Cell number measurement method applied to biological information

Abstract

The present invention discloses a method for measuring the number of cells applied to biological information, which relates to the technical field of cell measurement, and is used to improve the cell suspension polymerization situation and reduce the cell number measurement error problem. The method includes retrieving the information of the culture medium where the cells to be measured are located, analyzing the suspension change of the cells to be measured in combination with the culture time interval, setting a suspension threshold using the logarithmic combination method, screening out the suspended cells in the culture dish according to the suspension threshold, setting multiple monitoring intervals to monitor the cell viability and cell cycle in each monitoring interval and adjusting each monitoring area to generate cell levels, counting the number of suspended cells in each cell level to obtain the level density, measuring the size of the suspended cells, determining the maximum cell aggregation amount of each cell level, fusing the level density and the maximum cell aggregation amount, calculating the suspension aggregation index of each cell level using the polynomial regression algorithm and converting it into a measurement error value caused by the suspended cells, and marking the culture dish according to the measurement error value for cell measurement.

Description

Technical Field

[0001] The present invention relates to the field of cell measurement technology, and more specifically, to a method for measuring the number of cells applied to biological information. Background Art

[0002] Cell measurement technology is a method for quantitatively and qualitatively analyzing cell characteristics and behaviors. The application of cell measurement technology to biological information can provide more accurate biological cell data, which helps to improve the project efficiency of biological experiments or cell technologies.

[0003] The existing technology has the following deficiencies:

[0004] In the past, when measuring the number of cells in a biological target, for the convenience of measurement, cell samples were first extracted from the target and placed in a culture dish for measurement. However, before measurement, due to the mutual attraction between cells and the problem of culture medium materials, some cells appeared in a suspended or aggregated state, increasing the measurement error of the underlying cells and affecting the progress and accuracy of the results of biological projects. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the existing technology, an embodiment of the present invention provides a method for measuring the number of cells applied to biological information, which screens suspended cells and divides the suspended area through culture information, sets monitoring intervals in the culture dish to generate cell levels, analyzes the suspended and aggregated conditions of the suspended cells, and determines whether to select the current culture dish for cell measurement to solve the problems raised in the above background art.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A method for measuring the number of cells applied to biological information, comprising the following steps:

[0008] Step S1: Retrieve the information of the culture medium where the cells to be measured are located, analyze the suspension changes of the cells to be measured in combination with the culture time interval, and set a suspension threshold based on the default suspension amount of the culture medium information using the suspension changes of the cells to be measured;

[0009] Step S2: Screen out the suspended cells according to the suspension threshold and determine the suspended cell area of the culture dish. Set multiple monitoring intervals in the suspended cell area of the culture dish, detect the cell viability and cell cycle of each monitoring interval and calculate the stratification coefficient, and adjust each monitoring interval according to the stratification coefficient to generate cell levels;

[0010] Step S3: Count the number of suspended cells in each cell level to obtain the level density, randomly select multiple suspended cells in each cell level for size measurement, and determine the maximum cell aggregation amount of each cell level according to the size measurement results;

[0011] Step S4: Calculate the suspension aggregation index of each cell layer using the polynomial regression algorithm based on the layer density and the maximum cell aggregation amount of each cell layer, convert the suspension aggregation index of each cell layer into a measurement error value caused by suspended cells, select a culture dish for marking according to the measurement error value caused by suspended cells, and screen the marked culture dishes for cell measurement.

[0012] In a preferred embodiment, in step S1, the medium information is the growth factor concentration. Record the time point when the cells are transferred and cultured in the culture dish, and make a secondary record when measuring the suspension change. Take the time interval between the two time points as the culture time interval.

[0013] For each culture dish, analyze the suspension change of the cells to be measured using the logarithmic combination method in combination with the growth factor concentration and the culture time interval, and set a suspension threshold.

[0014] In a preferred embodiment, in step S1, use the logarithmic combination method to analyze the suspension change of the cells to be measured and set a suspension threshold. The specific steps are as follows:

[0015] Data preprocessing: Standardize the growth factor concentration and the culture time of each culture dish, and then merge them into a factor concentration data set and a culture time data set respectively.

[0016] Establish a suspension change system: Randomly select one data from the factor concentration data set and the culture time data set for factor combination: , where x is the data selected from the factor concentration data set, y is the data selected from the culture time data set, and H is the suspension coefficient of the culture dish obtained by calculation.

[0017] Set the number of system executions: Set the number of system executions to n times, and obtain n suspension coefficients of culture dishes by randomly selecting n data from the factor concentration data set and the culture time data set.

[0018] Set the suspension threshold: Establish a suspension change system with the data averages in the factor concentration data set and the culture time data set, calculate the suspension coefficient of the culture dish as the suspension calibration value, calculate the average value among the n suspension coefficients of the culture dishes obtained as the suspension true value, and set the suspension threshold through the suspension threshold adjustment formula: , where is the suspension true value, is the suspension calibration value, is the default suspension space, is the suspension threshold.

[0019] The default suspension space is the suspension space of the cells to be measured in the corresponding culture dish where the culture dish is stored in the cell information management system.

[0020] In a preferred embodiment, in step S2, the space within the suspension threshold of the culture dish is taken as the suspension cell region, and the cells to be tested within the suspension cell region in each culture dish are taken as the suspension cells in the corresponding culture dish;

[0021] Multiple monitoring regions are set from top to bottom in the suspension cell region according to a preset ratio. The suspension cells in each monitoring region are counted, and the survival rate of the suspension cells in the corresponding monitoring region is calculated. The cell cycle of the suspension cells in each monitoring region is detected, and the proportion of each cycle stage is counted.

[0022] In a preferred embodiment, in step S2, the weighted sum of the proportion of the cycle stage of the suspension cells in each monitoring region and the preset migration weight of each cell cycle is calculated to obtain the cell migration coefficient of each monitoring region;

[0023] The survival rate of the suspension cells in each monitoring region and the cell migration coefficient are comprehensively used to calculate the stratification coefficient by using the logistic regression algorithm. The specific steps are as follows:

[0024] A logistic regression formula is constructed by using the survival rate of the suspension cells in the monitoring region and the cell migration coefficient of the corresponding monitoring region: , where e is the natural base, z is the influencing parameter and z is the summation result of the survival rate of the suspension cells in the corresponding monitoring region and the cell migration coefficient, and L is the stratification coefficient of the corresponding monitoring region calculated by the logistic regression formula;

[0025] The same processing is performed on each monitoring region to obtain the stratification coefficients of each monitoring region. The average value of the stratification coefficients of all monitoring regions is taken as the stratification benchmark. By adjusting the space size of each monitoring region, the stratification coefficients of each monitoring region are changed so that the stratification coefficient of each monitoring region reaches the stratification benchmark. Then, the space size of each monitoring region is adjusted, and the adjusted monitoring regions are used as cell hierarchies for generation.

[0026] In a preferred embodiment, in step S3, the number of suspension cells in each cell hierarchy is counted to obtain the hierarchy density, and the hierarchy density is the ratio of the number of suspension cells in each cell hierarchy to the space volume of the corresponding cell hierarchy;

[0027] In each cell hierarchy, multiple suspension cells are randomly selected for size measurement, and the average value is calculated as the size standard of the suspension cells in the corresponding cell hierarchy. According to the preset single cell judgment condition, the suspension cells in each cell hierarchy are judged and processed. The suspension cell with the largest size in each cell hierarchy is selected for size recording, and the size recording result is used as the size to be evaluated of the corresponding cell hierarchy. The size to be evaluated of each cell hierarchy is compared with the size standard of the suspension cells in the corresponding cell hierarchy to determine the maximum cell aggregation amount of each cell hierarchy.

[0028] In a preferred embodiment, in step S3, the specific steps for determining the maximum cell aggregation amount of each cell layer according to the size measurement results are as follows:

[0029] Take the ratio of the size to be evaluated of each cell layer to the size standard of the suspended cells in the corresponding cell layer as the cell amount evaluation parameter of the corresponding cell layer, and round up the cell amount evaluation parameter of each cell layer to obtain the maximum cell aggregation amount of the corresponding cell layer.

[0030] In a preferred embodiment, in step S4, use the polynomial regression algorithm to calculate the suspension aggregation index of each cell layer by integrating the layer density and the maximum cell aggregation amount of each cell layer. The specific steps are as follows:

[0031] Normalize the input: Use the Max-Min algorithm to normalize the layer density and the maximum cell aggregation amount of the cell layer.

[0032] Construct a polynomial regression formula: Construct a polynomial regression formula with the normalized results of the layer density and the maximum cell aggregation amount of each cell layer: , where a is the normalized result of the layer density of each cell layer, b is the normalized result of the maximum cell aggregation amount of each cell layer, c is a preset adjustment parameter, and F is the suspension aggregation index.

[0033] The adjustment parameter c is used to control the numerical range of the suspension aggregation index.

[0034] In a preferred embodiment, in step S4, use the conversion formula to calculate the measurement error value caused by the suspended cells for the suspension aggregation index of each cell layer: , where C is the measurement error value caused by the suspended cells in each culture dish, and s is the conversion ratio.

[0035] Compare the measurement error value caused by the suspended cells in each culture dish with a preset error threshold. If the measurement error value caused by the suspended cells in the current culture dish exceeds the error threshold, then reject the current culture dish and do not perform cell number measurement; if the measurement error value caused by the suspended cells in the current culture dish is lower than the error threshold, then mark the current culture dish and perform cell number measurement.

[0036] The technical effects and advantages of the cell number measurement method of the present invention applied to biological information:

[0037] The present invention analyzes the suspension change of cells to be measured by retrieving the information of the culture medium where the cells to be measured are located and combining the culture time interval. Based on the default suspension amount of the culture medium information, a suspension threshold is set using the suspension change of the cells. The suspended cells in the culture dish are screened according to the suspension threshold, avoiding the decrease in the accuracy of subsequent analysis caused by the vague definition of suspension. Multiple monitoring intervals are set to monitor the cell viability and cell cycle in each monitoring interval and calculate the stratification coefficient. Each monitoring area is adjusted according to the stratification coefficient to generate cell levels. The cell levels are used to reduce the error tolerance of subsequent statistical cell distribution density. The number of suspended cells in each cell level is counted to obtain the level density. The size of the suspended cells is measured to determine the maximum cell aggregation amount in each cell level. The suspension aggregation index of each cell level is calculated by combining the level density and the maximum cell aggregation amount of each cell level and converted into a measurement error value caused by the suspended cells. The culture dish is marked according to the measurement error value for cell measurement, reducing the measurement error of cell data caused by the suspended cells. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a schematic diagram of a method for measuring the number of cells applied to biological information according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0039] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0040] The present invention analyzes the suspension change of cells to be measured by retrieving the information of the culture medium where the cells to be measured are located and combining the culture time interval. Based on the default suspension amount of the culture medium information, a suspension threshold is set using the suspension change of the cells. The suspended cells in the culture dish are screened according to the suspension threshold. Multiple monitoring intervals are set to monitor the cell viability and cell cycle in each monitoring interval and calculate the stratification coefficient. Each monitoring area is adjusted according to the stratification coefficient to generate cell levels. The number of suspended cells in each cell level is counted to obtain the level density. The size of the suspended cells is measured to determine the maximum cell aggregation amount in each cell level. The suspension aggregation index of each cell level is calculated by combining the level density and the maximum cell aggregation amount of each cell level and converted into a measurement error value caused by the suspended cells. The culture dish is marked according to the measurement error value for cell measurement, reducing the measurement error of cell data caused by the suspended cells.

[0041] Example, a method for measuring cell data applied to biological information, as Figure 1 shown, includes the following steps:

[0042] Step S1: Retrieve the culture medium information of the cells to be tested, analyze the suspension changes of the cells to be tested in combination with the culture time interval, and set a suspension threshold based on the default suspension amount of the culture medium information using the suspension changes of the cells to be tested;

[0043] Step S2: Screen out the suspended cells according to the suspension threshold and determine the suspended cell area of the culture dish. Set multiple monitoring intervals in the suspended cell area of the culture dish, detect the cell viability and cell cycle of each monitoring interval and calculate the stratification coefficient, and adjust each monitoring interval according to the stratification coefficient to generate cell levels;

[0044] Step S3: Count the number of suspended cells in each cell level to obtain the level density, randomly select multiple suspended cells in each cell level for size measurement, and determine the maximum cell aggregation amount of each cell level according to the size measurement results;

[0045] Step S4: Calculate the suspension aggregation index of each cell level by using the polynomial regression algorithm based on the level density and the maximum cell aggregation amount of each cell level, convert the suspension aggregation index of each cell level into a measurement error value caused by the suspended cells, select the culture dish for marking according to the measurement error value caused by the suspended cells, and screen and mark the culture dish for cell measurement.

[0046] The specific implementation is as follows:

[0047] In Step S1: Access the cell information management system to retrieve the culture medium information of the cells to be tested. The culture medium information is the growth factor concentration. Before measuring the cell quantity, it is necessary to transfer and culture the cells of the target body. Usually, randomly select multiple positions on the target body, extract equal - amount samples and transfer them to culture dishes made of the same material for storage, and obtain the cell quantity range interval of the target body by measuring the cells in multiple culture dishes respectively;

[0048] Record the time point when the cells are transferred and cultured into the culture dish, and make a second record when measuring the suspension change. Take the time interval between the two time points as the culture time interval;

[0049] For each culture dish, analyze the suspension changes of the cells to be tested and set the suspension threshold by using the logarithmic combination method in combination with the growth factor concentration and the culture time interval. The specific steps are as follows:

[0050] Data pre - processing: Standardize the growth factor concentration and culture time of each culture dish, and then merge them into a factor concentration data set and a culture time data set respectively;

[0051] Establish a suspension change system: Randomly select one data from the factor concentration data set and the culture time data set respectively for factor combination; , where x is the data selected from the factor concentration dataset, y is the data selected from the culture time dataset, and H is the calculated suspension coefficient of the petri dish;

[0052] Set the number of system executions: Set the number of system executions to n times, and obtain n suspension coefficients of petri dishes by randomly selecting n data from the factor concentration dataset and the culture time dataset;

[0053] Set the suspension threshold: Establish a suspension change system with the data averages in the factor concentration dataset and the culture time dataset, calculate the suspension coefficient of the petri dish as the suspension calibration value, calculate the average value among the obtained n suspension coefficients of the petri dishes as the suspension true value, and set the suspension threshold through the suspension threshold adjustment formula: , where is the suspension true value, is the suspension calibration value, is the default suspension space, is the suspension threshold.

[0054] It should be noted that the cell information management system is a software tool for managing and tracking cell lines and their related data, which contains the medium information of the cells to be tested. In this example, it is used to call the growth factor concentration of the petri dish. The higher the growth factor concentration of the cells to be tested in the petri dish, the lower the suspension amount of dead cells and the larger the suspension space. The longer the culture time of the cells to be tested, the increase in cell precipitation behavior and the larger the suspension space; the default suspension space is the suspension space of the cells to be tested in the corresponding petri dish stored in the cell information management system, and the suspension threshold is geometrically amplified or reduced through the default suspension space to reduce the measurement error of cell quantity caused by suspended cells.

[0055] In step S2, the space within the suspension threshold of the petri dish is used as the suspended cell area, and the cells to be tested within the suspended cell area in each petri dish are used as the suspended cells in the corresponding petri dish;

[0056] Set multiple monitoring areas from top to bottom in the suspended cell area according to a preset ratio, count the suspended cells in each monitoring area, calculate the survival rate of the suspended cells in the corresponding monitoring area, detect the cell cycle of the suspended cells in each monitoring area, and count the proportion of each cycle stage;

[0057] It should be noted that cells in a petri dish will have multiple cell cycles. For example, the interphase, synthesis phase, and mitosis phase. Each cell cycle will have cell migration due to specific physiological processes. Cell migration includes cell floating and cell sinking, which will affect the cell suspension trend.

[0058] The weighted sum of the proportion of the cell cycle stage of the suspended cells in each monitoring area and the preset migration weight of each cell cycle is calculated to obtain the cell migration coefficient of each monitoring area;

[0059] Using the survival rate of the suspended cells in each monitoring area and the cell migration coefficient, the stratification coefficient is calculated by using the logistic regression algorithm. The specific steps are as follows:

[0060] A logistic regression formula is constructed by using the survival rate of the suspended cells in the monitoring area and the cell migration coefficient of the corresponding monitoring area: , where e is the natural base, z is the influence parameter and z is the summation result of the survival rate of the suspended cells in the corresponding monitoring area and the cell migration coefficient, and L is the stratification coefficient of the corresponding monitoring area calculated by the logistic regression formula;

[0061] The same processing is performed on each monitoring area to obtain the stratification coefficient of each monitoring area. The average value of the stratification coefficients of all monitoring areas is taken as the stratification benchmark. By adjusting the spatial size of each monitoring area, the stratification coefficient of each monitoring area is changed so that the stratification coefficient of each monitoring area reaches the stratification benchmark. Then, the spatial size of each monitoring area is adjusted, and the adjusted monitoring areas are used as cell levels for generation.

[0062] It should be noted that the generated cell levels are used to reduce the error tolerance of subsequent statistical cell distribution density, and at the same time, the measurement influence caused by cell movement is reduced.

[0063] In step S3, the number of suspended cells in each cell level is counted to obtain the level density, and the level density is the ratio of the number of suspended cells in each cell level to the spatial volume of the corresponding cell level;

[0064] In each cell level, multiple suspended cells are randomly selected for size measurement and the average value is calculated as the size standard of the suspended cells in the corresponding cell level. According to the preset single cell judgment condition, the suspended cells in each cell level are judged and processed, and the suspended cell with the largest size in each cell level is selected for size recording. The size recording result is used as the size to be evaluated of the corresponding cell level. The size to be evaluated of each cell level is compared with the size standard of the suspended cells in the corresponding cell level to determine the maximum cell aggregation amount of each cell level;

[0065] The specific steps for determining the maximum cell aggregation amount of each cell level according to the size measurement results are as follows:

[0066] The ratio of the size to be evaluated of each cell level to the size standard of the suspended cells in the corresponding cell level is used as the cell amount evaluation parameter of the corresponding cell level, and the cell amount evaluation parameter of each cell level is rounded up to obtain the maximum cell aggregation amount of the corresponding cell level.

[0067] It should be noted that when counting cells, the interaction between cells will cause multiple cells to form aggregates, resulting in deviation in cell counting. The larger the size to be evaluated at each cell level, the greater the interaction between suspended cells within the corresponding cell level, the easier it is to form aggregates, and the greater the deviation in cell counting. In addition, the greater the density of suspended cells, the greater the deviation in counting the bottom-layer cells.

[0068] In step S4, the suspension aggregation index of each cell level is calculated using the polynomial regression algorithm by integrating the level density and the maximum cell aggregation amount of each cell level. The specific steps are as follows:

[0069] Normalized input: Use the Max-Min algorithm to normalize the level density and the maximum cell aggregation amount of the cell level: , where is the level density or the maximum cell aggregation amount of each cell level, is the maximum value of the level density or the maximum cell aggregation amount of all cell levels, is the minimum value of the level density or the maximum cell aggregation amount of all cell levels, is the result after normalizing the level density or the maximum cell aggregation amount of the corresponding cell level;

[0070] Construct a polynomial regression formula: Construct a polynomial regression formula using the results after normalizing the level density and the maximum cell aggregation amount of each cell level: , where a is the result after normalizing the level density of each cell level, b is the result after normalizing the maximum cell aggregation amount of each cell level, c is an adjustment parameter, and F is the suspension aggregation index.

[0071] It should be explained that in this example, the weight of each parameter in the above polynomial regression formula is set to 1, which can be adjusted according to the actual situation. The adjustment parameter c is used to control the value range of the suspension aggregation index, which is convenient for converting it into the measurement error value of suspended cells. The adjustment parameter c is not unique. For example, the adjustment parameter can be set to 0.2, etc., and no more analysis will be done here.

[0072] Calculate the measurement error value caused by suspended cells using the conversion formula with the suspension aggregation index of each cell level: , where C is the measurement error value caused by suspended cells in each culture dish, and s is the conversion ratio;

[0073] Compare the measurement error values caused by the suspended cells in each petri dish with a preset error threshold. If the measurement error value caused by the suspended cells in the current petri dish exceeds the error threshold, then exclude the current petri dish and do not perform cell count measurement; if the measurement error value caused by the suspended cells in the current petri dish is lower than the error threshold, then mark the current petri dish and perform cell count measurement.

[0074] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product.

[0075] Those of ordinary skill in the art can realize that the modules and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application of the technical solution and the inventive constraints. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0076] In addition, in each embodiment of this application, the various functional modules can be integrated into one processing module, or each module can exist physically alone, or two or more modules can be integrated into one module.

[0077] As described above, this is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in this application, and all should be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

[0078] Finally: The above are only the preferred embodiments of the present invention and are not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for measuring the number of cells applied to bioinformatics, characterized in that: The following steps are included: Step S1: Retrieve the culture medium information of the cells to be tested, analyze the suspension changes of the cells to be tested in combination with the culture time interval, and set the suspension threshold value based on the default suspension amount of the culture medium information and the suspension changes of the cells to be tested; Step S2: screening out suspended cells according to the suspension threshold and determining the suspended cell area of ​​the culture dish, setting multiple monitoring intervals in the suspended cell area of ​​the culture dish, detecting the cell activity and cell cycle of each monitoring interval and calculating the stratification coefficient, and adjusting each monitoring interval according to the stratification coefficient to generate a cell hierarchy; Step S3: Counting the number of suspended cells in each cell layer to obtain the layer density, randomly selecting a plurality of suspended cells in each cell layer to measure their size, and determining the maximum cell aggregation amount of each cell layer according to the size measurement results; Step S4: The suspension aggregation index of each cell layer is calculated by comprehensively considering the layer density of each cell layer and the maximum cell aggregation amount using a polynomial regression algorithm, and the suspension aggregation index of each cell layer is converted into a measurement error value caused by suspended cells. According to the measurement error value caused by suspended cells, culture dishes are selected for marking, and the marked culture dishes are screened for cell measurement.

2. The method for measuring the number of cells applied to biological information according to claim 1, characterized in that: In step S1, the culture medium information is the growth factor concentration, the time point when the cells are transferred to the culture dish is recorded, and a second record is made when the suspension change is measured, and the time interval between the two time points is used as the culture time interval; For each culture dish, combined with the growth factor concentration and culture time interval, the logarithmic binding method is used to analyze the suspension changes of the cells to be tested and set the suspension threshold.

3. The method for measuring the number of cells applied to biological information according to claim 2, characterized in that: In step S1, the logarithmic binding method is used to analyze the suspension changes of the cells to be tested and set the suspension threshold. The specific steps are as follows: Data preprocessing: The growth factor concentration and culture time of each culture dish were standardized and merged into the factor concentration data set and culture time data set respectively; Establish a suspension change system: randomly select a data in the factor concentration data set and the culture time data set for factor combination: , where x is the data selected from the factor concentration data set, y is the data selected from the culture time data set, and H is the culture dish suspension coefficient obtained by calculation; Set the number of system executions: Set the number of system executions to n times, and obtain the suspension coefficients of n culture dishes by randomly selecting n data from the factor concentration data set and the culture time data set; Set the suspension threshold: Use the average values ​​of the factor concentration data set and the culture time data set to establish a suspension change system and calculate the culture dish suspension coefficient as the suspension calibration value. Calculate the average value of the obtained n culture dish suspension coefficients as the true suspension value, and set the suspension threshold through the suspension threshold adjustment formula: ,in, is the true value of the suspension, is the suspension calibration value, It is the default floating space. is the suspension threshold; The default suspension space is the suspension space of the cells to be tested in the culture dish where the corresponding culture dish is located that has been stored in the cell information management system.

4. The method for measuring the number of cells applied to biological information according to claim 3, characterized in that: In step S2, the space of the culture dish within the suspension threshold is regarded as the suspension cell area, and the cells to be tested in the suspension cell area in each culture dish are regarded as the suspension cells in the corresponding culture dish; A plurality of monitoring areas are set from top to bottom in the suspended cell area according to a preset ratio, the suspended cells in each monitoring area are counted and the survival rate of the suspended cells in the corresponding monitoring area is calculated, the cell cycle of the suspended cells in each monitoring area is detected and the proportion of each cycle stage is counted.

5. The method for measuring the number of cells applied to biological information according to claim 1, characterized in that: In step S2, the cell migration coefficient of each monitoring area is obtained by weighted summing the cycle phase proportion of the suspended cells in each monitoring area and the preset migration weight of each cell cycle; The stratification coefficient was calculated by using a logistic regression algorithm based on the survival rate of suspended cells and the cell migration coefficient in each monitoring area. The specific steps are as follows: The logistic regression formula is constructed using the survival rate of suspended cells in the monitoring area and the cell migration coefficient of the corresponding monitoring area: , where e is the natural base, z is the influencing parameter and z is the sum of the survival rate of suspended cells and the cell migration coefficient in the corresponding monitoring area, and L is the stratification coefficient of the corresponding monitoring area calculated by the logistic regression formula; The same processing is performed on each monitoring area to obtain the stratification coefficient of each monitoring area. The average stratification coefficient of all monitoring areas is taken as the stratification benchmark. The stratification coefficient of each monitoring area is changed by adjusting the spatial size occupied by each monitoring area. When the stratification coefficient of each monitoring area reaches the stratification benchmark, the spatial size of each monitoring area is adjusted, and the adjusted monitoring areas are generated as cell levels.

6. The method for measuring the number of cells applied to biological information according to claim 5, characterized in that: In step S3, the suspended cells in each cell layer are counted to obtain the layer density, which is the ratio of the number of suspended cells in each cell layer to the volume of the corresponding cell layer space; In each cell level, a plurality of suspended cells are randomly selected for size measurement and the average value is calculated as the size standard of the suspended cells in the corresponding cell level. The suspended cells in each cell level are judged according to the preset single cell judgment conditions. The suspended cells with the largest size in each cell level are selected for size recording. The size recording result is used as the size to be assessed in the corresponding cell level. The size to be assessed in each cell level is compared with the size standard of the suspended cells in the corresponding cell level to determine the maximum cell aggregation amount in each cell level.

7. The method for measuring the number of cells applied to biological information according to claim 6, characterized in that: In step S3, the maximum cell aggregation amount of each cell level is determined according to the size measurement results. The specific steps are as follows: The ratio of the size to be assessed at each cell level to the size standard of suspended cells in the corresponding cell level is used as the cell amount assessment parameter of the corresponding cell level, and the cell amount assessment parameter of each cell level is rounded up as the maximum cell aggregation amount of the corresponding cell level.

8. The method for measuring the number of cells applied to biological information according to claim 7, characterized in that: In step S4, the suspension aggregation index of each cell layer is calculated by using a polynomial regression algorithm based on the layer density of each cell layer and the maximum cell aggregation amount. The specific steps are as follows: Normalized input: The Max-Min algorithm was used to normalize the cell layer density and the maximum cell aggregation; Constructing a polynomial regression formula: Constructing a polynomial regression formula based on the normalized results of the layer density of each cell layer and the maximum cell aggregation amount: , where a is the result of normalization of the layer density of each cell layer, b is the result of normalization of the maximum cell aggregation amount of each cell layer, c is the preset adjustment parameter, and F is the suspension aggregation index; The adjustment parameter c is used to control the numerical range of the suspension aggregation index.

9. The method for measuring the number of cells applied to biological information according to claim 8, characterized in that: In step S4, the suspension aggregation index of each cell level is converted into the measurement error value caused by the suspended cells using the conversion formula: , where C is the measurement error caused by the suspended cells in each culture dish, and s is the conversion ratio; The measurement error value caused by the suspended cells in each culture dish is compared with the preset error threshold. If the measurement error value caused by the suspended cells in the current culture dish exceeds the error threshold, the current culture dish is discarded and the cell number measurement is not performed; if the measurement error value caused by the suspended cells in the current culture dish is lower than the error threshold, the current culture dish is marked and the cell number measurement is performed.

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