[0005] The object of the present invention is to solve the disadvantages existing in the prior art, and a real-time monitoring method and system for the processing process of granular drugs are proposed.

[0006] To achieve the above object, the present invention adopts the following technical solutions: A real-time monitoring method for the processing process of granular drugs, comprising the following steps:

[0007] S1: Based on the photoacoustic signal data in the processing process of granular drugs, a fixed time interval is set, the photoacoustic signals of granular drugs under the conditions of thermally induced photoacoustic excitation and pressure-induced photoacoustic excitation are captured, the amplitudes of the photoacoustic signals are recorded, and a photoacoustic signal feature data set is obtained;

[0008] S2: Based on the photoacoustic signal feature data set, the amplitude of the photoacoustic signal is called, the contrast of the photoacoustic holographic image is adjusted, and the spatial distribution information of the target component is extracted to obtain the identification data of trace components of granular drugs;

[0009] S3: Based on the identification data of trace components of granular drugs, the fluorescence spectrum data is extracted, the fluorescence peak wavelength is recorded, the fluorescence peak shift rate per unit time is analyzed, the time points where the fluorescence peak drift rate exceeds the noise baseline fluctuation range are screened, and the interface change data of granular drugs is obtained;

[0010] S4: Based on the interface change data of granular drugs, a high-frame-rate optical imaging device is used to record the particle trajectory data, identify the deviation angle of the particle movement direction, and judge the spatial distribution of the flow field disturbance to obtain the flow field disturbance data set of the granular drug processing;

[0011] S5: Based on the flow field disturbance data set of the granular drug processing, the interface change data of granular drugs is called, and the deviation rate of the granular drug processing state is analyzed to obtain the real-time monitoring result of drug processing.

[0012] As a further solution of the present invention, the photoacoustic signal feature data set includes thermally induced photoacoustic signals, pressure-induced photoacoustic signals, and photoacoustic signal amplitudes. The identification data of trace components of granular drugs includes the signal gain coefficient of the target component, the contrast of the photoacoustic holographic image, and the spatial distribution information of the target component. The interface change data of granular drugs includes fluorescence spectrum data, fluorescence peak wavelength, and fluorescence peak shift rate. The flow field disturbance data set of the granular drug processing includes particle trajectory data, the deviation angle of the particle movement direction, and the spatial distribution of the flow field disturbance. The real-time monitoring result of drug processing includes the interface change data of granular drugs, the deviation rate of the granular drug processing state, and the flow field disturbance data set of the granular drug processing.

[0013] As a further solution of the present invention, the specific steps for obtaining the photoacoustic signal feature data set are as follows:

[0014] S111: Based on the photoacoustic signal data during the processing of granular drugs, set a fixed time interval, detect the amplitude of the photoacoustic signal at each time point, record the data, and obtain a photoacoustic signal amplitude sequence;

[0015] S112: Based on the photoacoustic signal amplitude sequence, identify the amplitude change rate between adjacent time points, and use the formula:

[0016] ;

[0017] Calculate the photoacoustic signal change rate for each time interval, and establish a photoacoustic signal change rate sequence;

[0018] Among them, represents the photoacoustic signal change rate, and represent the photoacoustic signal amplitudes at adjacent time points, represents the time interval;

[0019] S113: Call the photoacoustic signal change rate sequence, screen the time points with a change rate exceeding the threshold, and extract the corresponding photoacoustic signal amplitudes to obtain a photoacoustic signal feature data set.

[0020] As a further solution of the present invention, the steps for obtaining the identification data of trace components of granular drugs are specifically as follows:

[0021] S211: Based on the photoacoustic signal feature data set, call the photoacoustic signal amplitude, calculate the initial gain coefficient of the target component signal, and perform normalization processing to obtain a normalized gain coefficient;

[0022] S212: Based on the normalized gain coefficient, by adjusting the contrast parameter of the differential region of the photoacoustic holographic image, use the formula:

[0023] ;

[0024] Calculate to obtain a contrast optimization parameter;

[0025] Among them, is the contrast optimization parameter, is the normalized gain coefficient of the th pixel point, is the photoacoustic signal intensity of the th pixel point, is the average normalized gain coefficient of the pixel points, is the total number of pixel points;

[0026] S213: Based on the contrast optimization parameter, adjust the pixel distribution of the photoacoustic holographic image, extract the spatial distribution information of the target component, and obtain the identification data of trace components of granular drugs.

[0027] As a further solution of the present invention, the steps for obtaining the data on the change of the interface of the granular drug are specifically as follows:

[0028] S311: Based on the data on the identification of trace components of the granular drug, extract the fluorescence spectrum data, analyze the fluorescence intensity at each wavelength, determine the position of the spectral peak, record the peak wavelength, calculate the wavelength change amount per unit time, and obtain the fluorescence peak offset rate;

[0029] S312: Invoke the fluorescence peak offset rate, identify the noise baseline fluctuation range, compare the offset rate at each time point with the noise baseline fluctuation range, screen out the time points exceeding the fluctuation range, evaluate the cumulative change amount of the offset rate, and use the formula:

[0030] ;

[0031] Calculate the average value of the fluorescence peak drift deviation;

[0032] Among them, represents the average value of the fluorescence peak drift deviation, represents the fluorescence peak offset rate at the th time point, represents the average value of the noise baseline fluctuation range, represents the total number of time points exceeding the noise baseline fluctuation range, represents the weight coefficient at the th time point, represents the time interval at the th time point, represents the total measurement time length;

[0033] S313: Based on the average value of the fluorescence peak drift deviation, statistically analyze the change trend of the time points, analyze the acceleration change of the offset rate, and combine the time accumulation to calculate the interface dynamic change rate, so as to obtain the data on the change of the interface of the granular drug.

[0034] As a further solution of the present invention, the steps for obtaining the data set of the disturbance of the processing flow field of the granular drug are specifically as follows:

[0035] S411: Based on the data on the change of the interface of the granular drug, extract the optical imaging data of the particles, use a high-frame-rate optical imaging device to record the trajectory data of the particles in the time series, analyze the motion trajectory vector of the particles, and obtain the particle trajectory data;

[0036] S412: Invoke the particle trajectory data, extract the motion directions of the particles at adjacent time points, analyze the angular change trend in the time series, judge the influence of the fluid action on the particle motion, adjust the disturbance parameters in combination with the particle force condition, identify the offset fluctuation range of the particles in the disturbance area, and use the formula:

[0037] ;

[0038] Calculate the deviation angle of the particle movement direction;

[0039] Wherein, represents the deviation angle of the particle movement direction, , represent the X-axis coordinates of the particle at time points and , , represent the corresponding Y-axis coordinates, represents the vertical displacement of the particle under fluid perturbation;

[0040] S413: Invoke the deviation angle of the particle movement direction, identify the spatial distribution of the flow field perturbation region, analyze the movement trend of the particle in the perturbation region, and obtain the particle drug processing flow field perturbation data set.

[0041] As a further solution of the present invention, the steps for obtaining the real-time monitoring result of drug processing are specifically as follows:

[0042] S511: Based on the particle drug processing flow field perturbation data set, invoke the particle drug interface change data, analyze the particle drug morphology change rate and the particle aggregation degree, and obtain the particle morphology change coefficient;

[0043] S512: According to the particle morphology change coefficient, evaluate the change deviation amount of the particle drug processing state, and combine the particle drug interface change data, and use the formula:

[0044] ;

[0045] Identify the change deviation situation of the particle drug processing state at each moment, and obtain the particle processing deviation rate;

[0046] Wherein, represents the particle processing deviation rate, represents the maximum value of the particle morphology change coefficient, represents the minimum value of the particle morphology change coefficient, represents the average change value of the particle morphology change trend, represents the fluctuation value of the particle interface change data, represents the particle processing state deviation adjustment factor;

[0047] S513: Based on the particle processing deviation rate, analyze the real-time change situation of the particle drug processing state, and combine the dynamic trend of the processing state deviation to monitor whether the particle drug processing state is stable, and obtain the real-time monitoring result of drug processing.

[0048] The real-time monitoring system for the granule drug processing process is used to execute the real-time monitoring method for the granule drug processing process, and the system includes:

[0049] Based on the photoacoustic signal data in the granule drug processing process, the photoacoustic signal control module controls the photoacoustic excitation power and intensity, synchronizes the change amplitude of the photoacoustic signal, calculates the deviation of the photoacoustic signal response time, and obtains the photoacoustic signal stability data;

[0050] Based on the photoacoustic signal stability data, the target component gain calculation module calculates the target component signal gain coefficient, adjusts the amplitude contrast of the photoacoustic signal, analyzes the peak change of the target component signal, compares the difference distribution between the target component signal gain coefficient and the background signal, screens the gain region, analyzes the enhancement degree of the target component signal, extracts the spatial distribution information of the target component, and obtains the spatial distribution data of the target component;

[0051] Based on the spatial distribution data of the target component, the granule interface change tracking module calls the fluorescence spectrum characteristics, calculates the fluorescence peak wavelength offset rate, compares the fluorescence peak wavelength offset rate with the interface stability threshold, screens the fluorescence drift moments exceeding the interface stability threshold, and obtains the granule drug interface change data;

[0052] Based on the granule drug interface change data, the flow field disturbance identification module combines the change of the granule trajectory, identifies the deviation angle of the granule movement direction, compares the deviation angle of the granule movement direction with the consistency of the fluid movement direction, analyzes the flow field disturbance trend of the granule trajectory, and obtains the granule drug processing flow field disturbance data;

[0053] Based on the granule drug processing flow field disturbance data, the processing deviation monitoring module analyzes the deviation change rate of the granule movement direction, compares the deviation change rate of the granule movement direction with the flow field stability parameter, calculates the granule drug processing state deviation rate, and obtains the real-time monitoring result of the drug processing.

[0054] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0055] In the present invention, through the capture of photoacoustic signals based on a fixed time interval, the continuous monitoring of the formation process of particulate drugs is ensured. By combining the dual conditions of thermally induced photoacoustic excitation and pressure-induced photoacoustic excitation, the coverage of photoacoustic signal acquisition for particulate drugs is improved, and a more comprehensive record of the particulate state is achieved. The gain coefficient of the target component signal is calculated using the amplitude of the photoacoustic signal, and the contrast of the photoacoustic holographic image is adjusted to make the spatial distribution information of the target component more intuitive, enhancing the recognition accuracy of trace components. By extracting fluorescence spectral data and combining the recording of the fluorescence peak wavelength, the fluorescence peak shift rate per unit time is analyzed, and the noise baseline fluctuation range is compared to accurately screen the time points with abnormal peak drift, enhancing the sensitivity to the interface changes of particulate drugs and improving the accuracy and stability of detection. Combining a high-frame-rate optical imaging device to obtain particulate trajectory data, identifying the deviation angle of the particulate movement direction, and analyzing the spatial distribution of flow field disturbances make the interaction relationship between the particulate movement state and the processing environment clearer, enhancing the quantitative analysis ability of flow field disturbances during the processing of particulate drugs. Through the correlation analysis of the particulate drug interface change data and the flow field disturbance data, combined with the calculation of the particulate processing state deviation rate, a real-time monitoring system is constructed to achieve precise control of the particulate drug processing process, provide efficient data support for optimizing the processing technology, and ensure the quality stability and consistency of the finished particulate drugs. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 is a schematic diagram of the working process of the present invention;

[0057] Figure 2 is a flowchart for obtaining the photoacoustic signal feature data set in the present invention;

[0058] Figure 3 is a flowchart for obtaining the particulate drug trace component identification data in the present invention;

[0059] Figure 4 is a flowchart for obtaining the particulate drug interface change data in the present invention;

[0060] Figure 5 is a flowchart for obtaining the particulate drug processing flow field disturbance data set in the present invention;

[0061] Figure 6 is a flowchart for obtaining the real-time monitoring results of drug processing in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0062] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0063] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present invention. In addition, in the description of the present invention, the meaning of "a plurality of" is two or more, unless otherwise specifically defined.

[0064] Embodiment 1: Please refer to Figure 1 , the present invention provides a technical solution: a real-time monitoring method for the processing of granular drugs, including the following steps:

[0065] S1: Based on the photoacoustic signal data during the processing of granular drugs, set a fixed time interval, capture the photoacoustic signals of granular drugs under the conditions of thermally induced photoacoustic excitation and pressure-induced photoacoustic excitation, record the amplitudes of the photoacoustic signals, and obtain a photoacoustic signal feature data set;

[0066] S2: Based on the photoacoustic signal feature data set, call the amplitudes of the photoacoustic signals, calculate the gain coefficient of the target component signal, adjust the contrast of the photoacoustic holographic image, extract the spatial distribution information of the target component, and obtain the identification data of trace components of granular drugs;

[0067] S3: Based on the identification data of trace components of granular drugs, extract the fluorescence spectrum data, record the fluorescence peak wavelength, analyze the fluorescence peak shift rate per unit time, compare the fluorescence peak shift rate with the noise baseline fluctuation range, screen the time points when the fluorescence peak drift rate exceeds the noise baseline fluctuation range, and obtain the interface change data of granular drugs;

[0068] S4: Based on the interface change data of granular drugs, use a high-frame-rate optical imaging device to record the particle trajectory data, identify the deviation angle of the particle movement direction, and judge the spatial distribution of the flow field disturbance to obtain a flow field disturbance data set for the processing of granular drugs;

[0069] S5: Based on the flow field disturbance data set for the processing of granular drugs, call the interface change data of granular drugs, analyze the deviation rate of the processing state of granular drugs, and obtain the real-time monitoring result of drug processing.

[0070] The photoacoustic signal feature dataset includes thermally induced photoacoustic signals, pressure-induced photoacoustic signals, and photoacoustic signal amplitudes. The identification data of trace components of particulate drugs includes the signal gain coefficient of the target component, the contrast of photoacoustic holographic images, and the spatial distribution information of the target component. The data on the interface change of particulate drugs includes fluorescence spectral data, fluorescence peak wavelength, and fluorescence peak shift rate. The dataset of flow field disturbances during particulate drug processing includes particle trajectory data, the deviation angle of particle movement direction, and the spatial distribution of flow field disturbances. The real-time monitoring results of drug processing include the data on the interface change of particulate drugs, the deviation rate of the processing state of particulate drugs, and the dataset of flow field disturbances during particulate drug processing.

[0071] Please refer to Figure 2 , and the specific steps for obtaining the photoacoustic signal feature dataset are as follows:

[0072] S111: Based on the photoacoustic signal data during the processing of particulate drugs, set a fixed time interval, detect the photoacoustic signal amplitude at each time point, record the data, and obtain a sequence of photoacoustic signal amplitudes;

[0073] First, for the photoacoustic signals under thermally induced photoacoustic excitation and pressure-induced photoacoustic excitation, set a fixed time interval, and monitor the photoacoustic signal amplitude at each time point through precise equipment. This process can be applied during drug manufacturing to ensure the stability and quality control of drug particles in the production process. For example, during the operation of a compressor, the photoacoustic signals emitted from the particles are regularly recorded by the monitoring equipment, and their amplitude changes are analyzed to monitor the physical state changes of the particles. This monitoring helps pharmaceutical engineers adjust the machine settings to optimize the processing process of drug particles. Finally, the recorded sequence data of photoacoustic signal amplitudes reflects the photoacoustic response characteristics of drug particles at different time points. From the data, the stability of the particles during the manufacturing process and its changing trend can be analyzed. This dataset provides key information for pharmaceutical quality control.

[0074] S112: Based on the sequence of photoacoustic signal amplitudes, identify the amplitude change rate between adjacent time points, and use the formula:

[0075] ;

[0076] Calculate the photoacoustic signal change rate for each time interval and establish a sequence of photoacoustic signal change rates;

[0077] Among them, represents the photoacoustic signal change rate, and represent the photoacoustic signal amplitudes at adjacent time points, represents the time interval;

[0078] In actual application scenarios, this calculation can be used for quality monitoring of granular drugs in the pharmaceutical process. For example, in granulation, the uniformity of drug granules directly affects the dissolution rate and drug efficacy release of the final tablets. It is necessary to monitor the changes in photoacoustic signals of the granules at different time points in real time to ensure the stability of the processing process;

[0079] First, on a certain pharmaceutical production line, set the measurement time interval to and collect the photoacoustic signal amplitudes of the granular drugs at consecutive time points and For example, at the measured photoacoustic signal amplitude is and at the measured photoacoustic signal amplitude is ;

[0080] Substitute the data into the formula to calculate the change rate of the photoacoustic signal:

[0081] ;

[0082] The value indicates that between and the change rate of the photoacoustic signal amplitude is It can be seen that this change rate is relatively large, indicating that the granules may have undergone significant physical changes due to changes in environmental temperature or pressure. If the calculated change rate is much larger than the normal fluctuation range of the production process (for example, the normal fluctuation range is ), it means that the physical state of the granular drug has undergone a mutation, such as agglomeration or decomposition, and it is necessary to adjust the production parameters for correction;

[0083] To ensure data integrity, the change rates of the photoacoustic signal amplitudes at different time points throughout the production process should be continuously calculated, and a change rate sequence should be constructed. Set the measurement point and calculate the change rates of all adjacent points. For example, at the measured value is , then:

[0084] ;

[0085] This change rate is within the normal fluctuation range, indicating that the state of the drug granules is relatively stable at this stage;

[0086] The above calculation process finally obtains a series of change rate data, that is, the photoacoustic signal change rate sequence, which can be used later to analyze the state changes of drug granules at different processing stages and provide a basis for adjusting process parameters.

[0087] S113: Call the photoacoustic signal change rate sequence, screen the time points where the change rate exceeds the threshold, extract the corresponding photoacoustic signal amplitudes, and obtain the photoacoustic signal feature data set;

[0088] In practice, it can be applied to automatic alarm. For example, in a chemical plant, by setting a threshold value to monitor the operating status of key equipment, when the change rate of the photoacoustic signal suddenly exceeds the preset threshold, the system will automatically trigger an alarm to notify the operator to conduct inspections or maintenance. Set a reasonable change rate threshold based on historical data and the normal operating range of the equipment. Through real-time data monitoring, continuously track the change rate of the photoacoustic signal, and use data processing algorithms to continuously compare the change rate of each measurement point with the threshold. Once a change rate exceeding the threshold is detected, the system will automatically record the amplitude of the photoacoustic signal at that time point and add it to the feature dataset for subsequent analysis and processing to generate a photoacoustic signal feature dataset.

[0089] Please refer to Figure 3 , and the steps for obtaining the identification data of trace components of granular drugs are specifically as follows:

[0090] S211: Based on the photoacoustic signal feature dataset, call the amplitude of the photoacoustic signal, calculate the initial gain coefficient of the target component signal, and perform normalization processing to obtain the normalized gain coefficient;

[0091] By comparing the amplitudes of the photoacoustic signals within different time windows, a preliminary calculation of the gain coefficient can be obtained. For example, in the pharmaceutical process, the trace analysis of drug components is crucial. Assume that the amplitude of the photoacoustic signal recorded within a specific time window is , and the amplitude within another window is , and estimate through . This step can help R & D personnel identify and adjust the proportion of active ingredients in the drug formula to ensure the safety and effectiveness of the drug. For the refinement process, first, photoacoustic signals need to be collected in a laboratory environment, then analyze the signal differences in different time periods through dedicated software, and further use a calculation model to process the data to obtain the normalized gain coefficient. Specific data involved in this process, such as photoacoustic wavelength, acoustic wave reception sensitivity, etc., need to be obtained through precise equipment. For example, the photoacoustic wavelength is set in the visible light range, and the sensitivity is adjusted according to experimental requirements, which will directly affect the subsequent drug quality control steps.

[0092] S212: Based on the normalized gain coefficient, by adjusting the contrast parameter of the differentiated area of the photoacoustic holographic image, use the formula:

[0093] ;

[0094] Calculate to obtain the contrast optimization parameter;

[0095] Among them, is the contrast optimization parameter, is the normalized gain coefficient of the th pixel point, is the The photoacoustic signal intensity of a pixel is the average normalized gain coefficient of the pixel is the total number of pixels

[0096] After determining the normalized gain coefficient of each pixel it is necessary to adjust the contrast of the holographic image, which is crucial in biological tissue imaging, material analysis, and trace drug component detection. For example, in the process of drug analysis, researchers need to analyze the distribution of active ingredients in drug particles through photoacoustic imaging technology to ensure the consistency and stability of the formulation. Set up an experimental scenario. In a batch of drug samples, perform signal analysis at the pixel level on the holographic image. The photoacoustic signal intensity of each pixel is measured by experimental equipment. For example, obtain the photoacoustic signal intensity of different regions of drug particles through a photoacoustic detection device. Set the measured values of the signal intensity of some regional pixels to be respectively ;

[0097] The corresponding normalized gain coefficient is ;

[0098] Calculate the average normalized gain coefficient

[0099] ;

[0100] Calculate the first part of the contrast adjustment parameter :

[0101] ;

[0102] ;

[0103] Calculate the second part, that is, the variance term of the gain coefficient

[0104] ;

[0105] ;

[0106] ;

[0107] Finally, calculate the contrast adjustment parameter ;

[0108] The calculation results show that under the experimental conditions, through the weighted calculation of the normalized gain coefficient and the photoacoustic signal intensity, the obtained contrast optimization parameter is 1.0767, which means that this value can be used to further adjust the contrast of the photoacoustic holographic image to optimize the visualization effect of different components. In practical applications, the adjusted contrast parameter can be used to enhance the spatial distribution of the target component, enabling researchers to more accurately identify the structural information of trace components in drug particles and improving the accuracy of pharmaceutical quality monitoring.

[0109] S213: Based on the contrast optimization parameter, adjust the pixel distribution of the photoacoustic holographic image, extract the spatial distribution information of the target component, and obtain the identification data of trace components in granular drugs;

[0110] After adjusting the contrast of the holographic image, extract the spatial distribution information of the target component. For example, in materials science research, understanding the microstructure of materials has an important impact on improving material properties. For instance, during the research and development of new alloys, by adjusting the image contrast, scientists can more clearly observe the internal microstructure distribution of the alloy, thereby making accurate predictions about the physical properties of the material. First, scientists need to use image processing software to adjust the pixel distribution of the holographic image according to the previously obtained contrast optimization parameter, and then use advanced image analysis techniques to extract the spatial structure information of the material. This information will directly reflect the internal composition of the material, and finally obtain the identification data of trace components in granular drugs, which not only helps to evaluate the efficacy of the drug, but also ensures the accuracy and reproducibility of image analysis. By adjusting the contrast, ensure that every part of the image can be correctly analyzed to obtain reliable structural information.

[0111] Please refer to Figure 4 , and the steps for obtaining the data of the interface change of granular drugs are specifically as follows:

[0112] S311: Based on the identification data of trace components in granular drugs, extract the fluorescence spectrum data, analyze the fluorescence intensity at each wavelength, determine the position of the spectral peak, record the peak wavelength, calculate the wavelength change amount per unit time, and obtain the fluorescence peak shift rate;

[0113] The fluorescence spectrum data of the drug is extracted for subsequent analysis. This process involves the analysis of spectral signals and the precise positioning of spectral peaks. For example, within a certain wavelength range, determine the peak wavelength corresponding to the strongest fluorescence intensity, and then record the peak data, which is particularly important for monitoring the fluorescence changes of the drug. Each change in the fluorescence peak wavelength is precisely measured to calculate the change amount of the fluorescence peak wavelength per unit time. The statistics of this change amount are achieved through time series analysis. The time series data is differenced to extract the rate information, which provides an important indicator for determining the stability of drug components and obtains the fluorescence peak shift rate.

[0114] S312: Call the fluorescence peak offset rate, identify the noise baseline fluctuation range, compare the offset rate at each time point with the noise baseline fluctuation range, screen out the time points beyond the fluctuation range, evaluate the cumulative change amount of the offset rate, and use the formula:

[0115] ;

[0116] Calculate the average value of the fluorescence peak drift deviation;

[0117] Among them, represents the average value of the fluorescence peak drift deviation, represents the fluorescence peak offset rate at the th time point, represents the average value of the noise baseline fluctuation range, represents the total number of time points beyond the noise baseline fluctuation range, represents the weight coefficient of the th time point, represents the th time interval of the time point, represents the total measurement time length;

[0118] By comparing the offset rate at each time point with the preset noise baseline fluctuation range, screen out the time points whose offset rate exceeds the noise baseline range. For example, during the experiment, the fluctuation range of the fluorescence peak is set to nm, while the fluorescence peak offset rate at some time points reaches nm / s, far exceeding the noise baseline. This time point is marked as a key change point. This screening uses the ratio of the offset rate at each time point to the noise baseline fluctuation range. If the ratio is greater than 1, it is determined that this time point is a significant offset point. Since the data weights at different time points may be different, a weight coefficient is introduced to correct the influence of different time periods. For example, during the initial decomposition stage of the drug, the change rate is relatively fast, and higher weights are assigned to the data in this stage, such as , while in the later stage, it tends to be stable, and the weight is reduced to . Further adjust the error caused by the time interval difference. For example, if a certain time point is , and the total experiment time is , then the time correction term , and finally calculate the average value of the fluorescence peak drift deviation;

[0119] Suppose 5 time points beyond the noise baseline are selected in the experiment, and the corresponding fluorescence peak offset rates are set to , , , , (unit: nm / s), and the weight coefficients are set as , , , , , and the time intervals are , , , , . Substitute them into the formula for calculation as follows:

[0120] ;

[0121] Calculate the absolute values of each item:

[0122] ;

[0123] ;

[0124] ;

[0125] The average value of the fluorescence peak drift deviation finally calculated is 0.1677 nm / s. This value indicates that under the current experimental conditions, the average drift deviation of the time points exceeding the noise baseline is relatively large. Further analyzing the change trend of this value can evaluate the interfacial dynamic changes of the particulate drug and be used for subsequent drug stability judgment.

[0126] S313: Based on the average value of the fluorescence peak drift deviation, statistically analyze the change trend of the time points, analyze the acceleration change of the offset rate, and combine the time accumulation to calculate the interfacial dynamic change rate to obtain the interfacial change data of the particulate drug;

[0127] By calculating the increase and decrease relationship of the average drift deviation in different time periods, judge the change pattern in the time series. For example, if the average value of the fluorescence peak drift deviation gradually increases in the first half of the experiment and then decreases in the second half, this indicates that the drug molecules are more active in the initial stage of the reaction and then gradually stabilize. This kind of information is extremely crucial for understanding the dynamic changes of the drug. Through the calculation with the cumulative data in the time dimension, not only can the rate of the fluorescence peak drift be obtained, but also important data on drug stability and reaction speed can be provided, and the interfacial change data of the particulate drug can be obtained. The data will ultimately be used to evaluate the quality and stability of the drug and provide a scientific basis for the monitoring of the pharmaceutical process.

[0128] Please refer to Figure 5 for the specific steps to obtain the dataset of the flow field disturbance of the particulate drug processing:

[0129] S411: Based on the data of the interface change of granular drugs, extract the optical imaging data of the granules. Use a high-frame-rate optical imaging device to record the trajectory data of the granules over time series, analyze the motion trajectory vector of the granules, and obtain the granule trajectory data;

[0130] Under the monitoring of the high-frame-rate optical imaging device, the motion trajectory of the granules can be accurately recorded. The optical device continuously acquires images at a fixed time interval (such as 0.01 seconds). Each frame of the image contains the position information of the granules in three-dimensional space. Identify the coordinates of each frame of the image, extract the spatial coordinates of the granules, and calculate their trajectories based on the spatial displacement of the granules between adjacent frames. For example, if the coordinates of the granule at time are (2.5, 3.6, 1.2), and at time are (2.8, 3.9, 1.3), then the motion trajectory vector within this time interval can be expressed as (0.3, 0.3, 0.1), and so on. Traverse the entire time series. After recording the position information of all time points, call the coordinate change amount to calculate the motion trajectory vector of the granules and obtain the granule trajectory data.

[0131] S412: Call the granule trajectory data, extract the motion directions of the granules at adjacent time points, analyze the angular change trend in the time series, judge the influence of fluid action on the motion of the granules, adjust the perturbation parameters in combination with the force conditions of the granules, identify the offset fluctuation range of the granules in the perturbation area, and use the formula:

[0132] ;

[0133] Calculate the offset angle of the granule motion direction;

[0134] Among them, represents the offset angle of the granule motion direction, , represent the X-axis coordinates of the granule at time points and , , represent the corresponding Y-axis coordinates, represents the vertical displacement of the granule under fluid perturbation;

[0135] Gradually analyze each time point in the time series, calculate the offset angle of the granule at each time point. If the angular change of the granule at multiple time points is abnormal, it can be judged as caused by external perturbation. For example, if the granule moves along the direction of (1, 0, 0) at time, and changes to (0.8, 0.6, 0) at time, then the offset angle can be calculated using the formula:

[0136] ;

[0137] Assume that the coordinates of the particle at are (3.5, 2.2, 1.8), and at are (3.9, 2.6, 1.85). Then substitute into the formula to calculate the deflection angle:

[0138] ;

[0139] ;

[0140] ;

[0141] The calculated deflection angle of the particle movement direction is 1.41. If this angle exceeds the flow field perturbation determination threshold, it indicates that the particle is affected by the perturbation.

[0142] S413: Invoke the deflection angle of the particle movement direction, identify the spatial distribution of the flow field perturbation region, analyze the movement trend of the particle in the perturbation region, and obtain the particle drug processing flow field perturbation dataset;

[0143] Judge the movement trend of the particles in the perturbation region. Based on the offset data of multiple particles in the perturbation region, extract the perturbation center and the influence range. For example, if multiple particles to all show a deflection angle higher than 1.2 within the range, it can be judged that there is a flow field perturbation in this region. By statistically analyzing the perturbation characteristics of the particles at different time periods and calculating the dynamic change trend of the perturbation region, the particle drug processing flow field perturbation dataset is obtained.

[0144] Please refer to Figure 6 for the specific steps to obtain the real-time monitoring results of drug processing:

[0145] S511: Based on the particle drug processing flow field perturbation dataset, invoke the particle drug interface change data, analyze the particle drug morphology change rate and the particle aggregation degree, and obtain the particle morphology change coefficient;

[0146] First, obtain the flow field disturbance data and interface change data of granular drugs during the processing. For example, during the pharmaceutical process, use high-precision sensors and high-speed imaging devices to monitor the movement state of granules in a mixer or reactor in real time, record their speed, position, and shape changes, analyze the data, calculate the morphological change rate and aggregation degree of the granules. The morphological change rate can be determined by measuring the size changes of the granules at different time points. For example, assume that at the initial moment, the average diameter of the granules is 100 microns. After a period of time, the measurement shows that the average diameter increases to 105 microns. Then the morphological change rate is (105 - 100) / time interval. The aggregation degree can be evaluated by calculating the average distance between granules or the number of aggregates. For example, use image processing technology to identify the number and size of aggregates, count the number of aggregates and the average size per unit volume, and normalize the calculation results to obtain the granular morphological change coefficient. This coefficient can be expressed as the ratio of the morphological change rate to the aggregation degree. For example, assume that the morphological change rate is 0.05 microns / second and the aggregation degree is 10 aggregates per cubic millimeter. Then the granular morphological change coefficient is 0.05 / 10 = 0.005. Use this coefficient to evaluate the stability and consistency of the granules during the processing.

[0147] S512: According to the granular morphological change coefficient, evaluate the change deviation of the processing state of the granular drug, and combine with the granular drug interface change data, using the formula:

[0148] ;

[0149] Identify the change deviation of the processing state of the granular drug at each moment to obtain the granular processing deviation rate;

[0150] where, represents the granular processing deviation rate, represents the maximum value of the granular morphological change coefficient, represents the minimum value of the granular morphological change coefficient, represents the average change value of the granular morphological change trend, represents the fluctuation value of the granular drug interface change data, represents the granular processing state deviation adjustment factor;

[0151] First, determine the maximum and minimum values of the granular morphological change coefficient. For example, in a batch production, the maximum coefficient monitored is 0.008 and the minimum coefficient is 0.002. Calculate the average change value and the fluctuation value. The average change value can be obtained by averaging all measured morphological change coefficients. Assume that 100 measurements are taken and the sum of the coefficients is 0.5. Then the average change value is 0.5 / 100 = 0.005. The fluctuation value can be obtained by calculating the standard deviation of the coefficients. Assume that the standard deviation is 0.001;

[0152] Among them, represents the particle processing deviation rate, represents the maximum value (0.008) of the particle morphology change coefficient, represents the minimum value (0.002) of the particle morphology change coefficient, represents the average change value (0.005) of the particle morphology change trend, represents the fluctuation value (0.001) of the particle interface change data, represents the particle processing state deviation adjustment factor, and the adjustment factor is determined according to process requirements and historical data. For example, it is set to 1.2, and substitute the value into the calculation:

[0153] ;

[0154] The calculated particle processing deviation rate is 1.2, indicating that there is a certain degree of deviation in the processing process, and its impact needs to be evaluated according to process specifications.

[0155] S513: Based on the particle processing deviation rate, analyze the real-time changes in the particle drug processing state, and combine with the dynamic trend of the processing state deviation to monitor whether the particle drug processing state is stable, and obtain the real-time monitoring result of drug processing;

[0156] First, continuously obtain the data of the particle morphology change coefficient and the processing deviation rate during the processing process. For example, install an on-line monitoring device on the production line to record the particle morphology change coefficient and calculate the corresponding deviation rate every minute, and compare the real-time data with the preset stability standard. For example, set the acceptance range of the deviation rate to 0.8 to 1.0. If the monitored deviation rate exceeds this range, an alarm will be triggered to prompt the operator to check the production parameters and equipment status, and combine with the dynamic trend of the processing state deviation to judge the stability and consistency of the processing process. For example, if the deviation rate continues to rise, it indicates equipment wear or raw material quality fluctuations, and timely adjustment is required to ensure product quality.

[0157] The real-time monitoring system for the particle drug processing process is used to execute the above real-time monitoring method for the particle drug processing process. The system includes:

[0158] The photoacoustic signal control module controls the photoacoustic excitation power and intensity based on the photoacoustic signal data during the particle drug processing process, synchronizes the amplitude change of the photoacoustic signal, calculates the deviation of the photoacoustic signal response time, and obtains the photoacoustic signal stability data;

[0159] The target component gain calculation module calculates the signal gain coefficient of the target component based on the photoacoustic signal stability data, adjusts the amplitude contrast of the photoacoustic signal, analyzes the peak change of the target component signal, compares the difference distribution between the signal gain coefficient of the target component and the background signal, screens the gain region, analyzes the enhancement degree of the target component signal, extracts the spatial distribution information of the target component, and obtains the spatial distribution data of the target component;

[0160] The particle interface change tracking module calls the fluorescence spectrum characteristics based on the spatial distribution data of the target component, calculates the fluorescence peak wavelength shift rate, compares the fluorescence peak wavelength shift rate with the interface stability threshold, screens the fluorescence drift moments exceeding the interface stability threshold, and obtains the particle drug interface change data;

[0161] The flow field disturbance identification module combines the particle trajectory change based on the particle drug interface change data, identifies the deviation angle of the particle movement direction, compares the deviation angle of the particle movement direction with the consistency of the fluid movement direction, analyzes the flow field disturbance trend of the particle trajectory, and obtains the particle drug processing flow field disturbance data;

[0162] The processing deviation monitoring module analyzes the deviation change rate of the particle movement direction based on the particle drug processing flow field disturbance data, compares the deviation change rate of the particle movement direction with the flow field stability parameter, calculates the processing state deviation rate of the particle drug, and obtains the real-time monitoring result of the drug processing.

[0163] The above are only the preferred embodiments of the present invention, and do not limit the present invention in other forms. Any person skilled in the art may use the disclosed technical content to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A real-time monitoring method for a granular drug processing process, characterized in that: The following steps are involved: S1: Based on the photoacoustic signal data during the processing of granular drugs, a fixed time interval is set to capture the photoacoustic signals of granular drugs under the conditions of thermal photoacoustic excitation and pressure photoacoustic excitation, and the amplitude of the photoacoustic signal is recorded to obtain the photoacoustic signal feature data set; S2: Based on the photoacoustic signal feature data set, the photoacoustic signal amplitude is called, the contrast of the photoacoustic holographic image is adjusted, the spatial distribution information of the target component is extracted, and the micro-component identification data of the granular medicine is obtained; S3: Based on the micro-component identification data of the granular drug, extract the fluorescence spectrum data, record the fluorescence peak wavelength, analyze the fluorescence peak shift rate per unit time, screen the time point when the fluorescence peak drift rate exceeds the noise baseline fluctuation range, and obtain the granular drug interface change data; The steps for obtaining the particle drug interface change data are specifically as follows: S311: extracting fluorescence spectrum data based on the micro-component identification data of the granular medicine, analyzing the fluorescence intensity of each wavelength, determining the peak position of the spectrum, recording the peak wavelength, calculating the wavelength change per unit time, and obtaining the fluorescence peak shift rate; S312: Call the fluorescence peak shift rate, identify the noise baseline fluctuation range, compare the shift rate at each time point with the noise baseline fluctuation range, screen the time points beyond the fluctuation range, evaluate the cumulative change of the shift rate, and use the formula: ； The mean value of fluorescence peak drift deviation was calculated; in, represents the mean value of fluorescence peak drift deviation, Representative The fluorescence peak shift rate at each time point is Represents the mean value of the noise baseline fluctuation range, Represents the total number of time points that are beyond the range of noise baseline fluctuations, Representative The weight coefficient of each time point, Representative The time interval between time points, Represents the total measurement time length; S313: Based on the mean value of the fluorescence peak drift deviation, the time point change trend is counted, the acceleration change of the offset rate is analyzed, and the dynamic change rate of the interface is calculated in combination with the time accumulation to obtain the particle drug interface change data; S4: Based on the particle-drug interface change data, a high frame rate optical imaging device is used to record particle trajectory data, identify the deviation angle of the particle movement direction, determine the spatial distribution of the flow field disturbance, and obtain a particle drug processing flow field disturbance data set; S5: Based on the particle drug processing flow field disturbance data set, the particle drug interface change data is called, the particle drug processing state deviation rate is analyzed, and the drug processing real-time monitoring result is obtained.

2. The real-time monitoring method for the granular drug processing process according to claim 1, characterized in that: The photoacoustic signal feature data set includes thermal photoacoustic signals, pressure-induced photoacoustic signals, and photoacoustic signal amplitudes; the granular drug trace component identification data includes target component signal gain coefficient, photoacoustic holographic image contrast, and target component spatial distribution information; the granular drug interface change data includes fluorescence spectrum data, fluorescence peak wavelength, and fluorescence peak offset rate; the granular drug processing flow field disturbance data set includes particle trajectory data, particle movement direction offset angle, and flow field disturbance spatial distribution; the drug processing real-time monitoring results include granular drug interface change data, granular drug processing state deviation rate, and granular drug processing flow field disturbance data set.

3. The real-time monitoring method for the granular drug processing process according to claim 1, characterized in that: The steps for acquiring the photoacoustic signal feature data set are specifically as follows: S111: Based on the photoacoustic signal data during the processing of the granular medicine, a fixed time interval is set, the photoacoustic signal amplitude at each time point is detected, the data is recorded, and a photoacoustic signal amplitude sequence is obtained; S112: Based on the photoacoustic signal amplitude sequence, the amplitude change rate of adjacent time points is identified using the formula: ； Calculate the photoacoustic signal change rate at each time interval and establish a photoacoustic signal change rate sequence; in, represents the rate of change of photoacoustic signal, and represents the photoacoustic signal amplitude at adjacent time points, represents a time interval; S113: calling the photoacoustic signal change rate sequence, screening the time points where the change rate exceeds the threshold, extracting the corresponding photoacoustic signal amplitude, and obtaining a photoacoustic signal feature data set.

4. The real-time monitoring method for the granular drug processing process according to claim 3, characterized in that: The specific steps for obtaining the identification data of trace components of the granular medicine are as follows: S211: Based on the photoacoustic signal feature data set, calling the photoacoustic signal amplitude, calculating the initial gain coefficient of the target component signal, and performing normalization processing to obtain a normalized gain coefficient; S212: Based on the normalized gain coefficient, by adjusting the contrast parameter of the differential area of ​​the photoacoustic holographic image, the formula is used: ； Calculate the contrast optimization parameters; in, Optimize the parameters for contrast, For the The normalized gain coefficient of pixels, For the The photoacoustic signal intensity of each pixel is is the average normalized gain coefficient of the pixel points, is the total number of pixels; S213: Based on the contrast optimization parameters, the pixel distribution of the photoacoustic holographic image is adjusted to extract the spatial distribution information of the target component to obtain the micro-component identification data of the granular medicine.

5. The real-time monitoring method for the granular drug processing process according to claim 1, characterized in that: The steps for acquiring the particle drug processing flow field disturbance data set are specifically as follows: S411: extracting optical imaging data of particles based on the particle-drug interface change data, using a high frame rate optical imaging device to record trajectory data of particles in a time series, analyzing the motion trajectory vector of the particles, and obtaining particle trajectory data; S412: Call the particle trajectory data, extract the movement direction of the particles at adjacent time points, analyze the angle change trend in the time series, determine the impact of the fluid on the particle movement, adjust the disturbance parameters based on the particle force conditions, identify the offset fluctuation range of the particles in the disturbance area, and use the formula: ； The deviation angle of the particle movement direction is calculated; in, represents the deviation angle of the particle motion direction, , Represents particles at time point and The X-axis coordinate, , represents the corresponding Y-axis coordinate, Represents the vertical displacement of particles under fluid disturbance; S413: calling the particle movement direction offset angle, identifying the spatial distribution of the flow field disturbance area, analyzing the movement trend of the particles in the disturbance area, and obtaining a particle drug processing flow field disturbance data set.

6. The real-time monitoring method for the granular drug processing process according to claim 5, characterized in that: The steps for obtaining the real-time monitoring results of drug processing are specifically as follows: S511: Based on the particle drug processing flow field disturbance data set, call the particle drug interface change data, analyze the particle drug morphology change rate and particle aggregation degree, and obtain the particle morphology change coefficient; S512: Based on the particle morphology variation coefficient, the variation deviation of the particle drug processing state is evaluated, and combined with the particle drug interface variation data, the formula is used: ； Identify the variation deviation of the particle drug processing state at each moment and obtain the particle processing deviation rate; in, represents the particle processing deviation rate, represents the maximum value of the particle morphology variation coefficient, represents the minimum value of the particle morphology variation coefficient, The average change value representing the trend of particle morphology change, Represents the fluctuation value of the particle interface change data, represents the particle processing state deviation adjustment factor; S513: Based on the particle processing deviation rate, the real-time changes of the particle drug processing state are analyzed, and combined with the dynamic trend of the processing state deviation, whether the particle drug processing state is stable is monitored to obtain the real-time monitoring result of drug processing.

7. A real-time monitoring system for the processing of granular drugs, characterized in that: According to any one of claims 1 to 6, the real-time monitoring method for the granular drug processing process comprises: The photoacoustic signal control module controls the photoacoustic excitation power and intensity, synchronizes the photoacoustic signal change amplitude, calculates the photoacoustic signal response time deviation, and obtains the photoacoustic signal stability data based on the photoacoustic signal data during the particle drug processing; The target component gain calculation module calculates the target component signal gain coefficient based on the photoacoustic signal stability data, adjusts the photoacoustic signal amplitude contrast, analyzes the target component signal peak change, compares the difference distribution between the target component signal gain coefficient and the background signal, screens the gain area, analyzes the target component signal enhancement degree, extracts the target component spatial distribution information, and obtains the target component spatial distribution data; The particle interface change tracking module calls the fluorescence spectrum characteristics based on the target component spatial distribution data, calculates the fluorescence peak wavelength shift rate, compares the fluorescence peak wavelength shift rate with the interface stability threshold, screens the fluorescence drift time exceeding the interface stability threshold, and obtains the particle drug interface change data; The flow field disturbance identification module identifies the deviation angle of the particle movement direction based on the particle drug interface change data and the particle trajectory change, compares the consistency of the particle movement direction deviation angle with the fluid movement direction, analyzes the flow field disturbance trend of the particle trajectory, and obtains the particle drug processing flow field disturbance data; The processing deviation monitoring module analyzes the particle movement direction deviation change rate based on the particle drug processing flow field disturbance data, compares the particle movement direction deviation change rate with the flow field stability parameter, calculates the particle drug processing state deviation rate, and obtains the real-time monitoring result of drug processing.

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