Monitoring, regulating and controlling system for intelligent medical intermediate preparation production line

Abstract

The invention relates to a monitoring, regulating and controlling system for an intelligent medical intermediate preparation production line, which belongs to the technical field of industrial automation control and comprises a data acquisition module for constructing a process state data set based on real-time temperature data and real-time chemical component concentration data; the risk index calculation module, the causal decoupling module and the decision output module are used for identifying a process problem or a raw material problem as a current dominant risk according to a comparison result of the causal contribution factor and a preset contribution threshold value, and generating a differential intervention instruction matched with the dominant risk. According to the method, real-time temperature and concentration data are collected, the data are converted into two risk indexes with clear physical significance, and physical risks caused by process problems such as local hot spots or fluid dead zones can be accurately quantified by constructing the hydrodynamic instability index.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial automation control, and in particular to a monitoring and control system for an intelligent pharmaceutical intermediate preparation production line. Background Art

[0002] The preparation of high-value, chiral pharmaceutical intermediates is a critical step in the modern pharmaceutical industry. The production process, particularly the multi-step exothermic crystallization stage, places extremely stringent control requirements on process conditions. This process is characterized by complex system states, nonlinearity, multivariable coupling, and high sensitivity to small perturbations.

[0003] At present, process analysis technologies are widely used on production lines. For example, in-situ Raman spectroscopy or Fourier transform infrared spectroscopy are used to monitor the concentration of chemical components in real time; distributed fiber optic sensors are used to monitor the temperature field inside the reactor. Computational fluid dynamics simulation is also often used for offline simulation of flow and temperature fields under ideal conditions.

[0004] However, existing technologies have significant limitations: When a system undergoes a critical transition that deviates from the preset trajectory, such as a sudden abnormal crystallization rate or a sharp increase in by-products, the root cause may be very complex. For example, trace amounts of non-target configurational isomers in the raw materials may introduce unexpected catalytic side reaction pathways; or uneven stirring may lead to local hot spots or fluid dead zones in the reactor, thereby triggering kinetic instability of the main reaction. The existing technology lacks an effective model that can perform real-time and quantitative causal decoupling in these coupled and competing failure pathways.

[0005] Without accurate attribution, operators often make empirical, undifferentiated interventions. When faced with an anomaly, should they immediately adjust the stirring rate to improve mixing, or trigger a quality risk alert for the batch of raw materials and consider terminating the reaction? Incorrect decisions can not only result in the scrapping of an entire batch of high-value products but can also trigger safety incidents such as thermal runaway.

[0006] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0007] This invention aims to address the existing technical challenge of achieving real-time, quantitative causal decoupling of critical system transitions caused by the coupling of raw material quality and process conditions during the preparation of intelligent pharmaceutical intermediates. By constructing an integral intelligent analysis system, encompassing multi-source data acquisition, quantitative indicator calculation, and differentiated decision output, this system can identify declines in system dynamic resilience in real time and clearly identify the dominant factors driving these changes, providing a scientific basis for adopting optimal intervention strategies.

[0008] The technical solution of the present invention is: a monitoring and control system for an intelligent pharmaceutical intermediate production line, comprising: Data acquisition module: used to collect real-time temperature data and real-time chemical component concentration data of the production process, and construct a process status data set based on the real-time temperature data and the real-time chemical component concentration data; Risk index calculation module: used to perform the following steps: Step 1: Calculating a fluid dynamics instability index based on the real-time temperature data and the preset reference temperature data in the process state data set; Step 2: calculating an isomer catalytic risk index based on the real-time chemical component concentration data in the process state data set; Step 3: weighted fusion of the fluid dynamics instability index and the isomer catalytic risk index to generate a system dynamics resilience index; Causal decoupling module: This module is used to respond to the event that the system dynamics resilience index falls below the preset resilience threshold. By comparing the instantaneous rate of change of the fluid dynamics instability index and the isomer catalytic risk index, it calculates the causal contribution factor used to determine the dominant risk source. Decision output module: used to identify process problems or raw material problems as the current dominant risks based on the comparison results of the causal contribution factor and the preset contribution threshold, and generate differentiated intervention instructions that match the dominant risks.

[0009] In this embodiment, the risk index calculation module is specifically used to: Calculating a weighted mean square error of a normalized difference between the real-time temperature and the reference temperature based on the real-time temperature data and the preset reference temperature data collected by the data collection module to generate the fluid dynamics instability index; The reference temperature data is obtained by running offline computational fluid dynamics simulation software and simulating and calculating under ideal process conditions.

[0010] In this embodiment, the risk index calculation module is specifically used to: Calculating the relative generation rate of characteristic by-products and main products based on the real-time chemical component concentration data collected by the data acquisition module; Multiplying the relative generation rate by a preset proportionality coefficient to generate the isomer catalysis risk index, which is used to characterize the threat level of the side reaction catalyzed by the trace isomer to the main reaction; The proportionality coefficient is a semi-empirical parameter obtained by conducting a series of trace isomer doping experiments on a laboratory scale and calibrating the generation rates of the target product and the by-products through linear regression fitting.

[0011] In this embodiment, the risk index calculation module is further used to: The hydrodynamic instability index and the isomer catalytic risk index are multiplied by their corresponding preset weight coefficients and then summed to obtain an instantaneous comprehensive failure rate; Based on the instantaneous comprehensive failure rate, the system dynamics resilience index is constructed through an exponential decay function to comprehensively evaluate the system's ability to resist risks and maintain stable operation; The weight coefficient is preset based on statistical analysis of historical data of a specific chemical process or combined with domain expert knowledge to reflect the relative sensitivity of the process to the two types of risks.

[0012] In this embodiment, the causal decoupling module is specifically used to: Utilizing the historical time series of the hydrodynamic instability index and the isomer catalytic risk index, calculating their respective instantaneous change rates; Multiplying each instantaneous change rate by its corresponding preset weight coefficient to obtain a weighted change rate; The proportion of the weighted change rate of the fluid dynamic instability index in the sum of the absolute values ​​of the two weighted change rates is calculated to solve the causal contribution factor.

[0013] In this embodiment, the decision output module is specifically used to: When the causal contribution factor is higher than a preset first contribution threshold, it is determined that the dominant cause of the current system instability is fluid dynamics inhomogeneity, and the differential intervention instruction including an instruction to increase the stirring rate or check the cooling system is generated.

[0014] In this embodiment, the decision output module is further specifically configured to: When the causal contribution factor is lower than a preset second contribution threshold, the dominant cause of the current system instability is determined to be a side reaction catalyzed by trace isomers, and the differentiated intervention instruction is generated, including triggering a raw material batch quality alarm or preparing a safe shutdown instruction.

[0015] In this embodiment, the decision output module is further specifically configured to: When the causal contribution factor is not lower than the second contribution threshold and not higher than the first contribution threshold, it is determined that the system faces a coupling problem, and the differentiated intervention instruction is generated to recommend taking comprehensive intervention measures.

[0016] In this embodiment, the data acquisition module is specifically used to: Acquiring the real-time temperature data through a distributed fiber Bragg grating sensor array deployed in the reactor; The real-time chemical component concentration data is calculated from the real-time spectrum by in-situ Raman spectroscopy combined with a partial least squares chemometric model.

[0017] The present invention provides an intelligent monitoring and control system for pharmaceutical intermediate production lines through improvements. Compared with the prior art, it has the following improvements and advantages: This system not only collects real-time temperature and concentration data but also converts this data into two risk indices with clear physical meaning. By constructing a fluid dynamics instability index, it can accurately quantify the physical risks associated with process issues such as localized hot spots or fluid dead zones. By constructing an isomer catalysis risk index, it can quantify in real time the chemical risks of side reactions triggered by ppm-level impurities in the raw materials. This dual-path quantitative assessment transforms the understanding of the production process from vague state anomalies to clear risk attribution.

[0018] The core advancement of this invention lies in its causal decoupling module. When the overall health of the system, namely the system dynamics resilience index, decreases, the system does not issue a general alarm. Instead, it calculates causal contribution factors to diagnose the dominant risk sources leading to system instability in real time and quantitatively. This achieves a leap from blind intervention to precise control. Based on a clear root cause diagnosis, the decision output module can generate differentiated intervention instructions. If the diagnosis is a fluid dynamics problem, the instructions will focus on increasing the stirring rate or checking the cooling system; if the diagnosis is a trace isomer catalysis problem, the instructions will shift to triggering a raw material batch alarm or preparing for a safe shutdown. This precisely matched control strategy avoids the ineffective operation of traditional control, significantly improves the success rate of intervention, and effectively reduces the risk of material loss and batch failure.

[0019] This system achieves a leap from passive response to proactive early warning: it decouples risk sources by analyzing the instantaneous rate of change of the risk index, which is much more sensitive than waiting for the risk index to exceed a fixed threshold. This dynamic trend-based judgment mechanism enables the system to identify the dominant direction of problems at an early stage of risk accumulation, buying valuable response time for operators and transforming traditional passive emergency response into proactive risk intervention and critical transition warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The present invention will be further explained below in conjunction with the accompanying drawings and Examples: Figure 1 The present invention is a flowchart of a monitoring and control system for an intelligent pharmaceutical intermediate preparation production line. DETAILED DESCRIPTION

[0021] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to specific embodiments. Example

[0022] See also Figure 1 The present invention provides a technical solution for a monitoring and control system for an intelligent pharmaceutical intermediate production line. The specific steps include: Data acquisition module: used to collect real-time temperature data and real-time chemical component concentration data of the production process, and construct a process status data set based on the real-time temperature data and the real-time chemical component concentration data; the data acquisition module obtains temperature data in real time through a distributed fiber Bragg grating sensor array deployed in the reactor, uses in-situ Raman spectroscopy combined with a partial least squares chemometric model to calculate the chemical component concentration data from the real-time spectrum, and constructs a process status data set containing temperature field distribution and concentration change trajectory.

[0023] The risk index calculation module is used to perform the following steps: Step 1: Calculating a fluid dynamics instability index based on the real-time temperature data and the preset reference temperature data in the process state data set; Step 2: calculating an isomer catalytic risk index based on the real-time chemical component concentration data in the process state data set; Step 3: weighted fusion of the fluid dynamics instability index and the isomer catalytic risk index to generate a system dynamics resilience index; The risk index calculation module generates a fluid dynamics instability index based on the real-time temperature data and the preset reference temperature data in the process status dataset by calculating the weighted mean square error of the normalized difference between the real-time temperature and the reference temperature. This index can accurately quantify the degree of deviation between the fluid dynamics state in the reactor and the ideal state. When local hot spots or fluid dead zones appear in the reactor, the fluid dynamics instability index increases significantly, indicating an increase in the risk of process problems.

[0024] Based on the real-time chemical component concentration data in the process status dataset, the isomer catalysis risk index is generated by calculating the relative generation rate of characteristic by-products and main products and multiplying it by a preset proportional coefficient. This index can quantify in real time the degree of threat posed by side reactions catalyzed by trace isomers to the main reaction. When non-target configuration isomers exist at the ppm level in the raw materials, the isomer catalysis risk index rises sharply, indicating an aggravation of the risk of raw material problems. The risk index calculation module weightedly integrates the fluid dynamics instability index and the isomer catalysis risk index, and constructs the system dynamics resilience index through an exponential decay function. This index comprehensively evaluates the system's ability to resist risks and maintain stable operation. The closer the value is to 1, the better the system resilience, and the lower the value, the closer the system is to the critical transition point.

[0025] Causal decoupling module: This module is used to respond to the event that the system dynamics resilience index falls below the preset resilience threshold. By comparing the instantaneous rate of change of the fluid dynamics instability index and the isomer catalytic risk index, it calculates the causal contribution factor used to determine the dominant risk source. This causal contribution factor can perform real-time and quantitative causal decoupling in coupled and competitive failure pathways. When the causal contribution factor approaches 1, it indicates that fluid dynamics heterogeneity is the dominant causal factor. When the causal contribution factor approaches 0, it indicates that the side reaction catalyzed by trace isomers is the dominant causal factor. The decision-making output module identifies process problems or raw material problems as the current dominant risk based on the comparison results of the causal contribution factor and the preset contribution threshold, and generates differentiated intervention instructions that match the dominant risk, realizing the upgrade from blind intervention to precise control, significantly improving production success rate and reducing material loss.

[0026] Decision output module: used to identify process problems or raw material problems as the current dominant risks based on the comparison results of the causal contribution factor and the preset contribution threshold, and generate differentiated intervention instructions that match the dominant risks.

[0027] In this embodiment, the risk index calculation module is specifically used to: Calculating a weighted mean square error of a normalized difference between the real-time temperature and the reference temperature based on the real-time temperature data and the preset reference temperature data collected by the data collection module to generate the fluid dynamics instability index; The reference temperature data is obtained by running offline computational fluid dynamics simulation software and performing simulation calculations under ideal process conditions.

[0028] The calculation of the hydrodynamic instability index uses the following mathematical model:

[0029] Parameter explanation: : The hydrodynamic instability index at the moment; :time. : sensor space point index; : Total number of temperature measurement points; : Always Real-time temperature collected at the point; : Offline CFD simulation reference temperature corresponding to the point; : The weight factor of the point.

[0030] The reference temperature data is obtained by running offline computational fluid dynamics simulation software under ideal process conditions. The reference temperature data represents the three-dimensional temperature field distribution under ideal steady-state operating conditions in the reactor. The fluid dynamics instability index can represent the degree of deviation between the fluid dynamics state in the reactor and the ideal state in real time. When the reactor has local hot spots or fluid dead zones due to uneven stirring, the difference between the real-time temperature and the reference temperature increases significantly, and the fluid dynamics instability index increases accordingly, providing a quantitative basis for identifying process problems. This calculation method is based on The calculation realizes normalization processing, eliminates the influence of temperature dimension, and ensures that the index is dimensionless; the importance of key monitoring points is highlighted through weighted processing, and the accurate quantification of fluid dynamics heterogeneity is achieved.

[0031] In this embodiment, the risk index calculation module is specifically used to: Calculating the relative generation rate of characteristic by-products and main products based on the real-time chemical component concentration data collected by the data acquisition module; Multiplying the relative generation rate by a preset proportionality coefficient to generate the isomer catalysis risk index, which is used to characterize the threat level of the side reaction catalyzed by the trace isomer to the main reaction; The proportionality coefficient is a semi-empirical parameter obtained by conducting a series of trace isomer doping experiments on a laboratory scale and calibrating the generation rates of the target product and the by-products through linear regression fitting.

[0032] The isomer catalytic risk index is calculated using the following mathematical model:

[0033] Specifically, : The isomer catalytic risk index at the moment; : Main product concentration; : Concentration of characteristic by-products generated by isomer catalysis; : The instantaneous rate of change of the concentration of the corresponding substance, that is, the generation rate; : proportional coefficient; : A small non-negative constant that prevents the denominator from being zero.

[0034] Proportional coefficient By conducting trace isomer doping experiments at a laboratory scale and calibrating the semi-empirical parameters obtained by linear regression fitting of the generation rates of target products and by-products, the isomer catalysis risk index can quantify in real time the threat posed by side reactions catalyzed by trace isomers to the main reaction. Its construction logic is derived from the concept of selectivity in chemical reaction kinetics, and the risk level is defined by comparing the relative generation rates of target by-products and main products. When non-target configurational isomers are present in the raw materials at the ppm level, these isomers introduce unexpected catalytic side reaction pathways, resulting in a significant increase in the generation rate of characteristic by-products relative to the generation rate of the main product. The isomer catalysis risk index increases accordingly, providing a quantitative basis for identifying raw material problems. This calculation method eliminates the influence of reaction scale by comparing relative generation rates and ensures the accuracy of risk assessment through the calibration of proportionality coefficients.

[0035] In this embodiment, the risk index calculation module is further used to: The hydrodynamic instability index and the isomer catalytic risk index are multiplied by their corresponding preset weight coefficients and then summed to obtain an instantaneous comprehensive failure rate; Based on the instantaneous comprehensive failure rate, the system dynamics resilience index is constructed through an exponential decay function to comprehensively evaluate the system's ability to resist risks and maintain stable operation; The weight coefficient is preset based on statistical analysis of historical data of a specific chemical process or combined with domain expert knowledge to reflect the relative sensitivity of the process to the two types of risks.

[0036] The calculation of the system dynamics resilience index uses the following mathematical model:

[0037] Parameter explanation: : The system dynamics resilience index at the moment, with a value range of (0,1]; : weight coefficient of fluid dynamics risk; : weight coefficient of isomer risk; : hydrodynamic instability index; :Isomeric catalytic risk index.

[0038] The weight coefficient is based on statistical analysis of historical data of a specific chemical process or is preset in combination with domain expert knowledge to reflect the relative sensitivity of the process to the two types of risks. The system dynamics resilience index comprehensively evaluates the system's ability to resist risks and maintain stable operation. Its mathematical form draws on the failure rate model in reliability engineering, and the weighted sum of the two risk indices is regarded as the instantaneous comprehensive failure rate of the system. The value range of the system dynamics resilience index is (0,1]. The closer the value is to 1, the better the system resilience is, and the lower the value is, the closer the system is to the critical transition point. When the fluid dynamics instability index or the isomer catalysis risk index increases, the instantaneous comprehensive failure rate increases, and the system dynamics resilience index decreases accordingly, providing a quantitative indicator for the early warning of the critical transition of the system. This construction method realizes the nonlinear mapping of risk index to resilience index through an exponential decay function, and reflects the sensitivity differences of different processes to different risk types through the setting of weight coefficients.

[0039] In this embodiment, the causal decoupling module is specifically used to: Utilizing the historical time series of the hydrodynamic instability index and the isomer catalytic risk index, calculating their respective instantaneous change rates; Multiplying each instantaneous change rate by its corresponding preset weight coefficient to obtain a weighted change rate; The proportion of the weighted change rate of the fluid dynamic instability index in the sum of the absolute values ​​of the two weighted change rates is calculated to solve the causal contribution factor.

[0040] The calculation of the causal contribution factor uses the following mathematical model:

[0041] Parameter explanation: : The causal contribution factor of the moment; : the instantaneous rate of change of the hydrodynamic instability index; : Instantaneous change rate of isomer catalytic risk index; , : The weight coefficient defined above.

[0042] The derivation of the causal contribution factor is based on the total differential of the system dynamics resilience index over time. The chain rule decomposition yields the contribution of each risk item's rate of change to the overall rate of change. The causal contribution factor ranges from 1, indicating that the dominant causal factor for the current system instability is fluid dynamics heterogeneity, to 0, indicating that the dominant causal factor is a side reaction catalyzed by trace isomers. The causal decoupling module achieves real-time quantitative decoupling of coupled risks by comparing the instantaneous rates of change of the two risk indices, resolving the technical challenge of quantitatively distinguishing between process and raw material issues in real time when a system anomaly occurs. This solution ensures comparability of the rates of change for different risk types through the introduction of weight coefficients, and quantitatively attributing the dominant causal factors through weight calculation.

[0043] In this embodiment, the decision output module is specifically used to: When the causal contribution factor is higher than the preset first contribution threshold, the dominant cause of the current system instability is determined to be fluid dynamics inhomogeneity, and the differentiated intervention instruction is generated, including instructions to increase the stirring rate or check the cooling system. After receiving the value of the causal contribution factor, the decision output module immediately compares it with the first contribution threshold. When the causal contribution factor exceeds the first contribution threshold, the system determines that fluid dynamics inhomogeneity is the dominant source of risk, indicating that there are process problems such as local hot spots, fluid dead zones or uneven mixing in the reactor. The differentiated intervention instruction provides precise control measures for the root causes of fluid dynamics inhomogeneity, including specific operational instructions such as immediately increasing the stirring rate to improve the mixing effect, checking the operating status of the cooling system to eliminate local hot spots, and adjusting the feed rate to optimize the reaction kinetic balance.

[0044] In this embodiment, the decision output module is further specifically configured to: When the causal contribution factor is lower than the preset second contribution threshold, the dominant cause of the current system instability is determined to be the side reaction catalyzed by the trace isomer, and the differentiated intervention instruction is generated, including triggering a raw material batch quality alarm or preparing a safe shutdown instruction; the decision output module immediately compares the value of the causal contribution factor with the second contribution threshold after receiving it. When the causal contribution factor is lower than the second contribution threshold, the system determines that the side reaction catalyzed by the trace isomer is the dominant risk source, indicating that the current batch of raw materials contains excessive non-target configuration isomers; the differentiated intervention instruction provides emergency risk control measures for the side reaction catalyzed by the trace isomer, including immediately triggering a quality alarm for the batch of raw materials to trace the source of the raw materials, suspending the addition of materials to prevent the problem from expanding, preparing a safe shutdown procedure to avoid thermal runaway, and other key operational instructions.

[0045] In this embodiment, the decision output module is further specifically configured to: When the causal contribution factor is not lower than the second contribution threshold and not higher than the first contribution threshold, it is determined that the system faces a coupling problem, and the differentiated intervention instruction is generated to recommend taking comprehensive intervention measures; the decision output module identifies the complex situation where fluid dynamics heterogeneity and side reactions catalyzed by trace isomers contribute significantly to system instability at the same time through accurate judgment of the numerical range of the causal contribution factor. At this time, a single intervention measure cannot effectively solve the multiple risks faced by the system; the differentiated intervention instruction provides a systematic comprehensive control plan for the complexity of the coupling problem, including simultaneously adjusting the stirring rate and cooling system parameters to improve the fluid dynamics state, starting a deep detection program for raw material quality to confirm the isomer content, and implementing phased process parameter optimization to balance the interaction between the two types of risks. Multi-dimensional operational guidance.

[0046] In this embodiment, the data acquisition module is specifically used to: Acquiring the real-time temperature data through a distributed fiber Bragg grating sensor array deployed in the reactor; The real-time chemical component concentration data is calculated from the real-time spectrum using in-situ Raman spectroscopy combined with a partial least squares chemometrics model. A distributed fiber Bragg grating sensor array uses a multi-point distributed deployment approach, with temperature monitoring points located at key locations within the reactor, including near the feed inlet, around the agitator, at the edges of the reactor wall, and at the bottom, where temperature gradients are likely to occur. This allows for comprehensive monitoring of the three-dimensional temperature field within the reactor. The in-situ Raman spectroscopy system acquires chemical bond vibration information through the Raman scattering effect of laser-excited sample molecules. The partial least squares chemometrics model accurately converts spectral data into concentration data by establishing a quantitative relationship between spectral characteristics and chemical component concentrations.

[0047] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An intelligent monitoring and control system for a pharmaceutical intermediate production line, characterized in that: include: Data acquisition module: used to collect real-time temperature data and real-time chemical component concentration data of the production process, and construct a process status data set based on the real-time temperature data and the real-time chemical component concentration data; Risk index calculation module: used to perform the following steps: Step 1: Calculating a fluid dynamics instability index based on the real-time temperature data and the preset reference temperature data in the process state data set; Step 2: calculating an isomer catalytic risk index based on the real-time chemical component concentration data in the process state data set; Step 3: weighted fusion of the fluid dynamics instability index and the isomer catalytic risk index to generate a system dynamics resilience index; Causal decoupling module: This module is used to respond to the event that the system dynamics resilience index falls below the preset resilience threshold. By comparing the instantaneous rate of change of the fluid dynamics instability index and the isomer catalytic risk index, it calculates the causal contribution factor used to determine the dominant risk source. Decision output module: used to identify process problems or raw material problems as the current dominant risks based on the comparison results of the causal contribution factor and the preset contribution threshold, and generate differentiated intervention instructions that match the dominant risks.

2. The intelligent monitoring and control system for pharmaceutical intermediate production line according to claim 1, characterized in that: The risk index calculation module is specifically used to: Calculating a weighted mean square error of a normalized difference between the real-time temperature and the reference temperature based on the real-time temperature data and the preset reference temperature data collected by the data collection module to generate the fluid dynamics instability index; The reference temperature data is obtained by running offline computational fluid dynamics simulation software and performing simulation calculations under ideal process conditions.

3. The intelligent monitoring and control system for pharmaceutical intermediate production line according to claim 1, characterized in that: The risk index calculation module is specifically used to: Calculating the relative generation rate of characteristic by-products and main products based on the real-time chemical component concentration data collected by the data acquisition module; Multiplying the relative generation rate by a preset proportionality coefficient to generate the isomer catalysis risk index, which is used to characterize the threat level of the side reaction catalyzed by the trace isomer to the main reaction; The proportionality coefficient is a semi-empirical parameter obtained by conducting a series of trace isomer doping experiments on a laboratory scale and calibrating the generation rates of the target product and the by-products through linear regression fitting.

4. The intelligent monitoring and control system for pharmaceutical intermediate production line according to claim 3, characterized in that: The risk index calculation module is also used to: The hydrodynamic instability index and the isomer catalytic risk index are multiplied by their corresponding preset weight coefficients and then summed to obtain an instantaneous comprehensive failure rate; Based on the instantaneous comprehensive failure rate, the system dynamics resilience index is constructed through an exponential decay function to comprehensively evaluate the system's ability to resist risks and maintain stable operation; The weight coefficient is preset based on statistical analysis of historical data of a specific chemical process or combined with domain expert knowledge to reflect the relative sensitivity of the process to the two types of risks.

5. The intelligent monitoring and control system for pharmaceutical intermediate production line according to claim 4, characterized in that: The causal decoupling module is specifically used for: Utilizing the historical time series of the hydrodynamic instability index and the isomer catalytic risk index, calculating their respective instantaneous change rates; Multiplying each instantaneous change rate by its corresponding preset weight coefficient to obtain a weighted change rate; The proportion of the weighted change rate of the fluid dynamic instability index in the sum of the absolute values ​​of the two weighted change rates is calculated to solve the causal contribution factor.

6. The intelligent monitoring and control system for pharmaceutical intermediate production line according to claim 5, characterized in that: The decision output module is specifically used for: When the causal contribution factor is higher than a preset first contribution threshold, it is determined that the dominant cause of the current system instability is fluid dynamics inhomogeneity, and the differential intervention instruction including an instruction to increase the stirring rate or check the cooling system is generated.

7. The intelligent monitoring and control system for pharmaceutical intermediate production line according to claim 6, characterized in that: The decision output module is further specifically used for: When the causal contribution factor is lower than a preset second contribution threshold, the dominant cause of the current system instability is determined to be a side reaction catalyzed by trace isomers, and the differentiated intervention instruction is generated, including triggering a raw material batch quality alarm or preparing a safe shutdown instruction.

8. The intelligent monitoring and control system for pharmaceutical intermediate production line according to claim 7, characterized in that: The decision output module is further specifically used for: When the causal contribution factor is not lower than the second contribution threshold and not higher than the first contribution threshold, it is determined that the system faces a coupling problem, and the differentiated intervention instruction is generated to recommend taking comprehensive intervention measures.

9. The intelligent monitoring and control system for pharmaceutical intermediate production line according to claim 1, characterized in that: The data acquisition module is specifically used for: Acquiring the real-time temperature data through a distributed fiber Bragg grating sensor array deployed in the reactor; The real-time chemical component concentration data is calculated from the real-time spectrum by in-situ Raman spectroscopy combined with a partial least squares chemometric model.

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