A data processing method for evaluating the aging state of an oocyte in vitro

Abstract

The application belongs to the technical field of data analysis, and particularly relates to a data processing method for evaluating the aging state of an ex vivo oocyte, which comprises the following steps: performing extreme value normalization on multi-source biochemical data to obtain a comprehensive energy value and a comprehensive damage value; performing algebraic calculation on the comprehensive damage value and the comprehensive energy value to obtain an aging arrest value; performing moving average calculation on the comprehensive damage value to obtain a recent damage average value, and performing natural logarithm decay calculation on the basis of the recent damage average value to obtain an exhaustion acceleration index; obtaining an effective detachment duration according to a detachment duration and a limit tolerance time, and performing nonlinear survival decay calculation on the basis of the aging arrest value and the exhaustion acceleration index to obtain a comprehensive potential value; obtaining an activity determination threshold value according to a historical potential sequence, performing moving average filtering on the comprehensive potential value to obtain a smoothed potential value, and generating a control instruction in combination with the activity determination threshold value. According to the smoothed potential value, the application improves the accuracy of the evaluation of the aging state of the ex vivo oocyte.

Description

Technical Field

[0001] This invention relates to the field of data processing technology. More specifically, this invention relates to a data processing method for assessing the senescence status of isolated oocytes. Background Technology

[0002] In the daily operation of in vitro oocyte processing and culture, assessing the senescence status of in vitro oocytes is a core step in ensuring culture quality. Its main task is to monitor multi-source biochemical data of in vitro oocytes in real time, so as to promptly detect and trigger intervention mechanisms when cells experience energy depletion or structural degradation. This prevents the retention of pathological cells and the resulting waste of resources, ensuring the viability of batch samples and the reliability of subsequent operations.

[0003] Existing cell status assessment systems typically rely on biochemical sensor probes deployed within culture equipment to acquire biochemical indicators and apply the traditional single static threshold comparison method. By comparing the difference between single-dimensional biochemical data and preset standard parameters, the current survival status of isolated oocytes is determined.

[0004] However, in long-term in vitro monitoring environments, assessing the senescence status of isolated oocytes faces the dual challenges of physiological changes and hardware degradation. Firstly, the degradation of isolated oocytes is not a linear decline of a single indicator; it is characterized by a mutual constraint between the accumulation of oxidative damage and energy supply. With increasing isolation time, cellular senescence exhibits a non-linear acceleration, causing irregular fluctuations in the rate of deterioration of internal lipotoxicity and oxidative damage.

[0005] Secondly, multi-source biochemical sensing probes used to extract mevalonate concentration, mitochondrial membrane potential, sphingomyelin concentration, and reactive oxygen species levels will experience probe aging and shift, increased noise, and baseline drift after long-term operation. This physical characteristic of distorted static reference parameters, combined with the physiological characteristics of nonlinear aging arrest in isolated oocytes, increases the difficulty of data acquisition and processing. Summary of the Invention

[0006] To address the technical problem of bias in assessing the aging state of isolated oocytes using traditional methods, this invention provides a data processing method for assessing the aging state of isolated oocytes, comprising: collecting multi-source biochemical data from isolated oocytes; the multi-source biochemical data including mevalonate concentration, mitochondrial membrane potential, sphingomyelin concentration, and reactive oxygen species (ROS) level; reading the upper and lower limit calibration values ​​corresponding to each item of the multi-source biochemical data, and performing extreme value normalization accordingly to obtain mevalonate normalized value, potential normalized value, lipid normalized value, and ROS normalized value; taking the minimum value among the mevalonate normalized value and potential normalized value as the comprehensive energy value, and taking the maximum value among the lipid normalized value and ROS normalized value as the comprehensive damage value; The aging arrest value is calculated based on the comprehensive damage value and the comprehensive energy value; the temporal characteristics of the comprehensive damage value are extracted and a moving average is calculated to obtain the recent damage mean; the exhaustion acceleration index is obtained based on the recent damage mean; the effective extraction time is determined based on the extraction time and the limit tolerance time of the isolated oocytes; the comprehensive potential value is obtained based on the limit tolerance time, the effective extraction time, the aging arrest value, and the exhaustion acceleration index; the activity judgment threshold is generated based on the dynamic distribution of the historical potential sequence; the comprehensive potential value is time-series filtered to obtain the smoothed potential value; historical judgment identifiers are extracted, and the evaluation result is determined by combining the smoothed potential value and the activity judgment threshold, and control instructions for automated processing of isolated oocytes are generated accordingly.

[0007] This invention utilizes sliding window extreme value calculation to dynamically extract historical data limit calibration values, measuring the dynamic distribution trajectory of biochemical sequences and eliminating underlying mapping interference caused by probe aging. By comprehensively analyzing damage and energy values, it extrapolates aging inhibition values, measuring the nonlinear scaling relationship between damage degree and energy supply, thus avoiding early warning bias caused by the disconnect of single-dimensional biochemical indicators. It introduces natural logarithmic decay calculation to generate a decay acceleration index, measuring the corresponding evolution of recent damage mean and nonlinear mapping constant, preventing premature removal defects caused by fixed-time decay. Combining effective detachment time and extreme tolerance time for nonlinear survival decay extrapolation, it measures the coupling and synergistic relationship between time scale and dimensionless biochemical characteristics, balancing the magnitude misalignment between heterogeneous parameters. Based on conditional branch logic comparison, it smooths potential values ​​and historical judgment markers, measuring the causal evolution trajectory of the current potential state and temporal cache, blocking control flow discontinuities caused by power failure restarts, and improving the state perception stability and causal coherence of cell screening equipment under complex in vitro culture conditions.

[0008] Preferably, the step of reading the upper and lower limit calibration values ​​corresponding to each item of multi-source biochemical data and performing extreme value normalization accordingly to obtain the methylhydroxyl normalized value, potential normalized value, lipid normalized value, and active oxygen normalized value includes: simultaneously pushing the mevalonate concentration, mitochondrial membrane potential, sphingomyelin concentration, and reactive oxygen species level into a non-volatile storage region to construct a historical sequence array; based on the historical sequence array, using sliding window extreme value operation to extract the upper and lower limit calibration values ​​of methylhydroxyl, the upper and lower limit calibration values ​​of potential, the upper and lower limit calibration values ​​of lipid, and the upper and lower limit calibration values ​​of active oxygen corresponding to each item of multi-source biochemical data; and performing extreme value normalization according to the upper and lower limit calibration values ​​corresponding to each item of multi-source biochemical data to generate the methylhydroxyl normalized value, potential normalized value, lipid normalized value, and active oxygen normalized value.

[0009] This invention extracts the calibration boundary through sliding window extreme value operation and performs extreme value normalization, which measures the dynamic distribution trajectory of the historical sequence array, removes the residual interference of old abnormal extreme values ​​on the evaluation benchmark, and provides a dynamic evolution judgment basis for the calculation of comprehensive energy value and comprehensive damage value.

[0010] Preferably, the step of calculating the aging inhibition value based on the comprehensive damage value and the comprehensive energy value includes: calculating a first difference between the constant 1 and the comprehensive energy value, adding the square of the first difference to the square of the comprehensive damage value to obtain a first calculation term; calculating a second difference between the constant 1 and the comprehensive damage value, multiplying the comprehensive energy value by the second difference, and adding the product to the anti-overflow constant to obtain a second calculation term; and using the ratio of the first calculation term to the second calculation term as the aging inhibition value.

[0011] This invention uses algebraic calculations based on comprehensive damage value and comprehensive energy value to derive aging arrest value, which measures the nonlinear scaling relationship between structural damage and energy supply, reduces the risk of disconnection caused by indicator fragmentation, and provides a judgment basis with anti-noise overflow for determining the state of oocyte functional arrest.

[0012] Preferably, obtaining the deterioration acceleration index based on the recent damage mean includes: multiplying the recent damage mean by the deterioration curvature constant and taking the opposite number to obtain an index input term; performing exponentiation with the natural constant as the base and the index input term as the exponent to obtain a nonlinear decay term; calculating the difference between the upper limit constant and the lower limit constant of the exponent, multiplying the difference by the nonlinear decay term to obtain a dynamic interval mapping term; and adding the dynamic interval mapping term to the lower limit constant of the exponent to obtain the deterioration acceleration index.

[0013] This invention combines recent damage mean and boundary constant to calculate natural logarithmic decay to obtain the decay acceleration index, which measures the corresponding characteristics of the structural damage mean and the index mapping baseline, suppresses the defects of premature elimination or response lag caused by static depreciation coefficient, and ensures the time decay adaptive capability of the evaluation system.

[0014] Preferably, determining the effective detachment time based on the detachment time and the limit tolerance time of the isolated oocyte includes: reading the limit tolerance time and reading the detachment time output by the timer; and obtaining the effective detachment time by screening for the minimum value based on the detachment time and the limit tolerance time.

[0015] Preferably, the step of obtaining the comprehensive potential value based on the ultimate tolerance time, effective withdrawal time, aging inhibition value, and exhaustion acceleration index includes: quotienting the effective withdrawal time and the ultimate tolerance time to obtain the time consumption ratio; using the time consumption ratio as the base and the exhaustion acceleration index as the exponent to perform a power calculation to obtain a time loss term; calculating the difference between constant 1 and the time loss term to obtain a survival retention term; calculating the sum of the aging inhibition value and constant 1, and taking the reciprocal of the sum to obtain an inhibition compensation term; and multiplying the survival retention term and the inhibition compensation term to obtain the comprehensive potential value.

[0016] This invention calculates a comprehensive potential value by performing nonlinear survival decay calculation based on aging inhibition value, failure acceleration index and effective disengagement time. It measures the coupling and synergistic relationship between extreme tolerance time and multidimensional physiological decline characteristics, balances the dimensional misalignment interference of cross-dimensional indicators, and establishes a unified nonlinear depreciation benchmark for subsequent control command output.

[0017] Preferably, the generation of the activity determination threshold based on the dynamic distribution of historical potential sequences includes: reading the historical potential sequences; when the amount of sequence data has not reached the clustering start threshold, calling a preset factory baseline threshold as the activity determination threshold; and when the amount of sequence data reaches the threshold, triggering an asynchronous K-means clustering update operation based on the historical potential sequences to obtain the activity determination threshold.

[0018] Secondly, this invention provides a data processing system for assessing the aging state of isolated oocytes, comprising: a data acquisition module for acquiring multi-source biochemical data from isolated oocytes; the multi-source biochemical data including mevalonate concentration, mitochondrial membrane potential, sphingomyelin concentration, and reactive oxygen species (ROS) level; an extreme value normalization module for reading the upper and lower limit calibration values ​​corresponding to each item of the multi-source biochemical data, and performing extreme value normalization accordingly to obtain mevalonate normalization value, potential normalization value, lipid normalization value, and ROS normalization value; an energy and damage extraction module for taking the minimum value among the mevalonate normalization value and potential normalization value as the comprehensive energy value, and the maximum value among the lipid normalization value and ROS normalization value as the comprehensive damage value; and a retardation value and mean calculation module for calculating the comprehensive damage value and the comprehensive mean value. The system calculates aging inhibition values ​​based on energy values; extracts temporal characteristics of comprehensive damage values ​​and performs moving average calculations to obtain recent damage mean values; a potential value comprehensive calculation module is used to obtain the exhaustion acceleration index based on recent damage mean values; the effective extraction time is determined based on the extraction time and limit tolerance time of isolated oocytes; a comprehensive potential value is obtained based on limit tolerance time, effective extraction time, aging inhibition values, and exhaustion acceleration index; a threshold and filtering module is used to generate activity determination thresholds based on the dynamic distribution of historical potential sequences; a smoothed potential value is obtained by temporal filtering of the comprehensive potential value; and a control command generation module is used to extract historical determination identifiers, combine the smoothed potential value and activity determination thresholds to determine the evaluation results, and generate control commands for automated processing of isolated oocytes accordingly.

[0019] Preferably, the control command generation module includes: a working condition state discrimination submodule, used to obtain the current control logic running state and determine whether the current state is the first logic calculation working condition after a power failure and restart; a history identifier pass-through submodule, used to extract the history judgment identifier as the previous evaluation result and the previous control command for output in the first logic calculation working condition where a history judgment identifier exists; and a logic comparison and overwrite submodule, used to perform a logic comparison between the smoothing potential value and the activity judgment threshold to determine the current evaluation result and generate a control command in the first logic calculation working condition where it is not the first logic calculation working condition or where a history judgment identifier is lacking, and to overwrite the non-volatile storage area when the evaluation result undergoes a state reversal.

[0020] Preferably, the logic comparison and overwrite submodule includes: a high-quality state output unit, used to output a high-quality state result and generate a preferred workstation instruction if the smoothing potential value is greater than or equal to the activity determination threshold; a late-stage decay output unit, used to output a late-stage decay result and generate a cell removal instruction if the smoothing potential value is less than the activity determination threshold; and a state comparison and overwrite unit, used to compare the output evaluation result with the historical determination identifier, and only when the evaluation result undergoes a state reversal, overwrite the output evaluation result and the corresponding control instruction to the non-volatile storage area as a new historical determination identifier.

[0021] The beneficial effects of this invention are as follows: This invention processes multi-source biochemical data and extracts dynamic calibration limits through sliding window extreme value calculation, measuring the dynamic distribution trajectory of the sensor signal time-series array and suppressing the underlying mapping deviation caused by probe aging; it uses comprehensive damage value and comprehensive energy value to perform algebraic operations to deduce aging arrest value, measuring the nonlinear scaling correlation between cell structure damage and metabolic energy supply, preventing the missed detection of stagnation states caused by the separation of single-dimensional physiological indicators; and it combines recent damage mean and boundary constant to calculate natural logarithmic decay to generate a decay acceleration index, measuring the mapping relationship between the degree of damage evolution and the time decay baseline, thus mitigating the effects of aging. The assessment distortion caused by fixed-time penalties is eliminated; a comprehensive potential value is obtained by performing nonlinear survival decline calculation by integrating effective detachment time and aging inhibition characteristics, which measures the coupling and synergistic span of the extreme survival time window and dimensionless biochemical characteristics, and balances the dimensional misalignment between multidimensional physiological data; the potential value is smoothed by comparing and matching the conditional branch logic with historical judgment marks and generating control instructions, which measures the causal continuity between the current cell potential and the evolution of historical states, blocks the flow logic oscillation during equipment cold start and power failure restart, and improves the sensitivity of the cell sorting equipment to physiological decline perception and the continuity of closed-loop screening action under long-term in vitro culture conditions. Attached Figure Description

[0022] Figure 1 This is a flowchart of a data processing method for assessing the senescence status of isolated oocytes;

[0023] Figure 2 This is a comparison chart of cell detachment time and the trajectory of overall potential degradation;

[0024] Figure 3 It is a distribution diagram of multidimensional features and benchmark scale during cell viability detection;

[0025] Figure 4 This is a calculation mapping diagram of the input working conditions and aging inhibition values ​​during the cell sorting process;

[0026] Figure 5 This is a functional module diagram of a data processing system for assessing the senescence status of isolated oocytes. Detailed Implementation

[0027] 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, not all, of the embodiments of the present invention. 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.

[0028] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0029] This invention discloses a data processing method for assessing the senescence status of isolated oocytes, referring to... Figure 1 This includes steps S1 to S5:

[0030] S1. Collect multi-source biochemical data of isolated oocytes and push them into the historical sequence array. Use sliding window extreme value operation to dynamically extract the upper limit and lower limit calibration values ​​of each item. Based on this, perform extreme value normalization to obtain the comprehensive energy value and comprehensive damage value.

[0031] It should be noted that in the assessment of the aging state of isolated oocytes, isolated oocytes exhibit a dynamic interplay between oxidative damage and energy output. Due to aging shifts and baseline drift in biochemical sensing probes, these characteristics inherently distort static reference parameters at the data acquisition end. This limitation causes the data sequences output by different sensors to exhibit dimensional inversion, making it impossible for conventional algorithms to balance calculation weights, thus leading to assessment bias. Therefore, this invention utilizes sliding window extreme value calculation to measure the dynamic distribution trajectory of historical sequence arrays, dynamically extracting the upper and lower limit calibration values ​​of each item in multi-source biochemical data, providing a basis for subsequently obtaining comprehensive energy and comprehensive damage values.

[0032] It should be further explained that the sliding window extreme value calculation measures the historical sequence array by constructing a data observation interval. Specifically, the data observation interval is formed by extracting the front-end biochemical data within the historical sequence array according to the sliding window span parameter. As the current biochemical data is pushed into the historical sequence array, the data observation interval is synchronously shifted forward in time, and the old biochemical data at the end is simultaneously removed. In each extraction cycle, the various multi-source biochemical data contained in the data observation interval are sorted and compared. The biochemical data with the highest value among the various multi-source biochemical data is extracted as the corresponding upper limit calibration value, and the biochemical data with the lowest value is extracted as the corresponding lower limit calibration value. This removes the lingering interference effect of old abnormal extreme values ​​on the evaluation benchmark and provides a dynamic evolution measurement basis for dealing with probe drift.

[0033] Specifically, the mevalonate concentration, mitochondrial membrane potential, sphingomyelin concentration, and reactive oxygen species level of the isolated oocytes are collected in real time.

[0034] Mevalerate concentration, mitochondrial membrane potential, sphingolipid concentration and reactive oxygen species level were simultaneously pushed into the historical sequence array in the non-volatile storage region;

[0035] Using sliding window extreme value operation, the upper limit and lower limit values ​​of methylhydroxyl are obtained from the historical sequence array. The extreme value of mevalonic acid concentration is then normalized based on the upper limit and lower limit values ​​of methylhydroxyl to obtain the normalized value of methylhydroxyl.

[0036] Using sliding window extreme value operation, the upper limit and lower limit calibration values ​​of the potential are obtained from the historical sequence array. The extreme value of the mitochondrial membrane potential is normalized by the upper limit and lower limit calibration values ​​to obtain the potential normalization value.

[0037] Using sliding window extreme value operation, the upper limit and lower limit lipid calibration values ​​are obtained from the historical sequence array. The lipid normalization value is obtained by extreme value normalization of sphingomyelin concentration based on the upper limit and lower limit lipid calibration values.

[0038] Using sliding window extreme value calculation, the upper limit and lower limit values ​​of active oxygen are obtained from the historical sequence array. The active oxygen level is then normalized by extreme value normalization based on the upper limit and lower limit values ​​of active oxygen to obtain the normalized value of active oxygen.

[0039] The comprehensive energy value is obtained by taking the minimum value from the normalized values ​​of methylhydroxyl and potential; the comprehensive damage value is obtained by taking the maximum value from the normalized values ​​of lipid and active oxygen.

[0040] For example, the upper and lower calibration values ​​of various multi-source biochemical data are independently extracted by sliding window extreme value calculation based on the specific sensor range. Taking the upper calibration value of methylhydroxyl as an example, the upper calibration value of methylhydroxyl determines the mapping span and distortion correction strength of the extreme value normalization operation for abnormal high-order signals. The empirical value range is [2,5]. In this embodiment, the upper calibration value of methylhydroxyl is set to 2.45. Implementers can set the upper calibration value of methylhydroxyl according to the actual aging and drift degree of the detection probe. For example, when the probe is aging and the baseline fluctuates upwards significantly, the upper calibration value of methylhydroxyl can be appropriately increased to cover abnormal signals; when the probe is in a brand-new state and the signal-to-noise ratio is high, the upper calibration value of methylhydroxyl can be appropriately decreased to improve the high-order mapping resolution of the effective signal.

[0041] For example, taking the lower limit calibration value of methyl hydroxyl as an example, the lower limit calibration value of methyl hydroxyl determines the mapping span and distortion correction strength of the extreme value normalization operation on the underlying noise signal. The empirical range is [0.1, 1]. In this embodiment, the lower limit calibration value of methyl hydroxyl is set to 0.25. Implementers can set the lower limit calibration value of methyl hydroxyl according to the intensity of underlying electromagnetic interference. For example, when the ambient noise is strong, the lower limit calibration value of methyl hydroxyl can be appropriately increased to shield the ineffective noise; when the hardware shielding performance is excellent, the lower limit calibration value of methyl hydroxyl can be appropriately decreased to improve the low-bit mapping resolution of weak signals.

[0042] For example, the sliding window span parameter determines the historical sample capacity and update agility when dynamically extracting the boundary in the sliding window extreme value operation. The empirical value range is [10, 100]. In this embodiment, the sliding window span parameter is set to 50. Implementers can set the sliding window span parameter according to the baseline drift rate of the underlying sensor. For example, when the detection probe is aging and the baseline drift is severe, the sliding window span parameter can be appropriately reduced to improve the dynamic tracking of the limit boundary update. When the environmental interference is high and the reading oscillation is large, the sliding window span parameter can be appropriately increased to effectively equalize the boundary distortion caused by abnormal background noise. For short-wave abrupt anomalies, when the current multi-source biochemical data exceeds the corresponding upper limit calibration value of the current dynamic extraction or is lower than the corresponding lower limit calibration value, a nonlinear asymptotic mapping function is used for soft saturation compression to strictly clamp the output value within the closed interval [0, 1]. This preserves the characteristics of small gradient changes and, combined with the subsequent anti-overflow constant, eliminates the risk of algebraic collapse from the underlying operation logic.

[0043] S2. The aging inhibition value is obtained by algebraic calculation based on the comprehensive damage value and the comprehensive energy value.

[0044] It should be noted that in the assessment of aging status of isolated oocytes, isolated oocytes exhibit the characteristic of entering stagnation without causing substantial structural damage when encountering energy depletion. Due to the mutually restrictive relationship between internal oxidative damage accumulation and energy supply, this characteristic inherently limits the calculation of single-dimensional biochemical indicators at the data processing end. This limitation causes a disconnect between the calculated comprehensive damage value and the comprehensive energy value, leading to the inability of conventional algorithms to capture the true gap and resulting in warning bias. Therefore, this invention utilizes algebraic calculations to measure the nonlinear scaling relationship between the comprehensive damage value and the comprehensive energy value, providing a basis for subsequent determination of aging arrest status.

[0045] Specifically, the aging inhibition value is calculated based on the comprehensive damage value, the comprehensive energy value, and the spillover prevention constant;

[0046] Specifically, the aging arrest value satisfies the following relationship:

[0047] ;

[0048] In the formula, This represents the aging arrest value. The overall damage value, For the overall energy value, This is a constant to prevent overflow.

[0049] in, The higher the value, the greater the actual degree of lipotoxicity accumulation and oxidative damage within the isolated oocyte, leading to... As the value increases, it makes The larger the value tends to be, the more likely it is to identify a state of functional stagnation; The smaller the value, the more stable the state of oxidative damage inside the isolated oocyte, making it easier to... As the value decreases, it makes... The smaller the value, the more effectively it can suppress false rejections caused by environmental disturbances.

[0050] Furthermore, A higher value indicates more vigorous metabolism and a sufficient reserve of mitochondria within the isolated oocyte, thus... As the value increases, it makes The smaller the value, the more likely it is to achieve the technical benefits of retaining high vitality characteristics; The smaller the value, the more likely the isolated oocyte has encountered energy depletion or hypoxia stress. As the value decreases, it makes... The larger the value, the more effectively it can suppress the noise overflow caused by high-energy masking.

[0051] For example, the overflow prevention constant determines the overflow resistance capability when algebraic calculation encounters an extreme zero input. In this embodiment, the overflow prevention constant is set to 0.000001.

[0052] S3. Extract the temporal characteristics of the comprehensive damage value, and perform exponential mapping calculations in combination with preset boundary constants to obtain the decay acceleration index.

[0053] It should be noted that in the assessment of senescence status of isolated oocytes, the sensitivity of cells to in vitro time exhibits a non-linear, accelerated collapse characteristic with the accumulation of internal lipotoxicity and oxidative damage. Due to individual differences in resistance genes, the above characteristics, when applied to data processing, create a fixed time penalty weight, which can lead to premature elimination or delayed response. This limitation makes a single static time depreciation coefficient incompatible with the long-term preservation of viable samples and the rapid elimination of pathological samples, resulting in rigid misjudgments by conventional algorithms. Therefore, this invention utilizes the natural logarithm mapping to measure the relationship between the recent damage mean and the exponential boundary constant, providing a non-linear depreciation basis for subsequent calculation of the comprehensive potential value.

[0054] Specifically, the overall damage value for the current cycle is pushed into the status register;

[0055] The data in the status register is read and the moving average is calculated to obtain the recent destruction average. For the initial state of the register not being full at the beginning of sampling, the first value filling method is used. The overall destruction value of the current period is used to overwrite the entire status register to ensure the continuity of the moving average calculation.

[0056] Read the preset upper limit constant, lower limit constant, and decay curvature constant from the non-volatile memory area;

[0057] The decay acceleration index is calculated by natural logarithmic decay based on the recent mean damage, upper limit constant of the exponent, lower limit constant of the exponent, and decay curvature constant.

[0058] Specifically, the exhaustion acceleration index satisfies the following relationship:

[0059] ;

[0060] In the formula, The exhaustion acceleration index, The upper limit constant of the exponent. It is the lower bound constant of the exponent. The constant of the decay curvature. This represents the recent disruption of the mean.

[0061] in, The higher the value, the more substantial the internal damage to the isolated oocyte has accumulated and the worsening trend it shows. As the value decreases, it makes... The closer the value is to the lower limit constant of the exponent, the more likely it is to achieve the technical benefit of accelerating the triggering of the time penalty; The smaller the value, the better the structural homeostasis of the isolated oocyte at the current stage, making it more stable. As the value increases, it makes The closer the value is to the upper limit constant of the exponent, the more effectively it can suppress the forced degradation of high-quality evaluation indicators caused by the passage of survival time.

[0062] For example, the upper limit constant of the exponent determines the nonlinear dynamic upper limit span of the survival curve over time, with an empirical range of [3,5]. In this embodiment, the upper limit constant of the exponent is set to 4. Implementers can set the upper limit constant of the exponent based on the initial health baseline of the sample. For example, when the initial vitality of the sample population is extremely high, the upper limit constant of the exponent can be appropriately increased to grant a longer golden observation period; when the initial performance of the sample population is mediocre, the upper limit constant of the exponent can be appropriately decreased to tighten the margin for judging high potential.

[0063] For example, the exponential lower bound constant determines the nonlinear dynamic lower bound span of the survival curve over time, with an empirical range of [0.5, 1]. In this embodiment, the exponential lower bound constant is set to 1. Implementers can set the exponential lower bound constant according to the aggressiveness of the end-stage elimination strategy. For example, when it is necessary to quickly intercept end-stage pathological samples, the exponential lower bound constant can be appropriately reduced to accelerate the drop in potential value; when it is necessary to observe the recovery possibility of marginal samples, the exponential lower bound constant can be appropriately increased to provide a buffer for the drop.

[0064] For example, the decay curvature constant determines the convergence sensitivity of the natural logarithmic decay calculation towards the bottom, with an empirical range of [2, 6]. In this embodiment, the decay curvature constant is set to 4. Implementers can set the decay curvature constant according to the rate of deterioration of biochemical damage. For instance, when sample damage exhibits a step-like deterioration, the decay curvature constant can be appropriately increased to accelerate calculation convergence; when sample damage exhibits a gradual evolution, the decay curvature constant can be appropriately decreased to smooth the exponential decay process.

[0065] S4. Read the limit tolerance time and detachment time, and calculate the comprehensive potential value by nonlinear survival decline based on the aging retardation value and the failure acceleration index.

[0066] It should be noted that in the assessment of the aging status of isolated oocytes, isolated oocytes exhibit a unidirectional increase in aging over time after detachment from the maternal cell, with an upper limit to their survival window. Due to the difference in magnitude between time parameters and dimensionless biochemical indicators, these characteristics create an inherent limitation at the data processing end, making it impossible to calculate on the same scale. This limitation causes the data sequences of different dimensional indicators to exhibit dimensional misalignment, resulting in conventional algorithms being unable to balance the calculation weights and thus leading to assessment bias. Therefore, this invention utilizes nonlinear survival decay calculations to measure the coupling relationship between the ultimate tolerance time, effective detachment time, aging retardation value, and decay acceleration index, providing a basis for subsequent generation of control commands.

[0067] Specifically, the maximum tolerance time is read; the disengagement duration output by the timer is read; and the effective disengagement duration is obtained by filtering the minimum value between the disengagement duration and the maximum tolerance time.

[0068] The comprehensive potential value is obtained by nonlinear survival decline calculation based on the extreme tolerance time, effective detachment time, aging retardation value, and failure acceleration index.

[0069] Specifically, the comprehensive potential value satisfies the following relationship:

[0070] ;

[0071] In the formula, The overall potential value, For the ultimate endurance time, To effectively break free from time constraints, This represents the aging arrest value. This is the exhaustion acceleration index.

[0072] Furthermore, The higher the value, the lower the average recent damage to isolated oocytes, making them more susceptible to damage. The closer the value is to 0 in the early stages, the more... The closer the numerical value is to retaining high viability characteristics, the more effectively it can suppress the false rejection of high-quality oocytes in the early stage; The smaller the value, the more the mean recent damage to the isolated oocytes has cumulatively worsened, leading to... The slope of the value becomes steeper as the value increases over time, making... The faster the value drops, the more likely it is to identify diseased cells and trigger a recession warning.

[0073] in, The higher the value, the closer the time after the isolated oocyte leaves the mother's body is to the theoretical survival limit, making... As the value increases, it makes The smaller the value tends to be, the more likely it is to achieve the technical benefit of introducing a time penalty to identify the decay state; The smaller the value, the more likely the isolated oocyte is in the early stage of survival after detachment from the mother's body. As the value decreases, it makes... The closer the numerical values ​​are to preserving the baseline, the more effectively they can suppress the assessment bias of high-quality cells.

[0074] in, The higher the value, the greater the physiological stress experienced by the isolated oocyte, namely structural damage and energy depletion. As the value increases, it makes The smaller the value, the more likely it is to achieve the technical benefit of intercepting inferior cells; The smaller the value, the better the biochemical homeostasis of the isolated oocyte in the current cycle, making it more stable. As the value decreases, it makes... The larger the value, the more effectively it can suppress the devaluation of scores caused by over-calculation.

[0075] For example, the limit tolerance time determines the upper limit of the survival time of isolated oocytes under ideal conditions and its weight in the time dimension of the comprehensive potential value. The empirical range is [10, 24]. In this embodiment, the limit tolerance time is set to 12. Implementers can set the limit tolerance time according to the characteristics of the actual animal species and the culture medium formula. For example, when the sample has extremely strong viability or is in a high-quality culture medium, the limit tolerance time can be appropriately increased to extend the effective evaluation window; when the sample is extremely sensitive to the in vitro environment, the limit tolerance time can be appropriately decreased to quickly trigger the decline warning.

[0076] S5. Generate assessment results representing physiological state and control commands for the drive mechanism based on smoothing potential value and historical judgment identifier.

[0077] It should be noted that in the assessment of the senescence state of isolated oocytes, the underlying mechanism relies on decision boundaries to execute flow and elimination actions. Due to the risk of transient power loss during hardware operation, the above characteristics create inherent limitations at the data processing end, such as the clearing of status registers and the failure of rigid decision boundaries. This limitation causes the sequence output by the controller to exhibit discontinuous and oscillating characteristics, making it impossible for conventional algorithms to match causal relationships, thus resulting in missed detections. Therefore, this invention utilizes a conditional branch control algorithm to measure the correspondence between smoothing potential values ​​and historical decision markers, providing a basis for subsequent generation of high-quality state results, late-stage decay results, optimal workstation instructions, and cell elimination instructions.

[0078] Specifically, the historical potential sequence is read, and the activity determination threshold is obtained by triggering asynchronous K-means clustering update operation based on the historical potential sequence;

[0079] The comprehensive potential value of the current sampling period is pushed into the time-series buffer array, and the data in the time-series buffer array is subjected to moving average filtering to obtain the smoothed potential value;

[0080] The smoothing potential value is compared with the activity determination threshold. If the smoothing potential value is greater than or equal to the activity determination threshold, a high-quality status result is output and an optimal workstation instruction is generated. If the smoothing potential value is less than the activity determination threshold, a terminal decay result is output and a cell removal instruction is generated.

[0081] The current output evaluation result is compared with the historical decision identifier recorded in the non-volatile memory area. Only when the evaluation result is reversed is the current evaluation result and the corresponding control instruction overwritten to the non-volatile memory area as a new historical decision identifier.

[0082] For cold start and power failure restart scenarios, the following compensation logic is executed:

[0083] When the amount of data in the historical potential sequence has not reached the clustering start threshold, the asynchronous K-means clustering update operation is intercepted, and the preset factory baseline threshold is used as the activity determination threshold.

[0084] For cold start filtering, the time-series buffer array is filled with data during the array idle period after power failure restart or initial power-on.

[0085] For cold start conditions of control logic: read the historical judgment identifier recorded in the non-volatile memory area, and determine whether the current logic calculation is the first after a power failure and restart based on the controller status. In the case of the first logic calculation with a historical judgment identifier, directly extract the historical judgment identifier in the non-volatile memory area as the previous evaluation result and the previous control command for output; in the case of the first logic calculation without a historical judgment identifier, skip the comparison and directly trigger the execution status determination process, and overwrite the generated evaluation result to the non-volatile memory area to establish the first historical judgment identifier.

[0086] For example, the clustering start threshold determines the sample size and update sensitivity when the asynchronous update operation extracts the activity determination threshold. The empirical range is [1000, 5000]. In this embodiment, the clustering start threshold is set to 2000. Implementers can set the clustering start threshold according to the noise immunity requirements of the controller. For example, when the underlying interference noise is high, the clustering start threshold can be appropriately increased to average out the fluctuating interference.

[0087] For example, Figure 2 The figure shows a comparison of cell detachment time and overall potential degradation trajectory. Traditional static time-degradation strategies use a fixed ratio to estimate cell activity, which is greatly affected by individual sample differences. This leads to over-discounting of potential assessment in the early detachment stage and is prone to misjudgment of state and overestimation of activity under long-term degradation conditions. In contrast, this invention maintains smooth and stable overall potential in the early detachment stage of high-quality samples by extracting degradation lag characteristics, reducing the loss of activity misjudgment in the early stages of environmental changes. In the period of rapid deterioration of damaged samples, it achieves a low-hysteresis, rapid degradation response, reducing the risk of missed detection of inferior cells due to untimely degradation assessment. At the boundary between cell activity and degradation, it identifies degradation trends and adaptively matches grading thresholds, exhibiting dynamic following characteristics by locking the degradation baseline.

[0088] For example, Figure 3 This diagram illustrates the distribution of multidimensional features and baseline scale during cell viability detection. In the noise reduction phase, the invention constructs a lower threshold by physically truncating the noise floor of a heterogeneous sensor, ensuring stable and interference-resistant low-level feature signals and reducing the misjudgment rate caused by probe zero-point drift. During the quantization and unification phase comparing different physiological dimensions, scale alignment is achieved using a factory-standard equipotential circle, reducing the risk of weight imbalance due to dimensional inconsistencies. In the feature reconstruction region of asynchronous cell function decay, the differences in the decay rates of various physiological indicators are identified, and extreme value-normalized multidimensional vectors are extracted. By preserving objective degradation correlations, non-equilibrium hysteresis features are presented, suppressing dimensional imbalance and evaluation distortion during feature extraction.

[0089] For example, Figure 4The diagram shows the computational mapping between input conditions and aging arrest values ​​during cell sorting. In the stable operation period of high-quality samples, the invention introduces baseline bias compensation to retain a weak gradient, resulting in smooth dynamic differentiation of aging arrest values ​​and reducing the grading error of highly active homogeneous samples. During the transition period facing moderate decline, a smoothing function is used to achieve nonlinear pressure calculation, reducing the risk of incorrect screening of healthy samples due to rigid threshold segmentation. In the out-of-bounds abnormal zone where the comprehensive damage value and comprehensive energy value intersect, out-of-bounds data is identified and a constraint mapping mechanism is triggered. By limiting abnormal input signals, a high-gain amplitude limiting and blocking characteristic is presented, suppressing numerical oscillations and computational overflow, and enhancing the numerical safety and stability control of the pathological sample removal process.

[0090] For example, such as Figure 5 The diagram shown is a functional block diagram of a data processing system for assessing the aging state of isolated oocytes according to an embodiment of the present invention. The data processing system for assessing the aging state of isolated oocytes described in this invention can be installed in an electronic device. Depending on the functions implemented, the data processing system 100 for assessing the aging state of isolated oocytes may include: a data acquisition module 101, an extreme value normalization module 102, an energy and damage extraction module 103, a stagnation value and mean value calculation module 104, a potential value comprehensive calculation module 105, a threshold and filtering processing module 106, and a control command generation module 107.

[0091] The module described in this invention, also referred to as a unit, refers to a series of computer program segments that can be executed by the processor of an electronic device and perform a fixed function, and are stored in the memory of the electronic device. In this embodiment, the functions of each module / unit are as follows:

[0092] The data acquisition module 101 is used to acquire multi-source biochemical data from isolated oocytes; the multi-source biochemical data includes mevalonate concentration, mitochondrial membrane potential, sphingomyelin concentration, and reactive oxygen species level;

[0093] The extreme value normalization module 102 is used to read the upper limit calibration value and lower limit calibration value corresponding to each item of multi-source biochemical data, and perform extreme value normalization accordingly to obtain the methyl hydroxyl normalization value, potential normalization value, lipid normalization value and active oxygen normalization value respectively.

[0094] The energy and damage extraction module 103 is used to take the minimum value between the methylhydroxyl normalization value and the potential normalization value as the comprehensive energy value, and the maximum value between the lipid normalization value and the active oxygen normalization value as the comprehensive damage value.

[0095] The aging resistance value and mean value calculation module 104 is used to calculate the aging resistance value based on the comprehensive damage value and the comprehensive energy value; extract the temporal characteristics of the comprehensive damage value and perform a moving average calculation to obtain the recent damage mean value;

[0096] The potential value comprehensive calculation module 105 is used to obtain the exhaustion acceleration index based on the recent damage average; determine the effective extraction time based on the extraction time and limit tolerance time of the isolated oocyte; and obtain the comprehensive potential value based on the limit tolerance time, effective extraction time, aging retardation value and exhaustion acceleration index.

[0097] The threshold and filtering module 106 is used to generate an activity determination threshold based on the dynamic distribution of the historical potential sequence; and to perform time-series filtering on the comprehensive potential value to obtain a smoothed potential value.

[0098] The control instruction generation module 107 is used to extract historical judgment identifiers, combine them with smoothing potential value and activity judgment threshold to determine the evaluation result, and generate control instructions for automated processing of isolated oocytes accordingly.

[0099] In the several embodiments provided by this invention, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0100] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0101] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in a combination of hardware and software functional modules. It will be apparent to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from its spirit or essential characteristics.

[0102] The system also includes other components well known to those skilled in the art, such as communication buses and communication interfaces, the settings and functions of which are known in the art and will not be described in detail here.

Claims

1. A data processing method for assessing the senescence status of isolated oocytes, characterized in that, include: Collect multi-source biochemical data from isolated oocytes; the multi-source biochemical data includes mevalonate concentration, mitochondrial membrane potential, sphingomyelin concentration, and reactive oxygen species level; Read the upper and lower limit calibration values ​​of each item in the multi-source biochemical data, and perform extreme value normalization accordingly to obtain the methylhydroxyl normalization value, potential normalization value, lipid normalization value and active oxygen normalization value respectively. The minimum value between the normalized value of methylhydroxyl and the normalized value of potential is taken as the comprehensive energy value, and the maximum value between the normalized value of lipid and the normalized value of active oxygen is taken as the comprehensive damage value. The aging inhibition value is calculated based on the comprehensive damage value and the comprehensive energy value; the temporal characteristics of the comprehensive damage value are extracted and a moving average is performed to obtain the recent damage mean. The exhaustion acceleration index was obtained based on the recent average damage value; the effective detachment time was determined based on the detachment time and the limit tolerance time of the isolated oocytes. A comprehensive potential value is obtained based on the extreme tolerance time, effective disengagement time, aging retardation value and exhaustion acceleration index. The activity determination threshold is generated based on the dynamic distribution of historical potential sequences. Time-series filtering is applied to the comprehensive potential value to obtain a smoothed potential value; Historical judgment markers are extracted, and the evaluation results are determined by combining smoothing potential value and activity judgment threshold. Based on this, control instructions for automated processing of isolated oocytes are generated.

2. The data processing method for assessing the senescence status of isolated oocytes according to claim 1, characterized in that, The process of reading the upper and lower limit calibration values ​​corresponding to each item of multi-source biochemical data, and performing extreme value normalization accordingly to obtain the methylhydroxyl normalization value, potential normalization value, lipid normalization value, and active oxygen normalization value, includes: Mevalonate concentration, mitochondrial membrane potential, sphingolipid concentration, and reactive oxygen species level were simultaneously pushed into the non-volatile storage region to construct a historical sequence array; Based on historical sequence arrays, sliding window extreme value operations were used to extract the upper and lower limits of methylhydroxyl, the upper and lower limits of potential, the upper and lower limits of lipid, and the upper and lower limits of active oxygen, respectively, corresponding to various multi-source biochemical data. Extreme value normalization was performed based on the upper and lower limit calibration values ​​of each multi-source biochemical data to generate methylhydroxyl normalization value, potential normalization value, lipid normalization value and active oxygen normalization value.

3. The data processing method for assessing the senescence status of isolated oocytes according to claim 1, characterized in that, The aging inhibition value calculated based on the comprehensive damage value and the comprehensive energy value includes: Calculate the first difference between constant 1 and the comprehensive energy value, and add the square of the first difference to the square of the comprehensive damage value to obtain the first calculation term; The second difference between the calculation constant 1 and the comprehensive damage value is multiplied by the comprehensive energy value and the second difference, and the product is added to the anti-overflow constant to obtain the second calculation term; The ratio of the first calculation term to the second calculation term is used as the aging inhibition value.

4. The data processing method for assessing the senescence status of isolated oocytes according to claim 1, characterized in that, The process of obtaining the decay acceleration index based on the recent average damage includes: The exponential input term is obtained by multiplying the recent damage mean by the failure curvature constant and taking the negative number. Using the natural constant as the base and the exponent as the exponent, the nonlinear decay term is obtained by exponentiation. Calculate the difference between the upper limit constant and the lower limit constant of the exponent, and multiply the difference by the nonlinear decay term to obtain the dynamic interval mapping term; The dynamic interval mapping term is added to the lower limit constant of the exponent to obtain the exhaustion acceleration exponent.

5. The data processing method for assessing the senescence status of isolated oocytes according to claim 1, characterized in that, The determination of the effective detachment time based on the detachment time of the isolated oocyte and the limit tolerance time includes: Read the limit tolerance time and the timeout duration output by the timer; The effective detachment time is obtained by screening the minimum value between the detachment time and the limit tolerance time.

6. The data processing method for assessing the senescence status of isolated oocytes according to claim 1, characterized in that, The comprehensive potential value obtained based on extreme tolerance time, effective disengagement time, aging retardation value, and failure acceleration index includes: The effective escape time is divided by the limit tolerance time to obtain the time consumption ratio. The time loss term is obtained by exponentiation with the time consumption ratio as the base and the decay acceleration index as the exponent. The difference between constant 1 and the time loss term is calculated to obtain the survival retention term; Calculate the sum of the aging resistance value and the constant 1, and take the reciprocal of the sum to obtain the resistance compensation term; Multiply the survival retention term by the hindrance compensation term to obtain the overall potential value.

7. The data processing method for assessing the senescence status of isolated oocytes according to claim 1, characterized in that, The dynamic distribution-based generation of activity determination thresholds based on historical potential sequences includes: Read historical potential sequences. When the amount of sequence data does not reach the clustering start threshold, call the preset factory baseline threshold as the activity determination threshold. Once the amount of sequence data reaches a threshold, an asynchronous K-means clustering update operation is triggered based on the historical potential sequence to obtain the activity determination threshold.

8. A data processing system for assessing the senescence status of isolated oocytes, characterized in that, To implement the method according to any one of claims 1 to 7, comprising: The data acquisition module is used to collect multi-source biochemical data from isolated oocytes; the multi-source biochemical data includes mevalonate concentration, mitochondrial membrane potential, sphingomyelin concentration, and reactive oxygen species level; The extreme value normalization module is used to read the upper limit and lower limit calibration values ​​of each item in the multi-source biochemical data, and perform extreme value normalization accordingly to obtain the methylhydroxyl normalization value, potential normalization value, lipid normalization value and active oxygen normalization value respectively. The energy and damage extraction module is used to take the minimum value between the methylhydroxyl normalization value and the potential normalization value as the comprehensive energy value, and the maximum value between the lipid normalization value and the active oxygen normalization value as the comprehensive damage value. The module for calculating aging inhibition value and mean value is used to calculate the aging inhibition value based on the comprehensive damage value and comprehensive energy value; extract the temporal characteristics of the comprehensive damage value and perform moving average calculation to obtain the recent damage mean value; The potential value comprehensive calculation module is used to obtain the exhaustion acceleration index based on the recent damage average; determine the effective extraction time based on the extraction time and limit tolerance time of the isolated oocytes; and obtain the comprehensive potential value based on the limit tolerance time, effective extraction time, aging retardation value and exhaustion acceleration index. The threshold and filtering module is used to generate activity determination thresholds based on the dynamic distribution of historical potential sequences; and to perform time-series filtering on the comprehensive potential value to obtain a smoothed potential value. The control instruction generation module is used to extract historical judgment identifiers, combine them with smoothing potential values ​​and activity judgment thresholds to determine the evaluation results, and generate control instructions for automated processing of isolated oocytes.

9. A data processing system for assessing the senescence status of isolated oocytes according to claim 8, characterized in that, The control command generation module includes: The operating condition determination submodule is used to obtain the current operating status of the control logic and determine whether the current operation is the first logic calculation condition after a power failure and restart. The historical identifier pass-through submodule is used to extract the historical decision identifier as the previous evaluation result and the previous control instruction for output in the first logic calculation case where there is a historical decision identifier; The logic comparison and overwrite submodule is used to compare the smooth potential value with the activity determination threshold to determine the current evaluation result and generate control instructions in non-first logic calculation conditions or first logic calculation conditions that lack historical judgment identifiers, and overwrite the non-volatile storage area when the evaluation result is reversed.

10. A data processing system for assessing the senescence status of isolated oocytes according to claim 9, characterized in that, The logical comparison and overwrite submodule includes: The high-quality status output unit is used to output a high-quality status result and generate an optimal workstation instruction if the smoothing potential value is greater than or equal to the activity determination threshold. The terminal decay output unit is used to output the terminal decay result and generate a cell removal instruction if the smoothing potential value is less than the activity determination threshold. The state comparison and overwrite unit is used to compare the output evaluation result with the historical decision identifier. Only when the evaluation result undergoes a state flip, the output evaluation result and the corresponding control instruction are overwritten to the non-volatile storage area as a new historical decision identifier.

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