A stem cell culture information management method and system

Abstract

The present invention relates to the field of biological information technology, and in particular to a method and system for managing stem cell culture information. The method comprises the following steps: collecting basic culture process data during stem cell culture, wherein the basic culture process data includes cell count data, culture condition parameter data, and contamination status data; performing growth dynamics analysis on the basic culture process data to obtain cell growth characteristic data; establishing a cell growth prediction model based on the cell growth characteristic data to generate cell growth warning data; monitoring the culture process in real time based on the cell growth warning data, triggering a warning signal when abnormal fluctuations are detected, and generating a culture abnormality processing plan. The present invention establishes a growth prediction model based on cell growth characteristic data, which can monitor culture dynamics in real time and provide warning signals, significantly improving the detection and response speed of abnormal fluctuations and avoiding culture failure.

Description

Technical Field

[0001] The present invention relates to the field of biological information technology, and in particular to a stem cell culture information management method and system. Background Art

[0002] Stem cells are a type of cell with the ability to self-replicate and multidirectional differentiation potential. They play an important role in the development and tissue repair of organisms. During the culture process, stem cells need to be regularly separated and passaged to maintain the vitality and stability of the cell population. Cell separation usually uses enzymatic methods, by adding enzymes such as trypsin to disperse the cells, and then obtaining a cell pellet through operations such as centrifugation for cell passage. There are three main stem cell culture methods: traditional feeder layer culture method, feeder-free culture method, and direct stem cell culture method.

[0003] However, current methods for managing stem cell culture information often suffer from the following problems: Traditional methods typically detect problems only after stem cell culture failure, lack real-time early warning and dynamic control methods, and abnormal fluctuations cannot be promptly identified and addressed, resulting in unstable stem cell culture success rates, hindering research progress and clinical applications. Optimizing culture conditions is often achieved through trial and error, which is inefficient and lacks specificity. This approach, especially in large-scale culture or industrial production, leads to wasted resources and escalating costs. Summary of the Invention

[0004] Based on this, it is necessary for the present invention to provide a stem cell culture information management method and system to solve at least one of the above technical problems.

[0005] To achieve the above objectives, a stem cell culture information management method comprises the following steps:

[0006] Step S1: collecting basic data of the stem cell culture process, wherein the basic data of the culture process includes cell count data, culture condition parameter data, and contamination status data;

[0007] Step S2: performing growth kinetic analysis on the basic data of the culture process to obtain cell growth characteristic data; establishing a cell growth prediction model based on the cell growth characteristic data to generate cell growth warning data; monitoring the culture process in real time based on the cell growth warning data, triggering a warning signal when abnormal fluctuations are detected, and generating a culture abnormality treatment plan;

[0008] Step S3: Dynamically control the culture condition parameters according to the culture abnormality handling plan, and record the control effect in real time to generate parameter control response data; perform correlation analysis on the parameter control response data and the cell growth characteristic data to establish a culture condition optimization model;

[0009] Step S4: Based on the culture condition optimization model, the basic data of the culture process is mined and analyzed to generate the optimal culture parameter combination; a standardized culture plan is formulated based on the optimal culture parameter combination, and a culture quality assessment is performed to generate a culture quality assessment report.

[0010] By comprehensively collecting cell count data, culture condition parameter data, and contamination status data during the culture process, the present invention ensures the comprehensiveness and accuracy of the data, providing a solid foundation for subsequent analysis. Multi-source data integration enhances information coverage, enabling monitoring of every key factor in the culture process. In particular, automated equipment such as automated cell counters and microscope image acquisition systems reduces manual errors and improves the real-time nature and accuracy of the data. This high-quality data collection facilitates a deeper understanding of the relationship between cell growth dynamics and the culture environment. By analyzing growth dynamics of basic culture process data, core characteristic data of cell growth can be extracted, providing a scientific basis for further development of cell growth prediction models. Establishing a predictive model enables the early prediction of future cell growth trends and potential abnormalities, enabling preventive measures to be taken before problems arise. Real-time monitoring and early warning functions significantly enhance control over the culture process, reducing the risk of cell quality degradation or failure due to abnormal fluctuations. The generated culture anomaly handling plan provides operators with specific guidance and enhances the system's emergency response capabilities. By dynamically adjusting culture condition parameters, the system can optimize the culture environment in real time based on actual conditions, ensuring that cell growth conditions are always optimal. Real-time recording and analysis of regulatory effects not only improves the ability to assess the effectiveness of regulatory measures but also provides reference experience for similar problems in the future. Through correlation analysis, regulatory response data is linked to cell growth characteristic data, further improving the culture condition optimization model. This closed-loop dynamic regulation and optimization mechanism significantly improves the stability and success rate of culture, while also accumulating valuable data resources that facilitate the continuous improvement of the system. Through data mining analysis, the optimal combination of culture parameters is generated, providing a scientific basis for the development of standardized culture protocols. This process ensures the optimal configuration of culture conditions, thereby significantly improving culture efficiency and stem cell quality. The development of standardized protocols makes the culture process more reproducible and reliable, facilitating large-scale application. Furthermore, the generation of culture quality assessment reports further verifies the optimization results, helping researchers or operators to more intuitively understand the success rate and quality control level of the culture, thereby improving the standardization and efficiency of the overall operational process.

[0011] The present invention further provides a stem cell culture information management system for executing the above-mentioned stem cell culture information management method, wherein the stem cell culture information management system comprises:

[0012] A data acquisition module is used to collect basic data of the stem cell culture process, wherein the basic data of the culture process includes cell count data, culture condition parameter data and contamination status data;

[0013] The growth dynamics monitoring module is used to analyze the basic data of the culture process for growth dynamics and obtain cell growth characteristic data; establish a cell growth prediction model based on the cell growth characteristic data and generate cell growth warning data; monitor the culture process in real time based on the cell growth warning data, trigger a warning signal when abnormal fluctuations are detected, and generate a culture abnormality treatment plan;

[0014] The parameter control optimization module is used to dynamically control culture condition parameters according to the culture abnormality handling plan, record the control effect in real time, and generate parameter control response data; the parameter control response data is correlated with the cell growth characteristic data and the culture condition optimization model is established;

[0015] The cultivation plan generation module is used to mine and analyze the basic data of the cultivation process based on the cultivation condition optimization model to generate the optimal cultivation parameter combination; formulate a standardized cultivation plan based on the optimal cultivation parameter combination, conduct cultivation quality assessment, and generate a cultivation quality assessment report.

[0016] The data acquisition module of the present invention systematically collects key data from the stem cell culture process, providing basic data support for subsequent analysis. By acquiring real-time cell count data, culture condition data, and contamination status data, key changes during the culture process can be comprehensively monitored, ensuring the transparency and accuracy of the experimental process. In particular, the collection of contamination status data can promptly identify potential contamination risks, ensuring the effectiveness and reproducibility of the experiment. This facilitates subsequent optimization of the culture plan and reduces culture failures caused by environmental changes or contamination. The growth dynamics monitoring module uses the real-time collected basic data to analyze cell growth dynamics, identifying trends and patterns in cell growth and establishing an accurate growth prediction model. This enables dynamic monitoring during the culture process and predicts potential anomalies in advance. When growth data exhibits abnormal fluctuations, the system quickly triggers an early warning signal and generates a culture anomaly handling plan, enabling timely adjustments to the culture plan to prevent irreversible damage to cell growth caused by problems during the culture process. Real-time monitoring and early warning of the cell growth process can significantly improve culture efficiency, avoid unnecessary waste of resources, and reduce cell quality. The parameter control and optimization module adjusts culture condition parameters in real time based on anomalies detected during the culture process. By dynamically manipulating culture environment factors such as temperature, humidity, and gas composition, timely responses can be made to the different stages of cell growth. This module not only records the effects of these manipulations in real time but also integrates parameter manipulation response data with cell growth characteristic data through correlation analysis to establish a precise culture condition optimization model. This enables data-driven optimization of culture conditions, improving not only culture efficiency but also ensuring consistent cell quality. Ultimately, through manipulation and optimization, the success rate of culture and the predictability of cell growth can be significantly improved. The culture protocol generation module comprehensively analyzes basic culture process data and, in conjunction with the culture condition optimization model, accurately identifies patterns underlying the data and generates the optimal culture parameter combination. By developing standardized culture protocols, various cell culture conditions can be standardized, improving experimental consistency and reproducibility, and ensuring fully optimized cell growth conditions throughout the culture process. Furthermore, culture quality assessment and the generation of culture quality assessment reports help evaluate the overall culture performance, such as cell consistency, morphological homogeneity, and genetic stability, providing reliable quality assurance for subsequent research and applications. This process helps maintain stable cell quality across multiple experiments, enhances the controllability of the culture process, and reduces the risk of experimental failure. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0018] Figure 1 This is a schematic flow chart of the steps of the stem cell culture information management method of the present invention;

[0019] Figure 2 for Figure 1 Detailed step flow diagram of step S1;

[0020] Figure 3 for Figure 1 Detailed step flow chart of step S2 in FIG. DETAILED DESCRIPTION

[0021] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. It is obvious that the embodiments described are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of the present invention.

[0022] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0023] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0024] To achieve this, please refer to Figures 1 to 3 The present invention provides a method for managing stem cell culture information, the method comprising the following steps:

[0025] In the embodiment of the present invention, reference Figure 1 FIG. 1 is a flow chart showing the steps of a stem cell culture information management method according to the present invention. In this embodiment, the stem cell culture information management method includes the following steps:

[0026] Step S1: collecting basic data of the stem cell culture process, wherein the basic data of the culture process includes cell count data, culture condition parameter data, and contamination status data;

[0027] The embodiment of the present invention uses automated equipment to collect basic data of the culture process. The number of live cells and dead cells is recorded every 6 hours by an automatic cell counter, and cell morphological parameters are obtained in combination with microscope imaging to form cell counting data; the culture environment parameters are collected in real time by the built-in sensor of the incubator, including temperature (maintained at 37°C ± 0.1°C), humidity (relative humidity above 95%), CO2 concentration (5% ± 0.1%), and culture medium pH (controlled at 7.2-7.4); the image data of contamination conditions that may occur during the culture process are recorded by the microscope image acquisition system, including bacterial contamination and fungal contamination characteristics. Combined with the culture operation record data (such as the liquid change time is set to 48 hours, the liquid change volume is 80% of the culture medium volume, and the passage ratio is controlled to 1:3), the data is uniformly cleaned and standardized to generate basic data of the culture process including cell counting data, culture condition parameter data, and contamination status data.

[0028] Step S2: performing growth kinetic analysis on the basic data of the culture process to obtain cell growth characteristic data; establishing a cell growth prediction model based on the cell growth characteristic data to generate cell growth warning data; monitoring the culture process in real time based on the cell growth warning data, triggering a warning signal when abnormal fluctuations are detected, and generating a culture abnormality treatment plan;

[0029] The embodiment of the present invention analyzes the collected basic data of the culture process. First, the key data points in the logarithmic growth period are extracted from the cell activity data for 72 consecutive hours. The nonlinear least squares method is used to fit the exponential growth model to obtain the growth fitting curve model, and the model fitting goodness data and cell doubling time are calculated. By scoring and analyzing the dynamic stability of the doubling time, cell growth characteristic data are generated. Based on these characteristic data, a multidimensional cell growth prediction model including growth rate prediction, survival rate prediction and growth trend prediction is constructed. After cross-validation and parameter calibration to optimize the model performance, the confidence interval of the prediction result data is calculated. When the prediction result shows that the growth trend deviates from the preset range, an early warning signal is generated and combined with a historical abnormal event library to automatically formulate a culture abnormality handling plan.

[0030] Step S3: Dynamically control the culture condition parameters according to the culture abnormality handling plan, and record the control effect in real time to generate parameter control response data; perform correlation analysis on the parameter control response data and the cell growth characteristic data to establish a culture condition optimization model;

[0031] The embodiment of the present invention adjusts the culture condition parameters in real time according to the culture abnormality processing scheme. For example, when an abnormal fluctuation in CO2 concentration is detected (deviating from the set value by more than ±0.2%), the gas supply ratio is adjusted immediately; for abnormal culture medium pH value (such as below 7.2), NaHCO3 solution is added to slowly adjust it to the target range, while monitoring the changes in cell growth response; the parameter changes of each control operation are correlated with the corresponding cell growth characteristic data, and a quantitative mapping relationship between culture conditions and cell growth indicators is constructed. The optimization model is subjected to sensitivity analysis and iterative adjustment in multiple experimental verifications, thereby gradually improving the culture condition optimization model.

[0032] Step S4: Based on the culture condition optimization model, the basic data of the culture process is mined and analyzed to generate the optimal culture parameter combination; a standardized culture plan is formulated based on the optimal culture parameter combination, and a culture quality assessment is performed to generate a culture quality assessment report.

[0033] The present invention uses a culture condition optimization model to perform multi-dimensional data mining on the basic data of the historical culture process, screens out the key parameters that have the greatest impact on cell growth (for example, the influence weights of temperature, CO2 concentration, and passage ratio are 0.35, 0.30, and 0.25, respectively), and generates the optimal culture parameter combination based on this. A standardized culture protocol is formulated according to the optimal parameters, including setting the cell seeding density to 1×10 4 pieces / cm 2 The culture medium formula is DMEM+10% FBS, and the medium change cycle is 48 hours. Multiple repeated experiments are carried out under the standardized scheme. By recording key culture quality indicators such as cell growth consistency index (target value ≥0.90) and morphological uniformity score (target value ≥85 points), a culture quality assessment report is finally generated to provide a scientific reference for subsequent large-scale applications.

[0034] By comprehensively collecting cell count data, culture condition parameter data, and contamination status data during the culture process, the present invention ensures the comprehensiveness and accuracy of the data, providing a solid foundation for subsequent analysis. Multi-source data integration enhances information coverage, enabling monitoring of every key factor in the culture process. In particular, automated equipment such as automated cell counters and microscope image acquisition systems reduces manual errors and improves the real-time nature and accuracy of the data. This high-quality data collection facilitates a deeper understanding of the relationship between cell growth dynamics and the culture environment. By analyzing growth dynamics of basic culture process data, core characteristic data of cell growth can be extracted, providing a scientific basis for further development of cell growth prediction models. Establishing a predictive model enables the early prediction of future cell growth trends and potential abnormalities, enabling preventive measures to be taken before problems arise. Real-time monitoring and early warning functions significantly enhance control over the culture process, reducing the risk of cell quality degradation or failure due to abnormal fluctuations. The generated culture anomaly handling plan provides operators with specific guidance and enhances the system's emergency response capabilities. By dynamically adjusting culture condition parameters, the system can optimize the culture environment in real time based on actual conditions, ensuring that cell growth conditions are always optimal. Real-time recording and analysis of regulatory effects not only improves the ability to assess the effectiveness of regulatory measures but also provides reference experience for similar problems in the future. Through correlation analysis, regulatory response data is linked to cell growth characteristic data, further improving the culture condition optimization model. This closed-loop dynamic regulation and optimization mechanism significantly improves the stability and success rate of culture, while also accumulating valuable data resources that facilitate the continuous improvement of the system. Through data mining analysis, the optimal combination of culture parameters is generated, providing a scientific basis for the development of standardized culture protocols. This process ensures the optimal configuration of culture conditions, thereby significantly improving culture efficiency and stem cell quality. The development of standardized protocols makes the culture process more reproducible and reliable, facilitating large-scale application. Furthermore, the generation of culture quality assessment reports further verifies the optimization results, helping researchers or operators to more intuitively understand the success rate and quality control level of the culture, thereby improving the standardization and efficiency of the overall operational process.

[0035] As an embodiment of the present invention, refer to Figure 2 As shown, Figure 1 Detailed step flow diagram of step S1 in the embodiment of the present invention, step S1 includes the following steps:

[0036] Step S11: recording the number of living cells, the number of dead cells, and cell morphology parameters every 6 hours using an automatic cell counter to obtain cell counting data;

[0037] In this embodiment of the present invention, an automated cell counter (such as the Countess II FL automated cell counter) collects data from cultured cell samples every six hours. The procedure involves extracting 100 μL of cell suspension from a culture flask, adding an equal volume of trypan blue dye, and mixing thoroughly. Then, 10 μL of the mixture is placed on a cell counter slide for counting. The number of live and dead cells is recorded, and cell morphological parameters (such as average diameter, aspect ratio, and circularity) are extracted using the system's built-in image recognition algorithm. The output data includes a table of live / dead cell ratios, cell density, and morphological characteristics corresponding to the time point.

[0038] Step S12: collecting real-time data including temperature, humidity, CO2 concentration and culture medium pH value through the incubator sensor to obtain culture environment parameters;

[0039] The present invention utilizes the incubator's built-in environmental monitoring sensors to automatically record environmental parameters every 10 minutes. This includes incubation temperature data recorded by a temperature sensor (Pt100 thermal resistor), relative humidity data recorded by a humidity sensor (capacitive sensor), and CO2 concentration data recorded by a CO2 infrared analyzer. Furthermore, a culture medium pH probe monitors pH in real time (with a target range of 7.2-7.4). All collected data is automatically stored in the incubator's electronic recording system and output as a time-indexed environmental parameter curve file.

[0040] Step S13: Recording contamination status data using a microscope image acquisition system, including bacterial contamination image data and fungal contamination image data;

[0041] The present invention uses an image acquisition system equipped with a high-resolution microscope (such as the Nikon Eclipse Ti2) to regularly record image data of contamination conditions. The procedure involves extracting a small sample of culture medium from a culture flask, preparing a slide sample, and then performing microscopic observation to capture bacterial contamination characteristics (such as bacilli and cocci morphology) and fungal contamination characteristics (such as branching hyphae). The captured images are processed and annotated using contamination analysis software (such as ImageJ), which outputs contamination image files and quantitative assessment data (such as the proportion of contaminated area and the proportion of contamination types).

[0042] Step S14: collecting culture operation record data through an electronic recording system, including the medium change time, medium change volume, and passage ratio; merging the culture environment parameters and culture operation record data into culture condition parameter data;

[0043] In this embodiment, culture operation data, primarily including fluid changes and subcultures, is recorded through a laboratory electronic recording system (e.g., LabWare LIMS). Specifically, the process involves recording the fluid change time (e.g., every 48 hours) and volume (e.g., 80% of the total culture volume) during fluid changes, and the subculture ratio (e.g., 1:3) during subcultures. The system automatically correlates and integrates this data with incubator environmental parameter data to generate culture condition parameter data in a time series format.

[0044] Step S15: cleaning and standardizing the multi-source monitoring data to obtain basic data of the culture process, wherein the multi-source monitoring data includes cell counting data, contamination status data, and culture condition parameter data.

[0045] The embodiment of the present invention uses data processing software (such as a data cleaning script written in Python) to clean the multi-source monitoring data collected above, including removing outliers (such as outliers are defined as values ​​exceeding the mean ± 3 times the standard deviation) and processing missing values ​​(filled by interpolation). The cleaned data are uniformly standardized (such as normalized to the range of [0, 1] or Z-score standardization). The final output of the culture process basic data includes time-series cell count data, culture condition parameter data and contamination status data, providing a data foundation for consistency and reliability for subsequent analysis steps.

[0046] This invention utilizes an automated cell counter to regularly record the number of live and dead cells, as well as their morphological parameters. This not only reduces errors caused by manual operation but also provides high-frequency time-series data. This high-precision, real-time monitoring method helps capture dynamic changes in cell growth, promptly detect growth anomalies, and provide reliable data to support subsequent analysis and model building. This method significantly improves the quality of cell counting data and research efficiency. Real-time acquisition of key environmental parameters by incubator sensors ensures accurate monitoring of culture conditions. These parameters, such as temperature, humidity, CO2 concentration, and pH, directly impact the survival and growth of stem cells. Therefore, accurately capturing and recording these data helps quickly identify deviations in the culture environment and promptly adjust culture conditions to maintain an optimal growth environment for cells. This highly sensitive monitoring provides a reliable guarantee for improving culture efficiency and success rates. The microscope image acquisition system accurately records image data related to bacterial and fungal contamination, providing a key basis for early detection and precise treatment of contamination issues. Compared to traditional quantitative data, image data offers the advantages of being more intuitive and comprehensive, facilitating rapid diagnosis and tracing of contamination sources. This real-time image monitoring method allows contamination incidents to be addressed more quickly, thereby reducing negative impacts on cell culture and improving the reliability and safety of culture. The collection of culture operation record data through the electronic record system ensures the integrity and traceability of operation information. Information such as fluid change time, fluid change volume and subculture ratio are critical control variables in the culture process. When combined with the culture environment parameters, the generated culture condition parameter data can more comprehensively reflect the overall status of the culture conditions. This data integration method provides multi-dimensional data support for subsequent analysis, which helps to optimize culture conditions and achieve standardized operating procedures. Data cleaning and standardization are key steps to ensure the consistency and availability of multi-source data. Through this process, redundancy, errors and missing values ​​in the collection process can be eliminated, and data from different sources and units can be converted into a unified format, thereby significantly improving the accuracy of data analysis and the reliability of model training. The basic data of the culture process finally generated covers key elements such as cell count, contamination status and culture conditions, providing high-quality input data for subsequent modeling, prediction and optimization.

[0047] Preferably, step S15 includes the following steps:

[0048] Step S151: performing outlier detection and elimination on the cell counting data, eliminating data points that deviate from the mean by more than three standard deviations, thereby obtaining cell activity counting data;

[0049] The embodiment of the present invention uses a data cleaning script written in Python to detect and remove outliers in cell counting data. The specific operation is to first calculate the mean and standard deviation of the viable cell count, and mark the data points that deviate from the mean by more than three times the standard deviation as outliers; these outliers are eliminated by setting a threshold range (such as the mean ± 3 times the standard deviation), and replaced with the mean of the adjacent values ​​before and after the time series to maintain data continuity. Taking 48 hours of continuous culture data as an example, after cleaning, the output is an abnormality-free data table containing the number of viable cells at each time point, providing highly reliable cell activity count data for subsequent analysis.

[0050] Step S152: performing image quality assessment on the pollution status data, eliminating unqualified images such as blur and underexposure, thereby obtaining high-confidence pollution feature data;

[0051] This embodiment of the present invention utilizes image processing software (such as ImageJ) to perform image quality assessment on contamination status data. Specifically, the collected microscope images are individually sharpened, image edge sharpness is calculated using the Laplace operator, and a sharpness threshold is set (e.g., images above 20 are considered acceptable). Furthermore, a histogram analysis of the exposure conditions is performed to eliminate images with severely offset grayscale distributions or over- or under-exposure. After screening, high-confidence contamination feature data is output, including clear images of bacterial and fungal contamination and quantitative analysis results, for subsequent contamination feature extraction and evaluation.

[0052] Step S153: performing parameter correction processing on the culture condition parameter data to obtain culture condition parameter correction data, wherein the parameter correction processing specifically uses an interpolation algorithm to process missing culture environment parameters caused by sensor failure, and unifies the time format and operation terminology of the culture operation record data;

[0053] When correcting the culture condition parameter data, the embodiment of the present invention first uses linear interpolation to fill in the missing data caused by sensor failure. For example, if a breakpoint occurs in the CO2 concentration sensor during a certain period of time, interpolation repair is performed based on the values ​​of adjacent time points. Secondly, the culture operation record data is unified in time format, and inconsistent time records (such as "3 PM" and "15:00") are standardized to a 24-hour format, and operation terms (such as "medium change" and "change liquid") are translated for consistency. The corrected data is output in the form of a standardized parameter file for subsequent data integration.

[0054] Step S154: standardize the cell activity count data, high-confidence contamination feature data, and culture condition parameter correction data, and perform time series alignment processing to obtain basic culture process data.

[0055] In this embodiment of the present invention, cell viability count data, high-confidence contamination signature data, and culture condition parameter correction data are standardized. This standardization method involves normalizing numerical data to the range [0, 1] and converting categorical variables to numeric values ​​using one-hot encoding. Subsequently, the data is aligned using timestamps to ensure consistency in cell counts, contamination signatures, and culture condition parameters at the same time point. The final output data file, saved in CSV format, contains multi-dimensional, time-series-aligned data on the culture process, laying the foundation for further growth kinetic analysis.

[0056] The elimination of data points that deviate from the mean by more than three standard deviations is a classic method for eliminating outliers, effectively reducing the impact of instrument error or environmental interference on data. This process results in more accurate and reliable cell viability count data, providing higher-quality baseline data for subsequent analysis. By eliminating outliers, these data are prevented from causing bias in modeling, improving the reliability of cell growth feature analysis and prediction models. Image quality assessment eliminates blurry or underexposed images, ensuring high confidence in contamination status data. High-quality images can more clearly reflect contamination characteristics, facilitating accurate identification and subsequent treatment. This screening mechanism reduces the risk of misdiagnosis and misjudgment, providing a solid foundation for contamination source analysis and solution development, while also improving the overall reliability of culture process monitoring. Interpolation algorithms correct missing culture environment parameters caused by sensor failure, replenishing data integrity and reducing analytical bias caused by missing data. Furthermore, standardizing the time format and terminology of culture operation record data improves data consistency and usability. This correction process ensures the accuracy and compatibility of culture condition parameter data, providing more reliable input data for subsequent model training and optimization. Normalization eliminates dimensional differences between data sources, making cell viability counts, high-confidence contamination signatures, and culture condition parameters comparable, facilitating subsequent model calculations and analysis. Time series alignment further unifies multi-source data to the same time dimension, enhancing temporal consistency. These processing steps ultimately generate basic culture process data with high integrity, consistency, and analytical value, laying a solid foundation for accurate cell growth prediction and culture optimization.

[0057] As an embodiment of the present invention, refer to Figure 3 As shown, Figure 1 Detailed step flow diagram of step S2 in the embodiment of the present invention, step S2 includes the following steps:

[0058] Step S21: extracting the number of live cells and the number of dead cells from the cell activity counting data for 72 consecutive hours, thereby obtaining time-series cell activity data;

[0059] This example uses the Pandas library in Python to load 72 hours of continuous cell count data, which is divided into two columns based on timestamps: the number of live cells and the number of dead cells. This constructs time-series cell viability data. During extraction, the integrity of the time series is ensured, and unevenly spaced data points are filled using linear interpolation. For example, in one experiment, data recorded every six hours were precisely aligned. The resulting time-series cell viability data is expressed as the number of live and dead cells per hour, providing accurate input data for subsequent analysis.

[0060] Step S22: selecting data points of the logarithmic growth period of the time-series cell activity data, and fitting an exponential growth model based on the nonlinear least squares method to obtain a growth fitting curve model;

[0061] The embodiment of the present invention extracts the logarithmic growth period data points identified in the time series cell activity data, usually performs segmented analysis on the growth curve, and selects the stage with significantly increased growth rate as the logarithmic growth period by calculating the logarithmic change rate of the number of living cells. Then, the curve_fit function in the Scipy library is used to fit the selected data points to the exponential growth model based on the nonlinear least squares method. The fitting formula is N(t) = N0·e r·t , where N0 is the initial cell number and r is the growth rate. In one set of experimental data, data points from hours 12 to 36 were extracted. Fitting results showed that the exponential growth model curve can better describe the changes in cell activity.

[0062] Step S23: calculating the slope and determination coefficient of the growth fitting curve model, and performing a model fitting goodness evaluation to obtain model fitting goodness data;

[0063] The embodiment of the present invention uses the fitted growth curve model to calculate the slope of the curve (i.e., the growth rate parameter r) and the determination coefficient R 2 To evaluate the fitting effect. The specific method is to use NumPy to calculate the gradient to obtain the slope, and call the r2_score function in the Scikit-learn library to calculate the determination coefficient. In the experiment, the fitting slope of a cell culture data is 0.025 and the determination coefficient is 0.98, indicating a high goodness of fit. The result data is stored as a file containing the slope and R 2 The structured table provides reliable support for subsequent doubling time calculation.

[0064] Step S24: calculating the cell population doubling time based on the model goodness of fit data, and performing a doubling time stability assessment to generate doubling time dynamic stability data;

[0065] The embodiment of the present invention uses the formula The doubling time of the cell population is calculated, where r is the slope of the fitted curve. The stability of the fluctuation in the doubling time is also assessed. Specifically, the standard deviation of the doubling time is calculated using a sliding window method to quantify the amplitude of the fluctuation. For example, in one experiment, the doubling time was calculated to be 27.7 hours with a standard deviation of 2.3 hours, indicating that the doubling time remained stable during the logarithmic growth phase. Stability data are recorded as a dynamic stability curve of the doubling time to visually display the dynamic changes in the doubling time.

[0066] Step S25: extracting cell growth characteristic parameters from the doubling time dynamic stability data, thereby obtaining cell growth characteristic data;

[0067] This embodiment of the present invention extracts parameters from dynamic stability data of doubling time, including the mean, standard deviation, and extreme range of the doubling time, to generate a cell growth characteristic data table. For example, in one experimental data set, the mean doubling time was 28 hours, the standard deviation was 2 hours, and the maximum and minimum values ​​were 30 hours and 26 hours, respectively. The extracted growth characteristic parameters were stored in JSON format to provide input for a cell growth prediction model.

[0068] Step S26: establishing a cell growth prediction model based on the cell growth characteristic data to generate cell growth warning data;

[0069] This embodiment of the present invention constructs a cell growth prediction model based on cell growth feature data. It uses an LSTM (Long Short-Term Memory) network to model time series data. The input data includes the dynamic characteristics of the number of viable cells and doubling time, and the target output is the predicted value of the number of viable cells in the next 12 hours. During model training, the Adam optimizer and mean squared error (MSE) were selected as the optimization objective and loss function. Through training and validation on multiple sets of experimental data, the average prediction error of the prediction model was controlled within ±5%, ultimately generating cell growth warning data that includes risk factors.

[0070] Step S27: The culture process is monitored in real time based on the cell growth warning data. When an abnormal fluctuation is detected, a warning signal is triggered and a culture abnormality processing plan is generated.

[0071] This embodiment of the present invention utilizes cell growth warning data to monitor the culture process in real time. When the viable cell count deviates from the predicted value by more than ±10%, a warning signal is triggered. Based on the abnormality characteristics, a treatment plan for the culture anomaly is generated. For example, if the anomaly may be due to insufficient CO2 concentration or contamination, the treatment plan may recommend increasing the incubator ventilation frequency or replacing the culture medium. In one experiment, real-time monitoring detected a sharp drop in the viable cell growth rate at the 45th hour. This triggered a contamination warning and generated a treatment plan recommending thorough cleaning of the culture vessel, providing timely support for recovering the experimental results.

[0072] The present invention extracts the time series of the number of living and dead cells through continuous data recording for 72 hours, providing detailed dynamic change trends for analyzing the cell growth status. Long-term continuous monitoring can capture the key processes of cell proliferation and apoptosis, ensure the comprehensiveness and timeliness of the data, and provide a reliable basis for subsequent model fitting and growth feature extraction. Selecting data points in the logarithmic growth period can focus on the key stage of rapid cell growth, thereby more accurately reflecting the reproductive capacity of the cell population. Using nonlinear least squares fitting can effectively deal with data noise and generate an exponential growth model that conforms to the actual growth dynamics, providing accurate numerical expression for further analysis of cell growth trends. Calculating the slope of the fitting curve can quantify the cell growth rate, and the coefficient of determination can evaluate the degree of interpretation of the data by the fitting model. Through goodness of fit evaluation, the accuracy and reliability of the model can be verified, thereby ensuring that the generated growth model can truly reflect the cell growth characteristics, providing strong theoretical support for subsequent prediction and early warning. Doubling time is an important indicator for evaluating the proliferation capacity of a cell population, and its stability can reflect whether the culture conditions are uniform and appropriate. By calculating and evaluating the dynamic stability of doubling time, potential abnormal fluctuations during the culture process can be promptly identified, providing a scientific basis for optimizing culture conditions while ensuring efficient and consistent cell culture. Extracting characteristic cell growth parameters, such as growth rate and doubling time fluctuation amplitude, comprehensively characterizes cell growth behavior. The resulting cell growth characteristic data serves as the core input for subsequent predictive model development, enhancing the targeted and practical nature of dynamic monitoring of the culture process. Constructing a predictive model based on cell growth characteristic data enables quantitative prediction of cell growth trends. The generated early warning data can proactively identify potential anomalies and prevent cell culture failures. This model enhances the intelligence of the culture process, reduces the need for manual intervention, and significantly improves culture efficiency and success rate. By monitoring cell growth in real time and leveraging early warning data to rapidly detect and respond to abnormal fluctuations, culture accidents can be effectively avoided. Triggering early warning signals and generating anomaly resolution plans allows operators to quickly implement targeted measures to restore optimal culture conditions. This mechanism enhances the stability and safety of the culture system and reduces the risk of cell contamination and proliferation failure.

[0073] Preferably, step S24 includes the following steps:

[0074] Step S241: Calculating the cell population doubling time based on the model fit goodness data, thereby generating doubling time trend data;

[0075] The embodiment of the present invention uses the doubling time calculation formula Calculate the doubling time of a cell population, where r is the fitted growth rate. Organize the doubling times at different time points into trend data and generate a line chart using the Matplotlib library to display the changing trend. For example, in one experiment, the doubling times of cells cultured from passage 1 to passage 5 were 28.2 hours, 27.5 hours, 28.0 hours, 27.8 hours, and 27.6 hours, respectively. The generated doubling time trend data showed that the cell doubling time ranged within a stable ±0.5 hour range.

[0076] Step S242: comparing and analyzing the doubling times of different passage numbers based on the doubling time trend data, establishing a doubling time stability scoring system to evaluate the stability of the doubling time, thereby obtaining doubling time stability scoring data, wherein the doubling time stability scoring system is specifically as follows: when the coefficient of variation of the doubling time of the cells for three consecutive passages is less than 10%, the score is given a first level; when the coefficient of variation of the doubling time of the cells for three consecutive passages is between 10% and 20%, the score is given a second level; when the coefficient of variation of the doubling time of the cells for three consecutive passages is greater than 20%, the score is given a third level;

[0077] The present invention calculates the coefficient of variation (CV) between different passage times based on the doubling time trend data. The formula is: Where σ is the standard deviation of the doubling time, and μ is the mean. Doubling time stability is graded according to a scoring system, and the scoring data is stored as a structured table. For example, in one experiment, the coefficient of variation of cells was 7.8%, 11.3%, and 22.5%, respectively, corresponding to first, second, and third grade scores, providing data support for culture stability analysis.

[0078] Step S243: When the doubling time stability score data is the first level, the current culture conditions are recorded as high-quality culture condition data;

[0079] In this embodiment of the present invention, when the doubling time stability score is rated as level 1, key parameters of the current culture environment, including temperature, humidity, CO2 concentration, and culture medium composition, are recorded in a database to generate high-quality culture condition data. For example, in one experiment, when the score was rated as level 1, the recorded high-quality culture condition data included 37°C, 90% humidity, 5% CO2 concentration, and a culture medium formulation containing 10% fetal bovine serum.

[0080] Step S244: When the doubling time stability score data is the second level, the monitoring frequency is increased, and the trend of changes in the doubling time stability is analyzed. When the doubling time stability approaches the third level, the concentration change of the main growth factor is analyzed, and culture condition optimization data is generated. When the doubling time stability approaches the first level, the current culture conditions are recorded as high-quality culture condition data. When the doubling time stability remains unchanged, non-main growth factor regulation processing is performed, and the key regulation parameters are recorded as non-main growth factor adjustment data.

[0081] In the embodiment of the present invention, when the doubling time stability score is the second level, the system increases the monitoring frequency to once an hour and analyzes the stability change trend at the same time. If the doubling time tends to the third level, the concentration change of the main growth factor in the culture medium is detected by ELISA to generate optimization suggestion data; if the doubling time tends to the first level, the current culture conditions are recorded as high-quality culture conditions data; if the doubling time remains unchanged, the concentration of non-main growth factors (such as amino acids or trace elements) is adjusted, and the adjustment parameters are recorded as non-main growth factor adjustment data. For example, in a certain experiment, after the concentration of the main growth factor was adjusted from 50ng / mL to 75ng / mL, the doubling time tended to be stable.

[0082] Step S245: When the doubling time stability score data is the third level, extract the temperature and CO2 concentration fluctuation records of the culture environment, analyze the concentration changes of the main growth factors in the culture medium, and generate culture condition optimization data;

[0083] In this embodiment of the present invention, when the doubling time stability score is rated at level three, data on abnormal fluctuations in the culture environment are extracted, such as temperature fluctuations ranging from 36.5°C to 37.8°C and CO2 concentration fluctuations ranging from 4.7% to 5.2%. Subsequently, HPLC is used to detect changes in the concentrations of key growth factors in the culture medium to generate culture condition optimization data. For example, in one experiment, increasing the VEGF concentration from 20 ng / mL to 40 ng / mL increased the stability score to level two.

[0084] Step S246: Merge the high-quality culture condition data, the non-essential growth factor adjustment data, and the culture condition optimization data into the doubling time dynamic stability data.

[0085] The present invention integrates high-quality culture condition data, non-essential growth factor adjustment data, and culture condition optimization data into dynamic stability data for doubling time and stores it in a database file for subsequent optimization and analysis. For example, in one experiment, the final integrated dynamic stability data included the optimal culture temperature of 37°C, CO2 concentration of 5%, and the concentration of the primary growth factor of 50 ng / mL. It also recorded the situation where the stability score remained unchanged after adjusting the non-essential growth factor, providing a reliable parameter basis for subsequent experiments.

[0086] The present invention calculates cell doubling time from model goodness-of-fit data, efficiently and accurately quantifying cell growth cycle characteristics and converting them into easily analyzable doubling time trend data. The generation of trend data provides a comprehensive view of cell growth stability over long timescales, laying the foundation for further analysis and optimization of culture conditions. By comparing doubling times across different passages, it is possible to identify trends in cell stability during passage. The doubling time stability scoring system, which is graded based on the coefficient of variation, makes the assessment of cell culture conditions more intuitive and standardized. This scoring system provides a clear stability reference, helping to promptly identify potential issues during cell proliferation and ensure the controllability and consistency of the culture process. A first-level doubling time stability score indicates optimal culture conditions. Recording the current culture conditions as high-quality condition data not only provides valuable insights into successful culture processes but also provides a reliable reference for subsequent experiments and large-scale cell culture, reducing the trial-and-error cost of optimization. By increasing the monitoring frequency, doubling time trends can be captured promptly. When doubling time stability approaches the third level, analysis of changes in key growth factor concentrations is performed and culture condition optimization data is generated, allowing key factors to be quickly identified and adjusted to prevent further deterioration of cell status. When the doubling time stability approaches the first level, the current culture conditions are recorded as high-quality culture condition data and the effectiveness of optimization measures is summarized. If the doubling time stability remains unchanged, the culture environment is fine-tuned by manipulating non-critical growth factors to ensure that the cell culture remains within an acceptable range. Key control parameters are generated and experience with adjusting non-critical growth factors is accumulated. This process ensures flexibility and continuity in the dynamic optimization of culture conditions, improving overall cell culture stability. When the cell doubling time stability score drops to the third level, it indicates significant issues with the culture conditions. By extracting temperature and CO2 concentration fluctuation records, potential sources of environmental interference can be quickly identified. Combined with analysis of changes in key growth factor concentrations, a comprehensive diagnosis is made and optimization data generated, allowing for rapid adjustments to culture conditions. This measure effectively mitigates the risk of culture failure and improves the scientific and efficient nature of problem resolution. Integrating culture condition data from various sources to generate dynamic doubling time stability data provides a comprehensive and systematic overview of the adjustment history and optimization results of the culture process. This data not only serves as a basis for decision-making in the cell culture process but also can be used for subsequent model training and experience accumulation, significantly improving the level of intelligent cell culture management.

[0087] Preferably, step S25 includes the following steps:

[0088] Step S251: Calculate the cell viability at each time point based on the doubling time dynamic stability data, thereby drawing a survival rate change curve data;

[0089] The present invention uses the cell viability calculation formula based on the dynamic stability data of the doubling time Calculate cell viability at each time point and plot the data as a curve. For example, in one experiment, the number of viable cells recorded was 5 million, 5.2 million, and 5.3 million, respectively, with a total cell count of 5.5 million. The calculated viability rates were 90.9%, 94.5%, and 96.3%, respectively. Use Matplotlib in Python to plot the survival rate curve and analyze its dynamic changes over time.

[0090] Step S252: extracting key growth kinetic parameters based on the survival rate change curve data and the doubling time trend data, thereby obtaining growth kinetic parameter data, wherein the key growth kinetic parameters include growth rate, exponential growth coefficient, and maximum growth rate;

[0091] The present invention uses curve fitting to extract key growth kinetic parameters based on the survival rate change curve data and doubling time trend data. r·t , where r is the growth rate. The exponential growth coefficient and maximum growth rate are obtained by combining nonlinear least squares fitting. In one experiment, the fitted growth rate value was 0.03, the exponential growth coefficient was 2.1, and the maximum growth rate was 0.05. The results were stored as structured growth kinetic parameter data.

[0092] Step S253: segmenting the survival rate change curve data, dividing the culture period into an adaptation period, a logarithmic growth period, and a plateau period, thereby obtaining segmented survival curve data;

[0093] The present invention segments the survival rate curve data into segments, defining the acclimation period as the stage where the survival rate falls below 80%, the logarithmic growth period as the stage where the survival rate rapidly increases and the growth slope is the highest, and the plateau period as the stage where the survival rate stabilizes. For example, in one experiment, the acclimation period was 0-12 hours, the logarithmic growth period was 12-48 hours, and the plateau period was after 48 hours. Each segment of data was extracted, labeled, and stored as segmented survival curve data.

[0094] Step S254: Calculating the deviation between the mean survival rate of each period and the baseline curve based on the survival curve segmentation data and the preset survival rate baseline curve, thereby obtaining survival rate deviation value data;

[0095] This embodiment of the present invention compares the segmented survival curve data with a preset survival rate baseline curve to calculate the mean survival rate for each stage and the deviation from the baseline curve using the formula: Deviation = Actual Mean - Baseline Mean. For example, in an experiment, the actual survival rate mean is 85%, the baseline mean is 90%, and the deviation is -5%. The result is stored as survival rate deviation data.

[0096] Step S255: identifying abnormal fluctuations in survival rate based on the survival rate deviation value data according to a preset deviation threshold, thereby obtaining abnormal survival rate data;

[0097] This embodiment of the present invention identifies abnormal fluctuations in survival rate deviation data based on a preset deviation threshold (e.g., ±5%) and marks periods exceeding the threshold. For example, in an experiment, a deviation value of -7% was found during a certain period, exceeding the threshold. This was recorded as abnormal survival rate data, and the abnormal period was marked as 36-48 hours.

[0098] Step S256: extracting the culture operations within 24 hours before the abnormal period of the survival rate abnormality data, and performing pattern matching on the culture operations with a preset historical abnormal event library to determine the cause of the abnormal survival rate, thereby obtaining deviation traceability data;

[0099] This embodiment of the present invention extracts culture operation records for the 24 hours prior to the abnormal period corresponding to the abnormal survival rate data, including medium change times and medium composition adjustment records. A natural language processing algorithm is used to perform pattern matching with a historical abnormal event library to identify the causes of the abnormality. For example, in one experiment, the abnormality traceability results showed that there was a record of not changing the culture medium according to standard procedures 24 hours prior to the abnormal period. The deviation traceability data was stored as "untimely culture medium replacement."

[0100] Step S257: Merge the growth kinetic parameter data and the deviation tracing data into cell growth characteristic data.

[0101] This embodiment of the present invention integrates the extracted growth kinetic parameter data with the deviation traceability data to form complete cell growth characteristic data. For example, in one experiment, combining a growth rate of 0.03 and a maximum growth rate of 0.05 with the deviation traceability result of "untimely culture medium replacement" generates standardized cell growth characteristic data for subsequent analysis and prediction modeling.

[0102] By combining doubling time dynamic stability data with cell viability, the present invention can generate a survival rate curve reflecting cell growth. This curve helps provide information on cell viability at each time point during cell growth, facilitating tracking and assessment of cell growth status, and providing fundamental data support for subsequent growth kinetics analysis and anomaly detection. This step enhances the ability to monitor growth stability and health during cell culture in real time. By combining survival rate curve data with doubling time trend data, key growth kinetic parameters of cell growth, such as growth rate, exponential growth coefficient, and maximum growth rate, can be extracted. These parameters provide a deeper understanding of the fundamental characteristics of cell growth and, in turn, provide a precise basis for optimizing culture conditions. Key growth kinetic parameters facilitate the development of cell growth models, improving the predictability and accuracy of the culture process. Segmenting the survival rate curve into the adaptation phase, logarithmic growth phase, and plateau phase allows for detailed analysis of the different stages of cell growth. Each stage represents a distinct growth characteristic of the cell, and segmentation enables more precise analysis. For example, during the logarithmic growth phase, cells grow fastest, while the plateau phase may indicate that cells have reached their maximum growth capacity. Segmented data provides a clear reference for subsequent growth dynamics analysis and identification of abnormal fluctuations, ensuring that each critical stage of the culture process is properly monitored and optimized. By comparing the survival rate curve with a preset baseline survival rate curve, the deviation value of the survival rate at each stage can be calculated. The deviation value reflects whether there are any abnormal fluctuations during the culture process that deviate from expectations. If the survival rate deviates significantly from the baseline curve, it may indicate a problem with the culture environment or operation. This step helps detect potential abnormalities in the culture process and provides strong data support for subsequent identification and correction of abnormal fluctuations. By setting a deviation threshold to identify abnormal fluctuations in the survival rate deviation data, abnormal fluctuations in the survival rate can be detected promptly. This method can effectively and automatically identify abnormal fluctuations and provide early warnings, ensuring that potential problems in the cell culture process are discovered and addressed promptly. This step is important for improving culture stability and reducing failure rates, especially in large-scale cell culture or critical experiments. By reviewing the culture operations 24 hours prior to the abnormal survival rate and combining them with a historical abnormal event database for pattern matching, the specific cause of the abnormal survival rate can be accurately identified. This process ensures timely tracing of issues, avoids guesswork, and provides a scientific basis for adjusting culture conditions. This historical data-based analysis helps identify potential patterns and systemic issues, thereby optimizing future cell culture processes. Growth kinetic parameter data is integrated with deviation traceability data to ultimately generate cell growth signature data. This data includes key cell growth parameters and the specific causes of abnormal fluctuations, providing a comprehensive record and analysis of the cell growth process.By integrating this data, a comprehensive assessment of the health of cell cultures can be conducted, potential risks can be identified, culture protocols can be improved, and support can be provided for further automated culture management. These cell growth characteristic data provide a reliable basis for culture process optimization, quality control, and management.

[0103] Preferably, step S26 includes the following steps:

[0104] Step S261: constructing a multidimensional cell growth prediction model based on the cell growth characteristic data, wherein the multidimensional cell growth prediction model includes a cell growth rate prediction model, a survival rate prediction model, and a growth trend prediction model;

[0105] The embodiment of the present invention uses the Scikit-learn framework in Python to construct a multi-dimensional cell growth prediction model based on cell growth characteristic data, including a cell growth rate prediction model, a survival rate prediction model, and a growth trend prediction model. In the cell growth rate prediction model, a random forest regression algorithm is used to perform regression analysis on key growth dynamics parameters (such as growth rate and exponential growth coefficient); the survival rate prediction model uses a support vector machine classification algorithm to predict future survival rate intervals; and the growth trend prediction model uses a long short-term memory (LSTM) neural network to predict the survival rate and growth trend curve of future time periods. In a certain experiment, characteristic data of the last three passages (such as a growth rate of 0.03, an exponential growth coefficient of 2.1, and a survival rate of 95%) were selected for model training, and the obtained model accuracy was preliminarily evaluated to be 85%.

[0106] Step S262: calibrating and cross-validating the parameters of the multidimensional cell growth prediction model, and determining the prediction accuracy of the model by calculating the prediction confidence interval, thereby obtaining model prediction accuracy data; optimizing the multidimensional cell growth prediction model based on the model prediction accuracy data, and performing multidimensional prediction, thereby obtaining prediction result data;

[0107] The embodiment of the present invention calibrates the parameters of the constructed multi-dimensional cell growth prediction model and uses a grid search algorithm to adjust key hyperparameters. For example, the number of trees in the random forest is set to 100 and the maximum depth is set to 10. The prediction error (such as the mean square error is 0.02) and the confidence interval (95% confidence interval is ±0.01) are then calculated through five-fold cross-validation to evaluate the prediction accuracy of the model. For example, in a certain experiment, the accuracy of the survival rate prediction model reached 90%, but the confidence interval of the growth trend prediction model was wide. The learning rate of the LSTM model was further adjusted to 0.001 and the number of training rounds was increased. Finally, the overall prediction accuracy of the model was improved to 92%. The optimized model was used to generate cell growth trend prediction result data for the next 48 hours.

[0108] Step S263: setting thresholds for key cell growth indicators, including a minimum acceptable survival rate, a critical growth rate, and a maximum growth rate;

[0109] This embodiment of the present invention sets thresholds for key cell growth indicators based on cell culture requirements, including a minimum acceptable survival rate of 80%, a critical growth rate of 0.02 / h, and a maximum growth rate of 0.05 / h. In certain experimental scenarios, a survival rate below 80% may indicate a contaminated culture environment, a growth rate below 0.02 / h may indicate decreased cell activity, and a maximum growth rate exceeding 0.05 / h may indicate the presence of unnatural external interference. These thresholds are stored in the system configuration file as a benchmark for subsequent comparisons.

[0110] Step S264: The prediction result data is compared with the threshold value of the key cell growth indicator in real time. When the prediction result exceeds the preset threshold range, a cell growth abnormality warning signal is generated, thereby obtaining cell growth warning data.

[0111] This embodiment of the present invention compares the generated prediction data with the set thresholds for key cell growth indicators in real time. For example, in one experiment, the prediction indicated that the survival rate could drop to 78% over the next 24 hours, with a growth rate as low as 0.015 / hour, triggering a warning signal indicating abnormal cell growth. Based on the warning signal, the system records the abnormality information and generates a culture abnormality treatment plan, such as recommending immediate replacement of the culture medium and adjustment of the temperature and CO2 concentration of the culture environment. The warning data is also stored in a log file for subsequent analysis.

[0112] This invention constructs a multidimensional cell growth prediction model that can predict and analyze the cell growth process from multiple perspectives. The cell growth rate prediction model can estimate the rate of cell division and proliferation, the survival rate prediction model can reflect the health status of cells, and the growth trend prediction model can help infer the growth trend of cells over a period of time. This multidimensional prediction model provides comprehensive dynamic monitoring and prediction capabilities for cell culture, helping researchers to gain a deeper understanding of the cell growth process and make optimization adjustments. Parameter calibration and cross-validation help improve the accuracy and reliability of the prediction model. By optimizing the model and verifying its accuracy, it can ensure that the model can provide high-quality prediction results in practical applications. Calculating the prediction confidence interval helps measure the uncertainty of the prediction results and ensure that the error range is considered when making decisions. This process enhances the model's predictive ability, ensuring stable operation under different culture conditions and environments, further improving the controllability and prediction accuracy of cell growth. Setting thresholds for key cell growth indicators provides clear boundaries for judgment criteria during the cell culture process. These thresholds, including the minimum acceptable survival rate, critical growth rate, and maximum growth rate, can help quickly identify abnormal conditions during the culture process. For example, if the survival rate is lower than the minimum acceptable survival rate, or the growth rate exceeds the critical value, it may mean that there is a problem with the culture environment or operation. By setting these key indicators, more precise control can be provided for the cell culture process to ensure that cells grow within a reasonable range. By real-time monitoring and comparing the predicted results with the key indicator thresholds, an early warning signal can be issued as soon as abnormal cell growth occurs. This real-time early warning mechanism can effectively reduce unexpected situations in the culture process, such as cell decline, contamination or other abnormal phenomena, thereby avoiding experimental failures or production problems. This mechanism provides an intelligent means for the refined management of cell culture, which can adjust the culture conditions in a timely manner to ensure the stability and consistency of the culture results.

[0113] Preferably, step S3 includes the following steps:

[0114] Step S31: Accurately control the culture condition parameter data according to the culture abnormality processing plan, and continuously track and record the cell growth response of each control operation to generate parameter control response data;

[0115] The embodiment of the present invention accurately regulates the culture condition parameter data according to the culture abnormality handling scheme. For example, during the culture condition optimization process, when it is detected that the cell survival rate is lower than the set threshold, the CO2 concentration in the incubator is adjusted (from 5% to 6%), the temperature (from 37°C to 36.5°C) and the pH value of the culture medium (adjusted to 7.4). After each regulation, an automatic cell counter is used to continuously track and record cell growth, including cell activity, mortality rate, and morphological changes, to generate parameter regulation response data. After each operation, the recorded cell growth response data will include changes in cell activity before and after regulation (for example, the number of live cells increased by 20% after regulation), and these data will serve as the basis for subsequent analysis.

[0116] Step S32: performing correlation analysis on the parameter control response data and the cell growth characteristic data, and constructing a quantitative mapping relationship between the culture conditions and the cell growth indicators, thereby establishing an initial culture condition optimization model;

[0117] The embodiment of the present invention is based on the correlation analysis of each parameter regulation response data and cell growth characteristic data. The multivariate linear regression analysis method is used to associate culture conditions (such as temperature, humidity, CO2 concentration, etc.) with cell growth indicators (such as cell proliferation rate, survival rate, doubling time, etc.). Through analysis, it is found that the culture temperature has a positive effect on cell survival rate, and the CO2 concentration has a negative effect on cell doubling time. For example, it was found through analysis that when the CO2 concentration increased from 5% to 6%, the cell survival rate increased significantly by 5%, while the doubling time was shortened by about 10%. Based on this result, an initial culture condition optimization model was constructed, which can predict cell growth performance based on changes in culture conditions.

[0118] Step S33: cross-validate the initial culture condition optimization model and perform parameter sensitivity analysis to evaluate the prediction accuracy and stability of the model, and iteratively optimize the model parameters to obtain the culture condition optimization model.

[0119] The embodiment of the present invention performs cross-validation and parameter sensitivity analysis on the initial culture condition optimization model. First, the initial model is verified using different data sets, and the stability and prediction accuracy of the model are evaluated using 10-fold cross-validation. During the verification process, the prediction error of the model for different cell populations (such as the mean square error MSE of 0.03) is recorded, showing that the model has good adaptability. Subsequently, a sensitivity analysis is performed on the key parameters of the model to examine the effects of factors such as culture temperature and CO2 concentration on cell growth prediction, and it is found that temperature has a high sensitivity to the model prediction accuracy. Based on these analysis results, the model parameters are adjusted, and the algorithm structure and parameter weights of the model are optimized. Finally, the prediction accuracy of the optimized model is increased to 95%, and a stable culture condition optimization model is obtained.

[0120] The present invention can capture the cell's response to each adjustment in real time by precisely controlling the culture conditions and continuously tracking and recording the cell growth response of each operation. This continuous tracking provides researchers with key data for a deeper understanding of the relationship between cell growth and environmental conditions. The effect of each regulation can be directly fed back into the model optimization to ensure that the adjustment process can gradually improve the culture conditions. This data-based management helps to improve the controllability and optimizability of the culture process and provide a detailed basis for subsequent optimization of culture conditions. Correlating parameter control response data with cell growth characteristic data can reveal the quantitative relationship between culture conditions (such as temperature, pH value, nutrients, etc.) and cell growth indicators (such as cell proliferation rate, survival rate, etc.). The construction of this quantitative mapping relationship provides a more accurate adjustment tool for the cell culture process, which can achieve a transition from empirical regulation to data-based fine regulation. After establishing the initial culture condition optimization model, researchers can quickly adjust the culture parameters based on actual growth feedback to optimize the cell growth process. Cross-validation and parameter sensitivity analysis help to evaluate the prediction accuracy and stability of the initial model. Through cross-validation, the generalization ability of the model can be verified to ensure that cell growth responses can be reliably predicted under different experimental conditions. Parameter sensitivity analysis helps identify the culture parameters that most influence model results, enabling more effective adjustments. Through these evaluation and optimization steps, the initial model is continuously refined, gradually improving predictive accuracy and providing more reliable optimization recommendations for cell culture. The resulting culture condition optimization model can accurately guide the cell culture process and achieve efficient and stable culture results.

[0121] Preferably, step S4 includes the following steps:

[0122] Step S41: performing multi-dimensional data mining on the basic data of the culture process based on the culture condition optimization model to obtain correlation result data, wherein the multi-dimensional data mining specifically includes performing correlation analysis and pattern recognition on the cell count data, culture condition parameter data, and growth kinetic characteristic data;

[0123] The embodiment of the present invention performs multi-dimensional data mining on the basic data of the culture process based on the culture condition optimization model. For example, by combining cell counting data, culture condition parameter data and growth dynamics characteristic data, data mining methods (such as principal component analysis PCA and cluster analysis) are used to perform correlation analysis and pattern recognition on these data. In a specific application scenario, by analyzing the relationship between cell counting data and CO2 concentration and temperature changes, it is found that the increase in temperature is positively correlated with the cell growth rate, while the increase in CO2 concentration may inhibit cell proliferation. Through cluster analysis, the typical patterns of cell growth under different conditions are identified. For example, under high temperature and high CO2 concentration, the cell proliferation rate shows a pattern of obvious decline. This discovery provides a basis for the next step of optimizing culture conditions.

[0124] Step S42: performing feature weight evaluation on the correlation result data to screen the key parameters that have the most significant impact on cell growth, thereby generating the optimal culture parameter combination;

[0125] The embodiment of the present invention performs feature weight evaluation on the associated result data. Regression analysis and feature selection algorithms (such as LASSO regression) are used to evaluate the weight of each feature parameter based on the relationship between cell growth indicators (such as cell proliferation rate, survival rate) and different culture conditions (such as temperature, humidity, pH value, etc.). For example, the analysis results show that temperature and culture medium pH value have the most significant effects on cell proliferation rate, with temperature contributing 40% to the cell proliferation rate and pH value 30%. The rest, such as CO2 concentration, contributes less to the proliferation rate. Based on these weight evaluation results, the most influential parameters, such as temperature, pH value and culture medium composition, are screened out to generate the optimal culture parameter combination.

[0126] Step S43: formulating a standardized culture protocol based on the optimal culture parameter combination, wherein the standardized culture protocol includes specific parameters of cell seeding density, culture medium formulation, medium change cycle, passage ratio, and culture environment parameters;

[0127] The embodiment of the present invention formulates a standardized culture program based on the optimal culture parameter combination. For example, under the conditions of determining the temperature to be 37°C, the pH value to be 7.4, and the CO2 concentration to be 5%, a suitable cell seeding density (such as 5×10 4 cells / mL), a culture medium formula (e.g., DMEM supplemented with 10% fetal bovine serum), a medium exchange cycle of every 48 hours, and a subculture ratio of 1:3. Furthermore, specific culture environment parameters were established based on experimental data, such as maintaining a culture temperature of 37°C ± 0.5°C, humidity above 95%, and a CO2 concentration of 5%. This standardized protocol provides a unified operational guide for subsequent cell culture experiments.

[0128] Step S44: performing multiple repeated experiments to verify the standardized culture scheme, and recording the cell growth indicators of each experiment to obtain experimental result data;

[0129] The embodiments of the present invention conduct repeated experiments to verify the standardized culture scheme. Multiple cell lines are selected for culture, and experiments are carried out according to the standardized culture scheme. After each experiment, an automatic cell counter and a microscope are used to evaluate the proliferation of the cells, and growth indicators such as cell proliferation rate, morphological changes, and survival rate are recorded. For example, after 5 experiments, the average cell proliferation rate was 30% / day, and the survival rate was stable at more than 90%. By comparing the results of different experiments, it is ensured that the culture scheme can stably produce consistent cell growth performance in multiple experiments, and the experimental results data are recorded, including the cell proliferation curve, cell morphological distribution, and survival rate data of each experiment.

[0130] Step S45: Calculate key indicators of culture quality based on the experimental result data to generate a culture quality assessment report, where the key indicators of culture quality include cell growth consistency index, morphological uniformity score, gene stability rating, and overall culture success rate.

[0131] The embodiment of the present invention calculates the key indicators of culture quality based on the experimental result data. In combination with the experimental data, the formula is used to calculate the cell growth consistency index (such as the ratio of the standard deviation of the cell proliferation rate in each experiment to the mean), and the value is 0.05, indicating that the consistency of cell growth between different experiments is high. At the same time, the morphological uniformity score (such as the uniformity of cell morphology, the standard deviation of the morphological distribution is calculated using image analysis software, and the score is 95 points) and the gene stability rating (the consistency of gene expression after each culture is analyzed by gene sequencing, and a rating of A is given) are evaluated. Finally, the overall culture success rate is calculated based on the success rate of the experiment (such as 4 successes in 5 experiments, that is, a success rate of 80%). These key indicators will be summarized to form a culture quality assessment report to guide subsequent culture optimization.

[0132] Through multi-dimensional data mining, the present invention can deeply explore the potential correlations between various data types in the culture process, revealing the mutual influence between cell counts, culture conditions, and growth kinetics. This step, by performing correlation analysis and pattern recognition on this data, helps identify key factors affecting cell growth and potential optimization opportunities. Data mining not only helps us understand the effectiveness of existing culture conditions but also provides data support for future improvements to culture protocols, thereby improving the predictability and controllability of culture efficiency and cell quality. By evaluating the feature weights of the correlation result data, key parameters affecting cell growth can be effectively screened. This process helps identify factors that have a significant impact on cell growth, allowing these key parameters to be prioritized and optimized. By optimizing these parameters, cell growth effects can be maximized, resource waste can be reduced, and the efficiency of the culture process can be ensured. The resulting optimal culture parameter combination provides a theoretical basis for developing standardized culture protocols, making the culture process more precise and controllable, and helping to improve the success rate of cell culture. Standardized culture protocols developed based on the optimal culture parameter combination can unify and standardize every aspect of cell culture. Through standardization, human-induced differences can be eliminated, ensuring comparability and consistency between different experiments. Standardized culture protocols ensure consistent culture conditions, promote healthy cell growth, and lay the foundation for scalability and automation of cell culture. Furthermore, precise parameters such as medium formulation, passage ratio, and seeding density optimize the cell growth environment, improving cell proliferation rate and quality. Validating standardized culture protocols through repeated experiments ensures their stability and feasibility under diverse conditions. Recording cell growth metrics from each experiment provides reliable data for further analysis, ensuring that the standardized protocol achieves the expected results across a wide range of practical applications. This validation process can detect potential deviations or issues, allowing for timely protocol optimization. Accumulating experimental data further enhances the accuracy and reliability of culture protocols, ensuring that culture conditions are widely adaptable in practical applications. By calculating key culture quality indicators such as the cell growth consistency index, morphological uniformity score, and genetic stability rating, a comprehensive assessment of the success of the culture process can be achieved. These indicators reflect the impact of culture conditions on cell quality and help identify issues or areas for improvement. The resulting culture quality assessment report not only provides a basis for process control but also provides critical feedback for subsequent research and applications, ensuring the stability and reproducibility of cell culture quality. Through these assessments, researchers can optimize culture protocols in a targeted manner to improve the biological characteristics, morphological consistency, and genetic stability of cells, thereby providing higher quality cell populations for cell applications.

[0133] The present invention further provides a stem cell culture information management system for executing the above-mentioned stem cell culture information management method, wherein the stem cell culture information management system comprises:

[0134] A data acquisition module is used to collect basic data of the stem cell culture process, wherein the basic data of the culture process includes cell count data, culture condition parameter data and contamination status data;

[0135] The growth dynamics monitoring module is used to analyze the basic data of the culture process for growth dynamics and obtain cell growth characteristic data; establish a cell growth prediction model based on the cell growth characteristic data and generate cell growth warning data; monitor the culture process in real time based on the cell growth warning data, trigger a warning signal when abnormal fluctuations are detected, and generate a culture abnormality treatment plan;

[0136] The parameter control optimization module is used to dynamically control culture condition parameters according to the culture abnormality handling plan, record the control effect in real time, and generate parameter control response data; the parameter control response data is correlated with the cell growth characteristic data and the culture condition optimization model is established;

[0137] The cultivation plan generation module is used to mine and analyze the basic data of the cultivation process based on the cultivation condition optimization model to generate the optimal cultivation parameter combination; formulate a standardized cultivation plan based on the optimal cultivation parameter combination, conduct cultivation quality assessment, and generate a cultivation quality assessment report.

[0138] The data acquisition module of the present invention systematically collects key data from the stem cell culture process, providing basic data support for subsequent analysis. By acquiring real-time cell count data, culture condition data, and contamination status data, key changes during the culture process can be comprehensively monitored, ensuring the transparency and accuracy of the experimental process. In particular, the collection of contamination status data can promptly identify potential contamination risks, ensuring the effectiveness and reproducibility of the experiment. This facilitates subsequent optimization of the culture plan and reduces culture failures caused by environmental changes or contamination. The growth dynamics monitoring module uses the real-time collected basic data to analyze cell growth dynamics, identifying trends and patterns in cell growth and establishing an accurate growth prediction model. This enables dynamic monitoring during the culture process and predicts potential anomalies in advance. When growth data exhibits abnormal fluctuations, the system quickly triggers an early warning signal and generates a culture anomaly handling plan, enabling timely adjustments to the culture plan to prevent irreversible damage to cell growth caused by problems during the culture process. Real-time monitoring and early warning of the cell growth process can significantly improve culture efficiency, avoid unnecessary waste of resources, and reduce cell quality. The parameter control and optimization module adjusts culture condition parameters in real time based on anomalies detected during the culture process. By dynamically manipulating culture environment factors such as temperature, humidity, and gas composition, timely responses can be made to the different stages of cell growth. This module not only records the effects of these manipulations in real time but also integrates parameter manipulation response data with cell growth characteristic data through correlation analysis to establish a precise culture condition optimization model. This enables data-driven optimization of culture conditions, improving not only culture efficiency but also ensuring consistent cell quality. Ultimately, through manipulation and optimization, the success rate of culture and the predictability of cell growth can be significantly improved. The culture protocol generation module comprehensively analyzes basic culture process data and, in conjunction with the culture condition optimization model, accurately identifies patterns underlying the data and generates the optimal culture parameter combination. By developing standardized culture protocols, various cell culture conditions can be standardized, improving experimental consistency and reproducibility, and ensuring fully optimized cell growth conditions throughout the culture process. Furthermore, culture quality assessment and the generation of culture quality assessment reports help evaluate the overall culture performance, such as cell consistency, morphological homogeneity, and genetic stability, providing reliable quality assurance for subsequent research and applications. This process helps maintain stable cell quality across multiple experiments, enhances the controllability of the culture process, and reduces the risk of experimental failure.

[0139] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced therein.

[0140] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A method for managing stem cell culture information, characterized in that: The following steps are involved: Step S1: collecting basic data of the stem cell culture process, wherein the basic data of the culture process includes cell count data, culture condition parameter data, and contamination status data; Step S2: performing growth kinetic analysis on the basic data of the culture process to obtain cell growth characteristic data; establishing a cell growth prediction model based on the cell growth characteristic data to generate cell growth warning data; monitoring the culture process in real time based on the cell growth warning data, triggering a warning signal when abnormal fluctuations are detected, and generating a culture abnormality processing plan. Step S2 includes: Step S21: extracting the number of live cells and the number of dead cells from the cell activity counting data for 72 consecutive hours, thereby obtaining time-series cell activity data; Step S22: selecting data points of the logarithmic growth period of the time-series cell activity data, and fitting an exponential growth model based on the nonlinear least squares method to obtain a growth fitting curve model; Step S23: calculating the slope and determination coefficient of the growth fitting curve model, and performing a model fitting goodness evaluation to obtain model fitting goodness data; Step S24: calculating the cell population doubling time based on the model goodness of fit data, and performing a doubling time stability assessment to generate doubling time dynamic stability data; Step S25: extracting cell growth characteristic parameters from the doubling time dynamic stability data, thereby obtaining cell growth characteristic data; Step S26: establishing a cell growth prediction model based on the cell growth characteristic data to generate cell growth warning data; Step S27: monitoring the culture process in real time based on the cell growth warning data, triggering a warning signal when abnormal fluctuations are detected, and generating a culture abnormality processing plan; Step S3: Dynamically control the culture condition parameters according to the culture abnormality handling plan, and record the control effect in real time to generate parameter control response data; perform correlation analysis on the parameter control response data and the cell growth characteristic data to establish a culture condition optimization model; Step S4: Based on the culture condition optimization model, the basic data of the culture process is mined and analyzed to generate the optimal culture parameter combination; a standardized culture plan is formulated based on the optimal culture parameter combination, and a culture quality assessment is performed to generate a culture quality assessment report.

2. The stem cell culture information management method according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: recording the number of living cells, the number of dead cells, and cell morphology parameters every 6 hours using an automatic cell counter to obtain cell counting data; Step S12: Collect information including temperature, humidity, Real-time data of concentration and culture medium Values, thereby obtaining the culture environment parameters; Step S13: Recording contamination status data using a microscope image acquisition system, including bacterial contamination image data and fungal contamination image data; Step S14: collecting culture operation record data through an electronic recording system, including the medium change time, medium change volume, and passage ratio; merging the culture environment parameters and culture operation record data into culture condition parameter data; Step S15: cleaning and standardizing the multi-source monitoring data to obtain basic data of the culture process, wherein the multi-source monitoring data includes cell counting data, contamination status data, and culture condition parameter data.

3. The stem cell culture information management method according to claim 2, characterized in that: Step S15 includes the following steps: Step S151: performing outlier detection and elimination on the cell counting data, eliminating data points that deviate from the mean by more than three standard deviations, thereby obtaining cell activity counting data; Step S152: performing image quality assessment on the pollution status data, eliminating unqualified images such as blur and underexposure, thereby obtaining high-confidence pollution feature data; Step S153: performing parameter correction processing on the culture condition parameter data to obtain culture condition parameter correction data, wherein the parameter correction processing specifically uses an interpolation algorithm to process missing culture environment parameters caused by sensor failure, and unifies the time format and operation terminology of the culture operation record data; Step S154: standardize the cell activity count data, high-confidence contamination feature data, and culture condition parameter correction data, and perform time series alignment processing to obtain basic culture process data.

4. The stem cell culture information management method according to claim 3, characterized in that: Step S24 includes the following steps: Step S241: Calculating the cell population doubling time based on the model fit goodness data, thereby generating doubling time trend data; Step S242: comparing and analyzing the doubling times of different passage numbers based on the doubling time trend data, establishing a doubling time stability scoring system to evaluate the stability of the doubling time, thereby obtaining doubling time stability scoring data, wherein the doubling time stability scoring system is specifically as follows: when the coefficient of variation of the doubling time of the cells for three consecutive passages is less than 10%, the score is given a first level; when the coefficient of variation of the doubling time of the cells for three consecutive passages is between 10% and 20%, the score is given a second level; when the coefficient of variation of the doubling time of the cells for three consecutive passages is greater than 20%, the score is given a third level; Step S243: When the doubling time stability score data is the first level, the current culture conditions are recorded as high-quality culture condition data; Step S244: When the doubling time stability score data is the second level, the monitoring frequency is increased, and the trend of changes in the doubling time stability is analyzed. When the doubling time stability approaches the third level, the concentration change of the main growth factor is analyzed, and culture condition optimization data is generated. When the doubling time stability approaches the first level, the current culture conditions are recorded as high-quality culture condition data. When the doubling time stability remains unchanged, non-main growth factor regulation processing is performed, and the key regulation parameters are recorded as non-main growth factor adjustment data. Step S245: When the doubling time stability score data is the third level, extract the temperature of the culture environment and Record concentration fluctuations, analyze concentration changes of major growth factors in the culture medium, and generate culture condition optimization data; Step S246: Merge the high-quality culture condition data, the non-essential growth factor adjustment data, and the culture condition optimization data into the doubling time dynamic stability data.

5. The stem cell culture information management method according to claim 4, characterized in that: Step S25 includes the following steps: Step S251: Calculate the cell viability at each time point based on the doubling time dynamic stability data, thereby drawing a survival rate change curve data; Step S252: extracting key growth kinetic parameters based on the survival rate change curve data and the doubling time trend data, thereby obtaining growth kinetic parameter data, wherein the key growth kinetic parameters include growth rate, exponential growth coefficient, and maximum growth rate; Step S253: segmenting the survival rate change curve data, dividing the culture period into an adaptation period, a logarithmic growth period, and a plateau period, thereby obtaining segmented survival curve data; Step S254: Calculating the deviation between the mean survival rate of each period and the baseline curve based on the survival curve segmentation data and the preset survival rate baseline curve, thereby obtaining survival rate deviation value data; Step S254: identifying abnormal fluctuations in survival rate based on the survival rate deviation value data according to a preset deviation threshold, thereby obtaining abnormal survival rate data; Step S256: extracting the culture operations within 24 hours before the abnormal period of the survival rate abnormality data, and performing pattern matching on the culture operations with a preset historical abnormal event library to determine the cause of the abnormal survival rate, thereby obtaining deviation traceability data; Step S257: Merge the growth kinetic parameter data and the deviation tracing data into cell growth characteristic data.

6. The stem cell culture information management method according to claim 5, characterized in that: Step S26 includes the following steps: Step S261: constructing a multidimensional cell growth prediction model based on the cell growth characteristic data, wherein the multidimensional cell growth prediction model includes a cell growth rate prediction model, a survival rate prediction model, and a growth trend prediction model; Step S262: calibrating and cross-validating the parameters of the multidimensional cell growth prediction model, and determining the prediction accuracy of the model by calculating the prediction confidence interval, thereby obtaining model prediction accuracy data; optimizing the multidimensional cell growth prediction model based on the model prediction accuracy data, and performing multidimensional prediction, thereby obtaining prediction result data; Step S263: setting thresholds for key cell growth indicators, including a minimum acceptable survival rate, a critical growth rate, and a maximum growth rate; Step S264: The prediction result data is compared with the threshold value of the key cell growth indicator in real time. When the prediction result exceeds the preset threshold range, a cell growth abnormality warning signal is generated, thereby obtaining cell growth warning data.

7. The stem cell culture information management method according to claim 6, characterized in that: Step S3 includes the following steps: Step S31: Accurately adjust the culture condition parameter data according to the culture abnormality processing plan, and continuously track and record the cell growth response of each adjustment operation to generate parameter adjustment response data; Step S32: performing correlation analysis on the parameter control response data and the cell growth characteristic data, and constructing a quantitative mapping relationship between the culture conditions and the cell growth indicators, thereby establishing an initial culture condition optimization model; Step S33: cross-validate the initial culture condition optimization model and perform parameter sensitivity analysis to evaluate the prediction accuracy and stability of the model, and iteratively optimize the model parameters to obtain the culture condition optimization model.

8. The stem cell culture information management method according to claim 7, characterized in that: Step S4 includes the following steps: Step S41: performing multi-dimensional data mining on the basic data of the culture process based on the culture condition optimization model to obtain correlation result data, wherein the multi-dimensional data mining specifically includes performing correlation analysis and pattern recognition on the cell count data, culture condition parameter data, and growth kinetic characteristic data; Step S42: performing feature weight evaluation on the correlation result data to screen the key parameters that have the most significant impact on cell growth, thereby generating the optimal culture parameter combination; Step S43: formulating a standardized culture protocol based on the optimal culture parameter combination, wherein the standardized culture protocol includes specific parameters of cell seeding density, culture medium formulation, medium change cycle, passage ratio, and culture environment parameters; Step S44: performing multiple repeated experiments to verify the standardized culture scheme, and recording the cell growth indicators of each experiment to obtain experimental result data; Step S45: Calculate key indicators of culture quality based on the experimental result data to generate a culture quality assessment report, where the key indicators of culture quality include cell growth consistency index, morphological uniformity score, gene stability rating, and overall culture success rate.

9. A stem cell culture information management system, characterized in that: Used to execute the stem cell culture information management method according to claim 1, the stem cell culture information management system comprising: A data acquisition module is used to collect basic data of the stem cell culture process, wherein the basic data of the culture process includes cell count data, culture condition parameter data and contamination status data; The growth dynamics monitoring module is used to analyze the basic data of the culture process for growth dynamics and obtain cell growth characteristic data; establish a cell growth prediction model based on the cell growth characteristic data and generate cell growth warning data; monitor the culture process in real time based on the cell growth warning data, trigger a warning signal when abnormal fluctuations are detected, and generate a culture abnormality treatment plan; The parameter control optimization module is used to dynamically control culture condition parameters according to the culture abnormality handling plan, record the control effect in real time, and generate parameter control response data; the parameter control response data is correlated with the cell growth characteristic data and the culture condition optimization model is established; The cultivation plan generation module is used to mine and analyze the basic data of the cultivation process based on the cultivation condition optimization model to generate the optimal cultivation parameter combination; formulate a standardized cultivation plan based on the optimal cultivation parameter combination, conduct cultivation quality assessment, and generate a cultivation quality assessment report.

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