Method for evaluating the cryopreservation efficiency of marine biological samples

Abstract

This invention relates to the field of cryopreservation evaluation technology and discloses a method for evaluating the cryopreservation effectiveness of marine biological samples. The method acquires multi-dimensional monitoring datasets within the current preservation device in real time to comprehensively understand the preservation environment; dynamically sets the effectiveness evaluation cycle based on the total sample volume to make the evaluation more aligned with actual needs; continuously collects temperature distribution maps within the evaluation cycle and calculates the average temperature value over the entire cycle as a basic evaluation threshold; performs abnormal fluctuation detection on the temperature distribution map based on the basic evaluation threshold and a temperature stability database to identify potential temperature anomaly regions; when potential anomaly regions exist, extracts the ice crystal growth rate and cell viability curves of biological samples in each region; confirms actual preservation anomaly events by comparing the deviation of the ice crystal growth rate from the reference rate and the abrupt change characteristics of the cell viability curve; and statistically analyzes the frequency of actual preservation anomaly events and calculates the initial effectiveness decay coefficient.

Description

Technical Field

[0001] This invention relates to the field of cryopreservation evaluation technology, specifically to a method for evaluating the cryopreservation effectiveness of marine biological samples. Background Technology

[0002] Marine biological samples contain abundant biological genetic resources, and cryopreservation is a crucial prerequisite for conducting marine biological research, protecting biological resources, and developing and utilizing them. Accurate assessment of cryopreservation effectiveness directly impacts the integrity of the samples and their subsequent application value. However, current methods for assessing the cryopreservation effectiveness of marine biological samples have many limitations.

[0003] Traditional evaluation methods often rely on a single temperature sensor for fixed-point monitoring, which can only obtain local temperature data and is difficult to reflect the overall temperature distribution characteristics within the storage device, easily overlooking temperature fluctuations in local areas. At the same time, the evaluation cycle is often fixed and does not take into account the differences in the total amount of stored samples. When the total amount of samples is large, a fixed cycle may lead to insufficient data collection, while when the total amount of samples is small, it will result in low evaluation efficiency.

[0004] In the anomaly detection phase, existing methods often rely on simple temperature thresholds as the judgment standard, lacking correlation analysis with historical preservation parameters. This makes it difficult to distinguish between normal temperature fluctuations and abnormal changes that may affect sample preservation. More importantly, traditional assessments do not integrate temperature changes with the physiological state of the biological sample itself, judging anomalies solely based on temperature data, which may lead to misjudgments. For example, some temperature fluctuations, even if they do not exceed the set threshold, may still cause abnormal ice crystal growth or decreased cell viability within the sample, and such potential preservation risks are difficult to identify in a timely manner.

[0005] Existing assessment methods lack quantitative analysis of the decline in preservation effectiveness and cannot track effectiveness change trends through historical data. This results in a lack of targeted maintenance and adjustment of preservation devices, thus affecting the long-term preservation of marine biological samples. These problems lead to insufficient accuracy and comprehensiveness in assessing the cryopreservation effectiveness of marine biological samples, making it difficult to meet the stringent requirements of practical applications. Summary of the Invention

[0006] The purpose of this invention is to provide a method for evaluating the cryopreservation efficiency of marine biological samples, so as to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides a method for evaluating the cryopreservation efficiency of marine biological samples, the method comprising the following operations:

[0008] A temperature stability database containing historical standard preservation parameters is constructed; a multi-dimensional monitoring dataset within the current preservation device is acquired in real time; the performance evaluation cycle is dynamically set based on the total sample volume in the multi-dimensional monitoring dataset; temperature distribution maps are continuously acquired within the performance evaluation cycle, and the average temperature value over the entire cycle is calculated as the basic evaluation threshold; based on the basic evaluation threshold and the temperature stability database, abnormal fluctuation detection is performed on the temperature distribution maps to identify and mark all potential temperature anomaly regions; when a marked potential anomaly region exists, the ice crystal growth rate and cell viability curves of biological samples in each region are extracted; actual preservation anomaly events are confirmed by comparing the deviation between the ice crystal growth rate and the reference rate and the abrupt change characteristics of the cell viability curve; the frequency of occurrence of the actual preservation anomaly events is statistically analyzed, and the initial performance decay coefficient is calculated.

[0009] Preferably, the operation of dynamically setting the performance evaluation cycle based on the total sample size in the multi-dimensional monitoring dataset includes: pre-setting a biological sample baseline capacity and a biological sample critical capacity; comparing the total sample size in the multi-dimensional monitoring dataset with the biological sample baseline capacity and the biological sample critical capacity in a hierarchical manner; if the total sample size is lower than or equal to the biological sample baseline capacity, then the performance evaluation cycle is defined as a long cycle; if the total sample size is higher than the biological sample baseline capacity but lower than or equal to the biological sample critical capacity, then the performance evaluation cycle is defined as a medium cycle; if the total sample size exceeds the biological sample critical capacity, then the performance evaluation cycle is defined as a short cycle; wherein the duration of the long cycle is longer than that of the medium cycle, and the duration of the medium cycle is longer than that of the short cycle.

[0010] Preferably, the operation of performing abnormal fluctuation detection on the temperature distribution map based on the basic evaluation threshold and temperature stability database specifically includes: dividing the performance evaluation period into several continuous time units; matching and verifying the temperature distribution map in each time unit with the standard temperature range in the temperature stability database; simultaneously calculating the deviation of the average temperature of each time unit from the basic evaluation threshold; when the average temperature of a certain time unit exceeds a set multiple of the basic evaluation threshold, marking the area corresponding to that time unit as a potential temperature anomaly area; when the temperature distribution map of a certain time unit does not match any standard temperature range in the temperature stability database, marking the area corresponding to that time unit as a potential temperature anomaly area.

[0011] Preferably, the operation of confirming an actual preservation anomaly by comparing the deviation of the ice crystal growth rate from the reference rate and the abrupt change characteristics of the cell survival rate curve includes: calling the standard ice crystal growth rate range for the corresponding biological sample type in the temperature stability database; if the ice crystal growth rate of the marked area deviates from the standard ice crystal growth rate range by more than an allowable threshold, then it is determined that an actual preservation anomaly has occurred in that area; analyzing the derivative change characteristics of the cell survival rate curve, if the derivative curve shows a discontinuous breakpoint, then it is determined that an actual preservation anomaly has occurred in that area.

[0012] Preferably, the operation of counting the occurrence frequency of actual preservation anomalies and calculating the initial performance decay coefficient includes: accumulating the number of all actual preservation anomalies within the performance evaluation period as the anomaly event frequency; traversing each actual preservation anomaly event and obtaining the biological sample volume data in that event; querying the standard volume data of the same type of biological sample in the temperature stability database; and generating the initial performance decay coefficient based on the relative deviation between the biological sample volume and the standard volume data, combined with the anomaly event frequency, using a weighted accumulation algorithm.

[0013] Preferably, the method further includes the following operations: extracting the biosample activity parameter sequences of all actual preservation anomalies, wherein the biosample activity parameter sequences include cell membrane permeability change curves and protein denaturation rate curves; merging the number of troughs in the cell membrane permeability change curves and the slope change of the protein denaturation rate curves into a feature vector; performing time-series alignment processing on the feature vectors of all actual preservation anomalies using a dynamic time warping algorithm; performing group analysis on the aligned feature vector set using a density clustering model; if a group contains at least two actual preservation anomalies and the corresponding preservation temperature range spans more than two temperature zone levels, then the group is determined to have a cross-temperature zone anomaly pattern.

[0014] Preferably, the processing operation of the cross-temperature zone anomaly mode includes: parsing the compositional features of the cross-temperature zone anomaly mode, wherein the compositional features include the total number of cross-temperature zone events and the proportion of the storage medium component for each event; inputting the total number of cross-temperature zone events and the proportion of the storage medium component into a historical response strategy library for feature matching; wherein the historical response strategy library stores different historical cross-temperature zone event features and their corresponding compensation factors; calculating the dynamic time distance between the current compositional feature and all historical cross-temperature zone event features in the historical response strategy library; if there is a valid historical feature with a dynamic time distance less than a set matching threshold, then the compensation factor corresponding to the smallest dynamic time distance is selected as the current adjustment coefficient; if all dynamic time distances exceed the set matching threshold, then the current adjustment coefficient is determined by linear proportional mapping based on the total number of cross-temperature zone events.

[0015] Preferably, the operation of determining the current adjustment coefficient based on the total number of cross-temperature zone events through linear proportional mapping is implemented in the following manner: A tiered interval division rule for the total number of cross-temperature zone events is pre-defined in the system parameter configuration library, dividing the total number of cross-temperature zone events into a low event quantity interval, a medium event quantity interval, and a high event quantity interval; when the total number of cross-temperature zone events falls into the low event quantity interval, an initial calculation base for the current adjustment coefficient is set; if the total number of cross-temperature zone events is in the medium event quantity interval, the initial calculation base is corrected based on the average event quantity increase over the last three evaluation periods in the historical response strategy library; when the total number of cross-temperature zone events enters the high event quantity interval, the range data of the total number of cross-temperature zone events occurring consecutively in the last two evaluation periods is extracted and normalized, and the normalization result is superimposed on the initial calculation base to establish a dynamic correction base; the current adjustment coefficient is generated based on the calculation result of multiplying the dynamic correction base by a preset proportional growth gradient.

[0016] Preferably, the method further includes: obtaining the final performance degradation coefficient after correction by the current adjustment coefficient; calculating the compression ratio coefficient for subsequent performance evaluation cycles based on the final performance degradation coefficient; the compression ratio coefficient and the final performance degradation coefficient are negatively correlated.

[0017] Preferably, the method further includes the following operations: obtaining a set of feature vectors of identified cross-temperature zone anomaly patterns; retrieving verified cross-temperature zone anomaly pattern templates stored in the historical pattern database; calculating the statistical difference value between the feature vector set and each verified cross-temperature zone anomaly pattern template; comparing the statistical difference value with multiple preset confidence threshold intervals; and determining the confidence level of the cross-temperature zone anomaly pattern based on the comparison results.

[0018] Compared with the prior art, the beneficial effects of the present invention are:

[0019] This method for evaluating the cryopreservation effectiveness of marine biological samples integrates preservation data from different periods and under different conditions by constructing a temperature stability database containing historical standard preservation parameters. This provides rich historical references for current evaluations, ensuring that the evaluation process is not isolated from past experience but is based on an understanding of historical patterns. This data-driven evaluation foundation helps to more clearly identify the differences between the current preservation state and the standard state, reducing judgment bias caused by a lack of references.

[0020] Real-time acquisition of multi-dimensional monitoring datasets within the current preservation device overcomes the limitations of traditional single-parameter monitoring, simultaneously covering various preservation-related environmental factors such as temperature, humidity, and gas concentration. The integration of multi-dimensional data allows for assessments that are no longer limited to changes in a single indicator, but rather provide a holistic understanding of the dynamics of the preservation environment, capturing the interactions between different factors and thus more comprehensively reflecting the actual operating status of the preservation device.

[0021] Dynamically setting the performance evaluation cycle based on the total sample size in the multi-dimensional monitoring dataset allows the evaluation process to adapt to actual preservation needs. When the total sample size is large, appropriately extending the evaluation cycle ensures sufficient monitoring data is obtained, avoiding biased evaluations due to insufficient data; when the total sample size is small, shortening the evaluation cycle improves evaluation efficiency and reduces unnecessary resource consumption. This dynamic adjustment mechanism makes the evaluation more flexible and adaptable to preservation scenarios of different scales.

[0022] By continuously collecting temperature distribution maps and calculating the average temperature value over the entire evaluation period, the baseline evaluation threshold is obtained. Compared to the traditional instantaneous temperature threshold, this method better reflects the overall temperature situation throughout the entire evaluation period. The average temperature over the entire period can smooth out short-term, occasional temperature fluctuations, avoid misjudgments caused by instantaneous anomalies, and make the setting of the evaluation threshold more closely match the temperature stability during actual preservation.

[0023] By performing anomaly fluctuation detection on temperature distribution maps based on fundamental assessment thresholds and a temperature stability database, potential temperature anomaly regions can be accurately located. This detection method, which combines historical data with real-time temperature distribution, can not only identify temperature changes that exceed the normal range, but also discover anomalies that, while not significantly deviating numerically, do not conform to historical standard trends, thereby improving the sensitivity and accuracy of anomaly detection.

[0024] When potentially anomalous regions are identified, the ice crystal growth rate and cell viability curves of biological samples in each region are extracted, directly linking temperature changes in the physical environment to the physiological state of the biological samples. The impact of temperature anomalies on biological samples is ultimately reflected in changes in their physiological state. Identifying anomalies through changes in ice crystal growth rate and cell viability avoids the limitations of relying solely on physical parameters, making the confirmation of anomalous events more consistent with the actual preservation state of the samples.

[0025] By comparing the deviation of ice crystal growth rate from the reference rate and the abrupt changes in cell viability curves, actual preservation anomalies can be identified, further distinguishing potential anomalies from true anomalies. Some temperature fluctuations may only cause minor physiological changes without substantially affecting sample preservation; through this multi-indicator comparison, false positives can be ruled out, ensuring that confirmed anomalies are truly related to preservation efficacy.

[0026] By statistically analyzing the frequency of actual storage anomalies and calculating the initial performance degradation coefficient, the changing trend of storage performance can be quantitatively described. Frequency statistics provide a direct understanding of how frequently anomalies occur, while the degradation coefficient reflects the rate of performance decline. This quantitative analysis helps to more clearly grasp the performance changes of the storage device, providing direction for subsequent maintenance and adjustments, and making storage management more targeted. Attached Figure Description

[0027] Figure 1 This is a schematic diagram illustrating the working principle of the method for evaluating the cryopreservation efficiency of marine biological samples as described in this invention.

[0028] Figure 2 Design diagram for abnormal fluctuation detection;

[0029] Figure 3 Design diagram for calculating the initial performance degradation coefficient;

[0030] Figure 4 The flowchart for calculating the final performance degradation coefficient. Detailed Implementation

[0031] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0032] Please see Figure 1 This invention provides a method for evaluating the cryopreservation efficiency of marine biological samples, the method comprising:

[0033] A temperature stability database containing historical standard preservation parameters was constructed, including data such as temperature range, ice crystal growth reference rate, and cell viability baseline curve of different types of marine biological samples under standard preservation conditions.

[0034] The system acquires multi-dimensional monitoring datasets in real time within the current storage device. These datasets include information such as total sample volume, real-time temperature of each area within the storage device, sample type, and sample volume.

[0035] The performance evaluation cycle is dynamically set based on the total number of samples in the multi-dimensional monitoring dataset;

[0036] Temperature distribution maps are continuously collected during the performance evaluation cycle, and the average temperature value of the entire cycle is calculated as the basic evaluation threshold. The temperature distribution maps are obtained by distributed temperature sensors and cover all sample storage areas in the storage device.

[0037] Based on the aforementioned basic evaluation threshold and temperature stability database, abnormal fluctuation detection is performed on the temperature distribution map to identify and mark all potential temperature anomaly areas.

[0038] When there are potentially abnormal regions marked, the ice crystal growth rate and cell survival rate curves of biological samples in each region are extracted. The ice crystal growth rate is monitored and calculated in real time using microscopic imaging technology, and the cell survival rate curve is plotted by periodic sampling and detection.

[0039] By comparing the deviation between the ice crystal growth rate and the reference rate, as well as the abrupt changes in the cell survival rate curve, the actual preservation abnormal events were identified.

[0040] The frequency of actual storage anomalies is statistically analyzed, and the initial performance degradation coefficient is calculated.

[0041] Example 1: See Figure 2 When dynamically setting the effectiveness evaluation cycle based on the total sample volume in the multi-dimensional monitoring dataset, the baseline and critical biological sample capacities must be pre-defined. The determination of the baseline and critical biological sample capacities requires comprehensive consideration of the structural characteristics of the preservation device, its cooling power, and the preservation characteristics of different marine biological samples. These values ​​are fixed after multiple tests and calibrations. They are stored in the system's parameter configuration module and can be adjusted according to the actual model of the preservation device and the type of sample used.

[0042] The total sample size in the multi-dimensional monitoring dataset is compared with the biological sample baseline capacity and the biological sample critical capacity in a hierarchical manner, and different performance evaluation cycles are determined based on the comparison results. If the total sample size is less than or equal to the biological sample baseline capacity, the performance evaluation cycle is defined as a long cycle. The long cycle is mainly set because when the sample size is small, the temperature environment inside the preservation device is relatively stable, and there is no need for overly frequent evaluations. Extending the evaluation cycle can reduce unnecessary resource consumption while ensuring the accuracy of the evaluation results. If the total sample size is higher than the biological sample baseline capacity but lower than or equal to the biological sample critical capacity, the performance evaluation cycle is defined as a medium cycle. The medium cycle is suitable for situations where the sample size is at a medium level. At this time, the temperature distribution inside the preservation device may fluctuate due to factors such as the density of sample placement. Using a medium cycle can capture potential abnormal changes in a timely manner without excessively increasing the workload of the evaluation. If the total sample size exceeds the biological sample critical capacity, the performance evaluation cycle is defined as a short cycle. The short cycle is used for scenarios with a large sample size. Due to the presence of a large number of samples, the heat load inside the preservation device is large, and the temperature is prone to instability. The short cycle can monitor temperature changes more intensively in order to quickly detect and handle anomalies.

[0043] When performing abnormal fluctuation detection on temperature distribution maps based on basic evaluation thresholds and temperature stability databases, the performance evaluation period is first divided into several continuous time units. The division of time units needs to be determined in conjunction with the length of the evaluation period. The time units corresponding to longer periods are relatively longer, and the time units corresponding to shorter periods are shorter. This ensures that there are a sufficient number of time units in each evaluation period to capture the temperature change trend.

[0044] Subsequently, the temperature distribution maps within each time unit were matched and validated against standard temperature ranges in the temperature stability database. These standard temperature ranges, compiled from extensive historical data and experimental results, cover the temperature ranges of different types of marine biological samples under normal preservation conditions. Matching and validation allows determination of whether the temperature distribution of the current time unit conforms to the standard conditions. Simultaneously, the deviation of the mean temperature for each time unit from the baseline assessment threshold is calculated; this deviation quantifies the degree of difference between the mean temperature and the baseline assessment threshold.

[0045] When the average temperature of a given time unit exceeds a predetermined multiple of the baseline assessment threshold, the corresponding region is marked as a potential temperature anomaly. The determination of this predetermined multiple must consider the tolerance of marine biological samples to temperature changes; different types of samples may require different predetermined multiples to ensure timely identification of significant temperature deviations. Similarly, when the temperature distribution map of a given time unit does not match any standard temperature range in the temperature stability database, the corresponding region is also marked as a potential temperature anomaly. This indicates that the current temperature distribution does not conform to any known normal pattern, posing a potential risk of anomaly and requiring further monitoring and analysis.

[0046] Throughout the abnormal fluctuation detection process, the temperature distribution map acquisition must cover all corners of the storage device to ensure accurate temperature monitoring of each sample storage area. The temperature stability database is updated regularly, incorporating new historical data and experimental results to ensure the timeliness and accuracy of the standard temperature range.

[0047] Example 2: See Figure 3When confirming actual preservation anomalies by comparing the deviation of ice crystal growth rate from the reference rate and the abrupt changes in cell viability curves, the first step is to retrieve the standard ice crystal growth rate range for the corresponding biological sample type from the temperature stability database. The standard ice crystal growth rate range stored in the temperature stability database is determined based on historical data accumulated during long-term cryopreservation of the same type of marine biological samples, covering ice crystal growth rate benchmarks at different preservation stages and under different temperature conditions. This data is categorized and organized, allowing for precise retrieval based on the type of biological sample, such as algae, shellfish, and fish, ensuring a complete match between the reference rate range and the currently tested biological sample type.

[0048] After obtaining the ice crystal growth rate of the biological sample in the labeled region, the deviation of this rate from the standard ice crystal growth rate range is calculated. The deviation is calculated based on the upper and lower limits of the standard range to determine the specific value by which the actual measured ice crystal growth rate exceeds the upper or falls below the lower limit. If the deviation exceeds the allowable threshold, an actual preservation anomaly is determined to have occurred in that region. The setting of the allowable threshold needs to comprehensively consider the characteristics of the biological sample. For samples sensitive to low temperatures, the allowable threshold is relatively small, while for samples with strong tolerance, the allowable threshold can be appropriately relaxed to accommodate the different tolerance levels of samples to changes in ice crystal growth rate.

[0049] Cell viability curves were extracted from biological samples from each region, and derivative curves were calculated to obtain derivative curves. Cell viability curves are plotted by periodically testing sample viability during the evaluation period, reflecting the trend of cell viability changes over time. Derivative curves further illustrate the rate and direction of viability change. By analyzing the characteristics of the derivative curves, it is possible to identify whether there are sudden or discontinuous changes in viability. If the derivative curve shows a discontinuity, meaning the rate of change before and after a certain time point is significantly discontinuous, it indicates that a sudden change in cell viability occurred at that time point, thus determining that an actual preservation anomaly event has occurred in that region.

[0050] When calculating the frequency of actual storage anomalies and the initial performance degradation coefficient, the number of all events identified as actual storage anomalies within the performance evaluation period is first accumulated and used as the anomaly frequency. During the statistical process, each marked potential anomaly area needs to be checked one by one to confirm the authenticity of each actual storage anomaly event, avoiding inaccurate frequency statistics due to misjudgment.

[0051] The process iterates through each actual saved anomaly event, extracting the corresponding biological sample volume data from the multi-dimensional monitoring dataset. During real-time acquisition, the multi-dimensional monitoring dataset records the specific volume information of each sample, including its initial volume and volume changes during storage, ensuring that the extracted data accurately reflects the volume status of the biological sample during the anomaly event.

[0052] The database retrieves standard volume data for similar biological samples from a temperature stability database. Standard volume data represents the typical volume range of this type of biological sample under standard preservation conditions, taking into account the normal physiological state of the sample and reasonable volume changes during preservation. By comparing the actual sample volume data with the standard volume data, the relative deviation between the two is calculated. The relative deviation is calculated based on the standard volume data and reflects the degree of deviation between the actual volume and the standard volume.

[0053] An initial performance degradation coefficient is generated using a weighted cumulative algorithm based on the relative deviation between the biological sample volume and the standard volume data, combined with the frequency of abnormal events. In the weighted cumulative algorithm, the relative deviation and the frequency of abnormal events are assigned corresponding weights, the allocation of which is determined based on their respective impacts on preservation performance. A larger relative deviation indicates that the sample's condition may have been affected, contributing significantly to performance degradation; a higher frequency of abnormal events indicates poor stability of the preservation environment, which also significantly affects preservation performance. By comprehensively considering these two factors through weighted cumulative calculation, the final calculated initial performance degradation coefficient objectively reflects the performance degradation of the preservation device within the current evaluation period.

[0054] Throughout the process, all data extraction, calculation, and analysis are completed through an automated system, reducing errors caused by human intervention. The system performs real-time data verification to ensure the accuracy of the data input into the algorithm, thereby guaranteeing that the calculated initial performance degradation coefficient truly reflects the actual performance of cryogenic storage.

[0055] Example 3: After confirming actual preservation anomalies, the bioactivity parameter sequences of all biological samples from these anomalies were extracted. These sequences included cell membrane permeability change curves and protein denaturation rate curves. The cell membrane permeability change curves were obtained using fluorescent probe technology. Fluorescent probes can penetrate the cell membrane and emit fluorescence at specific wavelengths. Changes in fluorescence intensity reflect changes in cell membrane permeability. The x-axis of the curve represents time, and the y-axis represents relative fluorescence intensity, thus visually presenting the fluctuations in permeability over time. The protein denaturation rate curves were generated using circular dichroism spectroscopy. Circular dichroism spectroscopy can capture changes in protein secondary structure. The denaturation rate is obtained by calculating the shift of spectral characteristic peaks per unit time. The curves, with time on the x-axis and denaturation rate on the y-axis, record the dynamic process of protein denaturation.

[0056] When extracting features from the cell membrane permeability change curve, the number of troughs is counted. A trough refers to a region in the curve where the relative fluorescence intensity is lower than that of adjacent time intervals. Each trough represents a significant decrease in cell membrane permeability, and the number of troughs reflects the frequency of permeability fluctuations. Simultaneously, the change in slope of the protein denaturation rate curve is calculated. The change in slope is the difference in slope between adjacent time intervals. This value indicates the magnitude of the denaturation rate change; a larger change in slope indicates poorer stability of the protein denaturation process.

[0057] The number of troughs and the change in slope are combined into an eigenvector. The eigenvector is in the form of (number of troughs, change in slope). For example, if the cell membrane permeability change curve of a certain actual preservation abnormal event has 3 troughs and the change in slope of the protein denaturation rate curve is 0.04 / h, then the corresponding eigenvector is (3, 0.04).

[0058] A dynamic time warping algorithm is employed to perform temporal alignment on the feature vectors of all actually stored anomalous events. This algorithm constructs time-curved paths to adjust feature vector sequences of different lengths to the same time dimension, eliminating feature differences caused by asynchronous occurrence of anomalous events. During processing, the algorithm calculates the distance between each feature vector to find the optimal alignment, ensuring that the feature vectors of different events match on the time axis, thus guaranteeing that subsequent analysis is based on a consistent time reference.

[0059] The aligned feature vector set is grouped and analyzed using a density clustering model. The density clustering model uses the Euclidean distance between feature vectors as a similarity measure. The formula for calculating the Euclidean distance is as follows:

[0060]

[0061] Where d represents the Euclidean distance between two eigenvectors, (x1,y1) and (x2,y2) are the coordinates of the two eigenvectors respectively, x1 and x2 are the number of troughs, and y1 and y2 are the slope changes.

[0062] When the Euclidean distance between two feature vectors is less than a set radius threshold, they are grouped into the same cluster. Through continuous iteration, multiple groups with similar features are formed.

[0063] Each group is analyzed to examine the number of actual storage anomalies within the group and the corresponding storage temperature ranges for these events. The storage temperature range is the temperature range recorded when the anomaly occurs. Based on a preset temperature zone classification standard, such as -100℃-90℃ as Level A, -90℃-80℃ as Level B, and -80℃~-70℃ as Level C, the number of temperature zone levels spanned by the temperature range is determined. If a group contains at least two actual storage anomalies and the corresponding storage temperature range spans more than two temperature zone levels, then the group is determined to have a cross-temperature zone anomaly pattern.

[0064] Throughout the process, it is essential to ensure the continuity and consistency of all data acquisition and processing. The acquisition frequency of cell membrane permeability change curves and protein denaturation rate curves must match the time units of the performance evaluation cycle. Feature vector extraction must be strictly performed according to a unified standard to guarantee the reliability of the grouping analysis results. The parameter settings of the density clustering model need to be adjusted to ensure that the grouping results accurately reflect the intrinsic correlation of feature vectors, avoiding over-clustering or under-clustering.

[0065] Example 4: The processing of cross-temperature zone anomaly patterns first requires analyzing the compositional characteristics of these patterns. The total number of cross-temperature zone events in the compositional characteristics refers to the total number of actual preservation anomalies identified as cross-temperature zone anomalies within the current performance evaluation period. The compositional proportion of the preservation medium for each event covers the percentage of various preservatives used during preservation. For example, when preserving certain marine invertebrate samples, the preservation medium may contain 20% glycerol, 10% ethylene glycol, and 70% seawater solution. These specific proportions are recorded in detail and incorporated into the compositional characteristics.

[0066] The total number of cross-temperature zone events and the composition ratio of the preservation medium are input into the historical response strategy database for feature matching. The database stores historical cross-temperature zone event features, including relevant data on various types of cross-temperature zone anomalies handled in the past. For example, in a particular cross-temperature zone anomaly event involving coral larvae samples, the total number of cross-temperature zone events was 5, the preservation medium consisted of 15% dimethyl sulfoxide and 85% artificial seawater, and the corresponding compensation factor was 0.3. By comparing the current composition characteristics with these historical characteristics, similar treatment experiences can be identified.

[0067] When calculating the dynamic temporal distance between the current composition characteristics and the characteristics of all historical cross-temperature zone events in the historical response strategy database, the trend of the total number of cross-temperature zone events and the similarity of the composition ratio of the preservation medium are taken into account. For example, if the current total number of cross-temperature zone events is 4, and the preservation medium contains 18% glycerol and 82% seawater, while a certain event in the historical database has a total of 3 cross-temperature zone events, and the preservation medium contains 20% glycerol and 80% seawater, the dynamic temporal distance will be calculated by quantifying the differences in quantity and ratio between the two.

[0068] If a valid historical feature has a dynamic time distance less than the set matching threshold, the compensation factor corresponding to the smallest dynamic time distance is selected as the current adjustment coefficient. For example, if the dynamic time distance between the current constituent feature and an event in the historical database is 0.08, which is less than the set matching threshold of 0.1, and the compensation factor corresponding to that historical event is 0.25, then 0.25 is used as the current adjustment coefficient. If all dynamic time distances exceed the set matching threshold, the current adjustment coefficient needs to be determined by linear proportional mapping based on the total number of cross-temperature zone events.

[0069] When determining the current adjustment coefficient based on the total number of cross-temperature zone events using a linear proportional mapping, it is necessary to follow the preset rules for classifying the total number of cross-temperature zone events in the system parameter configuration library. The specific ranges for the low, medium, and high event number ranges will be set according to the preservation characteristics of different marine biological samples. For example, for phytoplankton samples, the low event number range may be set to 1-2 events, the medium event number range to 3-5 events, and the high event number range to 6 events or more.

[0070] When the total number of cross-temperature zone events falls into the low event count range, the initial calculation base for the current adjustment coefficient is set. For example, in a cross-temperature zone anomaly pattern targeting copepod samples, the total number of cross-temperature zone events is 2, which falls into the low event count range, and the initial calculation base might be set to 0.15. If the total number of cross-temperature zone events is in the medium event count range, the initial calculation base needs to be adjusted based on the average event count increase over the past three evaluation periods in the historical response strategy database. Assuming that the number of cross-temperature zone events targeting krill samples was 3, 4, and 5 in the past three evaluation periods, the average event count increase is obtained by calculating the change in the number of events over these three periods, and this increase is then used to adjust the initial calculation base.

[0071] When the total number of cross-temperature zone events enters the high-event range, the range of consecutive cross-temperature zone event totals within the two most recent assessment periods is extracted. For example, if the total number of cross-temperature zone events for fish egg samples was 8 and 11 in the two most recent assessment periods, the range would be 3. After normalizing this range, the result is superimposed on the initial calculation base to establish a dynamic correction base. Finally, the current adjustment coefficient is generated based on the calculation result of multiplying the dynamic correction base by a preset proportional growth gradient. The preset proportional growth gradient is set according to the performance parameters of the storage device to ensure that the adjustment coefficient accurately reflects the impact of cross-temperature zone anomaly patterns.

[0072] Example 5: See Figure 4 The final performance degradation coefficient, corrected by the current adjustment factor, is obtained by multiplying the initial performance degradation coefficient by the current adjustment factor. The initial performance degradation coefficient reflects the natural degradation of storage performance based on statistics of actual storage anomalies within the current evaluation period, while the current adjustment factor quantifies the impact of cross-temperature zone anomaly modes. The combination of the two provides a more comprehensive representation of the actual performance status of the storage device. For example, if the initial performance degradation coefficient is 0.3 and the current adjustment factor is 0.2, then the final performance degradation coefficient is the result of multiplying 0.3 and 0.2.

[0073] The compression ratio coefficient for subsequent performance evaluation cycles is calculated based on the final performance degradation coefficient. The compression ratio coefficient is negatively correlated with the final performance degradation coefficient; that is, the larger the final performance degradation coefficient, the smaller the compression ratio coefficient, and the shorter the corresponding subsequent evaluation cycle. This means that when preservation performance degradation is significant, the evaluation cycle needs to be shortened to monitor the preservation status more intensively in order to promptly detect new anomalies. Conversely, when the final performance degradation coefficient is small, it indicates that the preservation status is relatively stable, and the evaluation cycle can be appropriately extended to reduce unnecessary monitoring frequency.

[0074] It is also necessary to obtain the feature vector set of the identified cross-temperature-zone anomalous patterns. These feature vectors were formed in the previous processing by merging the number of troughs in the cell membrane permeability change curve and the slope change of the protein denaturation rate curve. Each feature vector corresponds to a key feature of a specific cross-temperature-zone anomalous event. For example, a cross-temperature-zone anomalous pattern may contain three anomalous events, and its feature vectors may be (2, 0.06), (3, 0.05), and (2, 0.07), respectively. These vectors together constitute the feature vector set of this pattern.

[0075] The system retrieves validated cross-temperature zone anomaly pattern templates stored in the historical pattern database. These templates are built based on historically processed and confirmed cross-temperature zone anomaly patterns, and each template contains a set of typical feature vectors. For example, there might be a cross-temperature zone anomaly pattern template for seaweed samples in the historical templates, with a feature vector set of (2, 0.05), (3, 0.04), and (2, 0.06). This template has been validated multiple times and can accurately represent the characteristics of this type of anomaly pattern.

[0076] The statistical difference value is calculated between the feature vector set and each validated cross-temperature zone anomaly pattern template. This statistical difference value is a quantitative indicator calculated by comparing the differences between the two feature vector sets across various dimensions, and is used to measure the similarity between the current feature vector set and historical templates. A smaller difference value indicates greater similarity; a larger difference value indicates more significant feature differences.

[0077] The statistical difference value is compared with multiple preset confidence threshold intervals. These intervals are defined based on historical data and practical application requirements; for example, they can be divided into three intervals: [0, 0.2), [0.2, 0.5), and [0.5, 1.0]. Each interval corresponds to a different confidence level. The confidence level of the cross-temperature zone anomaly pattern is determined based on the comparison results. If the statistical difference value is 0.15 and falls within the [0, 0.2) interval, the corresponding confidence level is high; if the statistical difference value is 0.3 and falls within the [0.2, 0.5) interval, the confidence level is medium; and if the statistical difference value is 0.6 and falls within the [0.5, 1.0] interval, the confidence level is low. By determining the confidence level, the degree of matching between the current cross-temperature zone anomaly pattern and historically known patterns can be assessed.

[0078] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0079] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for evaluating the cryopreservation efficiency of marine biological samples, characterized in that, The process includes the following operations: constructing a temperature stability database containing historical standard preservation parameters; acquiring a multi-dimensional monitoring dataset within the current preservation device in real time; dynamically setting the performance evaluation cycle based on the total sample volume in the multi-dimensional monitoring dataset; continuously acquiring temperature distribution maps within the performance evaluation cycle and calculating the average temperature value over the entire cycle as a basic evaluation threshold; performing abnormal fluctuation detection on the temperature distribution maps based on the basic evaluation threshold and the temperature stability database, identifying and marking all potential temperature anomaly regions; when marked potential anomaly regions exist, extracting the ice crystal growth rate and cell viability curves of biological samples from each region; and confirming actual preservation anomaly events by comparing the deviation between the ice crystal growth rate and the reference rate and the abrupt change characteristics of the cell viability curve. The frequency of actual storage anomalies was statistically analyzed, and the initial performance degradation coefficient was calculated. The operation of confirming actual preservation anomalies by comparing the deviation of ice crystal growth rate from the reference rate and the mutation characteristics of cell survival rate curves includes: calling the standard ice crystal growth rate range for the corresponding biological sample type in the temperature stability database; if the ice crystal growth rate of the marked area deviates from the standard ice crystal growth rate range by more than the allowable threshold, it is determined that an actual preservation anomaly has occurred in that area. Analyze the derivative change characteristics of the cell survival rate curve. If a discontinuous breakpoint appears in the derivative curve, it is determined that an actual preservation abnormality event has occurred in that region.

2. The method for evaluating the cryopreservation efficiency of marine biological samples according to claim 1, characterized in that, The operation of dynamically setting the performance evaluation cycle based on the total sample size in the multi-dimensional monitoring dataset includes: pre-setting the biological sample baseline capacity and the biological sample critical capacity; comparing the total sample size in the multi-dimensional monitoring dataset with the biological sample baseline capacity and the biological sample critical capacity in a hierarchical manner; if the total sample size is lower than or equal to the biological sample baseline capacity, the performance evaluation cycle is defined as a long cycle; if the total sample size is higher than the biological sample baseline capacity but lower than or equal to the biological sample critical capacity, the performance evaluation cycle is defined as a medium cycle; if the total sample size exceeds the biological sample critical capacity, the performance evaluation cycle is defined as a short cycle; wherein the duration of the long cycle is longer than that of the medium cycle, and the duration of the medium cycle is longer than that of the short cycle.

3. The method for evaluating the cryopreservation efficiency of marine biological samples according to claim 2, characterized in that, The operation of detecting abnormal fluctuations in the temperature distribution map based on the basic evaluation threshold and temperature stability database specifically includes: dividing the performance evaluation period into several continuous time units; matching and verifying the temperature distribution map within each time unit with the standard temperature range in the temperature stability database; simultaneously calculating the deviation of the average temperature of each time unit from the basic evaluation threshold; when the average temperature of a certain time unit exceeds a set multiple of the basic evaluation threshold, marking the corresponding area of ​​that time unit as a potential temperature anomaly area; when the temperature distribution map of a certain time unit does not match any standard temperature range in the temperature stability database, marking the corresponding area of ​​that time unit as a potential temperature anomaly area.

4. The method for evaluating the cryopreservation efficiency of marine biological samples according to claim 1, characterized in that, The operation of statistically analyzing the frequency of actual preservation anomalies and calculating the initial performance decay coefficient includes: accumulating the number of all actual preservation anomalies within the cumulative performance evaluation period as the anomaly frequency; iterating through each actual preservation anomaly and obtaining the biological sample volume data for that event; querying the standard volume data of similar biological samples in the temperature stability database; and generating the initial performance decay coefficient based on the relative deviation between the biological sample volume and the standard volume data, combined with the anomaly frequency, using a weighted accumulation algorithm.

5. The method for evaluating the cryopreservation efficiency of marine biological samples according to claim 4, characterized in that, The process also includes the following operations: extracting the biosample activity parameter sequences of all actual preservation anomalies, wherein the biosample activity parameter sequences include cell membrane permeability change curves and protein denaturation rate curves; merging the number of troughs in the cell membrane permeability change curves and the slope change of the protein denaturation rate curves into a feature vector; performing time-series alignment processing on the feature vectors of all actual preservation anomalies using a dynamic time warping algorithm; performing group analysis on the aligned feature vector set using a density clustering model; if a group contains at least two actual preservation anomalies and the corresponding preservation temperature range spans more than two temperature zone levels, then the group is determined to have a cross-temperature zone anomaly pattern.

6. The method for evaluating the cryopreservation efficiency of marine biological samples according to claim 5, characterized in that, The processing operation for the cross-temperature zone anomaly mode includes: parsing the constituent features of the cross-temperature zone anomaly mode. The composition features include the total number of cross-temperature zone events and the proportion of the storage medium composition for each event; the total number of cross-temperature zone events and the proportion of the storage medium composition are input into the historical response strategy library for feature matching; the historical response strategy library stores different historical cross-temperature zone event features and their corresponding compensation factors; the dynamic time distance between the current composition feature and all historical cross-temperature zone event features in the historical response strategy library is calculated; if there is a valid historical feature with a dynamic time distance less than a set matching threshold, the compensation factor corresponding to the smallest dynamic time distance is selected as the current adjustment coefficient; if all dynamic time distances exceed the set matching threshold, the current adjustment coefficient is determined by linear proportional mapping based on the total number of cross-temperature zone events.

7. The method for evaluating the cryopreservation efficiency of marine biological samples according to claim 6, characterized in that, The operation of determining the current adjustment coefficient based on the total number of cross-temperature zone events through linear proportional mapping is implemented in the following way: the classification rules for the total number of cross-temperature zone events are set in advance in the system parameter configuration library, and the classification rules for the total number of cross-temperature zone events are divided into low event number interval, medium event number interval, and high event number interval; When the total number of cross-temperature zone events falls into the low event quantity range, the initial calculation base for the current adjustment coefficient is set; If the total number of cross-temperature zone events is in the medium event range, the initial calculation base is adjusted according to the average event increase in the past three evaluation periods in the historical response strategy library; When the total number of cross-temperature zone events enters the high event quantity range, extract the range data of the total number of cross-temperature zone events that occur consecutively in the two most recent evaluation periods and perform normalization processing. Then, superimpose the normalization result onto the initial calculation base to establish a dynamic correction base. The current adjustment coefficient is generated based on the calculation result of multiplying the dynamic correction base by the preset growth gradient.

8. The method for evaluating the cryopreservation efficiency of marine biological samples according to claim 7, characterized in that, Also includes: Obtain the final performance degradation coefficient after correction by the current adjustment factor; The compression ratio coefficient for subsequent performance evaluation cycles is calculated based on the final performance decay coefficient. The compression ratio coefficient and the final performance attenuation coefficient are negatively correlated.

9. The method for evaluating the cryopreservation efficiency of marine biological samples according to claim 8, characterized in that, It also includes the following operations: obtaining the feature vector set of the identified cross-temperature zone anomaly patterns; retrieving the verified cross-temperature zone anomaly pattern templates stored in the historical pattern database; calculating the statistical difference value between the feature vector set and each verified cross-temperature zone anomaly pattern template; comparing the statistical difference value with multiple preset confidence threshold intervals; and determining the confidence level of the cross-temperature zone anomaly pattern based on the comparison results.

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