A full process management method for cell cryopreservation

Abstract

The present application relates to the field of cell cryopreservation, and discloses a whole-process management method for cell cryopreservation, comprising the following steps: firstly, multi-dimensionally collecting cell survival indexes, screening high-quality cells in combination with a clustering algorithm, and completing standardized pretreatment; secondly, introducing a low-temperature tolerance response model algorithm to realize intelligent dynamic compounding of cryopreservation protectants; then, performing blockchain identity authentication and archiving on cell cryopreservation samples, and adopting an ice crystal targeting inhibition variable gradient freezing mode in combination with hierarchical intelligent storage management and control to ensure long-term storage stability of the samples; and finally, combining a time sequence evolution algorithm to perform activity attenuation prediction and periodic sampling evaluation on the stored samples, and dynamically grasping the storage state of the samples. The present application solves the technical defects of traditional manual cryopreservation, such as poor standardization, weak adaptability, low cell survival rate, sample management confusion, and inability to predict activity attenuation.

Description

Technical Field

[0001] This invention relates to the field of cell cryopreservation, and in particular to a method for the entire process management of cell cryopreservation. Background Technology

[0002] Cell cryopreservation is a core foundational technology in the fields of biological sample storage, biomedical research and development, and clinical cell therapy. Currently, the industry generally uses manual labor with simple equipment to complete cell cryopreservation operations, and the overall operation mode lacks a systematic process control system. Routine operations rely heavily on the experience of operators to control key steps such as cell pretreatment, cryopreservation solution preparation, and cooling and freezing. It is difficult to unify the operation standards between different personnel and different batches, and the sample processing parameters are highly random, making it impossible to form a standardized operation paradigm.

[0003] Existing cryopreservation management methods have many practical shortcomings. Initial cell screening is highly subjective, and inferior samples are easily mixed into cryopreservation batches. The fixed and uniform formulation of cryoprotectant solutions cannot adapt to the different tolerance characteristics of cells, easily causing cell membrane damage. The crude freezing and cooling methods are prone to generating intracellular ice crystals that damage cell structure. Furthermore, sample traceability information is fragmented, storage environment monitoring is lagging, cell viability decline during long-term storage cannot be predicted in a timely manner, sample loss rates are high, and the stability of thawing quality is poor, making it difficult to meet the requirements for high-precision, high-viability long-term cell preservation.

[0004] A comprehensive management method for cell cryopreservation addresses the shortcomings of existing technologies by constructing an intelligent management model that integrates standardized screening, adaptive solution preparation, step-by-step temperature control, dynamic monitoring, and data review. This unified approach standardizes operational parameters at each stage, effectively reduces human error, and precisely mitigates various risks of damage during cell cryopreservation. Summary of the Invention

[0005] This invention overcomes the shortcomings of the prior art and provides a method for the whole process management of cell cryopreservation.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows: The first aspect of this invention provides a method for the entire process management of cell cryopreservation, comprising the following steps: Cell viability indicators were collected from multiple dimensions, and the cells were pretreated to obtain qualified pretreated cell samples. We introduced a cell low-temperature tolerance response model algorithm and combined it with the basic characteristic information of pretreated qualified cell samples to dynamically formulate cell cryopreservation protectants. After the cell cryopreservation agent is prepared, its identity is confirmed using blockchain technology, resulting in a stored electronic file. Combined with the cell cryopreservation mixture sample after aliquoting, the ice crystal targeted inhibition variable gradient intelligent freezing process is carried out, and the frozen cell cryopreservation mixture sample is intelligently adapted for storage and management. We predict the temporal evolution and activity decay of frozen cell samples after storage, and periodically sample frozen cell samples for evaluation.

[0007] Furthermore, in a preferred embodiment of the present invention, the multi-dimensional acquisition of cell viability indicators and the pretreatment of cells to obtain pretreated qualified cell samples specifically involve: The cell samples that need to be cryopreserved are identified and labeled as target cell samples. Multidimensional survival indicators of the target cell samples are collected using cell detection equipment. The cell detection device includes a cell detection module and a data analysis module. The multidimensional survival indicators include the metabolic reaction rate of the target cell sample, cell membrane integrity, content of contaminants, and cell division and growth cycle status. In the data analysis module of the cell detection equipment, a cell endowment feature clustering algorithm is introduced, and standard survival indicators of similar cells in the target cell sample are obtained and compared with multidimensional survival indicators. Target cell samples whose feature similarity to multidimensional survival indicators is greater than a preset threshold are identified as qualified cell samples. By introducing a historical data network, basic characteristic information of cells in qualified cell samples is obtained, which is used to retrieve the pretreatment process and corresponding process parameters of qualified cell samples in combination with the historical data network. Output the pretreatment process and corresponding process parameters for qualified cell samples to obtain pretreated qualified cell samples.

[0008] Furthermore, in a preferred embodiment of the present invention, the introduction of a cell cryopreservation response model algorithm, combined with the basic characteristic information of pretreated qualified cell samples, for dynamic compounding of cell cryopreservation agents, specifically involves: Retrieve basic characteristic information of qualified cell samples, and pre-determine the storage and preservation market and actual application scenarios of qualified cell samples, and merge and package them into a combined associated dataset of qualified cell samples. In the data analysis module of the cell detection equipment, a combined associated dataset of qualified cell samples is input, and a cell low-temperature tolerance response model algorithm is introduced. Based on the cell low temperature tolerance response model algorithm, historical data on cryopreservation ratios of cells of the same type as qualified cell samples are retrieved from the historical data network. Combined with the combined association dataset of qualified cell samples, the theoretical mixing ratio of all protective components of the cryoprotectant for pretreated qualified cell samples is calculated. Obtain a sterile mixing container. Based on the theoretical mixing ratio of all protective components of the cryoprotectant for the pretreated qualified cell samples, add all protective components of the cryoprotectant for the pretreated qualified cell samples into the sterile mixing container to obtain a preliminary mixture of protective components. Combined with the cell detection module of the cell detection device, the internal osmotic pressure and pH of the preliminary protective component mixture are collected in real time and compared with the corresponding preset standard parameter range to determine whether the internal osmotic pressure and pH of the preliminary protective component mixture are maintained within the corresponding preset standard parameter range. If not, it is determined that the preliminary protective component mixture does not meet the cell tolerance requirements of qualified cell samples. The deviation values ​​of the internal osmotic pressure and real-time pH physicochemical parameters of the preliminary protective component mixture from the preset standard parameter range are recorded. Based on the deviation values, the mixing ratio values ​​of all protective components of the cryopreservation agent for pretreated qualified cell samples are dynamically adjusted until the internal osmotic pressure and real-time pH physicochemical parameters of the preliminary protective component mixture are maintained within the corresponding preset standard parameter range, thus obtaining the cell cryopreservation agent.

[0009] Furthermore, in a preferred embodiment of the present invention, after the cell cryopreservation agent is prepared, the cell cryopreservation agent is subjected to blockchain-based identity verification to obtain a stored electronic file, specifically as follows: The cell cryopreservation agent and qualified cell samples are mixed and homogenized during the mixing process to obtain a mixed cell cryopreservation sample. Determine the standard aliquot volume, and aliquot the cell cryopreservation mixture into a sealed cryopreservation container according to the standard aliquot volume; In the data analysis module of the cell detection equipment, a unique and tamper-proof traceability identifier is generated by combining the combined dataset of qualified cell samples corresponding to the cell cryopreservation mixed sample, the cryopreservation batch, and the cryopreservation time. This identifier is then bound to the corresponding sealed cryopreservation container number to build and store an electronic archive. The stored electronic archives lock down the transfer information of all cryopreserved mixed cell samples throughout the entire process.

[0010] Furthermore, in a preferred embodiment of the present invention, the combined cryopreservation mixed sample of cells after dispensing undergoes ice crystal-targeted inhibition variable gradient intelligent freezing treatment, and the frozen cryopreservation mixed sample is intelligently adapted for storage and management, specifically as follows: In the stored electronic archives, retrieve the cell attributes of the cryopreserved mixed samples, including the cell type and low temperature tolerance limit threshold in the basic characteristic information of the cells. The critical threshold for ice crystal formation in cells of the mixed cryopreserved cell samples was retrieved by searching the historical data network. This was used to divide the multi-stage freezing and cooling process of the mixed cryopreserved cell samples into stages including dehydration, ice crystal inhibition, and low-temperature fixation. Meanwhile, we continue to retrieve the cooling rate, cooling duration, and isothermal holding parameters of the multi-stage freezing and cooling process for different cell cryopreservation mixed samples through historical data networks, in order to establish the segmented cooling process curves for different types of cell cryopreservation mixed samples and calibrate them as target segmented cooling process curves. Obtain a cell freezing device and connect it to the data analysis module of a cell detection device. Place a pre-packaged, sealed cryopreservation container inside the cell freezing device and perform segmented gradient freezing of the mixed cell cryopreservation sample in the cell freezing device, based on the target segmented cooling process curve. During the segmented gradient freezing process of mixed cell cryopreservation samples, real-time freezing analysis is performed using the data analysis module of the cell detection equipment, and intelligent adaptation storage and management of the frozen mixed cell cryopreservation samples is carried out based on the analysis results.

[0011] Furthermore, in a preferred embodiment of the present invention, during the segmented gradient freezing process of the cell cryopreservation mixed sample, real-time freezing analysis is performed in conjunction with the data analysis module of the cell detection device, and intelligent adaptive storage and management of the frozen cell cryopreservation mixed sample is performed based on the analysis results, specifically as follows: During the segmented gradient freezing process of mixed cell cryopreservation samples, the data analysis module of the cell detection equipment is used to collect in real time the ambient temperature inside the cell freezing equipment, the real-time body temperature of the mixed cell cryopreservation samples, the cooling rate fluctuation value, and the time parameters of all freezing and cooling stages. All collected data are analyzed in real time through the data analysis module of the cell detection device, compared with the target segmented cooling process curve and the corresponding critical threshold for ice crystal formation, and the deviation value is calculated and marked as the freezing data deviation value. Define the freezing quality level corresponding to the freezing data deviation value. Based on the freezing data deviation value, output the freezing quality level in the segmented gradient freezing process of the cell cryopreservation mixed sample in real time, and record it into the stored electronic archive for archive update. At the same time, obtain the frozen cell cryopreservation mixed sample and label it as a cell cryopreservation sample. Obtain cell storage devices, retrieve the storage specifications and environmental standard parameters that need to be matched for different freezing quality levels through historical data networks, and input them into cell storage devices; The frozen cell samples are placed in a cell storage device, which then transports the frozen cell samples to the appropriate storage location based on the storage specifications and environmental standards required for different freezing quality levels. Different storage locations have different storage specifications and environmental standards. The storage specifications and environmental standards for the storage locations where frozen cell samples are stored are entered into the electronic storage archive for updating.

[0012] Furthermore, in a preferred embodiment of the present invention, the step of predicting the temporal evolution and activity decay of frozen cell samples after storage, and periodically sampling and evaluating frozen cell samples, specifically includes: Preset fixed-period sampling time points and randomly sample frozen cell samples in the cell storage device; Among them, the indicators for sampling and testing frozen cell samples include cell viability, morphological integrity, and proliferation capacity. Obtain the standard indicators for cryopreservation and compare them with the indicators of sampled frozen cell samples. Calculate the evolution law of cell viability decay in frozen cell samples and combine it with historical data network analysis to analyze the evolution law of cell viability decay in frozen cell samples and output the time node of cell viability failure. Cell quality is graded based on the viability failure time point of frozen cell samples, and classified into high-quality usable frozen cell samples, degraded usable frozen cell samples, and surrounding frozen cell samples. At the same time, the viability failure time point of the frozen cell samples and the corresponding cell quality grading results are entered into the stored electronic archive for archive updates.

[0013] This invention addresses the technical deficiencies in the existing technology and offers the following advantages: First, it collects cell viability indicators from multiple dimensions, uses clustering algorithms to screen high-quality cells, and completes standardized preprocessing. Second, it introduces a low-temperature tolerance response model algorithm to achieve intelligent dynamic compounding of cryopreservation agents. Subsequently, it establishes blockchain identity verification and archiving for cryopreserved cell samples and employs an ice crystal-targeted inhibition gradient freezing method, coupled with hierarchical intelligent warehousing management, to ensure the long-term storage stability of samples. Finally, it combines a time-series evolution algorithm to predict the activity decay of stored samples and conduct periodic sampling assessments, dynamically monitoring the sample storage status. This invention overcomes the technical shortcomings of traditional artificial cryopreservation, such as poor standardization, weak adaptability, low cell viability, chaotic sample management, and inability to predict activity decay. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other embodiments can be obtained from these drawings without creative effort.

[0015] Figure 1A flowchart illustrating a comprehensive management method for cell cryopreservation is shown. Figure 2 A flowchart illustrating the method for freezing mixed samples of cryopreserved cells and managing intelligent adaptive storage is presented. Detailed Implementation

[0016] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0017] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0018] Figure 1 A flowchart illustrating a comprehensive management method for cell cryopreservation is shown, including the following steps: S102: Collect cell survival indicators from multiple dimensions and perform cell survival pretreatment to obtain qualified pretreated cell samples; S104: Introducing a cell low-temperature tolerance response model algorithm, combined with the basic characteristic information of pretreated qualified cell samples, to dynamically compound cell cryopreservation agents; S106: After the cell cryopreservation agent is prepared, the cell cryopreservation agent is identified and registered using blockchain technology to obtain a stored electronic file. S108: Combined with the cell cryopreservation mixed sample after dispensing, perform ice crystal targeted inhibition variable gradient intelligent freezing treatment, and perform intelligent adaptation storage and management of the frozen cell cryopreservation mixed sample. S110: Predict the temporal evolution and activity decay of frozen cell samples after storage, and periodically sample frozen cell samples for evaluation.

[0019] Furthermore, in a preferred embodiment of the present invention, the multi-dimensional acquisition of cell viability indicators and the pretreatment of cells to obtain pretreated qualified cell samples specifically involve: The cell samples that need to be cryopreserved are identified and labeled as target cell samples. Multidimensional survival indicators of the target cell samples are collected using cell detection equipment. The cell detection device includes a cell detection module and a data analysis module. The multidimensional survival indicators include the metabolic reaction rate of the target cell sample, cell membrane integrity, content of contaminants, and cell division and growth cycle status. In the data analysis module of the cell detection equipment, a cell endowment feature clustering algorithm is introduced, and standard survival indicators of similar cells in the target cell sample are obtained and compared with multidimensional survival indicators. Target cell samples whose feature similarity to multidimensional survival indicators is greater than a preset threshold are identified as qualified cell samples. By introducing a historical data network, basic characteristic information of cells in qualified cell samples is obtained, which is used to retrieve the pretreatment process and corresponding process parameters of qualified cell samples in combination with the historical data network. Output the pretreatment process and corresponding process parameters for qualified cell samples to obtain pretreated qualified cell samples.

[0020] It is important to note that the process begins by identifying the cell samples to be cryopreserved as the testing targets. The cells' true cryopreservation potential is characterized across four core dimensions: cell metabolism, structural integrity, biosafety, and growth cycle. The multidimensional characteristics of the cells to be tested are automatically compared and fitted with the characteristics of high-quality standard cells of the same type in the database to identify cells within the optimal cryopreservation window. Unqualified samples due to aging, contamination, metabolic abnormalities, or abnormal cell cycles are automatically filtered out, retaining only high-quality cells. Subsequently, the process is linked to a historical process database, intelligently retrieving appropriate pretreatment parameters for dissociation, washing, and centrifugation based on the current cell type and physiological characteristics. A standardized pretreatment protocol is output and the cell pretreatment is completed, ensuring that the cell osmotic pressure, physiological state, and cleanliness meet the optimal standards for cryopreservation. The entire process requires no human intervention, significantly improving process stability and repeatability, and substantially increasing cell cryopreservation survival rates from the outset.

[0021] Furthermore, in a preferred embodiment of the present invention, the introduction of a cell cryopreservation response model algorithm, combined with the basic characteristic information of pretreated qualified cell samples, for dynamic compounding of cell cryopreservation agents, specifically involves: Retrieve basic characteristic information of qualified cell samples, and pre-determine the storage and preservation market and actual application scenarios of qualified cell samples, and merge and package them into a combined associated dataset of qualified cell samples. In the data analysis module of the cell detection equipment, a combined associated dataset of qualified cell samples is input, and a cell low-temperature tolerance response model algorithm is introduced. Based on the cell low temperature tolerance response model algorithm, historical data on cryopreservation ratios of cells of the same type as qualified cell samples are retrieved from the historical data network. Combined with the combined association dataset of qualified cell samples, the theoretical mixing ratio of all protective components of the cryoprotectant for pretreated qualified cell samples is calculated. Obtain a sterile mixing container. Based on the theoretical mixing ratio of all protective components of the cryoprotectant for the pretreated qualified cell samples, add all protective components of the cryoprotectant for the pretreated qualified cell samples into the sterile mixing container to obtain a preliminary mixture of protective components. Combined with the cell detection module of the cell detection device, the internal osmotic pressure and pH of the preliminary protective component mixture are collected in real time and compared with the corresponding preset standard parameter range to determine whether the internal osmotic pressure and pH of the preliminary protective component mixture are maintained within the corresponding preset standard parameter range. If not, it is determined that the preliminary protective component mixture does not meet the cell tolerance requirements of qualified cell samples. The deviation values ​​of the internal osmotic pressure and real-time pH physicochemical parameters of the preliminary protective component mixture from the preset standard parameter range are recorded. Based on the deviation values, the mixing ratio values ​​of all protective components of the cryopreservation agent for pretreated qualified cell samples are dynamically adjusted until the internal osmotic pressure and real-time pH physicochemical parameters of the preliminary protective component mixture are maintained within the corresponding preset standard parameter range, thus obtaining the cell cryopreservation agent.

[0022] It's important to note that by integrating three core variables—inherent cell physiological characteristics, planned storage duration, and post-resuscitation application scenarios—a dedicated dataset is created. This dataset is then fed into the algorithm model to establish a correlation between cell cryotolerance characteristics and cryoprotectant ratios. This dedicated cryotolerance response model accurately captures the differences in cryotolerance among different cell types, overcoming the shortcomings of traditional cryoprotectant preparation methods that lack differentiation and have low levels of intelligence. The cell cryotolerance response model algorithm relies on historical data, combined with the specific characteristics, storage, and application parameters of the current cell sample, to automatically calculate the optimal theoretical ratio of all components, including the cryoprotectant, serum, and culture medium. Based on this ratio, initial cryoprotectant preparation is performed, ensuring aseptic and standardized operation throughout the process. Subsequently, the core physicochemical indicators of the cryoprotectant are collected in real time, and the measured values ​​are precisely compared with the specific tolerance standard range for this type of cell. Deviations between the theoretical ratio and the actual solution state are identified. If parameters are not up to standard, the deviation value is recorded, and the allocation ratios of each group are dynamically adjusted until the parameters meet the standards, ultimately generating a dedicated cell cryoprotectant. In other words, the composition ratio is corrected by real-time physicochemical deviation and iteratively fine-tuned until the osmotic pressure and pH of the protective solution are fully adapted to the cell's low-temperature tolerance range, thus generating the final special protective solution. The advantage is that the microenvironment of the protective solution can be made to perfectly match the cell's low-temperature survival needs, thereby improving the cell cryopreservation survival rate and long-term storage stability from the root.

[0023] Furthermore, in a preferred embodiment of the present invention, after the cell cryopreservation agent is prepared, the cell cryopreservation agent is subjected to blockchain-based identity verification to obtain a stored electronic file, specifically as follows: The cell cryopreservation agent and qualified cell samples are mixed and homogenized during the mixing process to obtain a mixed cell cryopreservation sample. Determine the standard aliquot volume, and aliquot the cell cryopreservation mixture into a sealed cryopreservation container according to the standard aliquot volume; In the data analysis module of the cell detection equipment, a unique and tamper-proof traceability identifier is generated by combining the combined dataset of qualified cell samples corresponding to the cell cryopreservation mixed sample, the cryopreservation batch, and the cryopreservation time. This identifier is then bound to the corresponding sealed cryopreservation container number to build and store an electronic archive. The stored electronic archives lock down the transfer information of all cryopreserved mixed cell samples throughout the entire process.

[0024] It is important to note that the customized cryopreservation solution is thoroughly and uniformly mixed with the pre-treated qualified cell samples to form a homogeneous and stable cell cryopreservation mixture. This ensures that the cryoprotectant evenly coats each cell, guaranteeing that all cells receive equal cryoprotection and preventing cell damage caused by insufficient local protection. After quantitative aliquoting, core data such as cell characteristic datasets, batch information, and cryopreservation time are integrated and encrypted to generate a unique, tamper-proof traceability ID, which is then one-to-one bound to the cryopreservation container. Using blockchain-based ownership verification logic, the data is immutable and uniquely traceable, completely resolving issues such as sample confusion, information falsification, data loss, and batch mismatch. Finally, using the unique traceability identifier as the core, a dedicated electronic file is established to record, store, and lock the entire process of sample flow from cryopreservation, warehousing, sampling, to thawing.

[0025] Furthermore, in a preferred embodiment of the present invention, the step of predicting the temporal evolution and activity decay of frozen cell samples after storage, and periodically sampling and evaluating frozen cell samples, specifically includes: Preset fixed-period sampling time points and randomly sample frozen cell samples in the cell storage device; Among them, the indicators for sampling and testing frozen cell samples include cell viability, morphological integrity, and proliferation capacity. Obtain the standard indicators for cryopreservation and compare them with the indicators of sampled frozen cell samples. Calculate the evolution law of cell viability decay in frozen cell samples and combine it with historical data network analysis to analyze the evolution law of cell viability decay in frozen cell samples and output the time node of cell viability failure. Cell quality is graded based on the viability failure time point of frozen cell samples, and classified into high-quality usable frozen cell samples, degraded usable frozen cell samples, and surrounding frozen cell samples. At the same time, the viability failure time point of the frozen cell samples and the corresponding cell quality grading results are entered into the stored electronic archive for archive updates.

[0026] It should be noted that a fixed sampling period is set in advance, and frozen samples to be tested are randomly selected from the storage device. This is to avoid long-term storage with unknown conditions and to promptly grasp the true condition of the samples. Multiple tests are conducted focusing on cell viability, structural integrity, and growth and reproduction capacity. This comprehensive assessment of cell quality from core dimensions fully reflects the degree of storage degradation. The measured data are compared with pre-standard parameters to predict activity trends and estimate the time point when the samples become completely ineffective. Based on the degree of degradation and the time limit for failure, samples are classified into three categories: high-quality usable, degraded usable, and discarded. The grading results and failure times are recorded and archived. The purpose is to clearly define the usability of the samples and provide a basis for subsequent sampling and retrieval.

[0027] Figure 2 A flowchart illustrating a method for freezing mixed cell cryopreservation samples and managing their intelligent adaptation in storage is shown, including the following steps: S202: Combine the cell cryopreservation mixed samples after dispensing, perform ice crystal targeted inhibition variable gradient intelligent freezing treatment, and perform intelligent adaptation storage and management of the frozen cell cryopreservation mixed samples. S204: During the segmented gradient freezing process of mixed cell cryopreservation samples, real-time freezing analysis is performed using the data analysis module of the cell detection equipment, and intelligent adaptation storage and management of the frozen mixed cell cryopreservation samples is carried out based on the analysis results.

[0028] Furthermore, in a preferred embodiment of the present invention, the combined cryopreservation mixed sample of cells after dispensing undergoes ice crystal-targeted inhibition variable gradient intelligent freezing treatment, and the frozen cryopreservation mixed sample is intelligently adapted for storage and management, specifically as follows: In the stored electronic archives, retrieve the cell attributes of the cryopreserved mixed samples, including the cell type and low temperature tolerance limit threshold in the basic characteristic information of the cells. The critical threshold for ice crystal formation in cells of the mixed cryopreserved cell samples was retrieved by searching the historical data network. This was used to divide the multi-stage freezing and cooling process of the mixed cryopreserved cell samples into stages including dehydration, ice crystal inhibition, and low-temperature fixation. Meanwhile, we continue to retrieve the cooling rate, cooling duration, and isothermal holding parameters of the multi-stage freezing and cooling process for different cell cryopreservation mixed samples through historical data networks, in order to establish the segmented cooling process curves for different types of cell cryopreservation mixed samples and calibrate them as target segmented cooling process curves. Obtain a cell freezing device and connect it to the data analysis module of a cell detection device. Place a pre-packaged, sealed cryopreservation container inside the cell freezing device and perform segmented gradient freezing of the mixed cell cryopreservation sample in the cell freezing device, based on the target segmented cooling process curve. During the segmented gradient freezing process of mixed cell cryopreservation samples, real-time freezing analysis is performed using the data analysis module of the cell detection equipment, and intelligent adaptation storage and management of the frozen mixed cell cryopreservation samples is carried out based on the analysis results.

[0029] It's important to note that the process begins by extracting inherent attribute data such as cell type and low-temperature tolerance limits from electronic records. This aims to pinpoint the cell's tolerance boundaries and define a safe range for cooling parameters. Historical data is then used to identify critical points for ice crystal formation, resulting in three independent cooling stages: dehydration, crystal inhibition, and cell shaping. These three independent cooling stages proceed sequentially, addressing different cell water states at different stages and effectively reducing the risk of sharp ice crystals puncturing and damaging cell structure. The appropriate cooling rate, duration, and isothermal parameters for each stage are retrieved and combined to create a customized cooling curve for the cell type. This curve is then integrated into the freezing equipment, and gradient freezing is performed according to the target curve. A combined data analysis module enables seamless collaboration between the equipment and the data system, ensuring fully programmed temperature control, uniform batch freezing results, and significantly improved process repeatability.

[0030] Furthermore, in a preferred embodiment of the present invention, during the segmented gradient freezing process of the cell cryopreservation mixed sample, real-time freezing analysis is performed in conjunction with the data analysis module of the cell detection device, and intelligent adaptive storage and management of the frozen cell cryopreservation mixed sample is performed based on the analysis results, specifically as follows: During the segmented gradient freezing process of mixed cell cryopreservation samples, the data analysis module of the cell detection equipment is used to collect in real time the ambient temperature inside the cell freezing equipment, the real-time body temperature of the mixed cell cryopreservation samples, the cooling rate fluctuation value, and the time parameters of all freezing and cooling stages. All collected data are analyzed in real time through the data analysis module of the cell detection device, compared with the target segmented cooling process curve and the corresponding critical threshold for ice crystal formation, and the deviation value is calculated and marked as the freezing data deviation value. Define the freezing quality level corresponding to the freezing data deviation value. Based on the freezing data deviation value, output the freezing quality level in the segmented gradient freezing process of the cell cryopreservation mixed sample in real time, and record it into the stored electronic archive for archive update. At the same time, obtain the frozen cell cryopreservation mixed sample and label it as a cell cryopreservation sample. Obtain cell storage devices, retrieve the storage specifications and environmental standard parameters that need to be matched for different freezing quality levels through historical data networks, and input them into cell storage devices; The frozen cell samples are placed in a cell storage device, which then transports the frozen cell samples to the appropriate storage location based on the storage specifications and environmental standards required for different freezing quality levels. Different storage locations have different storage specifications and environmental standards. The storage specifications and environmental standards for the storage locations where frozen cell samples are stored are entered into the electronic storage archive for updating.

[0031] It should be noted that during the freezing operation, four types of operational data are collected simultaneously: ambient temperature, sample temperature, cooling rate fluctuations, and time consumed at each stage. This allows for a comprehensive understanding of the actual freezing operation and a complete reconstruction of the entire cooling process. The above data needs to be compared with preset cooling curves and ice crystal critical thresholds to calculate the deviation between the actual operating conditions and the standard process. The purpose is to quantitatively determine the compliance level of the freezing process and objectively measure the effectiveness of process execution. Based on the deviation value, corresponding quality levels are assigned, and the level information is simultaneously entered into the sample file. This completes the sample type marking during the freezing stage. Using a historical database, the specific storage specifications and environmental parameters corresponding to samples of different quality levels are retrieved and imported into the storage device control system, enabling the automatic transfer of samples to their corresponding designated storage locations. The actual storage location of the samples and the associated environmental parameters are recorded and simultaneously added to the electronic file for future reference, ensuring full traceability of storage information. This facilitates later retrieval, sampling, and process review, improving the standardization of sample management.

[0032] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for the entire process management of cell cryopreservation, characterized in that, Includes the following steps: Cell viability indicators were collected from multiple dimensions, and the cells were pretreated to obtain qualified pretreated cell samples. We introduced a cell low-temperature tolerance response model algorithm and combined it with the basic characteristic information of pretreated qualified cell samples to dynamically formulate cell cryopreservation protectants. After the cell cryopreservation agent is prepared, its identity is confirmed using blockchain technology, resulting in a stored electronic file. Combined with the cell cryopreservation mixture sample after aliquoting, the ice crystal targeted inhibition variable gradient intelligent freezing process is carried out, and the frozen cell cryopreservation mixture sample is intelligently adapted for storage and management. We predict the temporal evolution and activity decay of frozen cell samples after storage, and periodically sample frozen cell samples for evaluation.

2. The method for managing the entire process of cell cryopreservation according to claim 1, characterized in that, The process involves collecting cell viability indicators from multiple dimensions and pre-treating the cells to obtain qualified pre-treated cell samples. Specifically: The cell samples that need to be cryopreserved are identified and labeled as target cell samples. Multidimensional survival indicators of the target cell samples are collected using cell detection equipment. The cell detection device includes a cell detection module and a data analysis module. The multidimensional survival indicators include the metabolic reaction rate of the target cell sample, cell membrane integrity, content of contaminants, and cell division and growth cycle status. In the data analysis module of the cell detection equipment, a cell endowment feature clustering algorithm is introduced, and standard survival indicators of similar cells in the target cell sample are obtained and compared with multidimensional survival indicators. Target cell samples whose feature similarity to multidimensional survival indicators is greater than a preset threshold are identified as qualified cell samples. By introducing a historical data network, basic characteristic information of cells in qualified cell samples is obtained, which is used to retrieve the pretreatment process and corresponding process parameters of qualified cell samples in combination with the historical data network. Output the pretreatment process and corresponding process parameters for qualified cell samples to obtain pretreated qualified cell samples.

3. The method for managing the entire process of cell cryopreservation according to claim 1, characterized in that, The introduced cell cryoprotectant response model algorithm, combined with the basic characteristic information of pretreated qualified cell samples, enables dynamic compounding of cell cryoprotectants, specifically as follows: Retrieve basic characteristic information of qualified cell samples, and pre-determine the storage and preservation market and actual application scenarios of qualified cell samples, and merge and package them into a combined associated dataset of qualified cell samples. In the data analysis module of the cell detection equipment, a combined associated dataset of qualified cell samples is input, and a cell low-temperature tolerance response model algorithm is introduced. Based on the cell low temperature tolerance response model algorithm, historical data on cryopreservation ratios of cells of the same type as qualified cell samples are retrieved from the historical data network. Combined with the combined association dataset of qualified cell samples, the theoretical mixing ratio of all protective components of the cryoprotectant for pretreated qualified cell samples is calculated. Obtain a sterile mixing container. Based on the theoretical mixing ratio of all protective components of the cryoprotectant for the pretreated qualified cell samples, add all protective components of the cryoprotectant for the pretreated qualified cell samples into the sterile mixing container to obtain a preliminary mixture of protective components. Combined with the cell detection module of the cell detection device, the internal osmotic pressure and pH of the preliminary protective component mixture are collected in real time and compared with the corresponding preset standard parameter range to determine whether the internal osmotic pressure and pH of the preliminary protective component mixture are maintained within the corresponding preset standard parameter range. If not, it is determined that the preliminary protective component mixture does not meet the cell tolerance requirements of qualified cell samples. The deviation values ​​of the internal osmotic pressure and real-time pH physicochemical parameters of the preliminary protective component mixture from the preset standard parameter range are recorded. Based on the deviation values, the mixing ratio values ​​of all protective components of the cryopreservation agent for pretreated qualified cell samples are dynamically adjusted until the internal osmotic pressure and real-time pH physicochemical parameters of the preliminary protective component mixture are maintained within the corresponding preset standard parameter range, thus obtaining the cell cryopreservation agent.

4. The method for managing the entire process of cell cryopreservation according to claim 1, characterized in that, After the cell cryopreservation agent is prepared, a blockchain-based identity verification process is performed on the cell cryopreservation agent to obtain a stored electronic file, specifically as follows: The cell cryopreservation agent and qualified cell samples are mixed and homogenized during the mixing process to obtain a mixed cell cryopreservation sample. Determine the standard aliquot volume, and aliquot the cell cryopreservation mixture into a sealed cryopreservation container according to the standard aliquot volume; In the data analysis module of the cell detection equipment, a unique and tamper-proof traceability identifier is generated by combining the combined dataset of qualified cell samples corresponding to the cell cryopreservation mixed sample, the cryopreservation batch, and the cryopreservation time. This identifier is then bound to the corresponding sealed cryopreservation container number to build and store an electronic archive. The stored electronic archives lock down the transfer information of all cryopreserved mixed cell samples throughout the entire process.

5. The method for managing the entire process of cell cryopreservation according to claim 1, characterized in that, The combined cell cryopreservation mixture sample, after being dispensed, undergoes ice crystal-targeted inhibition variable gradient intelligent freezing treatment, and the frozen cell cryopreservation mixture sample is then intelligently adapted for storage and management, specifically as follows: In the stored electronic archives, retrieve the cell attributes of the cryopreserved mixed samples, including the cell type and low temperature tolerance limit threshold in the basic characteristic information of the cells. The critical threshold for ice crystal formation in cells of the mixed cryopreserved cell samples was retrieved by searching the historical data network. This was used to divide the multi-stage freezing and cooling process of the mixed cryopreserved cell samples into stages including dehydration, ice crystal inhibition, and low-temperature fixation. Meanwhile, we continue to retrieve the cooling rate, cooling duration, and isothermal holding parameters of the multi-stage freezing and cooling process for different cell cryopreservation mixed samples through historical data networks, in order to establish the segmented cooling process curves for different types of cell cryopreservation mixed samples and calibrate them as target segmented cooling process curves. Obtain a cell freezing device and connect it to the data analysis module of a cell detection device. Place a pre-packaged, sealed cryopreservation container inside the cell freezing device and perform segmented gradient freezing of the mixed cell cryopreservation sample in the cell freezing device, based on the target segmented cooling process curve. During the segmented gradient freezing process of mixed cell cryopreservation samples, real-time freezing analysis is performed using the data analysis module of the cell detection equipment, and intelligent adaptation storage and management of the frozen mixed cell cryopreservation samples is carried out based on the analysis results.

6. The method for managing the entire process of cell cryopreservation according to claim 5, characterized in that, In the segmented gradient freezing process of the mixed cell cryopreservation samples, real-time freezing analysis is performed using the data analysis module of the cell detection equipment. Based on the analysis results, intelligent adaptive storage and management of the frozen mixed cell cryopreservation samples is implemented. Specifically: During the segmented gradient freezing process of mixed cell cryopreservation samples, the data analysis module of the cell detection equipment is used to collect in real time the ambient temperature inside the cell freezing equipment, the real-time body temperature of the mixed cell cryopreservation samples, the cooling rate fluctuation value, and the time parameters of all freezing and cooling stages. All collected data are analyzed in real time through the data analysis module of the cell detection device, compared with the target segmented cooling process curve and the corresponding critical threshold for ice crystal formation, and the deviation value is calculated and marked as the freezing data deviation value. Define the freezing quality level corresponding to the freezing data deviation value. Based on the freezing data deviation value, output the freezing quality level in the segmented gradient freezing process of the cell cryopreservation mixed sample in real time, and record it into the stored electronic archive for archive update. At the same time, obtain the frozen cell cryopreservation mixed sample and label it as a cell cryopreservation sample. Obtain cell storage devices, retrieve the storage specifications and environmental standard parameters that need to be matched for different freezing quality levels through historical data networks, and input them into cell storage devices; The frozen cell samples are placed in a cell storage device, which then transports the frozen cell samples to the appropriate storage location based on the storage specifications and environmental standards required for different freezing quality levels. Different storage locations have different storage specifications and environmental standards. The storage specifications and environmental standards for the storage locations where frozen cell samples are stored are entered into the electronic storage archive for updating.

7. The method for managing the entire process of cell cryopreservation according to claim 1, characterized in that, The process of predicting the temporal evolution and activity decay of frozen cell samples after storage, and periodically sampling and evaluating frozen cell samples, specifically includes: Preset fixed-period sampling time points and randomly sample frozen cell samples in the cell storage device; Among them, the indicators for sampling and testing frozen cell samples include cell viability, morphological integrity, and proliferation capacity. Obtain the standard indicators for cryopreservation and compare them with the indicators of sampled frozen cell samples. Calculate the evolution law of cell viability decay in frozen cell samples and combine it with historical data network analysis to analyze the evolution law of cell viability decay in frozen cell samples and output the time node of cell viability failure. Cell quality is graded based on the viability failure time point of frozen cell samples, and classified into high-quality usable frozen cell samples, degraded usable frozen cell samples, and surrounding frozen cell samples. At the same time, the viability failure time point of the frozen cell samples and the corresponding cell quality grading results are entered into the stored electronic archive for archive updates.

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