Method for rapidly detecting impurity content in preparation process of organic silicon emulsion

Abstract

The invention discloses a method for rapidly detecting the impurity content in the preparation process of organic silicon emulsion, and relates to the technical field of impurity detection.The method comprises the steps that technological parameters of all current steps in the preparation process of the organic silicon emulsion are collected in real time; calculating the probability of various impurities introduced in the step; when the impurity introduction probability of the single step exceeds a first preset threshold value, generating a high-risk impurity list and triggering online directional detection; if the impurity introduction probabilities of all the steps do not exceed a first preset threshold value, constructing a transition probability matrix for describing the state change of the impurities in the whole process; calculating and predicting steady-state probability distribution of various impurities in the finished product; and performing finished product terminal detection on the impurities with the prediction probability exceeding a second preset threshold. The method has the advantages that multi-level early warning of impurity risks is achieved, source abnormity is positioned through the probability model, a transfer matrix tracks a full-process impurity migration path, accurate directional detection is combined with a terminal focusing strategy, and the impurity detection efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of impurity detection technology, specifically to a rapid method for detecting impurity content during the preparation of organosilicon emulsions. Background Technology

[0002] In the production of silicone emulsions, precise control of impurity content is crucial to ensuring the stability and performance of the final product. Currently, the industry generally relies on offline analysis methods for impurity detection, such as chromatography. However, this method suffers from significant detection lag. It cannot identify the risk sources of impurity formation in real time during production. While existing technologies can monitor basic process parameters such as reaction temperature and feed flow rate, they lack quantitative assessment models that dynamically correlate actual deviations from these parameters with the probability of specific impurity formation. Furthermore, existing methods struggle to effectively track the dynamic transformation pathways of impurities in complex multi-step reaction systems, such as side reactions, degradation, or cross-linking processes. This results in operators being unable to quickly pinpoint the specific stage of impurity generation when abnormal fluctuations occur in the production process, and are forced to passively wait for the final product test results, often leading to raw material waste and inconsistent batch product quality.

[0003] The current technological bottlenecks manifest in two key aspects. First, the correlation between deviations in process parameters and the types of impurities that may arise largely relies on the operator's experience and judgment, lacking a probabilistic analysis model driven by historical data. This makes it difficult to effectively predict the risk of impurity formation under complex operating conditions where multiple parameters are coupled and influence each other. Second, existing research and technologies mostly focus on impurity control within a single reaction step, neglecting the potential state migration phenomena of impurities in a continuously flowing reaction system. For example, oligomers generated by hydrolysis and condensation reactions may further undergo cyclization reactions to generate new impurities. This lack of understanding of the dynamic evolution of impurities throughout the entire process makes it impossible for manufacturers to accurately predict the distribution of latent impurities in the final product that are not easily detected directly. To ensure product quality, companies often have to expand the scope and items of end-point testing, which significantly increases the cost and time consumption of testing. Therefore, the industry urgently needs to establish a comprehensive and rapid detection mechanism that integrates real-time analysis of process parameters, dynamic calculation of impurity formation probability, and prediction of impurity migration and evolution throughout the entire process. Summary of the Invention

[0004] To address the aforementioned technical problems, a rapid method for detecting impurity content during the preparation of organosilicon emulsions is provided. This technical solution resolves at least one of the technical problems mentioned in the background section.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] A rapid method for detecting impurity content during the preparation of organosilicon emulsions includes:

[0007] Real-time acquisition of process parameters for each current step in the preparation of organosilicon emulsion, including reaction temperature, feed flow rate, stirring power, catalyst concentration, and system pH value;

[0008] Based on the degree of deviation between the current process parameters and the standard parameters, calculate the probability of various impurities introduced in this step;

[0009] When the probability of impurity introduction in a single step exceeds a first preset threshold, a list of high-risk impurities is generated and online targeted detection is triggered.

[0010] If the probability of impurity introduction in all steps does not exceed the first preset threshold, then a transition probability matrix describing the state changes of impurities throughout the entire process is constructed.

[0011] The steady-state probability distribution of various impurities in the finished product is predicted by calculating the transition probability matrix of the state changes of impurities throughout the entire process.

[0012] Impurities whose predicted probability exceeds a second preset threshold are subjected to final product testing.

[0013] Preferably, calculating the probability of various impurities introduced in this step based on the deviation of the current step's process parameters from the standard parameters specifically includes:

[0014] Call the pre-established database of mapping relationships between process parameter deviation and impurity types, and construct an impurity probability distribution analysis model corresponding to each step;

[0015] The impurity probability distribution corresponding to the current parameter combination is analyzed using an impurity probability distribution analysis model.

[0016] The impurity probability distribution analysis model is dynamically optimized based on historical data feedback.

[0017] Preferably, the impurity probability distribution analysis model is constructed by training a machine learning classification model or by constructing a logistic regression model;

[0018] The process of constructing the impurity probability distribution analysis model is as follows:

[0019] Based on product quality requirements, a threshold for impurity content is set;

[0020] Based on the mapping relationship database between process parameter deviation and impurity type, the data in the database is divided according to whether the content of impurities generated in the step exceeds the impurity content threshold, and status labels are attached as sample data.

[0021] A model for analyzing the probability distribution of impurities was trained using sample data.

[0022] Preferably, constructing the transition probability matrix describing the state changes of impurities throughout the entire process specifically includes:

[0023] The evolution of impurities in the reaction system is divided into three states: formation state, transformation state, and stable state;

[0024] Calculate the state transition probability of various impurities based on the principles of chemical reaction kinetics;

[0025] The specific calculation steps are as follows:

[0026] The tendency of state migration between impurities is calculated based on the principles of reaction kinetics.

[0027] Specifically:

[0028] ;

[0029] In the formula, This represents the tendency of impurity i to transform into impurity j. Let be the reaction rate constant for the conversion of impurity i to impurity j. The coefficients for satisfying other conditions required for impurity i to transform into impurity j;

[0030] Summarize all state transition tendencies among impurities and convert these tendencies into state transition probabilities.

[0031] Specifically:

[0032] ;

[0033] In the formula, Let i be the state transition probability from impurity i to impurity j. Let i be the set of impurity types transformed from impurity i. for The k-th element in The tendency of impurity i to transform into impurity k is considered.

[0034] By summing the state transition probabilities among all impurities, a transition probability matrix Z is constructed. , Let be the state transition probability of impurity i transforming into impurity j, and n be the total number of impurity types in the preparation process of organosilicon emulsion.

[0035] Preferably, the step of calculating and predicting the steady-state probability distribution of various impurities in the finished product by using the transition probability matrix of impurity state changes throughout the entire process specifically includes:

[0036] Based on the probability of various impurities that may be introduced in the steps, initialize the probability concentration distribution of various impurities in the initial step.

[0037] Based on the probability concentration distribution of various impurities in the initial step and the transition probability matrix, the conversion probability concentration distribution of various impurities is calculated iteratively.

[0038] When the difference between two consecutive iterations is less than the convergence threshold, the final conversion probability concentration distribution is output, and the steady-state probability distribution of various impurities in the finished product is calculated based on the content threshold of various impurities.

[0039] Preferably, the initialization of the probability concentration distribution of various impurities in the initial step based on the probability of various impurities that may be introduced in the step specifically includes:

[0040] For each type of impurity, the sum of the probabilities of introducing the impurity in each step is used as the initial distribution probability of the impurity.

[0041] Multiply the initial distribution probability of each type of impurity by the content threshold of each impurity to obtain the probability concentration distribution of each type of impurity in the initial step.

[0042] Preferably, the iterative calculation of the conversion probability concentration distribution of various impurities based on the probability concentration distribution of various impurities in the initial step, combined with the transition probability matrix, specifically includes:

[0043] Impurities in the same conversion process are clustered into the same group of conversion substances;

[0044] The probability concentration of each type of impurity participating in the transformation is obtained by multiplying the probability concentration distribution of each type of impurity in the initial step with the state transition tendency between impurities.

[0045] Using the first complete conversion among the group of convertible substances as the conversion termination condition, we analyzed the conversion amount of various impurities and the amount of generated impurities in the group of convertible substances.

[0046] After summarizing the total conversion and total generation of various impurities, updating the probability concentration distribution of various impurities, recalculating the transition probability matrix, and iteratively calculating the conversion probability concentration distribution of various impurities.

[0047] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0048] This invention, by quantifying the correlation between process parameter deviations and impurity generation probabilities in real time, can provide early warnings of potential impurity generation risks and trigger precise targeted detection, effectively overcoming the lag problem of traditional offline detection. Simultaneously, based on a constructed impurity state transition model, it predicts the steady-state distribution of various impurities, especially latent impurities, in the finished product. This allows the final quality inspection to be highly focused on potentially high-risk impurity categories, significantly reducing unnecessary testing items while ensuring product quality. This comprehensively improves detection efficiency and significantly reduces overall detection costs and quality feedback cycles, enabling rapid detection of impurity content during the preparation of silicone emulsions. Attached Figure Description

[0049] Figure 1 This is a flowchart of the rapid detection method for impurity content in the preparation process of organosilicon emulsion proposed in this scheme;

[0050] Figure 2 A flowchart illustrating the method for calculating the probabilities of various impurities introduced in the proposed scheme.

[0051] Figure 3 This is a flowchart of the method proposed in this scheme for predicting the steady-state probability distribution of various impurities in the finished product. Detailed Implementation

[0052] The following description is intended to disclose the invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art.

[0053] Reference Figure 1 As shown, a rapid method for detecting impurity content in the preparation process of organosilicon emulsion includes:

[0054] The system collects real-time process parameters for each step in the preparation of silicone emulsions, including reaction temperature, feed flow rate, stirring power, catalyst concentration, and system pH. Through a sensor network deployed at key nodes of the production line, it continuously and synchronously acquires core parameters reflecting the reaction progress and material mixing state. These dynamic changes in parameters serve as a direct input source for subsequent impurity risk prediction.

[0055] Based on the deviation of the current process parameters from the standard parameters, the probability of various impurities introduced in this step is calculated. Using a pre-established database mapping process parameter deviations to impurity types, combined with a constructed impurity probability distribution analysis model, such as a machine learning model trained on historical data, the real-time parameter deviation is quantified as the risk probability of specific impurities potentially arising in this step. This achieves a quantitative conversion from parameter anomalies to impurity risk.

[0056] When the probability of impurity introduction in a single step exceeds a first preset threshold, a list of high-risk impurities is generated and online targeted detection is triggered. In some preferred embodiments, the first preset threshold is set to 0.35, and this solution sets up early risk intervention. When the impurity generation risk in a certain step exceeds a preset warning level, the system will immediately identify the most likely high-risk impurity types to be generated in that step and automatically initiate online rapid detection for these specific impurities, such as near-infrared spectroscopy and Raman spectroscopy, to achieve rapid location and diagnosis of problematic steps and prevent the risk from spreading downstream.

[0057] If the probability of impurity introduction in all steps does not exceed a first preset threshold, a transition probability matrix describing the state changes of impurities throughout the entire process is constructed. When the initial risks of each step are low, the dynamic evolution of impurities in subsequent processes is considered. The tendency for different impurities to transform into each other is calculated, and finally, a transition probability matrix reflecting the migration and transformation patterns of impurities throughout the entire production chain is constructed. This provides a model basis for predicting the final impurity composition.

[0058] By calculating the transition probability matrix of impurities' state changes throughout the entire process, the steady-state probability distribution of various impurities in the finished product is predicted. Using the constructed transition probability matrix, combined with the initial impurity probability distribution introduced in each step as a starting point, the migration and transformation process of impurities throughout the entire process is simulated through iterative calculations. Finally, the probability distribution of various impurities present in the finished product is calculated. This reveals the hidden impurity risks that may evolve and arise during the process, even if the initial risk is low.

[0059] For impurities whose predicted probability exceeds a second preset threshold, finished product terminal testing is performed. In some preferred embodiments, the second preset threshold is set to 0.55. Based on the predicted steady-state probability distribution of finished product impurities, the system filters out impurity categories whose probability of presence exceeds a preset safety threshold. Finished product terminal testing focuses highly on these target impurities predicted as high-risk by the model for targeted analysis, greatly reducing blind and comprehensive testing items, and significantly improving the efficiency and cost-effectiveness of terminal testing while ensuring quality.

[0060] Reference Figure 2 As shown, in some preferred embodiments, calculating the probability of various impurities introduced in this step based on the deviation of the current step's process parameters from standard parameters specifically includes:

[0061] Call the pre-established database of mapping relationships between process parameter deviation and impurity types, and construct an impurity probability distribution analysis model corresponding to each step;

[0062] The impurity probability distribution corresponding to the current parameter combination is analyzed using an impurity probability distribution analysis model.

[0063] The impurity probability distribution analysis model is dynamically optimized based on historical data feedback.

[0064] The process of constructing the impurity probability distribution analysis model is as follows:

[0065] Based on product quality requirements, a threshold for impurity content is set;

[0066] Based on the mapping relationship database between process parameter deviation and impurity type, the data in the database is divided according to whether the content of impurities generated in the step exceeds the impurity content threshold, and status labels are attached as sample data.

[0067] A model for analyzing the probability distribution of impurities was trained using sample data.

[0068] In some preferred embodiments, the impurity probability distribution analysis model is constructed by training a machine learning classification model. Specifically, the input of the machine learning classification model is the deviation of process parameters, and the output is the probability that the impurity content introduced by the step exceeds the impurity content threshold.

[0069] In some other preferred embodiments, the impurity probability distribution analysis model is constructed using a logistic regression model;

[0070] The expression for the logistics regression model is:

[0071]

[0072] in, The probability that the impurity content introduced in the step exceeds the impurity content threshold. The deviation of the a-th process parameter in the step related to the content of introduced impurities. This represents the total number of process parameters for the current step that are related to the content of introduced impurities. These are the model coefficients, which are calculated based on the sample data using the least squares method.

[0073] The impurity probability distribution analysis model quantifies the correlation between process parameter deviation and impurity exceedance risk. This model accurately calculates the probability that the content of various impurities will exceed safety thresholds in specific steps, fundamentally changing the traditional fuzzy assessment method that relies on experience. Utilizing machine learning classification models or highly interpretable logistic regression models, the system not only adapts to the nonlinear effects of multi-parameter coupling on impurity generation under complex operating conditions but also dynamically optimizes by continuously incorporating historical detection data, enabling the prediction results to continuously evolve. This probability-based prediction mechanism provides a scientific basis for subsequent risk grading, significantly improving the accuracy and foresight of impurity risk identification.

[0074] In some preferred embodiments, constructing a transition probability matrix describing the state changes of impurities throughout the process specifically includes:

[0075] The evolution of impurities in the reaction system is divided into three states: formation state, transformation state, and stable state;

[0076] Calculate the state transition probability of various impurities based on the principles of chemical reaction kinetics;

[0077] The specific calculation steps are as follows:

[0078] The tendency of state migration between impurities is calculated based on the principles of reaction kinetics.

[0079] Specifically:

[0080] ;

[0081] In the formula, This represents the tendency of impurity i to transform into impurity j. Let be the reaction rate constant for the conversion of impurity i to impurity j. The coefficients for satisfying other conditions required for impurity i to transform into impurity j;

[0082] The method for determining the value is as follows: if all other conditions required for impurity i to transform into impurity j are met, the value is set to 1; if the other conditions required for impurity i to transform into impurity j cannot be fully met, the value of the condition with the lowest degree of satisfaction is compared with the value of the condition required for impurity i to transform into impurity j. For example, if the temperature required for impurity i to transform into impurity j is A, and the actual ambient temperature is B, then when the temperature is the minimum condition for fulfillment, then... ;

[0083] Summarize all state transition tendencies among impurities and convert these tendencies into state transition probabilities.

[0084] Specifically:

[0085] ;

[0086] In the formula, Let i be the state transition probability from impurity i to impurity j. Let i be the set of impurity types transformed from impurity i. for The k-th element in The tendency of impurity i to transform into impurity k is considered.

[0087] By summing the state transition probabilities among all impurities, a transition probability matrix Z is constructed. , Let be the state transition probability of impurity i transforming into impurity j, and n be the total number of impurity types in the preparation process of organosilicon emulsion.

[0088] By scientifically dividing the evolution of impurities in complex reaction systems into three states—formation, transformation, and stability—and accurately calculating the state migration tendency and probability based on the principles of chemical reaction kinetics, a quantitative modeling of the dynamic transformation path of impurities in continuous process flows has been achieved. This model fully considers the dynamic influence of the actual production environment on reaction conditions, introduces a condition satisfaction coefficient, and corrects theoretical migration tendencies in real time, adaptively reflecting the real impact of fluctuations in key factors such as temperature and concentration on impurity transformation paths. This dynamic modeling capability based on the nature of reactions solves the problem that traditional methods cannot track the migration, transformation, and accumulation of impurities between processes, providing a solid theoretical foundation for accurately predicting the distribution of latent impurities in finished products that are difficult to detect directly, and greatly improving the foresight and reliability of quality risk prediction.

[0089] Reference Figure 3 As shown, in some preferred embodiments, calculating and predicting the steady-state probability distribution of various impurities in the finished product using the transition probability matrix of impurity state changes throughout the entire process specifically includes:

[0090] Based on the probability of various impurities that may be introduced in the steps, initialize the probability concentration distribution of various impurities in the initial step.

[0091] Based on the probability concentration distribution of various impurities in the initial step and the transition probability matrix, the conversion probability concentration distribution of various impurities is calculated iteratively.

[0092] When the difference between two consecutive iterations is less than the convergence threshold, the final conversion probability concentration distribution is output, and the steady-state probability distribution of each type of impurity in the finished product is calculated based on the content threshold of each type of impurity. The steady-state probability distribution is the ratio of the final conversion probability concentration distribution of each type of impurity to the content threshold of each type of impurity.

[0093] By integrating the initial impurity introduction probability of each step with a full-process migration and transformation model, the system can, for the first time, accurately simulate the dynamic evolution trajectory of impurities in a complex reaction chain before the finished product is produced. Its core advantage lies in using an iterative computation mechanism to realistically recreate the chain effects of mutual transformation and accumulation between impurities, and ensuring the stability and reliability of the prediction results through convergence judgment. This changes the passive mode of traditional end-product testing, enabling proactive identification of high-risk latent impurities that are present in low concentrations in the initial steps but may accumulate and exceed limits in subsequent processes. Ultimately, risk ranking based on steady-state distribution allows end-product testing resources to be highly focused on the key impurities that truly affect product quality, significantly improving testing efficiency and optimizing quality control costs while ensuring quality and safety.

[0094] Specifically, based on the probabilities of various impurities that may be introduced in the steps, the initialization of the probability concentration distribution of various impurities in the initial step includes:

[0095] For each type of impurity, the sum of the probabilities of introducing the impurity in each step is used as the initial distribution probability of the impurity.

[0096] Multiply the initial distribution probability of each type of impurity by the content threshold of each impurity to obtain the probability concentration distribution of each type of impurity in the initial step.

[0097] By integrating the risk probabilities of impurities, calculated in the preceding steps and dispersed across various stages, into an initial impurity concentration distribution estimate for the entire process, the system estimating the overall impurity concentration distribution for the entire process. Specifically, for each specific impurity, the system sums the probability of its introduction in all production steps (i.e., the sum of the introduction probabilities of the impurity in each step), reflecting the overall risk level of the impurity at the source of the entire production chain. Then, to obtain a concentration dimension with practical physical meaning and compatible with subsequent migration models, the system multiplies the initial distribution probability of each impurity by its corresponding preset content threshold. This threshold represents the safe upper limit concentration of the impurity allowed in the product. Through this transformation, the final "probability concentration distribution" not only quantifies the relative likelihood of each impurity's presence at the beginning of the process, but more importantly, it provides a unified initial state based on actual concentration risk, laying a necessary and reasonable numerical foundation for subsequent simulations of the dynamic migration and transformation behavior of impurities throughout the reaction system based on the transition probability matrix.

[0098] Specifically, based on the probability concentration distributions of various impurities in the initial step combined with the transition probability matrix, the iterative calculation of the conversion probability concentration distributions of various impurities includes:

[0099] Impurities in the same conversion process are clustered into the same conversion substances. For example, in the conversion of impurity i to impurity j, the participation of impurity k and impurity m is required simultaneously. Then impurity i, impurity k and impurity m are considered as the same conversion substances in the conversion process of impurity j.

[0100] The probability concentration of each type of impurity participating in the transformation is obtained by multiplying the probability concentration distribution of each type of impurity in the initial step with the state transition tendency between impurities.

[0101] Specifically, if the probability concentration of impurity i is Then, in the same group of transformed substances consisting of impurities i, k, and m in the transformation process of impurity j, the probability concentration of impurity i participating in the transformation is: ;

[0102] Using the first complete conversion among the group of convertible substances as the conversion termination condition, we analyzed the conversion amount of various impurities and the amount of generated impurities in the group of convertible substances.

[0103] Specifically, if impurities i, k, and m are part of the same group of transformed substances in the transformation process of impurity j, When impurity i at a certain concentration is completely converted, impurities k and m still remain. In this case, the amount of impurity j generated when impurity i is completely converted is taken as the amount of impurity j generated, and the amount of impurity k and m consumed when impurity i is completely converted is taken as the amount of impurity k and m converted.

[0104] After summarizing the total conversion and total generation of various impurities, updating the probability concentration distribution of various impurities, recalculating the transition probability matrix, and iteratively calculating the conversion probability concentration distribution of various impurities;

[0105] After each iteration, the probability concentration of impurities is calculated as the probability concentration of impurities after the previous iteration minus the sum of all impurity conversions plus the sum of all impurity generations. After the update, the impurity content in the emulsion system changes, and it is necessary to recalculate the state transition tendency between impurities and update the state transition probability.

[0106] By clustering impurities participating in the same chemical transformation pathway into "group-transformed substances," the system can accurately capture the stoichiometric relationships and material balance constraints in multi-impurity synergistic reactions. The key innovation lies in introducing a termination condition mechanism based on the "first to be completely transformed"—the complete consumption of the impurity with the lowest concentration within the group serves as the termination point for that round of transformation; at this point, other impurities are consumed only in proportion to the reaction. This design realistically reflects the chemical essence of actual reactions where limiting reactants determine the amount of product formed.

[0107] At the computational level, the system uses the product of the initial probability concentration and the migration probability as the effective concentration actually participating in the conversion, and achieves material conservation through a dynamic update mechanism during the iteration process. More importantly, after each iteration, the system recalculates the state migration tendency and transition probability matrix among all impurities based on the updated impurity concentration distribution. This real-time feedback mechanism is crucial—it enables the model to adaptively reflect the impact of concentration changes on the reaction rate, as well as the disturbances to the conversion path caused by fluctuations in environmental parameters. Through this high-fidelity dynamic simulation, the final conversion concentration probability distribution can predict the cumulative behavior of latent impurities in complex reaction networks.

[0108] In summary, the advantages of this invention are as follows: by quantifying the correlation between process parameter deviations and impurity generation probabilities in real time, it can provide early warnings of risk points related to impurity generation and trigger precise targeted detection, effectively overcoming the lag problem of traditional offline detection; at the same time, based on the constructed impurity state transition model, it predicts the steady-state distribution of various impurities, especially latent impurities, in the finished product, enabling the final quality inspection to be highly focused on potentially high-risk impurity categories, significantly reducing unnecessary testing items while ensuring product quality, thereby comprehensively improving detection efficiency and significantly reducing overall detection costs and quality feedback cycles, achieving rapid detection of impurity content in the preparation process of organosilicon emulsions.

[0109] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. A rapid method for detecting impurity content during the preparation of organosilicon emulsions, characterized in that, include: Real-time acquisition of process parameters for each current step in the preparation of organosilicon emulsion, including reaction temperature, feed flow rate, stirring power, catalyst concentration, and system pH value; Based on the degree of deviation between the current process parameters and the standard parameters, calculate the probability of various impurities introduced in this step; When the probability of impurity introduction in a single step exceeds a first preset threshold, a list of high-risk impurities is generated and online targeted detection is triggered. If the probability of impurity introduction in all steps does not exceed the first preset threshold, then a transition probability matrix describing the state changes of impurities throughout the entire process is constructed. The steady-state probability distribution of various impurities in the finished product is predicted by calculating the transition probability matrix of the state changes of impurities throughout the entire process. Impurities whose predicted probability exceeds a second preset threshold are subjected to final product testing.

2. The rapid detection method for impurity content in the preparation process of organosilicon emulsion according to claim 1, characterized in that, The calculation of the probability of various impurities introduced in this step based on the deviation of the current process parameters from the standard parameters specifically includes: Call the pre-established database of mapping relationships between process parameter deviation and impurity types, and construct an impurity probability distribution analysis model corresponding to each step; The impurity probability distribution corresponding to the current parameter combination is analyzed using an impurity probability distribution analysis model. The impurity probability distribution analysis model is dynamically optimized based on historical data feedback.

3. The rapid detection method for impurity content in the preparation process of organosilicon emulsion according to claim 2, characterized in that, The impurity probability distribution analysis model is constructed by training a machine learning classification model or by constructing a logistic regression model. The process of constructing the impurity probability distribution analysis model is as follows: Based on product quality requirements, a threshold for impurity content is set; Based on the mapping relationship database between process parameter deviation and impurity type, the data in the database is divided according to whether the content of impurities generated in the step exceeds the impurity content threshold, and status labels are attached as sample data. A model for analyzing the probability distribution of impurities was trained using sample data.

4. The rapid detection method for impurity content in the preparation process of organosilicon emulsion according to claim 3, characterized in that, The construction of the transition probability matrix describing the state changes of impurities throughout the entire process specifically includes: The evolution of impurities in the reaction system is divided into three states: formation state, transformation state, and stable state; Calculate the state transition probability of various impurities based on the principles of chemical reaction kinetics; The specific calculation steps are as follows: The tendency of state migration between impurities is calculated based on the principles of reaction kinetics. Specifically: ； In the formula, This represents the tendency of impurity i to transform into impurity j. Let be the reaction rate constant for the conversion of impurity i to impurity j. The coefficients for satisfying other conditions required for impurity i to transform into impurity j; Summarize all state transition tendencies among impurities and convert these tendencies into state transition probabilities. Specifically: ； In the formula, Let i be the state transition probability from impurity i to impurity j. Let i be the set of impurity types transformed from impurity i. for The k-th element in The tendency of impurity i to transform into impurity k is considered. By summing the state transition probabilities among all impurities, a transition probability matrix Z is constructed. , Let be the state transition probability of impurity i transforming into impurity j, and n be the total number of impurity types in the preparation process of organosilicon emulsion.

5. The rapid detection method for impurity content in the preparation process of organosilicon emulsion according to claim 4, characterized in that, The calculation and prediction of the steady-state probability distribution of various impurities in the finished product using the transition probability matrix of impurity state changes throughout the entire process specifically includes: Based on the probability of various impurities that may be introduced in the steps, initialize the probability concentration distribution of various impurities in the initial step. Based on the probability concentration distribution of various impurities in the initial step and the transition probability matrix, the conversion probability concentration distribution of various impurities is calculated iteratively. When the difference between two consecutive iterations is less than the convergence threshold, the final conversion probability concentration distribution is output, and the steady-state probability distribution of various impurities in the finished product is calculated based on the content threshold of various impurities.

6. The rapid detection method for impurity content in the preparation process of organosilicon emulsion according to claim 5, characterized in that, The initialization of the probability concentration distribution of various impurities in the initial step based on the probability of various impurities that may be introduced in the steps specifically includes: For each type of impurity, the sum of the probabilities of introducing the impurity in each step is used as the initial distribution probability of the impurity. Multiply the initial distribution probability of each type of impurity by the content threshold of each impurity to obtain the probability concentration distribution of each type of impurity in the initial step.

7. The rapid detection method for impurity content in the preparation process of organosilicon emulsion according to claim 6, characterized in that, The iterative calculation of the conversion probability concentration distribution of various impurities based on the probability concentration distribution of various impurities in the initial step, combined with the transition probability matrix, specifically includes: Impurities in the same conversion process are clustered into the same group of conversion substances; The probability concentration of each type of impurity participating in the transformation is obtained by multiplying the probability concentration distribution of each type of impurity in the initial step with the state transition tendency between impurities. Using the first complete conversion among the group of convertible substances as the conversion termination condition, we analyzed the conversion amount of various impurities and the amount of generated impurities in the group of convertible substances. After summarizing the total conversion and total generation of various impurities, updating the probability concentration distribution of various impurities, recalculating the transition probability matrix, and iteratively calculating the conversion probability concentration distribution of various impurities.

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