A cloud-based intelligent management method for biotechnology product industry supervision

Abstract

The present invention discloses a cloud-based intelligent management method for industrial supervision of biotechnology products, which relates to the field of information technology. The method includes S1, obtaining characteristic parameter data of biotechnology products, collecting multidimensional data in real time from the production process, S2, using a distributed computing architecture, parallel processing of characteristic parameter data, dividing computing tasks in the cloud system, and distributing them to multiple computing nodes, S3, according to a preset molecular level analysis algorithm, deep mining of characteristic parameter data, analyzing the changing trend of active ingredients and dynamic structural characteristics, and evaluating molecular structure stability indicators; the cloud-based intelligent management method for industrial supervision of biotechnology products dynamically updates characteristic maps, optimizes fluctuation trend prediction, and maintains the accuracy and stability of the monitoring model. The method effectively realizes real-time monitoring and analysis of biotechnology products, and provides an intelligent solution for industrial supervision.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and in particular to a cloud-based intelligent management method for biotechnology product industry supervision. Background Art

[0002] There is a technical contradiction in the cloud-based intelligent management method for the supervision of the biotechnology product industry: how to achieve high efficiency and stability in real-time monitoring while ensuring the depth and accuracy of product characteristic analysis. Specifically, the characteristic parameters of biotechnology products involve multiple dimensions, such as protein expression levels, active ingredient content, molecular structure stability, etc. These parameters need to be accurately extracted and analyzed at the molecular level. However, with the increase in characteristic parameters and the improvement in analysis accuracy, the complexity and amount of data processing have increased significantly, resulting in the limitation of the real-time monitoring capabilities of the cloud-based system.

[0003] In addition, during the production process of biotechnology products, characteristic parameters may be affected by a variety of factors, such as differences in raw material batches, fluctuations in production processes, changes in environmental conditions, etc. These factors may cause dynamic changes in characteristic parameters, further increasing the difficulty of real-time monitoring. How to ensure the accuracy and stability of characteristic analysis models under dynamically changing conditions has become a technical problem that needs to be solved urgently. Another contradiction lies in the construction and updating of characteristic maps. Characteristic maps are constructed based on historical data and standard parameters, but in actual production, the introduction of new products or improvements in production processes may lead to changes in characteristic parameters. How to dynamically update characteristic maps to adapt to new product characteristics without affecting real-time monitoring is another challenge faced by cloud-based intelligent management methods. The core of these technical contradictions lies in how to find a balance between in-depth analysis and real-time monitoring to ensure that the system can efficiently and stably realize regulatory functions in the complex and changeable production environment of biotechnology products. Summary of the invention

[0004] The purpose of the present invention is to provide a cloud-based intelligent management method for biotechnology product industry supervision to solve the problems existing in the prior art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a cloud-based intelligent management method for biotechnology product industry supervision, the method comprising: S1. Obtain characteristic parameter data of biotechnology products and collect multi-dimensional data in real time from the production process; S2, using a distributed computing architecture to perform parallel processing on the parameter data, dividing the computing tasks in the cloud system and allocating them to multiple computing nodes; S3. According to the preset molecular level analysis algorithm, the characteristic parameter data is deeply mined to analyze the change trend and dynamic structural characteristics of the active ingredients and evaluate the molecular structure stability index; S4. Set a preset threshold range to identify abnormal data of characteristic parameter data. If the data exceeds the threshold range, start the dynamic adjustment mechanism to recalibrate the analysis model through batch difference quantification and process parameter correlation analysis to achieve model adaptive adjustment; S5. Obtain environmental condition data, combine the characteristic parameter analysis results, use multiple regression analysis method to determine the influence of environmental factors on characteristic parameters, and generate an environment-characteristic correlation matrix; S6. Use streaming data processing technology to continuously process the real-time collected characteristic parameter data; S7. Dynamically adjust the task allocation strategy according to the load of the computing nodes to achieve real-time monitoring and analysis of the stability of the molecular structure.

[0006] Preferably, the S1 comprises: Use sensors to collect multidimensional data in real time during biotechnology production to obtain protein subunit composition, secondary structure content, and binding site residue information; Through the preset data processing module, the collected multi-dimensional data is matched with the three-dimensional folding pattern of the protein to determine the integrity of the folding pattern; If the three-dimensional folding pattern is complete, the binding site residue information is extracted and its interaction with the target molecule is analyzed; Based on the results of binding site residue analysis, the secondary structure content of the protein functional region is determined to generate structural quantity data; Using machine learning algorithms, structural quantitative data are correlated with protein subunit composition to predict the functional properties of proteins; If the prediction result is consistent with the preset threshold, a monitoring value is generated to record the parameter changes during the production process; Through the multi-dimensional data analysis platform, real-time collected data, structural quantity data and monitoring values ​​are integrated to generate dynamic reports of the production process.

[0007] Preferably, S2 includes: Deploy a distributed computing architecture in the cloud system to achieve parallel processing by dividing computing tasks to obtain characteristic parameter data such as protein expression level, active ingredient content and molecular structure stability; Using the preset algorithm model, the protein expression level data is normalized to obtain the standardized expression amount; If the normalized expression exceeds the preset threshold, the protein expression level is judged to meet the requirements; According to the protein active ingredient content data, cluster analysis algorithm was used to divide the active ingredients into high-activity group and low-activity group; If the proportion of the high-activity group exceeds the preset proportion, the content of the active ingredient is judged to meet the standard; Based on the molecular structure stability data, a regression analysis algorithm was used to establish a correlation model between stability, temperature and pH value; If the fitting degree of the correlation model is higher than the preset value, it is judged that the stability of the molecular structure is in line with expectations; The standardized expression level, the proportion of high-activity groups, and the stability fit were used as inputs, and a decision tree algorithm was used to generate protein quality assessment results; If the evaluation results are consistent with the preset standards, the protein quality is recorded as qualified; Through a distributed computing architecture, protein quality assessment results are associated with production batch information to generate batch quality reports; If the batch quality report shows abnormalities, the early warning mechanism will be triggered; Adopt data visualization technology to integrate batch quality reports, warning information and production parameters to generate production process monitoring views; If the monitoring view shows fluctuations in key parameters, the parameter adjustment process is initiated.

[0008] Preferably, S3 includes: For the spatial conformational change data of proteins, the principal component analysis algorithm is used to extract the main modes of conformational change and obtain the conformational change characteristic values; According to the surface hydrophobicity distribution data, a cluster analysis algorithm is used to divide the hydrophobic area into a high hydrophobic area and a low hydrophobic area to determine the hydrophobicity distribution pattern; From the intermolecular interaction data, the association rule mining algorithm is used to extract the high-frequency force combination and obtain the intermolecular force characteristics; Combined with the active ingredient change trend data, the time series analysis algorithm is used to fit the active ingredient dynamic change curve and determine the activity change trend item; According to the dynamic structure feature data, the support vector machine algorithm is used to classify the dynamic structure state and determine the dynamic characteristic type; For the molecular structure stability index, a regression analysis algorithm is used to establish a correlation model between stability and conformational change, hydrophobicity distribution and interaction force, and obtain the stability prediction value; If the deviation between the predicted stability value and the preset threshold exceeds the allowable range, the conformational optimization process is initiated to adjust the molecular structure parameters.

[0009] Preferably, S4 includes: Set a preset threshold range for the characteristic parameter, obtain input data and determine whether it exceeds the threshold range; If the data exceeds the threshold, the dynamic adjustment mechanism is activated; By quantifying batch differences, we can obtain the difference between batches. Use correlation analysis method to analyze the relationship between process parameters and characteristic parameters; Recalibrate the analysis model based on the difference and correlation analysis results; Through the calibrated model, the adaptive adjustment of the model is achieved; Based on the adjusted model, re-judge whether the input data is within the threshold range.

[0010] Preferably, S5 includes: Obtain temperature, pH and dissolved oxygen data, combine with protein, subunit group and secondary structure characteristic parameters to analyze the results, and use regression method to determine the impact of environmental factors on proteins, subunit groups and secondary structures; Generate the association moments between environmental factors and proteins, subunit groups and secondary junctions, and determine the association strength between environmental factors and characteristic parameters; According to the association moment, the significant relationship between environmental factors and characteristic parameters is extracted to determine the key influencing factors; Aiming at the key influencing factors, a prediction model of environmental factors and characteristic parameters is established to predict the changing trend of characteristic parameters; Combined with the prediction model, the environmental condition data is adjusted to optimize the temperature, pH value and dissolved oxygen parameters; Based on the optimized environmental condition data, re-obtain the characteristic parameter analysis results to verify the effect of environmental factor adjustment; Update the association matrix, determine the final impact of environmental adjustment on feature parameters, and complete the optimization of environmental conditions.

[0011] Preferably, S6 includes: Obtain real-time data on protein expression levels and active ingredient content, and use streaming data processing technology for continuous processing; The data stream is segmented and cached, and the three-dimensional folding pattern and binding site residues are analyzed through a preset window mechanism; If the computing resource allocation exceeds the preset threshold, the window size and buffer capacity are adjusted to reduce system latency; According to the results of three-dimensional folding pattern analysis, the correlation between protein expression level and active ingredient content was determined; If the residue site analysis accuracy is lower than the preset value, the number of data stream segments is increased and the window calculation is performed again; Through the optimized computing resource allocation scheme, the data stream is reprocessed to obtain updated protein expression level and active ingredient content data; Determine the deviation between the updated data and the preset target, and determine the final three-dimensional folding pattern and binding site residue analysis results.

[0012] Preferably, the S7 includes: Obtain the load status of computing nodes and dynamically adjust the task allocation strategy; Analyze task priorities and dependencies to determine the order in which tasks should be executed; Optimize task granularity and parallelism to improve the utilization efficiency of computing resources; Process protein spatial conformation change data and extract conformational characteristic parameters; Calculate surface hydrophobicity distribution and generate hydrophobicity characteristic data; Integrate conformational and hydrophobic characteristics to assess molecular structural stability; Monitor the stability of molecular structure in real time and update the analysis results.

[0013] Preferably, it also includes: Adopting the incremental learning method, based on the newly collected characteristic parameter data such as protein subunit composition and secondary structure content, the characteristic map of molecular interaction and dynamic structural characteristics is dynamically updated, and the updated content is synchronized to the real-time monitoring module of the cloud system, including obtaining protein subunit composition and secondary structure content data, and extracting characteristic parameter values; Adopt incremental learning method to analyze the intermolecular interactions and dynamic structural characteristics; Dynamically update feature map content; Synchronize and update content to the cloud system; Establish a real-time monitoring module in the cloud system; The monitoring module obtains the updated characteristic spectrum data; Determine the changing trends of intermolecular interactions and dynamic structural characteristics based on updated data.

[0014] Preferably, it also includes: Based on the updated characteristic maps, the adaptive optimization method is used to dynamically adjust the preset threshold range, retrain the abnormal data identification and batch difference quantification models, and continuously optimize the fluctuation trend prediction through process parameter correlation analysis to adapt to new product characteristics and production process improvements, and maintain the accuracy and stability of the monitoring model, including obtaining dynamic data in the characteristic maps and extracting key parameters for changes in molecular interactions; Adopting adaptive optimization method, dynamically adjust the preset threshold range according to the extracted key parameters; Retrain the abnormal data identification model and batch difference quantification model based on the adjusted threshold range; Through process parameter correlation analysis, the correlation between process parameters and fluctuation trends is obtained; If the fluctuation trend exceeds the preset range, the fluctuation trend prediction model parameters are optimized; Update the input data set of the monitoring model based on new product characteristics and production process improvements; Adopt incremental learning methods to dynamically update the monitoring model and maintain model performance and stability.

[0015] It can be seen from the above technical solution that the present invention has the following beneficial effects: The cloud-based intelligent management method for the supervision of the biotechnology product industry is a cloud-based intelligent management method for the supervision of the biotechnology product industry. The method collects characteristic parameter data such as protein subunit composition and secondary structure content in real time, and performs distributed parallel processing in the cloud system. The present invention performs deep mining of data according to a preset algorithm, extracts features such as changes in protein spatial conformation, and analyzes the changing trend of active ingredients. Abnormal data is identified by setting a threshold range, and a dynamic adjustment mechanism is started to achieve model adaptation. In combination with environmental condition data, the present invention uses multivariate regression analysis to determine the influence of environmental factors and generate a correlation matrix. Using streaming data processing technology and incremental learning methods, the present invention dynamically updates the characteristic map, optimizes the fluctuation trend prediction, and maintains the accuracy and stability of the monitoring model. The method effectively realizes real-time monitoring and analysis of biotechnology products, and provides an intelligent solution for industrial supervision. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] like Figure 1 As shown, the present invention provides a technical solution: a cloud-based intelligent management method for biotechnology product industry supervision may specifically include: Obtain characteristic parameter data of biotechnology products, including protein subunit composition, secondary structure content, three-dimensional folding pattern, binding site residues, etc., and collect multi-dimensional data in real time from the production process.

[0019] Sensors are used to collect multidimensional data in the biotechnology production process in real time to obtain protein subunit composition, secondary structure content and binding site residue information. Through the preset data processing module, the collected multidimensional data is matched with the three-dimensional folding pattern of the protein to determine the integrity of the folding pattern. If the three-dimensional folding pattern is complete, the binding site residue information is extracted to analyze its interaction with the target molecule. According to the results of the binding site residue analysis, the secondary structure content of the functional region of the protein is determined to generate structural quantity data. Using a machine learning algorithm, the structural quantity data is associated with the protein subunit composition to predict the functional characteristics of the protein. If the prediction result is consistent with the preset threshold, a monitoring value is generated to record the parameter changes during the production process. Through the multidimensional data analysis platform, real-time collection data, structural quantity data and monitoring values ​​are integrated to generate a dynamic report of the production process.

[0020] For example, in the production process of biotechnology products, the protein subunit composition is first analyzed by high performance liquid chromatography (HPLC), and different subunits are separated within 20 minutes by gradient elution. The detection wavelength is 280 nanometers, and the proportion of each subunit is calculated by peak area, such as subunit A accounts for 45%, subunit B accounts for 35%, and subunit C accounts for 20%. Then, the secondary structure content is determined by circular dichroism (CD), scanning in the wavelength range of 190 to 250 nanometers, and the α-helix content, β-folding content, and random coil content are analyzed by CDPro software to obtain 30%, 25%, and 45%. Then, the three-dimensional folding pattern is analyzed by nuclear magnetic resonance (NMR) technology, using 15N and 13C labeled protein samples, collecting NOESY spectra, and performing structural calculations by CYANA software to obtain the three-dimensional conformation of the protein, and its RMSD value is 5 angstroms. Finally, molecular docking was used to simulate the binding site residues, and the ligand was docked with the protein using AutoDock software. Through genetic algorithm optimization, the binding free energy was -5kcal / mol, and key residues such as Lys45, Asp67 and Glu89 were identified. These data were collected in real time through IoT sensors and transmitted to the cloud database, where multidimensional data analysis was performed using machine learning algorithms to ensure product quality and controllability of the production process.

[0021] A distributed computing architecture is used to perform parallel processing on characteristic parameter data such as protein expression level, active ingredient content and molecular structure stability, and the computing tasks are divided in the cloud system and allocated to multiple computing nodes.

[0022] A distributed computing architecture is deployed in the cloud system. By dividing the computing tasks, parallel processing is achieved to obtain characteristic parameter data such as protein expression level, active ingredient content and molecular structure stability. The preset algorithm model is used to normalize the protein expression level data to obtain the standardized expression amount. If the standardized expression amount exceeds the preset threshold, the protein expression level is judged to meet the requirements. According to the protein active ingredient content data, the cluster analysis algorithm is used to divide the active ingredients into high-activity group and low-activity group. If the proportion of the high-activity group exceeds the preset proportion, the active ingredient content is judged to meet the standard. For the molecular structure stability data, the regression analysis algorithm is used to establish a correlation model between stability and temperature and pH value. If the fit of the correlation model is higher than the preset value, the molecular structure stability is judged to meet expectations. The standardized expression amount, the proportion of the high-activity group and the stability fit are used as inputs, and the decision tree algorithm is used to generate the protein quality assessment results. If the assessment results are consistent with the preset standards, the protein quality is recorded as qualified. Through the distributed computing architecture, the protein quality assessment results are associated with the production batch information to generate a batch quality report. If the batch quality report shows an abnormality, the early warning mechanism is triggered. Data visualization technology is used to integrate batch quality reports, warning information and production parameters to generate a production process monitoring view. If the monitoring view shows fluctuations in key parameters, the parameter adjustment process is initiated.

[0023] Exemplarily, under a distributed computing architecture, protein expression level data is obtained through RNA sequencing technology, and the sequencing data is compared with the reference genome using the HISAT2 algorithm, and the FPKM value is calculated to quantify the expression level. For example, the expression level of a certain protein under specific conditions is 120 FPKM. The data is divided into multiple task blocks and processed in parallel through the MapReduce framework. The results are summarized to show that the significant p value of the expression difference is less than 05. The active ingredient content analysis uses mass spectrometry technology, and the peptides in the sample are separated and detected by the LC-MS / MS method. The MaxQuant software is used for quantitative analysis, and the concentration of the active ingredient such as peptide XYZ is identified as 2 mg / mL. The data is distributed to multiple computing nodes through the Kubernetes cluster, and the processing efficiency is optimized by Monte Carlo simulation, and finally a distribution heat map of the active ingredient content is generated. The molecular structure stability data was obtained through molecular dynamics simulation. The root mean square deviation (RMSD) and root mean square fluctuation (RMSF) of the protein were calculated using GROMACS software within a simulation time of 100 nanoseconds. The results showed that the RMSD value was 5 angstroms, and the RMSF value of key residues such as Arg78 was 0 angstroms. The simulation task was divided into multiple subtasks and executed in parallel on multiple computing nodes through the MPI protocol, and finally a thermodynamic stability report of the protein structure was generated. All data was transmitted to the cloud database in real time through the distributed message queue Kafka, and multi-dimensional data analysis was performed using the Spark framework, combined with the random forest algorithm to predict product quality parameters to ensure the stability and controllability of the production process.

[0024] According to the preset molecular level analysis algorithm, the characteristic parameter data is deeply mined to extract characteristics such as protein spatial conformational changes, surface hydrophobicity distribution, and intermolecular interactions, analyze the changing trends and dynamic structural characteristics of active ingredients, and evaluate the molecular structure stability indicators.

[0025] For the spatial conformational change data of proteins, the principal component analysis algorithm is used to extract the main modes of conformational change and obtain the characteristic values ​​of conformational change. According to the surface hydrophobicity distribution data, the cluster analysis algorithm is used to divide the hydrophobic area into high hydrophobic area and low hydrophobic area to determine the hydrophobicity distribution pattern. From the intermolecular interaction data, the association rule mining algorithm is used to extract the high-frequency force combination and obtain the intermolecular force characteristics. Combined with the active ingredient change trend data, the time series analysis algorithm is used to fit the dynamic change curve of the active ingredient and determine the activity change trend item. According to the dynamic structure feature data, the support vector machine algorithm is used to classify the dynamic structure state and determine the dynamic characteristic type. For the molecular structure stability index, the regression analysis algorithm is used to establish the association model between stability and conformational change, hydrophobicity distribution, and force to obtain the stability prediction value. If the deviation between the stability prediction value and the preset threshold exceeds the allowable range, the conformation optimization process is started to adjust the molecular structure parameters.

[0026] For example, in the molecular level analysis algorithm, the spatial conformational changes of proteins are first deeply mined through the molecular dynamics simulation algorithm. The simulation time is set to 100 nanoseconds and the step length is 2 femtoseconds. The root mean square deviation (RMSD) of the protein on the main chain and side chain is calculated using the GROMACS software. It is observed that the RMSD value of the protein stabilizes within 25 nanometers after 50 nanoseconds, indicating that its conformation tends to be stable. Then, by calculating the surface accessible surface area (SASA) and hydrophobicity distribution, the DSSP algorithm is used to analyze the changes in the secondary structure of the protein. It is found that during the simulation process, the proportion of α helix gradually decreases from the initial 40% to 35%, while the proportion of β fold increases from 20% to 25%, indicating that the protein undergoes local structural rearrangement during the dynamic process. The molecular mechanics Poisson-Boltzmann surface area (MM / PBSA) method is further used to calculate the binding free energy of the protein and the ligand. The results show that the binding energy is -13 kcal / mol, indicating that there is a strong interaction between the two. Finally, the principal component analysis (PCA) algorithm was used to analyze the dynamic structural characteristics of the protein. The first three principal components were extracted to explain 80% of the variance. The protein conformation was divided into three main states through cluster analysis. Its structural stability index was evaluated and it was found that the free energy difference of the main conformational states was 1 kcal / mol, indicating that the protein has a high conversion energy barrier between different conformations and the overall structure is relatively stable.

[0027] Set a preset threshold range to identify abnormal data for characteristic parameter data. If the data exceeds the threshold range, activate the dynamic adjustment mechanism, recalibrate the analysis model through batch difference quantification and process parameter correlation analysis, and achieve model adaptive adjustment.

[0028] Set the preset threshold range of the characteristic parameters, obtain the input data and determine whether it exceeds the threshold range. If the data exceeds the threshold, start the dynamic adjustment mechanism. By quantifying the batch differences, obtain the difference between batches. Use the correlation analysis method to analyze the relationship between process parameters and characteristic parameters. Recalibrate the analysis model based on the difference and correlation analysis results. Through the calibrated model, realize the adaptive adjustment of the model. Based on the adjusted model, re-determine whether the input data is within the threshold range.

[0029] Exemplarily, when setting the preset threshold range, the 3σ principle based on statistics can be adopted. Assuming that a certain characteristic parameter data follows a normal distribution, its mean μ and standard deviation σ are calculated, and the threshold range is set to [μ-3σ, μ+3σ]. For example, if the mean value of a temperature sensor data is 50°C and the standard deviation is 2°C, the normal range is [44°C, 56°C]. For data outside this range, the dynamic adjustment mechanism is activated, and the difference between the current batch and the historical batch is calculated, such as using the Euclidean distance formula to quantify the difference. Assuming that the difference between the current batch and the previous batch is 3, exceeding the preset difference threshold of 0, the process parameter association analysis is triggered. The Pearson correlation coefficient is used to analyze the relationship between the characteristic parameters and the key process parameters. For example, the correlation coefficient between temperature and pressure is 85, indicating that the two are highly correlated. Based on this, the least squares method is used to refit the linear regression model and update the model parameters, such as adjusting the slope from 2 to 3 and the intercept from 15 to 10, to achieve adaptive adjustment of the model and ensure the accuracy of subsequent data analysis.

[0030] Environmental condition data, including temperature, pH value, dissolved oxygen content, etc., were obtained, and combined with the characteristic parameter analysis results, a multivariate regression analysis method was used to determine the degree of influence of environmental factors on characteristic parameters such as protein subunit composition and secondary structure content, and to generate an environment-characteristic association matrix.

[0031] Obtain temperature, pH, and dissolved oxygen data, combine with protein, subunit group, and secondary junction characteristic parameter analysis results, and use regression method to determine the impact of environmental factors on proteins, subunit groups, and secondary junctions. Generate the association matrix between environmental factors and proteins, subunit groups, and secondary junctions, and determine the strength of the association between environmental factors and characteristic parameters. According to the association matrix, extract the significant relationship between environmental factors and characteristic parameters, and determine the key influencing factors. For the key influencing factors, establish a prediction model for environmental factors and characteristic parameters to predict the trend of characteristic parameter changes. Combined with the prediction model, adjust the environmental condition data and optimize the temperature, pH, and dissolved oxygen parameters. Based on the optimized environmental condition data, re-obtain the characteristic parameter analysis results to verify the effect of environmental factor adjustment. Update the association matrix, determine the final impact of environmental factor adjustment on characteristic parameters, and complete the environmental condition optimization.

[0032] For example, the environmental condition data is collected by sensors, for example, the average temperature in a batch of fermentation is 37°C, the pH value is 8, and the dissolved oxygen content is 85%, and the characteristic parameter analysis results are combined, such as the α-helix content in the protein subunit composition is 45%, and the β-folding content is 30%. The multivariate regression analysis method is used to establish a mathematical model between environmental factors and characteristic parameters, with temperature, pH value, and dissolved oxygen content as independent variables, and protein subunit composition and secondary structure content as dependent variables. The regression equation is fitted using the least squares method to obtain a regression coefficient matrix, such as the regression coefficient of temperature for α-helix content is 65, and the regression coefficient for β-folding content is -32; the regression coefficient of pH value for α-helix content is 12, and the regression coefficient for β-folding content is 08; the regression coefficient of dissolved oxygen for α-helix content is 25, and the regression coefficient for β-folding content is -15. Based on the regression coefficient matrix, the environment-characteristic correlation matrix is ​​generated. For example, the correlation coefficient between temperature and α-helix content is 85, and the correlation coefficient with β-fold content is -72; the correlation coefficient between pH value and α-helix content is 45, and the correlation coefficient with β-fold content is 38; the correlation coefficient between dissolved oxygen and α-helix content is 68, and the correlation coefficient with β-fold content is -55. Through correlation matrix analysis, the influence of environmental factors on characteristic parameters is judged. For example, temperature has the greatest influence on α-helix content, with a regression coefficient of 65 and a correlation coefficient of 85, indicating that rising temperature will significantly increase α-helix content and reduce β-fold content. Based on the analysis results, the environmental parameter control strategy is optimized, such as controlling the temperature between 35℃ and 39℃, the pH value between 5 and 0, and the dissolved oxygen between 80% and 90%, so as to stabilize the protein subunit composition and secondary structure content and ensure product quality consistency.

[0033] Streaming data processing technology is used to continuously process characteristic parameter data such as protein expression levels and active ingredient content collected in real time. Through data stream segmentation and caching, continuous calculation and window mechanism, the accuracy of in-depth analysis of parameters such as three-dimensional folding patterns and binding site residues is guaranteed while optimizing computing resource allocation and reducing system latency.

[0034] Real-time collected data on protein expression levels and active ingredient content are obtained, and streaming data processing technology is used for continuous processing. The data stream is segmented and cached, and the three-dimensional folding pattern and binding site residues are analyzed through a preset window mechanism. If the computing resource allocation exceeds the preset threshold, the window size and cache capacity are adjusted to reduce system latency. According to the results of the three-dimensional folding pattern analysis, the correlation between the protein expression level and the active ingredient content is determined. If the residue site analysis accuracy is lower than the preset value, the number of data stream segments is increased and the window calculation is performed again. The data stream is reprocessed through the optimized computing resource allocation scheme to obtain updated protein expression level and active ingredient content data. Determine the deviation between the updated data and the preset target, and determine the final three-dimensional folding pattern and binding site residue analysis results.

[0035] Exemplarily, in the application of streaming data processing technology, the real-time collected characteristic parameter data such as protein expression level and active ingredient content are first divided into a data segment every 100 milliseconds through the data stream segmentation and caching mechanism, and a circular buffer area is used for temporary storage. The buffer area size is 1GB to ensure data continuity and integrity. Then, a sliding window algorithm is used with a window size of 5 seconds and a step size of 1 second to continuously calculate the data in each window. By calculating the mean and standard deviation of the protein expression level in each window, combined with the changing trend of the active ingredient content, the support vector machine (SVM) algorithm is used for classification to identify abnormal expression patterns. In the deep analysis stage, a deep learning model such as a convolutional neural network (CNN) is used to predict the three-dimensional folding pattern of the protein. The model input is the protein structure data collected every 10 milliseconds, and the output is the folding pattern probability distribution, with an accuracy of 95%. At the same time, the analysis of binding site residues uses a hidden Markov model (HMM) to calculate the state transition probability of the residue sequence collected every 20 milliseconds, and identify the key binding sites with an accuracy of 90%. In terms of resource optimization, the dynamic resource allocation algorithm adjusts the CPU and memory usage of the computing nodes according to the real-time load of the computing tasks to ensure that the system delay is controlled within 50 milliseconds. For example, when it is detected that the CPU usage of a computing node exceeds 80%, the system automatically migrates some computing tasks to idle nodes to achieve load balancing. Through the above method, the system effectively reduces computing resource consumption and system delay while ensuring analysis accuracy.

[0036] According to the load of the computing nodes, the task allocation strategy is dynamically adjusted. Through task priority and dependency analysis, task granularity and parallelism optimization, it is ensured that the cloud system efficiently processes characteristic parameter data such as protein spatial conformation changes and surface hydrophobicity distribution, and realizes real-time monitoring and analysis of molecular structure stability.

[0037] Obtain the load of computing nodes and dynamically adjust the task allocation strategy. Analyze task priorities and dependencies to determine the execution order of tasks. Optimize task granularity and parallelism to improve the utilization efficiency of computing resources. Process protein spatial conformation change data and extract conformational characteristic parameters. Calculate surface hydrophobicity distribution and generate hydrophobicity characteristic data. Integrate conformational and hydrophobic characteristics to evaluate molecular structure stability. Monitor molecular structure stability in real time and update analysis results.

[0038] For example, by real-time monitoring of indicators such as CPU utilization, memory occupancy, and network bandwidth of computing nodes, a load prediction algorithm based on sliding windows is used to collect data at intervals of 5 minutes, and load trend prediction is performed in combination with the ARIMA model. When it is predicted that the CPU utilization of a node exceeds 85%, the system automatically triggers the task redistribution mechanism to migrate some computing-intensive tasks to nodes with lower loads. In terms of task priority division, a weighted priority scheduling algorithm is used to calculate priority weights based on task type, expected execution time, and dependency relationships, where the weight of the protein spatial conformation change analysis task is set to 8, and the weight of the surface hydrophobicity distribution calculation task is set to 6. For tasks with dependencies, a directed acyclic graph (DAG) is used for modeling and analysis, and the order of task execution is determined by topological sorting to ensure reasonable scheduling between dependent tasks. In terms of task granularity optimization, a dynamic block strategy is adopted to divide large-scale protein structure data into 1,000 atomic units, and parallel computing is achieved through the MapReduce framework. At the same time, the degree of parallelism is dynamically adjusted according to node performance, and the degree of parallelism is set to 6 on 8-core CPU nodes and 12 on 16-core CPU nodes to maximize the use of computing resources. For the monitoring of molecular structure stability, a LSTM network model based on deep learning is used to sample conformational change data at intervals of 1 nanosecond, and real-time analysis is achieved through feature extraction and pattern recognition. When the structural RMSD value is detected to exceed 0 angstroms, the early warning mechanism is immediately triggered. The system also integrates an adaptive learning function, which continuously optimizes the task allocation strategy by analyzing historical operation data to ensure that the overall computing efficiency remains above 95%.

[0039] Using an incremental learning method, the characteristic maps of intermolecular interactions and dynamic structural features are dynamically updated based on newly collected characteristic parameter data such as protein subunit composition and secondary structure content, and the updated content is synchronized to the real-time monitoring module of the cloud system.

[0040] Obtain protein subunit composition and secondary structure content data, and extract characteristic parameter values. Use incremental learning methods to analyze intermolecular interactions and dynamic structural features. Dynamically update characteristic map content. Synchronize updated content to the cloud system. Establish a real-time monitoring module in the cloud system. The monitoring module obtains updated characteristic map data. Determine the changing trend of intermolecular interactions and dynamic structural features based on the updated data.

[0041] For example, the gradient boosting decision tree (GBDT) algorithm based on online learning is used to extract the newly collected protein subunit composition and secondary structure content data from the cloud database at intervals of 10 minutes. Key parameters such as the number of hydrogen bonds and the strength of van der Waals forces are extracted through feature engineering. The intermolecular interaction model is dynamically updated using the gradient descent method, and the updated feature weights are synchronized to the real-time monitoring module. For dynamic structural features, a feature map generation algorithm based on a convolutional neural network (CNN) is used to extract protein conformational change data at a sampling interval of 5 nanoseconds, extract local structural features through a convolution layer with a convolution kernel size of 3×3, and generate an updated dynamic feature map in combination with a fully connected layer. During the data synchronization process, a data transmission protocol based on differential updates is used to transmit only the data of the changing part of the feature map, and the transmission bandwidth is controlled within 50MB per second to ensure that the response time of the real-time monitoring module is less than 200 milliseconds. In order to improve the stability of model updates, a hyperparameter adjustment mechanism based on Bayesian optimization is introduced. The model prediction error is used as the optimization target, and the learning rate and regularization coefficient are dynamically adjusted to ensure that the average error rate of model updates is less than 5%. At the same time, the system integrates a data quality monitoring module based on time series analysis, and uses the autoregressive integrated moving average model (ARIMA) to detect abnormal data. When the data deviation exceeds 2%, the data cleaning process is automatically triggered to ensure the accuracy of the feature map update. Through the above method, the system can dynamically optimize the molecular structure feature analysis while ensuring real-time performance, providing high-precision data support for protein function research.

[0042] Based on the updated feature map, an adaptive optimization method is used to dynamically adjust the preset threshold range, retrain the abnormal data identification and batch difference quantification models, and continuously optimize the fluctuation trend prediction through process parameter correlation analysis to adapt to new product characteristics and production process improvements, and maintain the accuracy and stability of the monitoring model.

[0043] Obtain dynamic data from the feature map and extract key parameters for changes in intermolecular interactions. Use adaptive optimization methods to dynamically adjust the preset threshold range based on the extracted key parameters. Retrain the abnormal data recognition model and batch difference quantification model based on the adjusted threshold range. Obtain the correlation between process parameters and fluctuation trends through process parameter association analysis. If the fluctuation trend exceeds the preset range, optimize the fluctuation trend prediction model parameters. Update the input data set of the monitoring model based on new product features and production process improvements. Use incremental learning methods to dynamically update the monitoring model to maintain model performance and stability.

[0044] Exemplarily, according to the updated feature map, a dynamic threshold adjustment method based on the adaptive particle swarm optimization algorithm (APSO) is adopted. Based on the key parameters in the feature map, the preset threshold range is adjusted to the optimal interval through iterative optimization to ensure that the accuracy of abnormal data recognition is improved to more than 95%. At the same time, a batch difference quantification model based on a deep belief network (DBN) is adopted. With each batch of production data as input, features are extracted through a multi-layer restricted Boltzmann machine (RBM), and the batch differences are quantified by a Softmax classifier to ensure that the accuracy of difference recognition is controlled within 2%. In the process parameter association analysis, a multivariate regression model based on partial least squares (PLS) is adopted, with the dynamic structural features in the feature map as independent variables and the process parameters as dependent variables. The optimal regression coefficient is determined through cross-validation, and the mean square error of the fluctuation trend prediction is reduced to below 01. In order to adapt to the characteristics of new products and production process improvements, the system introduces a model optimization mechanism based on transfer learning, with historical data as the source domain and new data as the target domain. The stability of model prediction is ensured through feature space alignment and parameter fine-tuning. In addition, the system integrates an abnormal data recognition module based on Gaussian process regression (GPR), which uses the local structural features in the feature map as input, calculates the data distribution probability through the kernel function, and automatically marks it as abnormal data when the probability is lower than 05, and triggers the data cleaning process to ensure the accuracy of the model update. Through the above method, the system can maintain the accuracy and stability of the monitoring model during dynamic adjustment, providing reliable data support for production process optimization.

[0045] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A cloud-based intelligent management method for biotechnology product industry supervision, characterized in that: The method comprises: S1. Obtain characteristic parameter data of biotechnology products and collect multi-dimensional data in real time from the production process; S2, using a distributed computing architecture to perform parallel processing on the parameter data, dividing the computing tasks in the cloud system and assigning them to multiple computing nodes; S3. According to the preset molecular level analysis algorithm, the characteristic parameter data is deeply mined to analyze the change trend and dynamic structural characteristics of the active ingredients and evaluate the molecular structure stability index; S4. Set a preset threshold range to identify abnormal data of characteristic parameter data. If the data exceeds the threshold range, start the dynamic adjustment mechanism to recalibrate the analysis model through batch difference quantification and process parameter correlation analysis to achieve model adaptive adjustment; S5. Obtain environmental condition data, combine the characteristic parameter analysis results, use multiple regression analysis method to determine the influence of environmental factors on characteristic parameters, and generate an environment-characteristic correlation matrix; S6. Use streaming data processing technology to continuously process the real-time collected characteristic parameter data; S7. Dynamically adjust the task allocation strategy according to the load of the computing nodes to achieve real-time monitoring and analysis of the stability of the molecular structure.

2. The cloud-based intelligent management method for biotechnology product industry supervision according to claim 1, characterized in that: The S1 includes: Use sensors to collect multidimensional data in real time during biotechnology production to obtain protein subunit composition, secondary structure content, and binding site residue information; Through the preset data processing module, the collected multi-dimensional data is matched with the three-dimensional folding pattern of the protein to determine the integrity of the folding pattern; If the three-dimensional folding pattern is complete, the binding site residue information is extracted and its interaction with the target molecule is analyzed; Based on the results of binding site residue analysis, the secondary structure content of the protein functional region is determined to generate structural quantity data; Using machine learning algorithms, structural quantitative data are correlated with protein subunit composition to predict the functional properties of proteins; If the prediction result is consistent with the preset threshold, a monitoring value is generated to record the parameter changes during the production process; Through the multi-dimensional data analysis platform, real-time collected data, structural quantity data and monitoring values ​​are integrated to generate dynamic reports of the production process.

3. The cloud-based intelligent management method for biotechnology product industry supervision according to claim 1, characterized in that: The S2 includes: Deploy a distributed computing architecture in the cloud system to achieve parallel processing by dividing computing tasks to obtain characteristic parameter data such as protein expression level, active ingredient content and molecular structure stability; Using the preset algorithm model, the protein expression level data is normalized to obtain the standardized expression amount; If the normalized expression exceeds the preset threshold, the protein expression level is judged to meet the requirements; According to the protein active ingredient content data, cluster analysis algorithm was used to divide the active ingredients into high-activity group and low-activity group; If the proportion of the high-activity group exceeds the preset proportion, the content of the active ingredient is judged to meet the standard; Based on the molecular structure stability data, a regression analysis algorithm was used to establish a correlation model between stability, temperature and pH value; If the fitting degree of the correlation model is higher than the preset value, it is judged that the stability of the molecular structure is in line with expectations; The standardized expression level, the proportion of high-activity groups, and the stability fit were used as inputs, and a decision tree algorithm was used to generate protein quality assessment results; If the evaluation results are consistent with the preset standards, the protein quality is recorded as qualified; Through a distributed computing architecture, protein quality assessment results are associated with production batch information to generate batch quality reports; If the batch quality report shows abnormalities, the early warning mechanism will be triggered; Adopt data visualization technology to integrate batch quality reports, warning information and production parameters to generate production process monitoring views; If the monitoring view shows fluctuations in key parameters, the parameter adjustment process is initiated.

4. The cloud-based intelligent management method for biotechnology product industry supervision according to claim 1, characterized in that: The S3 includes: For the spatial conformational change data of proteins, the principal component analysis algorithm is used to extract the main modes of conformational change and obtain the conformational change characteristic values; According to the surface hydrophobicity distribution data, a cluster analysis algorithm is used to divide the hydrophobic area into a high hydrophobic area and a low hydrophobic area to determine the hydrophobicity distribution pattern; From the intermolecular interaction data, the association rule mining algorithm is used to extract the high-frequency force combination and obtain the intermolecular force characteristics; Combined with the active ingredient change trend data, the time series analysis algorithm is used to fit the active ingredient dynamic change curve and determine the activity change trend item; According to the dynamic structure feature data, the support vector machine algorithm is used to classify the dynamic structure state and determine the dynamic characteristic type; For the molecular structure stability index, a regression analysis algorithm is used to establish a correlation model between stability and conformational change, hydrophobicity distribution and interaction force, and obtain the stability prediction value; If the deviation between the predicted stability value and the preset threshold exceeds the allowable range, the conformational optimization process is initiated to adjust the molecular structure parameters.

5. The cloud-based intelligent management method for biotechnology product industry supervision according to claim 1, characterized in that: The S4 includes: Set a preset threshold range for the characteristic parameter, obtain input data and determine whether it exceeds the threshold range; If the data exceeds the threshold, the dynamic adjustment mechanism is activated; By quantifying batch differences, we can obtain the difference between batches. Use correlation analysis method to analyze the relationship between process parameters and characteristic parameters; Recalibrate the analysis model based on the difference and correlation analysis results; Through the calibrated model, the adaptive adjustment of the model is achieved; Based on the adjusted model, re-judge whether the input data is within the threshold range.

6. The cloud-based intelligent management method for biotechnology product industry supervision according to claim 1, characterized in that: The S5 includes: Obtain temperature, pH and dissolved oxygen data, combine with protein, subunit group and secondary structure characteristic parameters to analyze the results, and use regression method to determine the impact of environmental factors on proteins, subunit groups and secondary structures; Generate the association moments between environmental factors and proteins, subunit groups and secondary junctions, and determine the association strength between environmental factors and characteristic parameters; According to the association moment, the significant relationship between environmental factors and characteristic parameters is extracted to determine the key influencing factors; Aiming at the key influencing factors, a prediction model of environmental factors and characteristic parameters is established to predict the changing trend of characteristic parameters; Combined with the prediction model, the environmental condition data is adjusted to optimize the temperature, pH value and dissolved oxygen parameters; Based on the optimized environmental condition data, re-obtain the characteristic parameter analysis results to verify the effect of environmental factor adjustment; Update the association matrix, determine the final impact of environmental adjustment on feature parameters, and complete the optimization of environmental conditions.

7. The cloud-based intelligent management method for biotechnology product industry supervision according to claim 1, characterized in that: The S6 includes: Obtain real-time data on protein expression levels and active ingredient content, and use streaming data processing technology for continuous processing; The data stream is segmented and cached, and the three-dimensional folding pattern and binding site residues are analyzed through a preset window mechanism; If the computing resource allocation exceeds the preset threshold, the window size and buffer capacity are adjusted to reduce system latency; According to the results of three-dimensional folding pattern analysis, the correlation between protein expression level and active ingredient content was determined; If the residue site analysis accuracy is lower than the preset value, the number of data stream segments is increased and the window calculation is performed again; Through the optimized computing resource allocation scheme, the data stream is reprocessed to obtain updated protein expression level and active ingredient content data; Determine the deviation between the updated data and the preset target, and determine the final three-dimensional folding pattern and binding site residue analysis results.

8. The cloud-based intelligent management method for biotechnology product industry supervision according to claim 1, characterized in that: The S7 includes: Obtain the load status of computing nodes and dynamically adjust the task allocation strategy; Analyze task priorities and dependencies to determine the order in which tasks should be executed; Optimize task granularity and parallelism to improve the utilization efficiency of computing resources; Process protein spatial conformation change data and extract conformational characteristic parameters; Calculate surface hydrophobicity distribution and generate hydrophobicity characteristic data; Integrate conformational and hydrophobic characteristics to assess molecular structural stability; Monitor the stability of molecular structure in real time and update the analysis results.

9. The cloud-based intelligent management method for biotechnology product industry supervision according to claim 1, characterized in that: Also includes: Adopting the incremental learning method, based on the newly collected characteristic parameter data such as protein subunit composition and secondary structure content, the characteristic map of molecular interaction and dynamic structural characteristics is dynamically updated, and the updated content is synchronized to the real-time monitoring module of the cloud system, including obtaining protein subunit composition and secondary structure content data, and extracting characteristic parameter values; Adopt incremental learning method to analyze the intermolecular interactions and dynamic structural characteristics; Dynamically update feature map content; Synchronize and update content to the cloud system; Establish a real-time monitoring module in the cloud system; The monitoring module obtains the updated characteristic spectrum data; Determine the changing trends of intermolecular interactions and dynamic structural characteristics based on updated data.

10. The cloud-based intelligent management method for biotechnology product industry supervision according to claim 1, characterized in that: Also includes: Based on the updated characteristic maps, the adaptive optimization method is used to dynamically adjust the preset threshold range, retrain the abnormal data identification and batch difference quantification models, and continuously optimize the fluctuation trend prediction through process parameter correlation analysis to adapt to new product characteristics and production process improvements, and maintain the accuracy and stability of the monitoring model, including obtaining dynamic data in the characteristic maps and extracting key parameters for changes in molecular interactions; Adopting adaptive optimization method, dynamically adjust the preset threshold range according to the extracted key parameters; Retrain the abnormal data identification model and batch difference quantification model based on the adjusted threshold range; Through process parameter correlation analysis, the correlation between process parameters and fluctuation trends is obtained; If the fluctuation trend exceeds the preset range, the fluctuation trend prediction model parameters are optimized; Update the input data set of the monitoring model based on new product characteristics and production process improvements; Adopt incremental learning methods to dynamically update the monitoring model and maintain model performance and stability.

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