Stem cell quality evaluation system and method based on multi-source data fusion analysis

Abstract

The invention relates to the related field of biomedical information management, discloses a stem cell quality evaluation system and method based on multi-source data fusion analysis, constructs an intelligent integrated data analysis platform, gets through information synchronization among stages of stem cell products, realizes unified data interaction management, and improves the quality of stem cells. By integrating stem cell associated data of each stage and performing consistent visual supervision processing, convenient query and comprehensive judgment of isolated information are realized, and internal quality control and risk use management of stem cell quality are facilitated.

Description

A Stem Cell Quality Assessment System and Method Based on Multi-Source Data Fusion Analysis Technical Field

[0001] This invention relates to the field of biomedical information management, specifically to a stem cell quality evaluation system and method based on multi-source data fusion analysis. Background Technology

[0002] The acquisition and use of stem cells involves many departments, processes, objects, and equipment. The quality of stem cells can be affected by various factors, such as different steps and the condition of the objects. Therefore, effective safety supervision is needed to conduct quality assessment.

[0003] In existing technologies, the existence of various processes, objects, and departments is relatively independent, making quality traceability difficult and supervision between various links relatively weak. When quality problems occur later, there are difficulties in supervision and traceability. If the problems are not effectively discovered in the early stage, it is difficult to deal with the source of sudden problems when storing and using the data later. Furthermore, when problems occur in the front-end process, due to the independence of information at each stage, it is also difficult to promptly and synchronously notify the risk. Summary of the Invention

[0004] The purpose of this invention is to provide a stem cell quality evaluation system and method based on multi-source data fusion analysis to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a stem cell quality evaluation method based on multi-source data fusion analysis, comprising: connecting with multiple information systems through a standardized data protocol to acquire multi-source associated data matching the target stem cells in real time or asynchronously; performing data preprocessing based on the data categories of the multi-source associated data, and performing corresponding feature fusion processing to generate a fusion feature vector for characterizing stem cell quality; constructing a visualized multidimensional quality map of stem cells based on the fusion feature vector, wherein the multidimensional quality map is used to visualize and output time series, quality index dimensions, process parameter dimensions, and risk warning information; updating the multidimensional quality map in real time, judging the fusion feature vector and updated variables according to the quality evaluation criteria under multiple features, and generating a visualized quality risk marker based on the judgment result.

[0006] As a further aspect of the present invention: the multi-source associated data includes source object data, cell state data, and cell quality data; the source object data is used to characterize the healthy genetic background of the stem cell source, related preparation equipment, and safety supervision data of the equipment supplier; the cell state data is used to characterize data including current cell activity indicators, proliferation capacity, and long-term monitoring indicators of metabolic activity; the cell quality data is used to characterize the genetic integrity, health stability, and basic safety and quality data of endotoxins in the cells.

[0007] As a further aspect of the present invention: the source data object includes non-influence-related data and influence-related data; the non-influence-related data is an external reference information object, used to characterize information data that is not necessarily related to stem cell quality, and when this part of the information data at the stem cell source changes, based on the safety of the production environment, the stem cells have a non-hereditary quality risk due to external pollution; the influence-related data is used to characterize information data that is biologically related to stem cell quality, and when this part of the data at the stem cell source changes, based on biological characteristics, the stem cells have a hereditary quality risk probability.

[0008] As a further embodiment of the present invention, the step of performing feature fusion processing to generate a fusion feature vector for characterizing stem cell quality includes: assigning dynamic weights based on biological importance to different types of data categories, wherein the biological importance is used to characterize the correlation value of the data category with the evaluation of cell safety and stability; extracting multiple related high-order feature data from the data categories and assigning values ​​based on the dynamic weights; performing dimensionality reduction and integration calculation based on a multimodal fusion algorithm to establish a consistent and standardized fusion feature vector.

[0009] As a further aspect of the present invention: the step of judging the fused feature vector and updated variables according to the quality evaluation criteria under multiple features, and generating a visual quality risk marker based on the judgment result includes: obtaining the data category of the updated data item, and making a basic judgment based on the quality evaluation criteria of the corresponding data category to evaluate whether the current state is within the allowable range of quality evaluation; calculating the update difference for the updated data item to evaluate the trend characteristics of quality change under the data category, and judging whether it is within the allowable range of quality evaluation; generating a visual quality risk marker based on the evaluation results of the basic judgment and the trend characteristics evaluation, wherein the visual risk marker is used to characterize the current abnormal numerical performance state of stem cells that is outside the allowable range.

[0010] This invention aims to provide a stem cell quality assessment system based on multi-source data fusion analysis, comprising: a multi-source information synchronization module, used to connect with multiple information systems through a standardized data protocol to acquire multi-source associated data matching the target stem cells in real time or asynchronously; a data feature fusion module, used to preprocess data based on the data categories of the multi-source associated data and perform corresponding feature fusion processing to generate a fusion feature vector for characterizing stem cell quality; a visualization processing module, used to construct a visualized multidimensional quality map of stem cells based on the fusion feature vector, the multidimensional quality map being used to visualize time series, quality index dimensions, process parameter dimensions, and risk warning information; and a real-time update evaluation module, used to perform real-time data synchronization updates on the multidimensional quality map, judge the fusion feature vector and update variables according to quality evaluation standards under multiple features, and generate a visualized quality risk marker based on the judgment result.

[0011] As a further aspect of the present invention: the multi-source associated data includes source object data, cell state data, and cell quality data; the source object data is used to characterize the healthy genetic background of the stem cell source, related preparation equipment, and safety supervision data of the equipment supplier; the cell state data is used to characterize data including current cell activity indicators, proliferation capacity, and long-term monitoring indicators of metabolic activity; the cell quality data is used to characterize the genetic integrity, health stability, and basic safety and quality data of endotoxins in the cells.

[0012] As a further aspect of the present invention: the source data object includes non-influence-related data and influence-related data; the non-influence-related data is an external reference information object, used to characterize information data that is not necessarily related to stem cell quality, and when this part of the information data at the stem cell source changes, based on the safety of the production environment, the stem cells have a non-hereditary quality risk due to external pollution; the influence-related data is used to characterize information data that is biologically related to stem cell quality, and when this part of the data at the stem cell source changes, based on biological characteristics, the stem cells have a hereditary quality risk probability.

[0013] As a further embodiment of the present invention: the data feature fusion module includes: a weight assignment unit, used to assign dynamic weights based on biological importance to different types of data categories, wherein the biological importance is used to characterize the correlation value of the data category with the evaluation of cell safety and stability; and a feature fusion unit, used to extract multiple related high-order feature data in the data category, assign values ​​based on the dynamic weights, perform dimensionality reduction and integration calculations based on a multimodal fusion algorithm, and establish a consistent and standardized fusion feature vector.

[0014] As a further embodiment of the present invention, it also includes: a basic dynamic judgment unit, used to obtain the data category of the updated data item and make a basic judgment based on the quality evaluation standard of the corresponding data category to evaluate whether the current state is within the allowable range of quality evaluation; a difference dynamic judgment unit, used to calculate the update difference of the updated data item to evaluate the trend characteristics of quality change under the data category and determine whether it is within the allowable range of quality evaluation; and a change risk labeling unit, used to generate a visual quality risk label based on the evaluation results of the basic judgment and the change trend characteristics evaluation, wherein the visual risk label is used to characterize the current state of abnormal numerical performance of stem cells that is outside the allowable range.

[0015] Compared with the prior art, the beneficial effects of the present invention are: it constructs an intelligent integrated data analysis platform, connects the information synchronization between various stages of stem cell products, realizes unified data interaction management, integrates stem cell-related data at various stages and performs consistent visual monitoring processing, realizes convenient query and comprehensive judgment of isolated information, and facilitates internal quality control and risk management of stem cell quality. Attached Figure Description

[0016] Figure 1 is a flowchart of a stem cell quality assessment method based on multi-source data fusion analysis.

[0017] Figure 2 is a flowchart of feature fusion processing in a stem cell quality assessment method based on multi-source data fusion analysis.

[0018] Figure 3 shows the composition of a stem cell quality assessment system based on multi-source data fusion analysis. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0020] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0021] As shown in Figure 1, a stem cell quality assessment method based on multi-source data fusion analysis provided by an embodiment of the present invention includes the following steps: S10, connecting with multiple information systems through a standardized data protocol to acquire multi-source association data matching the target stem cells in real time or asynchronously; S20, performing data preprocessing based on the data categories of the multi-source association data, and correspondingly performing feature fusion processing to generate a fusion feature vector for characterizing stem cell quality; S30, constructing a visualized multidimensional quality map of stem cells based on the fusion feature vector, wherein the multidimensional quality map is used to visualize and output time series, quality index dimensions, process parameter dimensions, and risk warning information; S40, performing real-time data synchronization updates on the multidimensional quality map, judging the fusion feature vector and updated variables according to the quality assessment criteria under multiple features, and generating a visualized quality risk marker based on the judgment result.

[0022] This embodiment presents a stem cell quality evaluation method based on multi-source data fusion analysis. An intelligent integrated data analysis platform is constructed to synchronize information across different stages of stem cell product development, achieving unified data interaction management. By integrating stem cell-related data from various stages and performing consistent, visualized monitoring, it facilitates convenient querying and comprehensive judgment of isolated information, enabling internal quality control and risk management of stem cell use. The acquisition and use of stem cells involves numerous departments, processes, objects, and equipment. The quality of stem cells can be affected by various factors, including different steps and the state of the objects. Therefore, effective safety supervision is needed for quality assessment. However, in existing technologies, the processes, objects, and departments are relatively independent, making quality traceability difficult. Quality handover between processes is often hindered by... Simple quality assessment results exchange leads to limited oversight between different stages, making it difficult to trace and monitor subsequent quality issues. The proposed solution involves establishing a central monitoring platform for consistent information exchange with multiple stakeholders. This platform synchronizes information from different stages and visualizes it, allowing regulators to assess the potential correlation between stem cell quality and other factors at each stage based on a visualized, multi-dimensional quality map. Real-time data detection and processing facilitate comprehensive stem cell quality evaluation and oversight. Compared to existing methods that rely solely on stem cell-based quality assessments, this approach offers superior systemicity, enabling timely detection of potential risks beyond effective stem cell testing methods. This allows for the implementation of corresponding risk management plans to mitigate potential consequences.

[0023] In another preferred embodiment of the present invention, the multi-source associated data includes source object data, cell state data, and cell quality data; the source object data is used to characterize the healthy genetic background of the stem cell source, the relevant preparation equipment, and the safety supervision data of the equipment supplier; the cell state data is used to characterize data including the current cell activity indicators, proliferation capacity, and long-term monitoring indicators of metabolic activity; the cell quality data is used to characterize the genetic integrity, health stability, and basic safety and quality data of endotoxins in the cells.

[0024] This embodiment describes multi-source correlation data, which is mainly used for cell quality evaluation. Therefore, it primarily includes three categories: source data, cell state data, and cell quality data. Cell state data is real-time (due to detection limitations, there is periodic synchronization) and is the most intuitive state data characterizing stem cell quality, including features such as cell activity. Cell quality data expresses the quality of the stem cells themselves, mainly including non-expressive biological characteristics, such as genetic data. Source data is the most extensive and diverse type, including medical or biological characteristics such as the healthy genetic background of the stem cell provider, as well as environmental and equipment safety characteristics involved in the stem cell preparation process. These are all indicators that may directly or indirectly affect stem cell quality, thus requiring comprehensive monitoring and evaluation.

[0025] In another preferred embodiment of the present invention, the source data object includes non-influence-related data and influence-related data. The non-influence-related data is an external reference information object used to characterize information data that is not necessarily related to stem cell quality. When this part of the information data at the stem cell source changes, based on the safety of the production environment, the stem cells have a non-hereditary quality risk due to external pollution. The influence-related data is used to characterize information data that is biologically related to stem cell quality. When this part of the data at the stem cell source changes, based on biological characteristics, the stem cells have a hereditary quality risk. In this embodiment, based on the aforementioned embodiments, the source data object is further divided. Based on the different objects, the final outcome for stem cells may be... The impact on life varies, thus requiring subsequent safety management procedures. The distinction here is based on whether it produces hereditary or non-hereditary effects. If it is a non-hereditary effect, it can be easily determined through screening and testing of cell state data (e.g., contamination quality issues caused by equipment, etc.). However, if certain biological characteristics indicate a potential hereditary problem, additional corresponding biological assessments are required. In practice, this is usually caused by the provider, so the provider's health status is typically assessed. When a provider experiences very high health problems, these problems may be induced by the genes themselves, requiring stem cell risk assessment.

[0026] As shown in Figure 2, in another preferred embodiment of the present invention, the step of performing feature fusion processing to generate a fusion feature vector for characterizing stem cell quality includes: S21, assigning dynamic weights based on biological importance to different types of data categories, wherein the biological importance is used to characterize the correlation value of the data category with the evaluation of cell safety and stability; S22, extracting multiple related high-order feature data from the data categories, assigning values ​​based on the dynamic weights, performing dimensionality reduction and integration calculations based on a multimodal fusion algorithm, and establishing a consistent and standardized fusion feature vector.

[0027] In this embodiment, the steps of feature fusion processing are described. This process is mainly a weight assignment process for each related data parameter. The dynamic weight of biological importance here is established based on the overall evaluation of a large amount of historical related biological data. The vectorized expression after dimensionality reduction through assignment is a reliable technical means for data visualization. It can not only realize the visualization transformation of multi-dimensional data, but also has a more accurate and intuitive result-oriented expression, which facilitates the readability of platform data and optimizes the difficulty of supervision.

[0028] As another preferred embodiment of the present invention, the step of judging the fused feature vector and the updated variable according to the quality evaluation criteria under multiple features, and generating a visual quality risk marker based on the judgment result includes: obtaining the data category of the updated data item, and making a basic judgment based on the quality evaluation criteria of the corresponding data category to evaluate whether the current state is within the allowable range of the quality evaluation; calculating the update difference for the updated data item to evaluate the trend characteristics of quality change under the data category, and judging whether it is within the allowable range of the quality evaluation; generating a visual quality risk marker based on the evaluation results of the basic judgment and the trend characteristics evaluation, wherein the visual risk marker is used to characterize the current state of abnormal numerical performance of stem cells that is outside the allowable range.

[0029] In this embodiment, the quality assessment and monitoring of the subsequent data update process is supplemented. Based on the previous embodiment, the quality of the initial state of the stem cells has been judged. In the storage management before use, the related data will still be updated. Therefore, it is necessary to judge the quality after the update and the quality change trend shown by the update. This is not only used to assess the current quality of the stem cells, but also to judge whether there are any risks or loopholes in the relevant management, and whether there may be previously undiscovered hidden gene problems (when the provider has health problems, or when the storage management conditions are determined but the quality change trend of the stem cells is obviously problematic). Based on this feature, it is also possible to quickly find the user and perform the corresponding gene risk assessment after the stem cells have been used.

[0030] As shown in Figure 3, the present invention also provides a stem cell quality evaluation system based on multi-source data fusion analysis, which includes: a multi-source information synchronization module 100, used to connect with multiple information systems through a standardized data protocol to acquire multi-source associated data matching the target stem cells in real time or asynchronously; a data feature fusion module 200, used to preprocess data based on the data categories of the multi-source associated data and perform corresponding feature fusion processing to generate a fusion feature vector for characterizing stem cell quality; a visualization processing module 300, used to construct a visualized multidimensional quality map of stem cells based on the fusion feature vector, wherein the multidimensional quality map is used to visualize and output time series, quality index dimensions, process parameter dimensions, and risk warning information; and a real-time update evaluation module 400, used to perform real-time data synchronization updates on the multidimensional quality map, judge the fusion feature vector and update variables according to the quality evaluation criteria under multiple features, and generate a visualized quality risk label based on the judgment result.

[0031] In another preferred embodiment of the present invention, the multi-source associated data includes source object data, cell state data, and cell quality data; the source object data is used to characterize the healthy genetic background of the stem cell source, the relevant preparation equipment, and the safety supervision data of the equipment supplier; the cell state data is used to characterize data including the current cell activity indicators, proliferation capacity, and long-term monitoring indicators of metabolic activity; the cell quality data is used to characterize the genetic integrity, health stability, and basic safety and quality data of endotoxins in the cells.

[0032] In another preferred embodiment of the present invention, the source data object includes non-influence-related data and influence-related data; the non-influence-related data is an external reference information object used to characterize information data that is not necessarily related to stem cell quality. When this part of the information data at the stem cell source changes, based on the safety of the production environment, the stem cells have a non-hereditary quality risk due to external pollution. The influence-related data is used to characterize information data that is biologically related to stem cell quality. When this part of the data at the stem cell source changes, based on biological characteristics, the stem cells have a hereditary quality risk. In another preferred embodiment of the present invention, the data feature fusion module 200 includes: a weight assignment unit, used to assign dynamic weights based on biological importance to different types of data categories, the biological importance being used to characterize the correlation value of the data category to the evaluation of cell safety and stability; and a feature fusion unit, used to extract multiple related high-order feature data in the data category, assign values ​​based on the dynamic weights, perform dimensionality reduction and integration calculations based on a multimodal fusion algorithm, and establish a consistent and standardized fusion feature vector.

[0033] As another preferred embodiment of the present invention, it further includes: a basic dynamic judgment unit, used to obtain the data category of the updated data item and make a basic judgment based on the quality evaluation standard of the corresponding data category to evaluate whether the current state is within the allowable range of quality evaluation; a difference dynamic judgment unit, used to calculate the update difference of the updated data item to evaluate the trend characteristics of quality change under the data category and determine whether it is within the allowable range of quality evaluation; and a change risk labeling unit, used to generate a visual quality risk label based on the evaluation results of the basic judgment and the change trend characteristics evaluation, wherein the visual risk label is used to characterize the current state of abnormal numerical performance of stem cells that is outside the allowable range.

[0034] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0035] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the disclosure in the specification and embodiments. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the claims.

[0036] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A stem cell quality assessment method based on multi-source data fusion analysis, characterized in that, The process includes: connecting with multiple information systems through a standardized data protocol to acquire multi-source correlation data matching the target stem cells in real time or asynchronously; performing data preprocessing based on the data categories of the multi-source correlation data and corresponding feature fusion processing to generate a fusion feature vector for characterizing stem cell quality; constructing a visualized multidimensional quality map of stem cells based on the fusion feature vector, wherein the multidimensional quality map is used to visualize and output time series, quality index dimensions, process parameter dimensions, and risk warning information. The multidimensional quality map is updated in real time. The fused feature vector and updated variables are judged according to the quality evaluation criteria under multiple features. Based on the judgment results, a visual quality risk label is generated.

2. The stem cell quality assessment method based on multi-source data fusion analysis according to claim 1, characterized in that, The multi-source associated data includes source object data, cell state data, and cell quality data; the source object data is used to characterize the healthy genetic background of the stem cell source, related preparation equipment, and safety supervision data of the equipment supplier. The cell state data is used to characterize the cell's current activity indicators, proliferation capacity, and long-term monitoring indicators of metabolic activity; the cell quality data is used to characterize the cell's genetic integrity, health stability, and basic safety and quality data related to endotoxins.

3. The stem cell quality assessment method based on multi-source data fusion analysis according to claim 2, characterized in that, The source data objects include non-influence-related data and influence-related data; the non-influence-related data are external reference information objects used to characterize information data that is not necessarily related to stem cell quality. When this part of the information data at the stem cell source changes, based on the safety of the production environment, the stem cells have a non-hereditary quality risk due to external pollution. The impact correlation data is used to characterize information data that is biologically related to stem cell quality. When this part of the stem cell source data changes, the probability of stem cells having a genetic quality risk is determined based on biological characteristics.

4. The stem cell quality assessment method based on multi-source data fusion analysis according to claim 3, characterized in that, The step of performing feature fusion processing to generate a fusion feature vector for characterizing stem cell quality includes: assigning dynamic weights based on biological importance to different types of data categories, wherein the biological importance is used to characterize the correlation value of the data category with the evaluation of cell safety and stability; extracting multiple related high-order feature data from the data categories and assigning values ​​based on the dynamic weights; performing dimensionality reduction and integration calculation based on a multimodal fusion algorithm to establish a consistent and standardized fusion feature vector.

5. The stem cell quality assessment method based on multi-source data fusion analysis according to claim 4, characterized in that, The step of judging the fused feature vector and updated variables according to the quality evaluation criteria under multiple features, and generating a visual quality risk marker based on the judgment result includes: obtaining the data category of the updated data item, and making a basic judgment based on the quality evaluation criteria of the corresponding data category to evaluate whether the current state is within the allowable range of quality evaluation; calculating the update difference for the updated data item to evaluate the trend characteristics of quality change under the data category, and judging whether it is within the allowable range of quality evaluation; generating a visual quality risk marker based on the evaluation results of the basic judgment and the trend characteristics evaluation, wherein the visual risk marker is used to characterize the current state of abnormal numerical performance of stem cells that is outside the allowable range.

6. A stem cell quality assessment system based on multi-source data fusion analysis, characterized in that, Includes: a multi-source information synchronization module, used to connect with multiple information systems through standardized data protocols to acquire multi-source correlation data matching the target stem cells in real time or asynchronously; The data feature fusion module is used to preprocess data based on the data categories of multi-source associated data and perform feature fusion processing accordingly to generate a fusion feature vector for characterizing stem cell quality. The visualization processing module is used to construct a visualized multidimensional quality map of stem cells based on the fusion feature vector. The multidimensional quality map is used to visualize and output time series, quality index dimension, process parameter dimension, and risk warning information in graphical form. The real-time update evaluation module is used to perform real-time data synchronization updates on the multi-dimensional quality map, judge the fused feature vector and updated variables according to the quality evaluation criteria under multiple features, and generate a visual quality risk marker based on the judgment result.

7. The stem cell quality assessment system based on multi-source data fusion analysis according to claim 6, characterized in that, The multi-source associated data includes source object data, cell state data, and cell quality data; the source object data is used to characterize the healthy genetic background of the stem cell source, related preparation equipment, and safety supervision data of the equipment supplier. The cell state data is used to characterize the cell's current activity indicators, proliferation capacity, and long-term monitoring indicators of metabolic activity; the cell quality data is used to characterize the cell's genetic integrity, health stability, and basic safety and quality data related to endotoxins.

8. The stem cell quality assessment system based on multi-source data fusion analysis according to claim 7, characterized in that, The source data objects include non-influence-related data and influence-related data; the non-influence-related data are external reference information objects used to characterize information data that is not necessarily related to stem cell quality. When this part of the information data at the stem cell source changes, based on the safety of the production environment, the stem cells have a non-hereditary quality risk due to external pollution. The impact correlation data is used to characterize information data that is biologically related to stem cell quality. When this part of the stem cell source data changes, the probability of stem cells having a genetic quality risk is determined based on biological characteristics.

9. The stem cell quality assessment system based on multi-source data fusion analysis according to claim 8, characterized in that, The data feature fusion module includes: a weight assignment unit, used to assign dynamic weights based on biological importance to different types of data categories, wherein the biological importance is used to characterize the correlation value of the data category with the evaluation of cell safety and stability; and a feature fusion unit, used to extract multiple related high-order feature data in the data category, assign values ​​based on the dynamic weights, perform dimensionality reduction and integration calculations based on a multimodal fusion algorithm, and establish a consistent and standardized fusion feature vector.

10. The stem cell quality assessment system based on multi-source data fusion analysis according to claim 9, characterized in that, Also includes: The basic dynamic judgment unit is used to obtain the data category of the updated data item and make a basic judgment based on the quality evaluation standard of the corresponding data category to evaluate whether the current state is within the allowable range of quality evaluation. The difference dynamic judgment unit is used to calculate the update difference for the updated data item in order to evaluate the change trend characteristics of the data category and determine whether it is within the allowable range of the quality evaluation; the change risk labeling unit is used to generate a visual quality risk label based on the evaluation results of the basic judgment and change trend characteristics evaluation. The visual risk label is used to characterize the abnormal numerical performance of the current stem cells that are outside the allowable range.

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