Drug production quality risk management and control method, device and system and storage medium

Abstract

The invention discloses a drug production quality risk management and control method, device and system and a storage medium, and belongs to the technical field of drug production risk control. The drug production quality risk management and control method comprises the following steps: monitoring a production environment area in real time and obtaining monitoring data of a monitoring point; determining risk points according to the monitoring data; determining corresponding questionnaire generation prompt information based on the risk points; obtaining an investigation test result corresponding to the investigation test; and obtaining a risk management and control result corresponding to the production environment area according to the survey test result and the monitoring data. Through real-time monitoring, risk point accurate identification, personalized investigation prompting, comprehensive analysis and risk management and control result generation, effective management and control of quality risks in the drug production process are realized, the production efficiency and the product quality are improved, and the safety and reliability of drugs are ensured.

Description

Technical Field

[0001] The present invention relates to the field of drug production risk control technology, and in particular to a drug production quality risk control method, device, and storage medium. Background Art

[0002] Drug quality and risk management are the core links of the pharmaceutical industry. Their importance is not only reflected in ensuring the safety of patients' medication, but also the key to the sustainable development and market competitiveness of enterprises. With the rapid development of the pharmaceutical industry, the number of drug types is increasing and the production process is becoming more complex, which makes drug quality control and risk management face more challenges. Traditional quality management and risk assessment often rely on manual operations, which is not only inefficient, but also easily interfered by human factors, resulting in untimely and inaccurate risk identification and handling.

[0003] Existing pharmaceutical production units generally use manual methods to discover and troubleshoot risk points when conducting risk and quality management. This process usually involves multiple links, including risk identification, cause analysis, problem solving, etc. However, due to the limitations of manual operation, this method has many shortcomings. First, manual identification of risk points often requires a lot of time and energy, and is easily limited by personal experience and knowledge level, resulting in incomplete and inaccurate risk identification. Secondly, the layers of manual arrangements in the risk investigation process prolong the problem-solving cycle, affecting the efficiency of pharmaceutical production and the speed of market response. In addition, with the continuous expansion of drug types and production scales, the number of risk points and quality problems managed manually is increasing, and labor costs are also increasing, which brings a greater economic burden to enterprises.

[0004] In the existing technical system, a major problem faced by pharmaceutical production units is the efficiency and accuracy of risk management. Due to reliance on manual operations, the process of discovering and troubleshooting risk points is cumbersome and time-consuming, which not only affects the continuity and stability of pharmaceutical production, but also increases the operating costs of enterprises. At the same time, the subjectivity and uncertainty of manual operations may also lead to errors in risk assessment, thus affecting the quality and safety of drugs.

[0005] In addition, existing drug quality and risk management technologies have also shown their limitations when dealing with complex and changing production environments. With the advancement of science and technology and the diversification of market demand, more and more variables are involved in the drug production process. Traditional manual management methods are difficult to adapt to such changes and it is difficult to achieve rapid response and effective control of risks. This not only affects the quality and efficacy of drugs, but may also pose a potential threat to patients' health.

[0006] In summary, the existing drug quality and risk management technologies have obvious deficiencies in efficiency, accuracy, cost control and adaptability. These defects not only affect the economic benefits of drug production, but also pose challenges to the safety and reliability of drugs. Therefore, there is an urgent need to develop more efficient, accurate, economical and adaptable drug quality and risk management control technologies to meet the development needs of the modern pharmaceutical industry. Summary of the invention

[0007] In a first aspect, the present invention provides a method for drug production quality risk control, which monitors a production environment area in real time and obtains monitoring data corresponding to monitoring points in the production environment area;

[0008] Determining risk points based on the monitoring data;

[0009] Determine a corresponding investigator based on the risk point, generate prompt information for the investigator to perform an investigative test; and obtain an investigative test result corresponding to the investigative test;

[0010] A risk control result corresponding to the production environment area is obtained based on the investigation and test results and the monitoring data.

[0011] In an optional implementation manner, the acquiring monitoring data corresponding to the monitoring points in the production environment area includes:

[0012] Based on the production environment area, obtaining the detection specimen corresponding to the monitoring point;

[0013] Detecting the detection specimen to obtain detection data, determining a corresponding processing workstation according to the detection data, and obtaining raw data through the processing workstation;

[0014] The monitoring data is obtained by performing calculations based on the original data.

[0015] In an optional embodiment, the monitoring point in the production environment area is a drug extraction device; the drug extraction tank of the drug extraction device includes a plurality of movable sampling devices; wherein the movable sampling device includes a sampling body and a temperature probe provided on the sampling body;

[0016] The obtaining of the detection specimen corresponding to the monitoring point based on the production environment area includes:

[0017] Acquire the temperature measurement data in the drug extraction tank and the spatial coordinates corresponding to the temperature measurement data through the temperature probe of the movable sampling device;

[0018] Based on the spatial coordinates, constructing a data matrix corresponding to the drug extraction tank according to the temperature measurement data;

[0019] Based on the data matrix, determining a temperature uniform area within the drug extraction tank;

[0020] Based on the coordinate range corresponding to the temperature uniform area, the movable sampling device is moved into the coordinate range to obtain the test sample.

[0021] In an optional embodiment, based on the coordinate range corresponding to the temperature uniform area, the movable sampling device is moved into the coordinate range to obtain the test sample, including:

[0022] Based on the coordinate range corresponding to the temperature uniform area, the volume of the temperature uniform area is calculated;

[0023] According to the volume, the number of sampling points in the temperature uniform area is calculated;

[0024] The number of sampling points is calculated by the following formula:

[0025]

[0026] Wherein, n represents the number of sampling points; k represents the empirical coefficient; A represents the volume of the temperature uniform area;

[0027] Based on the number of sampling points, n sampling coordinate points are generated according to the coordinate range, and the movable sampling device is controlled to move within the coordinate range and sample at the sampling coordinate points to obtain the detection specimen.

[0028] In an optional implementation manner, determining the risk point according to the monitoring data includes:

[0029] Calculate the median of the monitoring data corresponding to the monitoring point and obtain the absolute median difference;

[0030] The Modified Z-Score is calculated based on the absolute median difference and the median. The Modified Z-Score calculation formula is:

[0031]

[0032] Among them, x i Represents the i-th monitoring data; represents the median; MAD represents the absolute median difference;

[0033] If the monitoring data x i If the absolute value of the corresponding Modified Z-Score is greater than 3.5, the monitoring data is excluded as an outlier;

[0034] If the monitoring data x i If the absolute value of the corresponding Modified Z-Score is not greater than 3.5, the monitoring data is regarded as a normal value;

[0035] Taking an average value of the monitoring data as normal values ​​to obtain a monitoring mean value, and comparing the monitoring mean value with a preset threshold range;

[0036] If the monitored mean value exceeds the preset threshold range, the monitored point is used as the risk point.

[0037] In an optional implementation manner, the determining a corresponding investigator based on the risk point and generating prompt information for the investigator to perform an investigative test include:

[0038] Generate action item information based on the risk point, and determine the investigator corresponding to the risk point;

[0039] Writing the action item information into the investigator's action list;

[0040] Taking the action item information for the risk point in the action list as current information and taking other action item information different from the current information as comparison information; obtaining the establishment time of the current information and action information for the same risk point in the action list;

[0041] Determine whether there is any comparison information in the action list whose interval with the creation time of the current information is less than a preset time threshold;

[0042] If so, the current information and the comparison information for the same risk point whose establishment time interval is less than the preset time threshold are combined as an action item information, and the prompt information is generated.

[0043] In an optional implementation manner, obtaining a risk control result corresponding to the production environment area according to the survey test result and the monitoring data includes:

[0044] comparing the survey test results with the monitoring data;

[0045] The error value is calculated using the following formula:

[0046] E=|x s -x h |;

[0047] Wherein, E represents the error value; x s Represents the monitoring data; x h represents the results of the survey test described;

[0048] Compare E with the error threshold T;

[0049] If E≤T, it is determined that the data matches, and the risk control result is that there is a risk;

[0050] If E>T, it is determined that the data does not match, the risk control result is that the data is questionable, and the step is returned to obtain the monitoring data corresponding to the monitoring point in the production environment area.

[0051] In a second aspect, the present invention provides a drug production quality risk control device, comprising:

[0052] An acquisition module is used to monitor the production environment area in real time and obtain monitoring data corresponding to the monitoring points in the production environment area;

[0053] A determination module, used to determine risk points based on the monitoring data;

[0054] The acquisition module is further used to determine the corresponding investigator based on the risk point, generate prompt information for the investigator to perform an investigative test; and obtain the investigative test result corresponding to the investigative test;

[0055] An output module is used to obtain risk control results corresponding to the production environment area based on the investigation and test results and the monitoring data.

[0056] In a third aspect, the present invention provides a drug production quality risk control system, comprising a memory and a processor, wherein the memory stores a drug production quality risk control program, and the processor runs the drug production quality risk control program to enable the drug production quality risk control system to execute the drug production quality risk control method as described in any one of the aforementioned embodiments.

[0057] In a fourth aspect, the present invention provides a computer-readable storage medium, on which a drug production quality risk control program is stored. When the drug production quality risk control program is executed by a processor, the drug production quality risk control method as described in any one of the aforementioned embodiments is implemented.

[0058] The drug production quality risk control method provided in this application has the following beneficial effects:

[0059] By real-time monitoring of the production environment area, environmental changes can be captured in a timely manner, real-time data of key monitoring points can be obtained, and the continuity of the production process and the accuracy of the data can be ensured.

[0060] Using monitoring data to determine risk points helps to quickly identify quality problems or potential risks that may arise in the production process, thereby achieving precise control of the production environment.

[0061] Generating prompt information for investigative testing for specific investigators based on risk points ensures the pertinence and effectiveness of the investigation and improves the efficiency of risk assessment.

[0062] By combining monitoring data and investigation and test results, the risk status of the production environment area can be comprehensively assessed, providing a more comprehensive and in-depth analysis for risk management and control.

[0063] The final risk management and control results will help guide production adjustments and decisions, ensure the quality and safety of drug production, and reduce the impact of potential risks on production.

[0064] In summary, this application achieves effective management and control of quality risks in the drug production process through real-time monitoring, accurate identification of risk points, personalized investigation prompts, comprehensive analysis and generation of risk control results, improves production efficiency and product quality, and ensures the safety and reliability of drugs. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to more clearly illustrate the technical solution of the present invention, the following will briefly introduce the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and should not be regarded as limiting the scope of protection of the present invention. In each of the drawings, similar components are numbered similarly.

[0066] Figure 1 It is a structural diagram of the hardware operating environment involved in the embodiment of the drug production quality risk control method of the present invention;

[0067] Figure 2 This is a flow chart of Example 1 of the drug production quality risk control method of the present invention;

[0068] Figure 3 This is a schematic diagram of a detailed process of step S100 in Example 2 of the method for risk control of drug production quality of the present invention;

[0069] Figure 4 This is a schematic diagram of a detailed process of step S110 in Example 2 of the method for risk control of drug production quality of the present invention;

[0070] Figure 5 This is a schematic flow chart of the refinement of step S200 in Example 3 of the method for risk control of drug production quality of the present invention;

[0071] Figure 6 This is a schematic flow chart of the refinement of step S200 in Example 3 of the method for risk control of drug production quality of the present invention;

[0072] Figure 7 This is a schematic diagram of a detailed process of step S300 in Example 4 of the method for risk control of drug production quality of the present invention;

[0073] Figure 8 This is a schematic flow chart of the refinement of step S400 in Example 5 of the method for risk control of drug production quality of the present invention;

[0074] Fig. 9 Schematic diagram of module connection of a drug production quality risk control device in an embodiment of the present invention. DETAILED DESCRIPTION

[0075] The embodiments of the present invention are described in detail below, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions.

[0076] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0077] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0078] 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.

[0079] like Figure 1 , which is a schematic diagram of the structure of the hardware operating environment of the terminal involved in the embodiment of the present invention.

[0080] The drug production quality risk control system provided in the embodiment of the present invention can be a PC, or a mobile terminal device such as a smart phone, a tablet computer or a portable computer. The drug production quality risk control system may include: a processor 1001, such as a CPU, a network interface 1004, a user interface 1003, a memory 1005 and a communication bus 1002. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen, an input unit such as a keyboard, and a remote control. The optional user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface). The memory 1005 may be a high-speed RAM memory or a stable memory, such as a disk memory. The memory 1005 may also be a storage device independent of the aforementioned processor 1001. Optionally, the drug production quality risk control system may also include an RF (Radio Frequency) circuit, an audio circuit, a WiFi module, and the like. In addition, the pharmaceutical production quality risk control system can also be configured with other sensors such as gyroscopes, barometers, hygrometers, thermometers, infrared sensors, etc., which will not be repeated here.

[0081] Those skilled in the art will understand that Figure 1 The pharmaceutical production quality risk control system shown in the figure does not constitute a limitation thereof, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently. Figure 1 As shown, the memory 1005 as a computer-readable storage medium may include an operating system, a data interface control program, a network connection program, and a drug production quality risk management and control program.

[0082] Example 1

[0083] Reference Figure 2 Embodiment 1 of the present invention provides a method for drug production quality risk control, comprising:

[0084] Step S100, real-time monitoring of the production environment area, and obtaining monitoring data corresponding to the monitoring points in the production environment area;

[0085] The above-mentioned production environment area is a designated area set up in a pharmaceutical production unit, for example, it may include but is not limited to a production workshop, storage warehouse, testing laboratory, research and development laboratory, etc.

[0086] The monitoring points mentioned above are specific units designated in the production environment area that need to be monitored. For example, they may include but are not limited to production equipment, such as extraction tanks, concentration tanks, and production-related equipment, such as operating devices, packaging devices, and detection or experimental related equipment, such as HPLC, UV spectrophotometer, etc.

[0087] This step aims to monitor a specific production environment in real time by setting up monitoring points to collect data, such as the device nodes of specific equipment in the production workshop, and the data obtained, so as to obtain and update the production environment data in real time, timely monitor the status of the production unit and environmental changes, and help to quickly respond to possible risks and abnormal situations.

[0088] Step S200, determining risk points according to the monitoring data;

[0089] As mentioned above, based on the data collected in step S100, this step analyzes these data to identify risk points that may lead to certain risks, such as unqualified raw materials, unqualified extracted materials, failure of testing equipment, abnormal temperature and humidity in the workshop, etc., so as to determine the risk points and take preventive measures in advance to reduce the probability of risk situations, thereby improving the management of pharmaceutical production units.

[0090] Step S300, determining a corresponding investigator based on the risk point, generating prompt information for the investigator to perform an investigative test; and obtaining an investigative test result corresponding to the investigative test;

[0091] As mentioned above, the investigators are the relevant management staff corresponding to the monitoring points, who are responsible for checking the risk points that appear, so as to determine whether there are risks at the current risk points and whether there is the possibility of further control.

[0092] The above-mentioned prompt information can be provided through multiple terminals such as APP, email, and phone.

[0093] The above-mentioned investigation test results are test results obtained by the investigator after repeated testing or inspection on the risk points. The testing method of the test results can be the same as the testing method of the monitoring data.

[0094] This step involves assigning appropriate management personnel to conduct in-depth investigations and tests based on the identified risk points to validate the risk points and conduct more detailed analysis.

[0095] Through professional investigation and testing, the causes and conditions of risk occurrence can be determined more accurately, which helps to develop more personalized and effective risk management and control plans.

[0096] Step S400, obtaining a risk control result corresponding to the production environment area according to the investigation and test result and the monitoring data.

[0097] Combined with the investigation and test results and preliminary monitoring data, a comprehensive analysis is conducted to form risk assessment and control recommendations for the entire production environment. The final risk control results can provide specific guidance on how to improve the environment and adjust management strategies to reduce the occurrence of future risk events.

[0098] In summary, this embodiment achieves effective management and control of quality risks in the drug production process through real-time monitoring, accurate identification of risk points, personalized investigation prompts, comprehensive analysis and generation of risk control results, improves production efficiency and product quality, and ensures the safety and reliability of drugs.

[0099] Example 2

[0100] Reference Figure 3 Embodiment 2 of the present invention provides a method for controlling the quality risk of drug production. Based on the above embodiment 1, step S100, obtaining monitoring data corresponding to the monitoring points in the production environment area, includes:

[0101] Step S110, based on the production environment area, obtaining the detection specimen corresponding to the monitoring point;

[0102] In a pharmaceutical manufacturing environment, this step involves collecting samples from key production control points. Test specimens can be intermediate samples of production extracted at specific control points, for example, extract samples obtained from extraction tanks. These samples are critical to assessing the quality and consistency of the production process.

[0103] By obtaining these samples, we can ensure that the production process meets the predetermined quality standards, detect problems in production in a timely manner, and take corresponding measures.

[0104] An automated sampling system can be used to take samples directly from the production line to reduce human error and ensure the representativeness of the sample.

[0105] Step S120, testing the test specimen to obtain test data, determining a corresponding processing workstation according to the test data, and obtaining raw data through the processing workstation;

[0106] Necessary tests are carried out on the samples obtained, such as chemical composition analysis, active ingredient content determination, etc., to obtain specific data on production quality. Based on these data, appropriate processing workstations are selected for further data processing, such as data standardization and preliminary analysis.

[0107] The above steps can help to timely understand any deviations or anomalies in the production process, adjust production parameters in time, and ensure product quality.

[0108] For example, samples are analyzed using analytical instruments such as high performance liquid chromatography (HPLC) and gas chromatography (GC), and the data is processed by a computer system for further analysis.

[0109] It should be noted that different test data require corresponding processing workstations to open and obtain raw data. For example, Agilent HPLC data and Shimadzu HPLC data both require dedicated workstations to read and open.

[0110] Step S130, obtaining monitoring data by calculation based on the original data.

[0111] As mentioned above, the raw data obtained from the processing workstations are used to generate final data for monitoring the production process through complex calculation analysis, such as trend analysis, quality control charts, etc.

[0112] The resulting monitoring data provides a comprehensive view of the entire production process, helping management make more informed decisions to improve production efficiency and product quality.

[0113] Apply statistical process control (SPC) and data analysis software (such as MiniTab, JMP) to analyze the data in order to identify and correct any anomalies in the production process.

[0114] Through these steps, pharmaceutical factories can monitor the production process in real time, ensure that the quality of the products meets the specified standards, and quickly respond to any potential production problems. This not only improves production efficiency, but also ensures the quality and safety of the final product.

[0115] Further, the monitoring point in the production environment area is a drug extraction device; the drug extraction tank of the drug extraction device includes a plurality of movable sampling devices; wherein the movable sampling device includes a sampling body and a temperature probe arranged on the sampling body;

[0116] As mentioned above, the method adopted in this embodiment needs to be based on specific equipment.

[0117] The above-mentioned monitoring points are drug extraction equipment in the production environment area. The drug extraction equipment may include a drug extraction tank, a heater, a feeding device, a mixing device, a filtering device, a central control device, and the like.

[0118] The movable sampling device described above may be structured so that a motor (stepping motor or servo motor) may be installed therein to control its movement in the tank, including vertical and horizontal directions.

[0119] Furthermore, the sampling device can be moved by rails or slides, such as by installing a rail or slide system in the extraction tank, and the sampling device can be moved to a specified position along these rails. These rails need to be resistant to chemical corrosion and high temperature.

[0120] For example, in a drug extraction tank, a plurality of rod-shaped slide rail arrays that can move up and down are provided, and each rod-shaped slide rail is provided with a corresponding movable sampling device. The movable sampling device can move up and down along the rod-shaped slide rail, and the slide rail can also be extended and retracted to increase or decrease the height or position in the drug extraction tank.

[0121] In the above mechanism, the structure for controlling the movement of the movable sampling device belongs to conventional technical means and will not be described in detail here.

[0122] The movable device needs to include a sampling body, including a suction device for extracting samples, a container for containing liquid medicine, and a temperature probe for detecting the temperature of the current area. The container for containing liquid medicine can be connected to a container outside the drug extraction device through a heat-resistant pipeline, so that the sampled liquid medicine is pumped into the external container, and the container is transported to the sample processing and detection device for further detection.

[0123] This highly automated and precisely controlled system ensures that the sampling device can be safely and accurately moved to the designated position in a complex drug extraction environment, thereby improving the efficiency and quality of sampling.

[0124] refer to Figure 4 The step S110, based on the production environment area, obtains the detection specimen corresponding to the monitoring point, including:

[0125] Step S111, obtaining temperature measurement data in the drug extraction tank and the spatial coordinates corresponding to the temperature measurement data through the temperature probe of the movable sampling device;

[0126] It should be noted that different temperatures in different areas during drug extraction will affect the content of the main ingredients in the drug. This is because the solubility, stability and reaction rate of many chemical components are significantly affected by temperature. Especially in the extraction of Chinese herbal medicines, due to the different chemical properties of various active ingredients, their sensitivity to temperature is also different, so temperature control is crucial to ensure extraction efficiency and ingredient quality.

[0127] In traditional Chinese medicine extraction, temperature has a significant impact on the extraction efficiency and stability of specific chemical components. Some traditional Chinese medicine components are extremely sensitive to temperature due to their unique chemical properties and solubility characteristics. For example, the following drugs:

[0128] 1. Ginseng - Ginsenosides: Ginseng is a widely used medicinal material in traditional Chinese medicine. It contains a variety of ginsenosides, which have anti-fatigue, anti-aging and immunomodulatory effects. The extraction efficiency of ginsenosides is strongly affected by temperature: at lower temperatures, the solubility of some ginsenosides is low, which may lead to low extraction efficiency. Although high temperature can increase the solubility and extraction rate of most ginsenosides, too high temperature may cause some sensitive saponins to decompose, thereby reducing their content.

[0129] 2. Coptis chinensis-choline (Berberine): Coptis chinensis is a commonly used Chinese herbal medicine. The main active ingredient is choline, which has obvious antibacterial and anti-inflammatory effects. The extraction of choline is significantly affected by temperature: the stability of choline will decrease at high temperatures, leading to decomposition. Therefore, the extraction temperature needs to be carefully controlled to avoid excessive temperature loss of active ingredients.

[0130] 3. Tea Tree - Tea Polyphenols: Tea polyphenols in tea are the main active chemical components, which have antioxidant, antibacterial and cardiovascular disease risk reduction effects. The extraction efficiency and stability of tea polyphenols are greatly affected by temperature: high temperature will accelerate the oxidation and degradation of tea polyphenols, so it is usually recommended to extract at a lower temperature to maintain its activity and concentration. However, low-temperature extraction is prone to low extraction rate. In workshop production, the extraction temperature often needs to be strictly controlled.

[0131] 4. Danshen-Tanshinones: Danshen is a commonly used medicinal material in traditional Chinese medicine. It mainly contains active ingredients such as Tanshinone IIA, which has the effects of improving blood circulation and anti-inflammatory. The extraction of Tanshinones is also affected by temperature: the chemical structure of Tanshinones is easily changed at high temperatures, which may lead to decomposition. Moderate temperature helps to maximize the extraction efficiency while maintaining the integrity of the chemical structure.

[0132] These examples show that temperature control during the extraction of traditional Chinese medicine is crucial to ensuring the content and quality of active ingredients in the extract. Correct temperature settings can not only improve extraction efficiency, but also avoid thermal degradation of sensitive ingredients, thereby ensuring the efficacy and stability of the extract.

[0133] However, pharmaceutical production units often use tons of drug extraction tanks. Due to their large scale and volume, it is likely that temperature differences will occur at different locations during the extraction process. This phenomenon is called temperature gradient and is caused by several factors:

[0134] 1. Heat transfer efficiency: In large extraction tanks, the heat transfer from the heating source to various parts of the tank may be uneven, especially at different heights of the tank and in areas far away from the heat source.

[0135] 2. Mixing effect: In large-capacity tanks, the stirring and mixing effect of materials is crucial to temperature uniformity. If the design of the mixing equipment is unreasonable or the stirring speed is insufficient, it will lead to uneven temperature distribution.

[0136] 3. Tank material and design: The material and design of the tank also affect the efficiency of heat conduction. For example, the insulation material and thickness of the tank, the shape and size of the tank will affect the distribution of heat.

[0137] 4. External environmental conditions: The ambient temperature, humidity and other environmental factors of the tank will also affect the internal temperature distribution.

[0138] In addition, if a material mixing system is used, although the material can be mixed to a certain extent to achieve a uniform effect, the mixing time is longer due to the large size of the tank itself.

[0139] Therefore, in the pharmaceutical production process, when large-scale drug extraction equipment is used, there is uneven temperature during multiple sampling, and the main components lead to uneven content, resulting in false positives and "false risks", thus forming false alarms, which seriously affect the efficiency of pharmaceutical production and risk investigation, and cause a significant increase in manpower and material costs.

[0140] This step uses a temperature probe mounted on a movable sampling device to obtain temperature data at different locations in the extraction tank and the spatial coordinates corresponding to these data. This helps to understand the temperature distribution in the tank. By obtaining detailed temperature distribution information, the uniformity of the extraction process can be ensured, which affects the quality of the final drug. This also helps to detect abnormal temperature areas in a timely manner and prevent quality problems.

[0141] Step S112, constructing a data matrix corresponding to the drug extraction tank based on the spatial coordinates and the temperature measurement data;

[0142] As described above, a data matrix is ​​constructed using the collected temperature data and the corresponding spatial coordinates, which will reflect the temperature distribution map inside the extraction tank.

[0143] The constructed data matrix can intuitively display the spatial distribution of temperature, facilitating the analysis and monitoring of temperature uniformity throughout the extraction process.

[0144] Specifically, data visualization software (such as MATLAB, Python's matplotlib library) can also be used to generate a heat map of temperature distribution.

[0145] Step S113, determining a temperature uniform area in the drug extraction tank based on the data matrix;

[0146] As mentioned above, in a larger drug extraction tank, excluding the stirring factor, there will still be some areas with relatively uniform temperature and areas with large temperature changes. The reasons may include the following:

[0147] Heat transfer efficiency: Due to the large size of large extraction tanks, there may be a delay in the propagation of heat from the heating source to various parts of the tank, resulting in uneven heat distribution. This delay is caused by physical limitations in the heat conduction and convection processes, especially in areas of the tank that are farthest from the heat source.

[0148] Tank shape and structure: The design of the tank, including its shape and internal structure such as flow control plates or baffles, has a direct impact on the dynamics of heat flow and material flow. Different designs may lead to uneven distribution of heat in the tank, affecting heating efficiency.

[0149] Tank material and insulation performance: The thermal conductivity of the tank material and the performance of the insulation layer also greatly affect the retention and transfer of heat energy within the tank. The thermal conductivity of the material and the effectiveness of the insulation layer directly determine how the heat energy is distributed within the tank and the extent of heat loss.

[0150] Environmental influences: External environmental conditions, such as ambient temperature and humidity, can also affect the temperature distribution within the tank. For example, if a portion of the tank is exposed to direct sunlight or is in a cooler environment, a significant temperature gradient may form.

[0151] In the above steps, the data matrix is ​​analyzed to identify those areas where the temperature changes are small and evenly distributed. These areas indicate that the drug content in these areas during the extraction process is relatively uniform, the data is stable, and can be representative to a certain extent.

[0152] By identifying areas of uniform temperature, representative and consistent sampling from these areas can be ensured, thereby improving the accuracy of product quality control.

[0153] Implementation: Apply statistical analysis methods, such as standard deviation, variance analysis, etc., to determine the uniformity of temperature distribution.

[0154] Step S114, based on the coordinate range corresponding to the temperature uniform area, the movable sampling device is moved into the coordinate range to obtain the test sample.

[0155] According to the identified temperature uniform areas, the sampling device is controlled to move to these areas to collect samples, ensuring that the samples obtained are more representative and consistent, which is beneficial to subsequent quality analysis and product evaluation.

[0156] Through these steps, not only can the temperature distribution in the drug extraction process be efficiently monitored and controlled, but also the collected samples can be guaranteed to have high quality and consistency, thereby effectively improving the control level of the entire production process and the quality of the final product. The implementation of this system requires a high degree of automation technology and precise temperature control equipment to ensure the accuracy of data and the effectiveness of sampling.

[0157] For further reference, Figure 5 The step S114, based on the coordinate range corresponding to the temperature uniform area, moves the movable sampling device into the coordinate range to obtain the test specimen, including:

[0158] Step S1141, calculating the volume of the temperature uniform area based on the coordinate range corresponding to the temperature uniform area;

[0159] This step provides data support for the subsequent determination of the appropriate number of sampling points by calculating the volume of the temperature uniform area. Volume calculation relies on the coordinate range determined by temperature monitoring and data analysis, and usually involves geometric or stereo measurement methods.

[0160] Accurate volume calculation can help ensure the representativeness and scientific nature of sampling, so that the sampling results more accurately reflect the actual conditions of the area.

[0161] The temperature sensor data can be received through an integrated software system, combined with the design parameters of the tank, and the volume can be calculated using geometric formulas (such as the volume formulas of cubes and cylinders).

[0162] Step S1142: Calculate the number of sampling points in the temperature uniform area according to the volume; the number of sampling points is calculated by the following formula:

[0163]

[0164] Wherein, n represents the number of sampling points; k represents the empirical coefficient; A represents the volume of the temperature uniform area;

[0165] As described above, based on the volume of the temperature uniform area, Formula 1 is used to determine a reasonable number of sampling points, where n is the number of sampling points, k is an empirical coefficient (usually set within a certain range based on the number of n actually required), and A is the volume.

[0166] As mentioned above, the adjustment of the empirical coefficient k needs to be considered based on a variety of factors, including the concentration of the drug solution, the sensitivity of the drug components, the uniformity of the distribution of the components during the extraction process, etc. The following are some situations where the k value may need to be adjusted:

[0167] 1. High concentration of drug solution: When the concentration of drug solution is high, the distribution of drug components in the liquid may be uneven, especially those components that are difficult to dissolve or easy to precipitate. In this case, increasing the number of sampling points (i.e. increasing the k value) can help better evaluate and monitor the uniformity of the components in the entire extraction tank.

[0168] 2. Thermal sensitivity of ingredients: For heat-sensitive pharmaceutical ingredients, temperature fluctuations may cause localized degradation or transformation of the ingredients. If it is known that certain areas may experience this due to improper temperature control, increasing the number of sampling points in those areas can help more accurately monitor and assess the impact.

[0169] 3. Important stages in the extraction process: At critical stages in the extraction process, such as when the extraction is just beginning or when the extraction is nearly complete, more intensive monitoring may be required to ensure the effectiveness and efficiency of the process. At this time, appropriately increasing the k value, that is, increasing the number of sampling points, can provide more data points to optimize and adjust the extraction parameters.

[0170] 4. Special medicinal material characteristics: The physical and chemical properties of different medicinal materials (such as viscosity, solubility, and particle size) may also affect the setting of sampling points. For example, for those medicinal materials that are prone to forming suspended matter or precipitation, it may be necessary to add sampling points in a specific area to ensure the representativeness of the sample.

[0171] 5. Regulatory requirements and quality control standards: According to the Good Manufacturing Practice (GMP) or other relevant regulations for pharmaceuticals, some products may require particularly strict quality control and verification. In these cases, increasing the number of sampling points can help meet these regulatory requirements and ensure the quality and consistency of the product.

[0172] By adjusting the k value, the number of sampling points can be flexibly set according to specific production conditions and needs to maximize extraction efficiency and ensure product quality. This strategy needs to be dynamically adjusted based on actual operating experience, experimental data and information monitored during the production process.

[0173] This method can flexibly adjust the number of sampling points according to the volume of the area to ensure the comprehensiveness and effectiveness of sampling.

[0174] This calculation can be implemented in the software of the control system, which automatically calculates the number of sampling points based on the input volume data.

[0175] Step S1143, based on the number of sampling points, n sampling coordinate points are generated according to the coordinate range, and the movable sampling device is controlled to move into the coordinate range and sample at the sampling coordinate points to obtain the detection specimen.

[0176] As described above, after the number of sampling points is calculated, specific sampling coordinate points are generated, and the movable sampling device is controlled to perform positioning and sampling according to these coordinate points.

[0177] By precisely controlling the sampling device to reach the specified coordinates, it can be ensured that each sampling point can be accurately sampled, thereby improving the representativeness of the sample and the accuracy of the analysis results.

[0178] A highly automated machine control system (such as the movable sampling device in this embodiment) is used to automatically drive the sampling device for precise sampling according to the generated coordinate points.

[0179] For example, the temperature uniform area is determined to be a cylinder through detection, the height of the cylinder is 2 meters, the radius of the bottom of the cylinder is 0.5 meters, and the k corresponding to the liquid is 6.034.

[0180] Calculate the volume of a cylinder using the formula V = πr 2 h = 1.5708 cubic meters.

[0181] Using formula 1,

[0182] Then, within the coordinate range of the temperature uniform area, 7 sampling coordinate points (x n ,y n ), and then control the movable sampling device to move to the coordinate range, and sample at the sampling coordinate point to obtain the test specimen.

[0183] The method provided in this embodiment not only improves the scientificity and accuracy of the sampling process, but also reduces human errors through automated control, thereby improving operational efficiency and safety. The implementation of these steps relies on advanced data processing software and automated mechanical control technology to ensure that high-quality data and samples can be obtained in a complex industrial environment.

[0184] Example 3

[0185] Reference Figure 6 Embodiment 3 of the present invention provides a method for controlling the quality risk of drug production. Based on the above embodiment 1, the step S200 determines the risk point according to the monitoring data, including:

[0186] Step S210, calculating the median of the monitoring data corresponding to the monitoring point, and obtaining the absolute median difference;

[0187] It should be noted that individual outliers may occur during the monitoring process of monitoring points due to measurement errors, data entry errors, equipment failures, or atypical production conditions. If these values ​​are not identified and processed, they may distort the results of statistical analysis, such as mean values, standard deviations, etc., leading to misleading conclusions. By effectively identifying and excluding these outliers, Modified Z-Score helps maintain the robustness and reliability of the analysis results.

[0188] In many applications, including production quality control and process monitoring, it is crucial to accurately understand the central tendency and variability of the data. The presence of outliers may significantly affect these statistical measures. Using Modified Z-Score can more accurately reflect the true data characteristics and ensure that decisions are based on accurate and real data analysis.

[0189] Compared with the traditional Z-Score, in this embodiment, the Modified Z-Score is calculated using the factor 0.6745 (which is derived from the normal distribution, so that the standard deviation of the standard normal variable is equal to the MAD) and MAD, making it more robust when facing a skewed distribution or a data set containing multiple outliers. This makes the Modified Z-Score particularly suitable for non-normal and abnormal data situations that are common in practical applications.

[0190] In a production environment, it is critical to monitor data quality in real time and respond quickly to possible production anomalies. Using Modified Z-Score allows real-time identification of abnormal fluctuations in data, supporting timely quality control and risk management measures, thereby reducing potential quality issues and production losses.

[0191] In pharmaceutical production and other strictly controlled industrial processes, maintaining process stability and product quality consistency is a core requirement. By excluding outliers in the data, the stability and compliance of the process can be more accurately assessed, thus ensuring that the product meets the predetermined quality standards.

[0192] In summary, the application of Modified Z-Score provides an effective tool for processing and analyzing complex data sets commonly found in actual production and scientific research data. By identifying and excluding outliers, this method helps improve the quality of data analysis, supports data-based decision making, and enhances the control of production processes and product quality.

[0193] First, calculate the median of the monitoring data, which is the value in the middle after all data values ​​are sorted by size. Next, calculate the absolute difference between each data point and the median, and then take the median of these absolute differences, which is called the median absolute difference (MAD). This is a method of measuring data variability, especially suitable for non-normally distributed data.

[0194] The median and MAD provide a robust (insensitive to outliers) description of the trend and dispersion in a data set and are suitable for situations where outliers or skewed distributions may be present.

[0195] Step S220: Calculate a Modified Z-Score based on the absolute median difference and the median. The calculation formula of the Modified Z-Score is:

[0196]

[0197] Among them, x i Represents the i-th monitoring data; represents the median; MAD represents the absolute median difference;

[0198] This calculation method can effectively identify outliers in the data, especially when the data contains outliers or non-normal distribution, and is more robust than the traditional Z-Score.

[0199] Step S230: If the monitoring data x i If the absolute value of the corresponding Modified Z-Score is greater than 3.5, the monitoring data is excluded as an outlier;

[0200] According to the Modified Z-Score results, if the absolute value of the Modified Z-Score of a data point is greater than 3.5, the data point is considered an outlier and excluded. This threshold is often used to identify extreme statistical outliers.

[0201] This method can remove noise from the data and improve the accuracy and reliability of subsequent analysis.

[0202] Step S240: If the monitoring data x i If the absolute value of the corresponding Modified Z-Score is not greater than 3.5, the monitoring data is regarded as a normal value;

[0203] Step S250, taking an average value of the monitoring data as normal values ​​to obtain a monitoring mean value, and comparing the monitoring mean value with a preset threshold range;

[0204] As mentioned above, at this time, there may be multiple normal values, and the values ​​may be close. Therefore, in order to reduce the occupation of system resources and avoid repeated and multiple comparisons, the normal values ​​that may be close in value are averaged to obtain the monitoring mean, so as to further evaluate the average value.

[0205] Step S260: If the monitoring mean value exceeds the preset threshold range, the monitoring point is used as the risk point.

[0206] In addition, if the monitoring mean value does not exceed the preset threshold range, the monitoring point will not be regarded as the risk point.

[0207] In the above, all the monitoring data that are considered normal are averaged to obtain the monitoring mean, and then the mean is compared with the preset threshold range to determine whether there is a risk.

[0208] This step can help determine whether the production process is under control and whether further risk control measures are needed. If the monitoring mean exceeds the threshold, it indicates that there may be production quality problems, which require further investigation or improvement measures.

[0209] The advantage of this method is its high sensitivity to outliers and adaptability to skewed data, making it an effective tool for monitoring production quality and identifying potential risks in a timely manner. By analyzing monitoring data in real time and responding in a timely manner, the safety and reliability of the production process can be significantly improved.

[0210] Example 4

[0211] Reference Figure 7 Embodiment 4 of the present invention provides a method for controlling the quality risk of drug production. Based on the above embodiment 1, the step S300 determines the corresponding investigator based on the risk point, and generates prompt information for the investigator to conduct an investigative test, including:

[0212] Step S310, generating action item information based on the risk point, and determining an investigator corresponding to the risk point;

[0213] This step involves identifying risk points, generating specific action items (tasks or measures) to address the risk, and assigning appropriate investigators to be responsible for these tasks. Investigators are selected based on their expertise, experience, or specific responsibilities related to the risk. This ensures that each identified risk point is handled promptly and effectively by a dedicated investigator, which can improve the professionalism and efficiency of handling risks.

[0214] Step S320, writing the action item information into the investigator's action list;

[0215] Add the generated action item information to the corresponding investigator’s action list. This may involve updating a database or risk management system to ensure that all information is properly recorded and tracked. By systematically recording action items, you can ensure that no important tasks are missed and facilitate subsequent monitoring and auditing.

[0216] Step S330, using the action item information for the risk point in the action list as current information and other action item information different from the current information as comparison information; obtaining the establishment time of the current information and action information for the same risk point in the action list;

[0217] As mentioned above, record the creation time of each action item. This helps to understand the urgency and time sensitivity of the task. You can prioritize more urgent tasks based on the timestamp of the task and allocate resources reasonably.

[0218] The current action item information for the risk point is taken as current information, and other action item information also exists in the action list; further, other action item information related to the current risk point is extracted.

[0219] Then, the extracted action items are sorted by establishing time.

[0220] It should be noted that for the same risk point, for example, drug extraction equipment, where the risk is excessive aristolochic acid content, if it occurs twice, two action item information will be generated. If the time interval between the two times is close, the investigator will have to repeat the operation test. Since the risks are the same, only one combined test is needed to achieve the purpose of investigation.

[0221] In this step, record the creation time of each action item information, which will help with subsequent time management and priority sorting. Recording the creation time allows tasks to be processed in chronological order, giving priority to the most urgent tasks, and removing "work redundancy" to avoid duplication of work.

[0222] Step S340, determining whether there is any comparison information in the action list whose interval with the creation time of the current information is less than a preset time threshold;

[0223] Step S350: If yes, the current information and the comparison information for the same risk point whose establishment time interval is less than the preset time threshold are combined as an action item information, and the prompt information is generated.

[0224] In addition, if not, no merging is performed, and the prompt information is generated according to the current information.

[0225] Check if there are any tasks that are very close in time to the current task, which may indicate that similar or related tasks need to be merged in logical time. This can avoid duplication of work, improve processing efficiency, and ensure that related tasks are considered together.

[0226] Combine tasks that are close in time and address the same risk point into a single task. This usually involves updating the task description to include all related action items.

[0227] Consolidating tasks reduces fragmentation, allowing investigators to focus more on issues while reducing administrative complexity.

[0228] Generate detailed instructions or prompts based on the merged task information to guide investigators on how to perform tasks. Provide clear guidance to help investigators perform tasks effectively and ensure that risk points are handled in the expected manner.

[0229] For example, for the risk of excessive aristolochic acid in drug extraction equipment, there are: action item information 1 (comparative information), March 10; action item information 2 (current information), March 12. The preset time threshold is 72 hours.

[0230] The creation time of action item information 1 and action item information 2 is 2 days and 48 hours, which is less than the preset time threshold of 72 hours. Therefore, the two action item information can be merged into one action item information without repeated execution.

[0231] Example 5

[0232] Reference Figure 8 Embodiment 5 of the present invention provides a method for drug production quality risk control. Based on the above embodiment 1, the step S400 obtains a risk control result corresponding to the production environment area according to the survey test result and the monitoring data, including:

[0233] Step S410, comparing the survey test result with the monitoring data;

[0234] This step involves comparing the survey test results obtained from the field or experiment with the data recorded in the real-time monitoring system. This comparison is to verify the accuracy of the monitoring data and confirm whether the survey results support the initial monitoring observations.

[0235] This comparison helps ensure that the data from the monitoring system is accurate and verifies the authenticity of the risk points. This is part of the verification step in risk management, ensuring that the measures taken are based on reliable data.

[0236] Step S420, calculating the error value by the following formula:

[0237] E=|xs -x h |;

[0238] Wherein, E represents the error value; x s Represents the monitoring data; x h represents the results of the survey test described;

[0239] As mentioned above, the calculated error value can quantify the difference between the investigation test results and the monitoring data, providing a numerical basis to determine whether the two are consistent.

[0240] Step S430, comparing E with an error threshold T;

[0241] In the above, the calculated error value E is compared with a preset error threshold T. This threshold defines the maximum acceptable error limit and is usually set based on production standards or quality control standards.

[0242] Step S440: If E≤T, it is determined that the data matches, and the risk control result is that there is a risk;

[0243] Step S450, if E>T, it is determined that the data does not match, the risk control result is that the data is questionable, and step S100 is executed again to obtain the monitoring data corresponding to the monitoring point of the production environment area.

[0244] The consistency between the investigation test results and the monitoring data can be determined by comparison. If the error is within an acceptable range (E≤T), the data is considered to match and the risk is confirmed; if the error exceeds the threshold (E>T), the data is questionable and further investigation or review may be required. Therefore, the initial step, i.e., S100, can be returned to obtain monitoring data again.

[0245] Furthermore, in this embodiment, a drug production quality risk control device is provided, comprising:

[0246] The acquisition module 10 is used to monitor the production environment area in real time and obtain the monitoring data corresponding to the monitoring points in the production environment area;

[0247] A determination module 20, configured to determine risk points based on the monitoring data;

[0248] The acquisition module 10 is further used to determine the corresponding investigator based on the risk point, generate prompt information for the investigator to perform an investigative test; and obtain the investigative test result corresponding to the investigative test;

[0249] The output module 30 is used to obtain the risk control result corresponding to the production environment area according to the investigation and test results and the monitoring data.

[0250] Furthermore, this embodiment provides a drug production quality risk control system, including a memory and a processor, wherein the memory stores a drug production quality risk control program, and the processor runs the drug production quality risk control program to enable the drug production quality risk control system to execute the drug production quality risk control method as described in any of the aforementioned embodiments.

[0251] Furthermore, this embodiment provides a computer-readable storage medium, on which a drug production quality risk control program is stored. When the drug production quality risk control program is executed by a processor, the drug production quality risk control method as described in any of the aforementioned embodiments is implemented.

[0252] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely schematic. For example, the flowcharts and structure diagrams in the accompanying drawings show the possible architecture, functions and operations of the devices, methods and computer program products according to multiple embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of a code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in an alternative implementation, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the structure diagram and / or the flow chart, and the combination of boxes in the structure diagram and / or the flow chart, can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0253] In addition, the functional modules or units in the various embodiments of the present invention may be integrated together to form an independent part, or each module may exist independently, or two or more modules may be integrated to form an independent part.

[0254] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a smart phone, a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0255] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for drug production quality risk control, characterized in that: Monitor the production environment area in real time and obtain monitoring data corresponding to the monitoring points in the production environment area; Determining risk points based on the monitoring data; Determine a corresponding investigator based on the risk point, and generate prompt information for conducting an investigative test on the investigator; and obtaining an investigative test result corresponding to the investigative test; A risk control result corresponding to the production environment area is obtained based on the investigation and test results and the monitoring data.

2. The method for drug production quality risk control according to claim 1, characterized in that: The obtaining of monitoring data corresponding to the monitoring points in the production environment area includes: Based on the production environment area, obtaining the detection specimen corresponding to the monitoring point; Detecting the detection specimen to obtain detection data, determining a corresponding processing workstation according to the detection data, and obtaining raw data through the processing workstation; The monitoring data is obtained by performing calculations based on the original data.

3. The method for controlling the risk of drug production quality according to claim 2, characterized in that: The monitoring point in the production environment area is a drug extraction device; the drug extraction tank of the drug extraction device includes a plurality of movable sampling devices; wherein the movable sampling device includes a sampling body and a temperature probe arranged on the sampling body; The obtaining of the detection specimen corresponding to the monitoring point based on the production environment area includes: Acquire the temperature measurement data in the drug extraction tank and the spatial coordinates corresponding to the temperature measurement data through the temperature probe of the movable sampling device; Based on the spatial coordinates, constructing a data matrix corresponding to the drug extraction tank according to the temperature measurement data; Based on the data matrix, determining a temperature uniform area within the drug extraction tank; Based on the coordinate range corresponding to the temperature uniform area, the movable sampling device is moved into the coordinate range to obtain the test sample.

4. The method for controlling the quality risk of drug production according to claim 3, characterized in that: The step of moving the movable sampling device into the coordinate range corresponding to the temperature uniform area to obtain the test specimen comprises: Based on the coordinate range corresponding to the temperature uniform area, the volume of the temperature uniform area is calculated; According to the volume, the number of sampling points in the temperature uniform area is calculated; The number of sampling points is calculated by the following formula: Wherein, n represents the number of sampling points; k represents the empirical coefficient; A represents the volume of the temperature uniform area; Based on the number of sampling points, n sampling coordinate points are generated according to the coordinate range, and the movable sampling device is controlled to move within the coordinate range and sample at the sampling coordinate points to obtain the detection specimen.

5. The method for drug production quality risk control according to claim 1, characterized in that: The determining of risk points according to the monitoring data includes: Calculate the median of the monitoring data corresponding to the monitoring point and obtain the absolute median difference; The Modified Z-Score is calculated based on the absolute median difference and the median. The Modified Z-Score calculation formula is: Among them, x i Represents the i-th monitoring data; represents the median; MAD represents the absolute median difference; If the monitoring data x i If the absolute value of the corresponding Modified Z-Score is greater than 3.5, the monitoring data is excluded as an outlier; If the monitoring data x i If the absolute value of the corresponding Modified Z-Score is not greater than 3.5, the monitoring data is regarded as a normal value; Taking an average value of the monitoring data as normal values ​​to obtain a monitoring mean value, and comparing the monitoring mean value with a preset threshold range; If the monitored mean value exceeds the preset threshold range, the monitored point is used as the risk point.

6. The method for drug production quality risk control according to claim 1, characterized in that: The determining a corresponding investigator based on the risk point and generating prompt information for performing an investigative test on the investigator includes: Generate action item information based on the risk point, and determine the investigator corresponding to the risk point; Writing the action item information into the investigator's action list; Taking the action item information for the risk point in the action list as current information and taking other action item information different from the current information as comparison information; obtaining the establishment time of the current information and action information for the same risk point in the action list; Determine whether there is any comparison information in the action list whose interval with the creation time of the current information is less than a preset time threshold; If so, the current information and the comparison information for the same risk point whose establishment time interval is less than the preset time threshold are combined as an action item information, and the prompt information is generated.

7. The method for drug production quality risk control according to claim 1, characterized in that: The risk control result corresponding to the production environment area is obtained according to the investigation and test result and the monitoring data, including: comparing the survey test results with the monitoring data; The error value is calculated using the following formula: E=|x s -x h |; Wherein, E represents the error value; x s Represents the monitoring data; x h represents the results of the survey test described; Compare E with the error threshold T; If E≤T, it is determined that the data matches, and the risk control result is that there is a risk; If E>T, it is determined that the data does not match, the risk control result is that the data is questionable, and the step is returned to obtain the monitoring data corresponding to the monitoring point in the production environment area.

8. A drug production quality risk control device, characterized in that: include: An acquisition module is used to monitor the production environment area in real time and obtain monitoring data corresponding to the monitoring points in the production environment area; A determination module, used to determine risk points based on the monitoring data; The acquisition module is further used to determine a corresponding investigator based on the risk point, and generate prompt information for conducting an investigative test on the investigator; and obtaining an investigative test result corresponding to the investigative test; An output module is used to obtain risk control results corresponding to the production environment area based on the investigation and test results and the monitoring data.

9. A drug production quality risk management and control system, characterized in that: It includes a memory and a processor, wherein the memory stores a drug production quality risk control program, and the processor runs the drug production quality risk control program to enable the drug production quality risk control system to execute the drug production quality risk control method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a drug production quality risk control program, and when the drug production quality risk control program is executed by the processor, the drug production quality risk control method according to any one of claims 1 to 7 is implemented.

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