An artificial intelligence umbilical cord stem cell quality detection data analysis method and system

Abstract

The application provides an artificial intelligence umbilical cord stem cell quality detection data analysis method and system, relates to the technical field of cell quality detection, and comprises the following steps: obtaining umbilical cord stem cell staining monitoring parameters, comparing the umbilical cord stem cell staining monitoring parameters with expected umbilical cord stem cell staining parameters, and obtaining an umbilical cord stem cell staining parameter deviation vector; collecting a first umbilical cord stem cell activity detection sample set, performing centralized value evaluation, and obtaining an umbilical cord stem cell activity value confidence interval; when the umbilical cord stem cell activity detection value does not belong to the umbilical cord stem cell activity value confidence interval, performing abnormal quality detection data identification on the umbilical cord stem cell activity detection value; and when the umbilical cord stem cell activity detection value belongs to the umbilical cord stem cell activity value confidence interval, performing normal quality detection data identification on the umbilical cord stem cell activity detection value. The technical problem of poor stability of umbilical cord stem cell quality detection results in the prior art is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of cell quality detection, and in particular to an artificial intelligence umbilical cord stem cell quality detection data analysis method and system. BACKGROUND

[0002] Umbilical cord stem cells have important application value in the field of regenerative medicine, and their quality directly determines the clinical efficacy and safety. However, the current quality control method, in order to cope with the volatility of the detection process, usually needs to perform multiple repeated detections on the same sample, and takes the mean or median as the final quality report value. This method, although intended to suppress random errors through averaging operations, has the disadvantage that when there are outliers in the detection results, it will significantly affect the representative value, that is, the traditional method cannot identify part of the abnormal data in the detection results, resulting in poor stability of the quality detection results. SUMMARY

[0003] The present application provides an artificial intelligence umbilical cord stem cell quality detection data analysis method and system to solve the technical problem of poor stability of umbilical cord stem cell quality detection results in the prior art.

[0004] The technical solution of the present application to solve the above technical problems is as follows:

[0005] In a first aspect, the present application provides an artificial intelligence umbilical cord stem cell quality detection data analysis method, comprising:

[0006] Obtaining umbilical cord stem cell staining monitoring parameters of umbilical cord stem cell activity detection samples, comparing with umbilical cord stem cell staining expected parameters to obtain umbilical cord stem cell staining parameter deviation vector;

[0007] Taking the umbilical cord stem cell staining parameter deviation vector, the umbilical cord stem cell staining expected parameters and the umbilical cord stem cell collection conditions as constraints, collecting a first umbilical cord stem cell activity detection sample set, performing a central value evaluation to obtain a umbilical cord stem cell activity value confidence interval, wherein the umbilical cord stem cell activity value is represented by the proportion of live cell number;

[0008] When the umbilical cord stem cell activity detection value does not belong to the umbilical cord stem cell activity value confidence interval, the umbilical cord stem cell activity detection value is marked as abnormal quality detection data;

[0009] When the umbilical cord stem cell activity detection value belongs to the umbilical cord stem cell activity value confidence interval, the umbilical cord stem cell activity detection value is marked as normal quality detection data.

[0010] In a second aspect, the present application provides an artificial intelligence umbilical cord stem cell quality detection data analysis system, comprising:

[0011] A deviation comparison module is configured to obtain an umbilical cord stem cell staining monitoring parameter of an umbilical cord stem cell activity detection sample, compare the umbilical cord stem cell staining monitoring parameter with an umbilical cord stem cell staining expected parameter, and obtain an umbilical cord stem cell staining parameter deviation vector.

[0012] A confidence analysis module is configured to collect a first umbilical cord stem cell activity detection sample set with the umbilical cord stem cell staining parameter deviation vector, the umbilical cord stem cell staining expected parameter and umbilical cord stem cell collection conditions as constraints, perform a central value evaluation, and obtain an umbilical cord stem cell activity value confidence interval, wherein the umbilical cord stem cell activity value is represented by a proportion of the number of living cells.

[0013] An abnormality identification module is configured to identify abnormal quality detection data when the umbilical cord stem cell activity detection value does not belong to the umbilical cord stem cell activity value confidence interval.

[0014] A normal identification module is configured to identify normal quality detection data when the umbilical cord stem cell activity detection value belongs to the umbilical cord stem cell activity value confidence interval.

[0015] The present application has the following beneficial effects:

[0016] Compared with the prior art, the umbilical cord stem cell activity detection sample is first obtained, the umbilical cord stem cell staining monitoring parameters are compared with the expected parameters of the umbilical cord stem cell staining, the umbilical cord stem cell staining parameter deviation vector is obtained, the key staining control attribute combination is accurately identified, the activity decline is attributed to the staining process parameters in advance from the cell culture results, the reduced dimension data (only the key attribute deviation is reserved) of the associated screening is provided for the subsequent activity confidence interval calculation, the biological effect of the quantitative deviation mapping is provided, and the core data basis for the standardization of the stem cell industrial production is provided. Secondly, the first umbilical cord stem cell activity detection sample set is collected under the constraints of the umbilical cord stem cell staining parameter deviation vector, the expected parameters of the umbilical cord stem cell staining and the umbilical cord stem cell collection conditions, the central value evaluation is performed, the umbilical cord stem cell activity value confidence interval is obtained, and the umbilical cord stem cell activity value confidence interval that can dynamically reflect the reasonable activity fluctuation range under the current process level is obtained, thereby providing a comparison standard for subsequent abnormal data identification. Thirdly, when the umbilical cord stem cell activity detection value does not belong to the umbilical cord stem cell activity value confidence interval, the abnormal quality detection data identification is performed on the umbilical cord stem cell activity detection value, the abnormal data is accurately identified, and subsequent management personnel can make targeted selection according to the indication, thereby ensuring the stability of the quality detection data. Finally, when the umbilical cord stem cell activity detection value belongs to the umbilical cord stem cell activity value confidence interval, the normal quality detection data identification is performed on the umbilical cord stem cell activity detection value, the clear numerical interval is determined to replace the traditional experience-based judgment, the standardization and automation of the umbilical cord stem cell quality evaluation are realized, the human subjective error is reduced, and clear classification basis is provided for subsequent quality tracing and data analysis.

[0017] Through the technical scheme, the recursive k-term combination screening (k increases from 1 to N) and the collection condition clustering analysis are adopted, the key factor combination is accurately identified from the multi-dimensional staining control attributes, the same sample is collected under the constraints of the umbilical cord stem cell staining parameter deviation vector, the expected parameters of the umbilical cord stem cell staining and the umbilical cord stem cell collection conditions, the central value processing is performed, the umbilical cord stem cell activity value confidence interval is obtained, and the abnormal quality detection data or the normal quality detection data identification is performed on the umbilical cord stem cell activity detection value. In this way, the abnormal quality detection data of the umbilical cord stem cell activity detection value is effectively identified, and the reliability and stability of the quality detection result are improved. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 A flowchart of an artificial intelligence umbilical cord stem cell quality detection data analysis method provided by the application is shown.

[0019] Figure 2In the artificial intelligence-based umbilical cord stem cell quality detection data analysis method provided by this invention, when k≤N, the k-item correlation of umbilical cord stem cell staining control attributes is sorted to obtain the flowchart of the k-item cell activity correlation control attributes.

[0020] Figure 3 This is a schematic diagram of the structure of an artificial intelligence-based umbilical cord stem cell quality detection data analysis system provided by the present invention.

[0021] In the attached diagram, the components represented by each number are as follows:

[0022] Deviation comparison module 11, confidence analysis module 12, anomaly identification module 13, and normal identification module 14. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0025] In the description of this invention, the term "for example" is used to mean "used as an example, illustration, or description." Any embodiment described as "for example" in this invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the invention. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that the invention can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid obscuring the description of the invention with unnecessary detail. Therefore, the invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed herein.

[0026] Example 1, as Figure 1 As shown, this embodiment of the invention provides an artificial intelligence-based method for analyzing umbilical cord stem cell quality detection data, including:

[0027] S10: obtain the umbilical cord stem cell staining monitoring parameter of the umbilical cord stem cell activity detection sample, compare the umbilical cord stem cell staining monitoring parameter with the umbilical cord stem cell staining expected parameter, and obtain an umbilical cord stem cell staining parameter deviation vector.

[0028] The umbilical cord stem cell staining process is dynamically coupled with multiple parameters such as dye concentration, temperature, pH value, etc. The traditional method cannot identify key abnormal parameters, resulting in ambiguous abnormal attribution, such as difficulty in distinguishing dye failure or temperature fluctuation, and passive correction relying on human experience, with a lagging response.

[0029] To solve the above problems, the application collects the inflammation feature parameters of the target user in the past preset time window to obtain a historical inflammation feature parameter sequence, and then inputs the historical inflammation feature parameter sequence into a pre-trained inflammation feature predictor to output the predicted inflammation feature parameters at the future preset time.

[0030] Specifically, step S10 in the method comprises:

[0031] When k≤N, the umbilical cord stem cell activity k-item correlation sorting is performed on the umbilical cord stem cell staining control attribute, k-item cell activity correlation control attribute is obtained, N≥k≥1, k is an integer, the initial value of k is equal to 1, and N represents the total number of control attributes;

[0032] When k>N, the cell activity correlation control attribute is obtained by taking the union of the k-item cell activity correlation control attribute;

[0033] According to the cell activity correlation control attribute, the umbilical cord stem cell staining monitoring parameter of the umbilical cord stem cell activity detection sample is collected, and the umbilical cord stem cell staining monitoring parameter is compared with the umbilical cord stem cell staining expected parameter to obtain the umbilical cord stem cell staining parameter deviation vector.

[0034] In the embodiment of the application, the umbilical cord stem cell staining process involves multiple dimensional control attributes, such as temperature, time, dye batch, etc. However, not all control attributes have equal influence on cell activity. In order to accurately identify key control attributes, the application adopts a dynamic recursive correlation sorting mechanism, sets an increasing integer k (initial value k=1, upper limit N is the total number of control attributes), when k≤N, k-item correlation sorting is performed, the k control attribute combination most significantly related to cell activity is screened out, and based on the screened cell activity correlation control attribute, the deviation vector of the actual umbilical cord stem cell staining monitoring parameter and the umbilical cord stem cell staining expected parameter is calculated as the retrieval constraint for subsequent activity confidence interval evaluation. Specifically:

[0035] First, when k≤N, umbilical cord stem cell activity k-item correlation sorting is performed on the umbilical cord stem cell staining control attribute, and k-item cell activity correlation control attribute is obtained, wherein N≥k≥1, k is an integer, representing the number of attributes to be screened at present, the initial value of k is equal to 1 and starts from 1 and recursively increases, and N represents the total number of control attributes, such as 4 control attributes of dye concentration, incubation time, pH value and temperature, then N=4, the correlation sorting selects the control attribute that has the greatest influence on the umbilical cord stem cell activity through statistical methods, avoiding noise interference.

[0036] Second, when k>N, the k-item cell activity correlation control attribute is taken as a union set, and the cell activity correlation control attribute is obtained. Specifically, when k increases to more than N (k>N), it means that all control attributes have been traversed, and all k-item cell activity correlation control attributes sorted out in history are taken as a union set to form a complete cell activity correlation control attribute set as the cell activity correlation control attribute.

[0037] Finally, according to the cell activity correlation control attribute, the umbilical cord stem cell staining monitoring parameters of the umbilical cord stem cell activity detection sample are collected, compared with the umbilical cord stem cell staining expected parameters, and the umbilical cord stem cell staining parameter deviation vector is obtained. The umbilical cord stem cell staining monitoring parameter refers to the key index data that can reflect the staining effect and cell state after staining the umbilical cord stem cells (such as trypan blue, fluorescent dye, etc.) through experimental equipment (such as microscope, flow cytometer, image analysis system) quantitative collection, including staining positive rate, fluorescence intensity, etc. The staining positive rate refers to the proportion of successfully stained living cells or dead cells in the total cells, such as blue dead cells in trypan blue staining, which can be directly related to cell activity, and the proportion of living cells = 1-dead cell staining positive rate. The fluorescence intensity is the mean, peak or standard deviation of the fluorescence signal intensity of the cell population after fluorescent dye labeling, and abnormal fluorescence intensity indicates uneven staining or damaged cell membrane integrity. The umbilical cord stem cell staining expected parameter is the ideal standard value pre-configured by the user end, and the umbilical cord stem cell staining parameter deviation vector = umbilical cord stem cell staining monitoring parameter-umbilical cord stem cell staining expected parameter.

[0038] Exemplarily, based on the screened cell activity correlation control attribute, the umbilical cord stem cell staining monitoring parameters of the umbilical cord stem cell activity detection sample are collected, such as staining positive rate 92%, fluorescence intensity mean 1500AU, and the preset umbilical cord stem cell staining expected parameters are staining positive rate 95%, fluorescence intensity mean 1600AU. The umbilical cord stem cell staining monitoring parameters and the umbilical cord stem cell staining expected parameters are compared item by item to obtain the umbilical cord stem cell staining parameter deviation vector: [Δ staining positive rate =-3%, Δ fluorescence intensity mean =-100AU].

[0039] In this way, by progressively optimizing the data dimensions through attribute screening, the bias vector is ensured to focus on the staining variables that have a significant impact on cell viability. This not only improves computational efficiency but also enhances the reliability of subsequent confidence interval assessments of viability values, providing a precise data foundation for automated stem cell quality testing.

[0040] Specifically, such as Figure 2 As shown, the phrase "when k≤N, perform k-item correlation sorting of umbilical cord stem cell activity control attributes to obtain k cell activity correlation control attributes" includes:

[0041] One cell viability-related control attribute up to k-1 cell viability-related control attributes are removed from the umbilical cord stem cell staining control attributes to obtain the retained umbilical cord stem cell staining control attributes.

[0042] When the total number of staining control attributes of the retained umbilical cord stem cells is ≤k-1, the process stops and outputs that k cell activity-related control attributes are empty.

[0043] When the total number of staining control attributes of the retained umbilical cord stem cells is greater than k-1, the staining control attributes of the retained umbilical cord stem cells are sorted by the correlation of k items of umbilical cord stem cell activity to obtain k cell activity-related control attributes.

[0044] In the embodiments of this application, such as Figure 2 As shown, firstly, up to k-1 cell activity-related control attributes are removed from the umbilical cord stem cell staining control attributes to obtain the remaining umbilical cord stem cell staining control attributes. For example, starting from k=1, the value of k is increased round by round: k=1, 2, 3, ... Before each round of correlation sorting, the cell activity-related control attributes selected in the previous k-1 rounds are deleted. For example, when k=3, the cell activity-related control attributes selected in the previous 2 rounds are deleted, ensuring that each round focuses on unselected attribute combinations. When k=1, the optimal solution for a single attribute is screened; when k=2, the synergistic effect of two attributes is screened from the remaining umbilical cord stem cell staining control attributes.

[0045] Secondly, when the total number of staining control attributes of retained umbilical cord stem cells is ≤k-1, the process stops, and the output shows that k cell viability-related control attributes are empty. This is because there are not enough retained umbilical cord stem cell staining control attributes to form k combinations. For example, if the total number of staining control attributes of retained umbilical cord stem cells is 2, k=3, since it is impossible to make a 3-item combination from 2 umbilical cord stem cell staining control attributes, the process stops directly, and the output shows that the 3 cell viability-related control attributes are empty.

[0046] Finally, when the total number of the remaining umbilical cord stem cell staining control attributes is greater than k-1, the umbilical cord stem cell activity k-item correlation sorting is performed on the remaining umbilical cord stem cell staining control attributes to obtain k-item cell activity correlation control attributes. In this way, by gradually removing the confirmed cell activity correlation control attributes, the search space is reduced, repeated calculation is avoided, the empty set is returned automatically when the remaining umbilical cord stem cell staining control attributes are insufficient, invalid calculation is prevented, and it is ensured that each round of sorting focuses on the unexplored correlation control attributes.

[0047] For example, if the total set of umbilical cord stem cell staining control attributes is [dye concentration, incubation time, pH value, temperature, centrifugal force], when k = 1, k-1 = 0 cell activity correlation control attributes are deleted from the umbilical cord stem cell staining control attributes to obtain the remaining umbilical cord stem cell staining control attributes [dye concentration, incubation time, pH value, temperature, centrifugal force]. Since the total number of the remaining umbilical cord stem cell staining control attributes is 5 > k-1 = 0, the umbilical cord stem cell activity 1-item correlation sorting is performed on the remaining umbilical cord stem cell staining control attributes to obtain 1-item cell activity correlation control attributes, for example, [dye concentration]. When k = 2, k-1 = 1 cell activity correlation control attributes are deleted from the umbilical cord stem cell staining control attributes to obtain the remaining umbilical cord stem cell staining control attributes [incubation time, pH value, temperature, centrifugal force]. Since the total number of the remaining umbilical cord stem cell staining control attributes is 4 > k-1 = 1, the umbilical cord stem cell activity 2-item correlation sorting is performed on the remaining umbilical cord stem cell staining control attributes to obtain 2-item cell activity correlation control attributes, for example, [incubation time, pH value]. When k = 3, one cell activity correlation control attribute is deleted from the umbilical cord stem cell staining control attributes until k-1 = 2 cell activity correlation control attributes are deleted to obtain the remaining umbilical cord stem cell staining control attributes [temperature, centrifugal force]. Since the total number of the remaining umbilical cord stem cell staining control attributes is 2 < k-1 = 2, the process is stopped, and the k-item cell activity correlation control attributes are empty.

[0048] Further, the “performing umbilical cord stem cell activity k-item correlation sorting on the remaining umbilical cord stem cell staining control attributes to obtain k-item cell activity correlation control attributes” comprises:

[0049] Accessing the staining control attribute deviation threshold value pre-configured by the user terminal for the remaining umbilical cord stem cell staining control attributes;

[0050] Performing k-item combination on the remaining umbilical cord stem cell staining control attributes to obtain a k-item staining control attribute combination set;

[0051] Based on the staining control attribute deviation threshold value, performing umbilical cord stem cell activity k-item correlation sorting on the k-item staining control attribute combination set to obtain k-item cell activity correlation control attributes.

[0052] In the embodiments of the present application, first, the user terminal retrieves the preconfigured staining control attribute deviation threshold for the staining control attribute of the retained umbilical cord stem cells. The staining control attribute deviation threshold is the maximum fluctuation range allowed by the staining control attribute. For example, the staining control attribute of the retained umbilical cord stem cells is [pH value, temperature, centrifugal force], and the user terminal retrieves the preconfigured staining control attribute deviation threshold, for example, pH value deviation ±0.3, temperature deviation ±1°C, and centrifugal force deviation ±5%, to provide a quantitative standard for subsequent combination screening.

[0053] Secondly, the staining control attribute of the retained umbilical cord stem cells is combined in k items to obtain a k-item staining control attribute combination set, wherein X is the total number of staining control attributes of the retained umbilical cord stem cells. For example, if the staining control attribute set of the retained umbilical cord stem cells is [dye concentration, incubation time, pH value, temperature, centrifugal force], when k = 2, there are 2-item staining control attribute combination sets, for example, [incubation time, pH value], [pH value, temperature], [temperature, centrifugal force], etc., as the 2-item staining control attribute combination set.

[0054] Finally, based on the staining control attribute deviation threshold, the k-item staining control attribute combination set is traversed to perform k-item correlation sorting of umbilical cord stem cell activity, and k-item cell activity correlation control attributes are obtained.

[0055] Specifically, the "based on the staining control attribute deviation threshold, traversing the k-item staining control attribute combination set to perform k-item correlation sorting of umbilical cord stem cell activity, and obtaining k-item cell activity correlation control attributes" includes:

[0056] extracting a first k-item staining control attribute combination from the k-item staining control attribute combination set;

[0057] retrieving a second umbilical cord stem cell activity detection sample set, wherein the second umbilical cord stem cell activity detection sample set has a one-to-one corresponding first umbilical cord stem cell activity detection record value set, a staining control attribute record value set, and an umbilical cord stem cell collection condition record value set;

[0058] performing cluster analysis on the umbilical cord stem cell collection condition record value set to obtain a plurality of clusters of umbilical cord stem cell collection condition record values;

[0059] based on the first cluster of umbilical cord stem cell collection condition record values of the plurality of clusters of umbilical cord stem cell collection condition record value sets, extracting a first cluster of staining control attribute record values from the staining control attribute record value set;

[0060] extracting a first staining control attribute record value and a second staining control attribute record value from the first cluster of staining control attribute record values;

[0061] extracting a first set of dyeing control attribute deviation thresholds from the dyeing control attribute deviation thresholds based on the first k-item dyeing control attribute combination, and storing the remaining dyeing control attribute deviation thresholds as a second set of dyeing control attribute deviation thresholds;

[0062] When the first set of dyeing control attribute deviations of the first dyeing control attribute record value and the second dyeing control attribute record value are respectively greater than the first set of dyeing control attribute deviation thresholds, and the second set of dyeing control attribute deviations are respectively less than or equal to the second set of dyeing control attribute deviation thresholds, comparing the first set of umbilical cord stem cell activity detection record values based on the first dyeing control attribute record value and the second dyeing control attribute record value, obtaining a first activity detection fluctuation value, and adding it into the activity detection fluctuation value set;

[0063] When the multi-cluster umbilical cord stem cell collection condition record value traversal is completed, the mean value of the activity detection fluctuation value set is calculated, and is set as the first k-item dyeing control attribute combination correlation coefficient;

[0064] When the first k-item dyeing control attribute combination correlation coefficient is greater than or equal to the correlation coefficient threshold, the first k-item dyeing control attribute combination is added into the k-item cell activity correlation control attribute.

[0065] In the embodiment of the application, first, a k-item dyeing control attribute combination is randomly extracted from the k-item dyeing control attribute combination set as the first k-item dyeing control attribute combination, for example, [pH value, temperature].

[0066] Secondly, a second umbilical cord stem cell activity detection sample set is retrieved from historical data, wherein the second umbilical cord stem cell activity detection sample set has a one-to-one corresponding first umbilical cord stem cell activity detection record value set, a dyeing control attribute record value set and an umbilical cord stem cell collection condition record value set. Umbilical cord stem cell activity refers to the proportion of living cells. Umbilical cord stem cell collection conditions include donor physical condition (such as donor physical condition), umbilical cord collection condition (such as time window), umbilical cord site selection, separation and culture, and cryopreservation and recovery condition, etc. Precondition for activity detection.

[0067] Thirdly, cluster analysis is performed on the umbilical cord stem cell collection condition record value set to obtain multi-cluster umbilical cord stem cell collection condition record values. This is to eliminate environmental interference. The sample collection conditions of the same cluster umbilical cord stem cell collection condition record value set after clustering are similar. At this time, the interference of the collection condition can be excluded, and the activity difference can be attributed to the dyeing parameter.

[0068] Further, based on the first cluster of umbilical cord stem cell collection condition record values of the set of umbilical cord stem cell collection condition record values, the first cluster of staining control attribute record values is extracted from the set of staining control attribute record values, and the first staining control attribute record value, such as pH = 7.2, temperature = 25°C, centrifugal force 3050 rpm, and the second staining control attribute record value, such as pH = 6.8, temperature = 27°C, centrifugal force 3000 rpm, are extracted from the first cluster of staining control attribute record values.

[0069] Further, based on the first k staining control attribute combination, the first set of staining control attribute deviation thresholds, such as pH value ± 0.3, temperature ± 1°C, is extracted from the staining control attribute deviation threshold, and the remaining staining control attribute deviation threshold, such as centrifugal force ± 100 rpm, is stored as the second set of staining control attribute deviation thresholds.

[0070] Further, when the first set of staining control attribute deviations of the first staining control attribute record value and the second staining control attribute record value are greater than the first set of staining control attribute deviation thresholds, respectively, and the second set of staining control attribute deviations are less than or equal to the second set of staining control attribute deviation thresholds, respectively, based on the first staining control attribute record value and the second staining control attribute record value, the first umbilical cord stem cell activity detection record value set is compared to obtain the first activity detection fluctuation value, which is added to the activity detection fluctuation value set. The first set of staining control attribute deviation thresholds and the second set of staining control attribute deviation thresholds are set to focus on the first set of staining control attributes and isolate the target variable (the first set of staining control attributes) and the noise variable (the second set of staining control attributes). For example, the first set of staining control attribute deviations of the first staining control attribute record value and the second staining control attribute record value, such as pH deviation |7.2-6.8| = 0.4, temperature deviation |25-27| = 2°C, and the second set of staining control attribute deviations, such as centrifugal force deviation |3000-3050| = 50 rpm, are calculated, and then compared with the first set of staining control attribute deviation thresholds and the second set of staining control attribute deviation thresholds. Since the first set of staining control attribute deviations 0.4 > 0.3 and 2°C > 1°C, and the second set of staining control attribute deviations 50 rpm < 100 ± 100 rpm, the conditions are met simultaneously, based on the first umbilical cord stem cell activity detection record value set, the activity detection record values of the two groups of samples, such as 92% and 85%, are obtained, the first activity detection fluctuation value = |92%-85%| = 7% is calculated, and added to the activity detection fluctuation value set. The first activity detection fluctuation value can quantify the degree of activity detection fluctuation caused by changes in target attributes.

[0071] Further, the multiple cluster umbilical cord stem cell collection condition record values are traversed in the same way, and when the multiple cluster umbilical cord stem cell collection condition record values are traversed, the mean value of the activity detection fluctuation value set is calculated and set as the first k-item dyeing control attribute combination correlation coefficient. For example, the multiple cluster umbilical cord stem cell collection condition record values are traversed, the cluster 1 activity detection fluctuation value 7% is calculated, the cluster 2 activity detection fluctuation value 9% is calculated, and so on, the mean value is calculated to obtain the first k-item dyeing control attribute combination correlation coefficient, for example, 8%, which represents the average influence strength of the first k-item dyeing control attribute combination on the activity detection fluctuation in the multiple cluster umbilical cord stem cell collection condition record values.

[0072] Finally, when the first k-item dyeing control attribute combination correlation coefficient is greater than or equal to the correlation coefficient threshold value, the first k-item dyeing control attribute combination is added to the k-item cell activity correlation control attribute, wherein the correlation coefficient threshold value is used to filter weakly related dyeing control attribute combinations and avoid noise interference, and a person skilled in the art can dynamically set it according to the actual situation, such as 5%. For example, if the first k-item dyeing control attribute combination correlation coefficient is 8% and the correlation coefficient threshold value is 5%, the first k-item dyeing control attribute combination, for example, [pH, temperature], is added to the k-item cell activity correlation control attribute.

[0073] Specifically, the "cluster analysis on the umbilical cord stem cell collection condition record value set to obtain multiple cluster umbilical cord stem cell collection condition record values" comprises:

[0074] The collection condition Euclidean distance threshold value is configured through the user end;

[0075] The first umbilical cord stem cell collection condition record value and the second umbilical cord stem cell collection condition record value are extracted from the umbilical cord stem cell collection condition record value set;

[0076] The first umbilical cord stem cell collection condition record value and the second umbilical cord stem cell collection condition record value are subjected to the same attribute deviation calculation to obtain a first umbilical cord stem cell collection condition deviation set;

[0077] After the first umbilical cord stem cell collection condition deviation set is traversed and normalized, the Euclidean distance calculation is performed to obtain the collection condition Euclidean distance;

[0078] When the collection condition Euclidean distance is greater than the collection condition Euclidean distance threshold value, the first umbilical cord stem cell collection condition record value and the second umbilical cord stem cell collection condition record value are regarded as different classes, otherwise, the first umbilical cord stem cell collection condition record value and the second umbilical cord stem cell collection condition record value are regarded as the same cluster.

[0079] In the embodiments of the present application, first, the user end is configured to set the acquisition condition Euclidean distance threshold value to determine the similarity clustering standard of the acquisition condition. For example, the acquisition condition Euclidean distance threshold value is set to 0.25. The smaller the acquisition condition Euclidean distance threshold value is, the more similar the acquisition conditions of the samples in the same cluster are.

[0080] Secondly, the first and second umbilical cord stem cell acquisition condition record values are extracted from the umbilical cord stem cell acquisition condition record value set. For example, the first umbilical cord stem cell acquisition condition record value, such as {age 30 years old, temperature 4℃}, and the second umbilical cord stem cell acquisition condition record value, such as {age 28 years old, temperature 5℃}, are extracted from the umbilical cord stem cell acquisition condition record value set.

[0081] Thirdly, the first and second umbilical cord stem cell acquisition condition record values are subjected to the same attribute deviation calculation to obtain the first umbilical cord stem cell acquisition condition deviation set, wherein the same attribute deviation calculation can calculate the absolute deviation. For example, if the first umbilical cord stem cell acquisition condition record value is {age 30 years old, temperature 4℃} and the second umbilical cord stem cell acquisition condition record value is {age 28 years old, temperature 5℃}, the same attribute deviation calculation is performed: Δage = |30-28| = 2, Δtemperature = |4-5| = 1, and thus the first umbilical cord stem cell acquisition condition deviation set {Δage = 2, Δtemperature = 1} is calculated according to the same method.

[0082] Further, after the first umbilical cord stem cell acquisition condition deviation set is traversed for normalization processing, the Euclidean distance calculation is performed to obtain the acquisition condition Euclidean distance, wherein the normalization processing is performed to eliminate the dimensional difference of different condition attributes, and the normalization can be based on the maximum value of the same attribute in the first and second umbilical cord stem cell acquisition condition record values. For example, if the maximum value of the age in the first and second umbilical cord stem cell acquisition condition record values is 50 years old and the maximum value of the temperature is 5℃, then Δage = 2 and Δtemperature = 1 are normalized to 2 / 50 = 0.04 and 1 / 5 = 0.2 to obtain the normalized deviation set {0.04, 0.2}, and then the Euclidean distance calculation is performed, for example, The acquisition condition Euclidean distance is obtained.

[0083] Finally, when the collection condition Euclidean distance is greater than the collection condition Euclidean distance threshold value, the first umbilical cord stem cell collection condition record value and the second umbilical cord stem cell collection condition record value are regarded as different classes, otherwise, the first umbilical cord stem cell collection condition record value and the second umbilical cord stem cell collection condition record value are regarded as the same cluster. Exemplarily, if the collection condition Euclidean distance threshold value is 0.25, the collection condition Euclidean distance is 0.204, the first umbilical cord stem cell collection condition record value and the second umbilical cord stem cell collection condition record value are regarded as the same cluster, which indicates that the sample collection conditions are similar, and at this time, the interference of the collection condition can be excluded, and the activity difference can be attributed to the staining parameter, otherwise, the different classes indicate that the sample collection conditions are different, and the activity difference may be caused by the collection condition.

[0084] In summary, compared with the prior art, the present application obtains the umbilical cord stem cell staining monitoring parameter of the umbilical cord stem cell activity detection sample, compares the umbilical cord stem cell staining expected parameter, and obtains the umbilical cord stem cell staining parameter deviation vector. In this way, the key staining control attribute combination is accurately identified, the activity decline is attributed to the cell culture result in advance to the staining process parameter, the dimensionality reduction data (only the key attribute deviation is retained) of the subsequent activity confidence interval calculation is provided after the correlation screening, the biological effect of the quantitative deviation mapping is provided, and the core data basis for the standardization of the stem cell industrial production is provided.

[0085] S20: Collecting a first umbilical cord stem cell activity detection sample set with the umbilical cord stem cell staining parameter deviation vector, the umbilical cord stem cell staining expected parameter and umbilical cord stem cell collection conditions as constraints, performing central value evaluation, and obtaining an umbilical cord stem cell activity value confidence interval, wherein the umbilical cord stem cell activity value is represented by the proportion of live cell number.

[0086] In the embodiment of the present application, the homogenized sample set is screened from the historical library with the umbilical cord stem cell staining parameter deviation vector, the umbilical cord stem cell staining expected parameter (the standard value preset by the user end) and the umbilical cord stem cell collection conditions (such as donor age, storage temperature) as constraints, to ensure that the samples are highly comparable in the staining deviation range and the collection conditions. Then, the central value evaluation is performed on the first umbilical cord stem cell activity detection sample set by the box plot method, to obtain the umbilical cord stem cell activity value confidence interval.

[0087] Exemplarily, with the umbilical cord stem cell staining parameter deviation vector such as [Δstaining positive rate =-3%, Δfluorescence intensity mean =-100 AU], the umbilical cord stem cell staining expected parameter such as a staining positive rate 95%, a fluorescence intensity mean 1600 AU, and the umbilical cord stem cell collection condition such as a donor 25-30 years old, as a screening constraint, a plurality of umbilical cord stem cell activity detection samples meeting the above constraint conditions are collected from a database as a first umbilical cord stem cell activity detection sample set, and a living cell proportion range is [90.1%, 96.3%]. After the box plot method is used to remove outliers, an umbilical cord stem cell activity value confidence interval is calculated as [92.8%, 95.4%]. In this way, the umbilical cord stem cell activity value confidence interval that can dynamically reflect a reasonable activity fluctuation range under a current process level is obtained, and a comparison standard is provided for subsequent abnormal data identification.

[0088] S30: When the umbilical cord stem cell activity detection value does not belong to the umbilical cord stem cell activity value confidence interval, the umbilical cord stem cell activity detection value is identified as abnormal quality detection data.

[0089] In the embodiment of the present application, the umbilical cord stem cell activity value confidence interval is used as a standard threshold range, the umbilical cord stem cell activity value confidence interval represents a reasonable fluctuation range of the umbilical cord stem cell activity value under normal circumstances, and when the actual activity detection value of a sample exceeds the umbilical cord stem cell activity value confidence interval, the umbilical cord stem cell activity detection value is marked as abnormal quality detection data. This identification means that the activity state of the sample deviates from the normal range, and there may be a quality risk, which needs to be further investigated, such as sample contamination, detection operation error, abnormal activity of stem cells, etc. In this way, the abnormal data is accurately identified, and subsequent management personnel can make targeted selection according to the indication, thereby ensuring the stability of the quality detection data.

[0090] S40: When the umbilical cord stem cell activity detection value belongs to the umbilical cord stem cell activity value confidence interval, the umbilical cord stem cell activity detection value is identified as normal quality detection data.

[0091] In the embodiment of the present application, the umbilical cord stem cell activity value confidence interval is used as a standard threshold range, the umbilical cord stem cell activity value confidence interval represents a reasonable fluctuation range of the umbilical cord stem cell activity value under normal circumstances, and when the actual activity detection value of a sample exceeds the umbilical cord stem cell activity value confidence interval, the umbilical cord stem cell activity detection value is marked as abnormal quality detection data. This identification means that the activity state of the sample deviates from the normal range, and there may be a quality risk, which needs to be further investigated, such as sample contamination, detection operation error, abnormal activity of stem cells, etc. In this way, the abnormal data is accurately identified, and subsequent management personnel can make targeted selection according to the indication, thereby ensuring the stability of the quality detection data.

[0092] In this way, the explicit numerical interval determination replaces the traditional empirical judgment, realizes the standardization and automation of the umbilical cord stem cell quality evaluation, reduces the human subjective error, and provides a clear classification basis for subsequent quality traceability and data analysis.

[0093] Further, the application also includes: when the number of umbilical cord stem cell activity detection values with normal quality detection data identification is greater than or equal to the fitting number, sending a fitting prompt to the user end.

[0094] In the embodiment of the application, when the cumulative number of activity detection values with normal quality identification reaches a preset fitting threshold (such as 1000 cases), a fitting prompt signal is automatically sent to the user end, triggering a confidence interval refitting process based on a new data set. In this way, the process lag is eliminated: the traditional static confidence interval (such as [92.3%, 95.7%]) cannot adapt to the new standard after the process upgrade, such as the average activity rising to 94.5%, and the application avoids misjudging the high activity sample under the new process as abnormal by continuously including the recent qualified samples to recalculate the interval, for example, updating to [94.1%, 96.5%].

[0095] In summary, the embodiment of the application has at least the following technical effects:

[0096] Compared with the prior art, the application first obtains the umbilical cord stem cell staining monitoring parameters of the umbilical cord stem cell activity detection sample, compares them with the umbilical cord stem cell staining expected parameters, and obtains the umbilical cord stem cell staining parameter deviation vector. In this way, the key staining control attribute combination is accurately identified, the activity decline is attributed to the staining process parameters in advance from the cell culture results, the dimensionality reduction data (only the key attribute deviation is retained) is provided for subsequent activity confidence interval calculation after the correlation screening, the deviation mapping biological effect is quantified, and the core data basis is provided for the standardization of stem cell industrial production.

[0097] Secondly, the application takes the umbilical cord stem cell staining parameter deviation vector, the umbilical cord stem cell staining expected parameters and the umbilical cord stem cell collection conditions as constraints, collects a first umbilical cord stem cell activity detection sample set, performs a central value evaluation, and obtains a umbilical cord stem cell activity value confidence interval. In this way, the umbilical cord stem cell activity value confidence interval that can dynamically reflect the reasonable activity fluctuation range under the current process level is obtained, providing a comparison standard for subsequent abnormal data identification.

[0098] Thirdly, the application identifies the umbilical cord stem cell activity detection value as abnormal quality detection data when the umbilical cord stem cell activity detection value does not belong to the umbilical cord stem cell activity value confidence interval. In this way, the abnormal data is accurately identified, and the subsequent management personnel can make targeted selection according to the indication, ensuring the stability of the quality detection data.

[0099] Finally, when the umbilical cord stem cell activity detection value belongs to the umbilical cord stem cell activity value confidence interval, the umbilical cord stem cell activity detection value is marked as normal quality detection data. In this way, the traditional empirical judgment is replaced by a clear numerical interval judgment, realizing the standardization and automation of umbilical cord stem cell quality evaluation, reducing human subjective errors, and providing clear classification basis for subsequent quality traceability and data analysis.

[0100] Through the above technical solutions, the present application adopts recursive k-term combination screening (k increases from 1 to N) and collection condition clustering analysis, accurately identifies key influencing factor combinations from multi-dimensional staining control attributes, and takes the umbilical cord stem cell staining parameter deviation vector, the umbilical cord stem cell staining expected parameter and the umbilical cord stem cell collection condition as constraints, collects homogeneous samples, and performs centralized value processing to obtain the umbilical cord stem cell activity value confidence interval, and accordingly marks the umbilical cord stem cell activity detection value as abnormal quality detection data or normal quality detection data. In this way, the abnormal quality detection data of the umbilical cord stem cell activity detection value is effectively identified, and the reliability and stability of the quality detection result are improved.

[0101] Embodiment two, as shown in Figure 3 the same inventive concept as the umbilical cord stem cell quality detection data analysis method provided in embodiment one, the present application embodiment further provides a kind of umbilical cord stem cell quality detection data analysis system of artificial intelligence, comprising:

[0102] deviation comparison module 11, for obtaining the umbilical cord stem cell staining monitoring parameter of umbilical cord stem cell activity detection sample, compared with umbilical cord stem cell staining expected parameter, obtain umbilical cord stem cell staining parameter deviation vector;

[0103] confidence analysis module 12, for taking the umbilical cord stem cell staining parameter deviation vector, the umbilical cord stem cell staining expected parameter and umbilical cord stem cell collection condition as constraints, collection first umbilical cord stem cell activity detection sample set, execute centralized value evaluation, obtain umbilical cord stem cell activity value confidence interval, wherein umbilical cord stem cell activity value is represented by the number of living cells proportion;

[0104] abnormal identification module 13, for when umbilical cord stem cell activity detection value does not belong to the umbilical cord stem cell activity value confidence interval, umbilical cord stem cell activity detection value is marked as abnormal quality detection data;

[0105] normal identification module 14, for when umbilical cord stem cell activity detection value belongs to the umbilical cord stem cell activity value confidence interval, umbilical cord stem cell activity detection value is marked as normal quality detection data.

[0106] Wherein, the deviation comparison module 11 is specifically used for:

[0107] when k≤N, the umbilical cord stem cell activity k-item correlation sorting of the umbilical cord stem cell staining control attribute is performed to obtain k-item cell activity correlation control attributes, N≥k≥1, k is an integer, the initial value of k is equal to 1, and N represents the total number of control attributes;

[0108] when k>N, the cell activity correlation control attributes are taken in a union set to obtain cell activity correlation control attributes;

[0109] According to the cell activity correlation control attribute, the umbilical cord stem cell staining monitoring parameters of the umbilical cord stem cell activity detection sample are collected, and the umbilical cord stem cell staining expected parameters are compared to obtain the umbilical cord stem cell staining parameter deviation vector.

[0110] Specifically, the "when k≤N, the umbilical cord stem cell activity k-item correlation sorting of the umbilical cord stem cell staining control attribute is performed to obtain k-item cell activity correlation control attributes" comprises:

[0111] One cell activity correlation control attribute to k-1 cell activity correlation control attributes are deleted from the umbilical cord stem cell staining control attribute to obtain remaining umbilical cord stem cell staining control attributes;

[0112] When the total number of remaining umbilical cord stem cell staining control attributes is less than or equal to k-1, the process is stopped, and it is output that the k-item cell activity correlation control attributes are empty;

[0113] When the total number of remaining umbilical cord stem cell staining control attributes is greater than k-1, the umbilical cord stem cell activity k-item correlation sorting of the remaining umbilical cord stem cell staining control attribute is performed to obtain k-item cell activity correlation control attributes.

[0114] Specifically, the "the umbilical cord stem cell activity k-item correlation sorting of the remaining umbilical cord stem cell staining control attribute is performed to obtain k-item cell activity correlation control attributes" comprises:

[0115] The user end pre-configured staining control attribute deviation threshold value for the remaining umbilical cord stem cell staining control attribute is called;

[0116] The remaining umbilical cord stem cell staining control attribute is k-item combined to obtain a k-item staining control attribute combination set;

[0117] Based on the staining control attribute deviation threshold value, the k-item staining control attribute combination set is traversed to perform umbilical cord stem cell activity k-item correlation sorting to obtain k-item cell activity correlation control attributes.

[0118] Specifically, the "based on the staining control attribute deviation threshold value, the k-item staining control attribute combination set is traversed to perform umbilical cord stem cell activity k-item correlation sorting to obtain k-item cell activity correlation control attributes" comprises:

[0119] extracting a first k-item dyeing control attribute combination from the k-item dyeing control attribute combination set;

[0120] calling a second umbilical cord stem cell activity detection sample set, wherein the second umbilical cord stem cell activity detection sample set has a one-to-one corresponding first umbilical cord stem cell activity detection record value set, a dyeing control attribute record value set and an umbilical cord stem cell collection condition record value set;

[0121] performing cluster analysis on the umbilical cord stem cell collection condition record value set to obtain a plurality of umbilical cord stem cell collection condition record value clusters;

[0122] extracting a first cluster dyeing control attribute record value from the dyeing control attribute record value set based on the first cluster umbilical cord stem cell collection condition record value of the plurality of umbilical cord stem cell collection condition record value clusters;

[0123] extracting a first dyeing control attribute record value and a second dyeing control attribute record value from the first cluster dyeing control attribute record value;

[0124] extracting a first group of dyeing control attribute deviation thresholds from the dyeing control attribute deviation threshold based on the first k-item dyeing control attribute combination, and storing the remaining dyeing control attribute deviation thresholds as a second group of dyeing control attribute deviation thresholds;

[0125] When the first dyeing control attribute record value and the second dyeing control attribute record value have a first group of dyeing control attribute deviations greater than the first group of dyeing control attribute deviation thresholds and a second group of dyeing control attribute deviations less than or equal to the second group of dyeing control attribute deviation thresholds, respectively, based on the first dyeing control attribute record value and the second dyeing control attribute record value, comparing the first umbilical cord stem cell activity detection record value set to obtain a first activity detection fluctuation value, and adding it to an activity detection fluctuation value set;

[0126] When the plurality of umbilical cord stem cell collection condition record value clusters are traversed, the mean value of the activity detection fluctuation value set is calculated and set as a first k-item dyeing control attribute combination correlation coefficient;

[0127] When the first k-item dyeing control attribute combination correlation coefficient is greater than or equal to a correlation coefficient threshold, the first k-item dyeing control attribute combination is added to the k-item cell activity correlation control attribute.

[0128] Further, the "performing cluster analysis on the umbilical cord stem cell collection condition record value set to obtain a plurality of umbilical cord stem cell collection condition record value clusters" comprises:

[0129] configuring a collection condition Euclidean distance threshold through a user terminal;

[0130] extracting a first umbilical cord stem cell collection condition record value and a second umbilical cord stem cell collection condition record value from the umbilical cord stem cell collection condition record value set;

[0131] performing homogeneity bias calculation on the first umbilical cord stem cell collection condition record value and the second umbilical cord stem cell collection condition record value to obtain a first umbilical cord stem cell collection condition bias set;

[0132] After performing normalization processing on the first umbilical cord stem cell collection condition bias set, performing Euclidean distance calculation to obtain a collection condition Euclidean distance;

[0133] When the collection condition Euclidean distance is greater than the collection condition Euclidean distance threshold, regarding the first umbilical cord stem cell collection condition record value and the second umbilical cord stem cell collection condition record value as different classes, otherwise, regarding the first umbilical cord stem cell collection condition record value and the second umbilical cord stem cell collection condition record value as the same cluster.

[0134] The confidence analysis module 12 is specifically configured to:

[0135] collecting a first umbilical cord stem cell activity detection sample set with the umbilical cord stem cell staining parameter bias vector, the umbilical cord stem cell staining expected parameter and the umbilical cord stem cell collection condition as constraints, performing centralized value evaluation to obtain an umbilical cord stem cell activity value confidence interval, wherein the umbilical cord stem cell activity value is represented by the proportion of the number of living cells.

[0136] The anomaly identification module 13 is specifically configured to:

[0137] When the umbilical cord stem cell activity detection value does not belong to the umbilical cord stem cell activity value confidence interval, performing anomaly quality detection data identification on the umbilical cord stem cell activity detection value.

[0138] The normal identification module 14 is specifically configured to:

[0139] When the umbilical cord stem cell activity detection value belongs to the umbilical cord stem cell activity value confidence interval, performing normal quality detection data identification on the umbilical cord stem cell activity detection value.

[0140] Further, it further includes: when the number of umbilical cord stem cell activity detection values with the normal quality detection data identification is greater than or equal to the fitting number, sending a fitting prompt to the user end.

[0141] In summary, the embodiments of the present application have at least the following technical effects:

[0142] Compared with the prior art, firstly, by means of the deviation comparison module, the umbilical cord stem cell staining monitoring parameter of the umbilical cord stem cell activity detection sample is obtained, compared with the umbilical cord stem cell staining expected parameter, the umbilical cord stem cell staining parameter deviation vector is obtained, the key staining control attribute combination is accurately identified, the activity recession is attributed to the staining process parameter in advance from the cell culture result, the reduced dimension data (only the key attribute deviation is reserved) of the correlation screening is provided for subsequent activity confidence interval calculation, the biological effect of the quantitative deviation mapping is provided, and the core data basis for the standardization of the stem cell industrial production is provided. Secondly, by means of the confidence analysis module, the first umbilical cord stem cell activity detection sample set is collected by taking the umbilical cord stem cell staining parameter deviation vector, the umbilical cord stem cell staining expected parameter and the umbilical cord stem cell collection condition as constraints, the central value evaluation is performed, the umbilical cord stem cell activity value confidence interval is obtained, the umbilical cord stem cell activity value confidence interval that can dynamically reflect the reasonable activity fluctuation range under the current process level is obtained, and the comparison standard for subsequent abnormal data identification is provided. Thirdly, by means of the abnormal identification module, when the umbilical cord stem cell activity detection value does not belong to the umbilical cord stem cell activity value confidence interval, the abnormal quality detection data identification is performed on the umbilical cord stem cell activity detection value, the abnormal data is accurately identified, subsequent management personnel can make targeted selection according to the indication, and the stability of the quality detection data is ensured. Finally, by means of the normal identification module, when the umbilical cord stem cell activity detection value belongs to the umbilical cord stem cell activity value confidence interval, the normal quality detection data identification is performed on the umbilical cord stem cell activity detection value, the traditional experiential judgment is replaced by the clear numerical interval judgment, the standardization and automation of the umbilical cord stem cell quality evaluation are realized, the human subjective error is reduced, and clear classification basis is provided for subsequent quality tracing and data analysis. In this way, the abnormal quality detection data of the umbilical cord stem cell activity detection value is effectively identified, and the reliability and stability of the quality detection result are improved.

[0143] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0144] Those skilled in the art will understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0145] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 means for functionally implementing the steps listed in the flowchart block or blocks.

[0146] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 means for functionally implementing the steps listed in the flowchart block or blocks.

[0147] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 means for functionally implementing the steps listed in the flowchart block or blocks.

[0148] While the preferred embodiments of the application have been described, additional variations and modifications can be made to the embodiments described and shown, and it is therefore intended that such additional variations and modifications be included within the scope of the application. Accordingly, it is the intent of the following claims to cover all such variations and modifications as falling within the scope of the application and its

[0149] It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the spirit or scope of the application. Thus, it is intended that the present application cover the modifications and variations of this application and its equivalent.

Claims

1. A method for analyzing umbilical cord stem cell quality detection data using artificial intelligence, characterized in that, include: The staining monitoring parameters of umbilical cord stem cells in the umbilical cord stem cell activity test sample are obtained and compared with the expected staining parameters of umbilical cord stem cells to obtain the umbilical cord stem cell staining parameter deviation vector. Constrained by the umbilical cord stem cell staining parameter deviation vector, the umbilical cord stem cell staining expected parameters, and the umbilical cord stem cell collection conditions, a first set of umbilical cord stem cell activity detection samples is collected, a central value assessment is performed, and the confidence interval of umbilical cord stem cell activity value is obtained, wherein the umbilical cord stem cell activity value is characterized by the proportion of live cells. When the umbilical cord stem cell activity test value does not fall within the confidence interval of the umbilical cord stem cell activity value, the umbilical cord stem cell activity test value is marked as abnormal quality test data; When the umbilical cord stem cell activity test value falls within the confidence interval of the umbilical cord stem cell activity value, the umbilical cord stem cell activity test value is marked as normal quality test data; This involves obtaining umbilical cord stem cell staining monitoring parameters, comparing them with expected umbilical cord stem cell staining parameters, and obtaining an umbilical cord stem cell staining parameter deviation vector, including: When k≤N, the staining control attributes of umbilical cord stem cells are sorted by the correlation of k items of umbilical cord stem cell activity to obtain k cell activity-related control attributes. N≥k≥1, k is an integer, the initial value of k is equal to 1, and N represents the total number of control attributes. When k > N, take the union of the k cell activity-related control attributes to obtain the cell activity-related control attributes. Based on the cell activity-related control attributes, the umbilical cord stem cell staining monitoring parameters of the umbilical cord stem cell activity detection samples are collected and compared with the expected umbilical cord stem cell staining parameters to obtain the umbilical cord stem cell staining parameter deviation vector. When k≤N, the staining control attributes of umbilical cord stem cells are sorted by the correlation of k cell activity to obtain k cell activity-related control attributes, including: One cell viability-related control attribute up to k-1 cell viability-related control attributes are removed from the umbilical cord stem cell staining control attributes to obtain the retained umbilical cord stem cell staining control attributes. When the total number of staining control attributes of the retained umbilical cord stem cells is ≤k-1, the process stops and outputs that k cell activity-related control attributes are empty. When the total number of staining control attributes of the retained umbilical cord stem cells is greater than k-1, the staining control attributes of the retained umbilical cord stem cells are sorted by the correlation of k items of umbilical cord stem cell activity to obtain k cell activity-related control attributes. Specifically, the staining control attributes of the retained umbilical cord stem cells are sorted by k-item correlation of umbilical cord stem cell activity to obtain k cell activity-related control attributes, including: Retrieve the staining control attribute deviation threshold pre-configured by the user terminal for the staining control attributes of the retained umbilical cord stem cells; The staining control attributes of the preserved umbilical cord stem cells are combined with k items to obtain a set of k staining control attribute combinations. Based on the staining control attribute deviation threshold, the set of k staining control attribute combinations is traversed to perform k-item correlation sorting of umbilical cord stem cell activity to obtain k cell activity correlation control attributes. Specifically, based on the staining control attribute deviation threshold, the set of k staining control attribute combinations is traversed to perform k-item correlation sorting of umbilical cord stem cell activity, obtaining k cell activity-related control attributes, including: Extract the first k-item combination of staining control attributes from the set of k-item combination of staining control attributes; Retrieve a second set of umbilical cord stem cell activity detection samples, wherein the second set of umbilical cord stem cell activity detection samples has a one-to-one corresponding set of first umbilical cord stem cell activity detection record values, a set of staining control attribute record values, and a set of umbilical cord stem cell collection condition record values; Cluster analysis was performed on the set of recorded values ​​of umbilical cord stem cell collection conditions to obtain multi-cluster recorded values ​​of umbilical cord stem cell collection conditions; Based on the first cluster of umbilical cord stem cell collection condition record values ​​from the set of multi-cluster umbilical cord stem cell collection condition record values, the first cluster of staining control attribute record values ​​is extracted from the set of staining control attribute record values. Extract the first staining control attribute record value and the second staining control attribute record value from the first cluster of staining control attribute record values; Based on the first k-item combination of staining control attributes, extract the first set of staining control attribute deviation thresholds from the staining control attribute deviation thresholds, and store the remaining staining control attribute deviation thresholds as the second set of staining control attribute deviation thresholds. When the first group of staining control attribute deviations of the first staining control attribute record value and the second staining control attribute record value are respectively greater than the first group of staining control attribute deviation threshold, and the second group of staining control attribute deviations are respectively less than or equal to the second group of staining control attribute deviation threshold, based on the first staining control attribute record value and the second staining control attribute record value, the first activity detection fluctuation value is obtained by comparing with the first umbilical cord stem cell activity detection record value set, and added to the activity detection fluctuation value set; Once the collection conditions and recorded values ​​of the multi-cluster umbilical cord stem cells have been traversed, the mean of the set of activity detection fluctuation values ​​is calculated and set as the correlation coefficient of the first k-th staining control attribute combination. When the correlation coefficient of the first k-item staining control attribute combination is greater than or equal to the correlation coefficient threshold, the first k-item staining control attribute combination is added to the k-item cell activity correlation control attribute.

2. The method as described in claim 1, characterized in that, Cluster analysis was performed on the set of recorded values ​​for umbilical cord stem cell collection conditions to obtain multi-cluster recorded values ​​for umbilical cord stem cell collection conditions, including: Configure the Euclidean distance threshold for data collection conditions via the user terminal; Extract the first umbilical cord stem cell collection condition record value and the second umbilical cord stem cell collection condition record value from the set of umbilical cord stem cell collection condition record values; The same attribute deviation is calculated for the first umbilical cord stem cell collection condition record value and the second umbilical cord stem cell collection condition record value to obtain the first umbilical cord stem cell collection condition deviation set; After traversing the first set of deviations for umbilical cord stem cell collection conditions and performing normalization, Euclidean distance calculation is performed to obtain the Euclidean distance of the collection conditions. If the Euclidean distance of the acquisition conditions is greater than the threshold value of the Euclidean distance of the acquisition conditions, the recorded values ​​of the first umbilical cord stem cell acquisition conditions and the second umbilical cord stem cell acquisition conditions are considered to be of different categories; otherwise, the recorded values ​​of the first umbilical cord stem cell acquisition conditions and the second umbilical cord stem cell acquisition conditions are considered to be in the same cluster.

3. The method as described in claim 1, characterized in that, Also includes: When the number of umbilical cord stem cell activity detection values ​​with the normal quality detection data identifier is greater than or equal to the fitted number, a fitting prompt is sent to the user terminal.

4. An artificial intelligence-based umbilical cord stem cell quality detection data analysis system, characterized in that, For performing the method according to any one of claims 1-3, comprising: The deviation comparison module is used to obtain the umbilical cord stem cell staining monitoring parameters of the umbilical cord stem cell activity detection sample, compare them with the expected umbilical cord stem cell staining parameters, and obtain the umbilical cord stem cell staining parameter deviation vector. The confidence analysis module is used to collect a first set of umbilical cord stem cell activity detection samples, perform central value assessment, and obtain the confidence interval of umbilical cord stem cell activity value, constrained by the umbilical cord stem cell staining parameter deviation vector, the umbilical cord stem cell staining expected parameters, and the umbilical cord stem cell collection conditions. The umbilical cord stem cell activity value is characterized by the proportion of live cells. An anomaly identification module is used to identify abnormal quality test data for umbilical cord stem cell activity test values ​​when the umbilical cord stem cell activity test value does not fall within the confidence interval of the umbilical cord stem cell activity value. The normal identification module is used to identify the umbilical cord stem cell activity detection value as normal quality detection data when the umbilical cord stem cell activity detection value falls within the confidence interval of the umbilical cord stem cell activity value.

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