A method and system for end-to-end traceability and safety supervision of biological samples

Abstract

This invention relates to the field of online monitoring technology, and is a method and system for full-process traceability and safety supervision of biological samples. The specific method includes: dividing the area requiring biological sample supervision into several sub-sample bank partitions; extracting the total equivalent value of historical sample activity decay in each sub-sample bank partition; simultaneously extracting the location data of cold chain monitoring nodes in each sub-sample bank partition; obtaining representative monitoring nodes for the region; and calculating the time-effect fluctuation coefficient and cold chain offset coefficient; substituting the time-effect fluctuation coefficient and cold chain offset coefficient into a sub-sample bank partition activity risk prediction strategy to predict the total activity risk value of samples in each sub-sample bank partition; and allocating cold chain resources according to the predicted total activity risk value of samples in each sub-sample bank partition. This invention solves the problem of mismatch between the decay risk of biological samples and the spatial environment in existing technologies.

Description

Technical Field

[0001] This invention relates to the field of online monitoring technology, and is a method and system for full traceability and safety supervision of biological samples. Background Technology

[0002] In the field of end-to-end traceability and safety supervision of biological samples (especially blood products), existing technologies have the following shortcomings: First, there is a spatial mismatch between cold chain resource allocation and the risk of sample activity decay. The existing equalization resource supply model ignores the dynamic risk gradient of sub-sample bank partitions, leading to hemolysis accidents in high-activity decay areas (such as high-frequency access areas and temperature-sensitive sample concentration areas) due to insufficient liquid nitrogen replenishment or shortage of constant temperature transportation capacity, while low-risk areas suffer from energy waste due to excessive refrigeration (for example, the daily energy consumption of ultra-low temperature storage is 2.8 times that of conventional cold storage). Second, there is a lack of modeling for the correlation between environmental disturbances and biological decay. Existing systems rely on static temperature threshold monitoring (such as single threshold alarms) and have not established a dynamic mapping relationship between cold chain deviation and sample deterioration rate, making it impossible to predict cascading deterioration risks caused by differences in door opening and closing frequency (for example, the daily access frequency in the core area is 4.6 times that of the edge area) or the distribution of hot and cold spots in equipment (for example, the temperature fluctuation range on the door side is 3.2-3.5℃ higher than that in the central area). Third, the traceability granularity and resource scheduling accuracy are insufficient. The data collection strategy based on uniform monitoring of all nodes generates a large amount of redundant and low-value data. Furthermore, the traceability cycle is not aligned with the biological half-life of the samples (e.g., 42 days for red blood cells and 5 days for platelets), resulting in a delay in resource scheduling response and increasing the risk of biological sample decay. Summary of the Invention

[0003] The technical problem to be solved by this invention is to address the issue of decay risk and spatial environment mismatch of biological samples in the prior art, and proposes a method and system for full traceability and safety supervision of biological samples.

[0004] To achieve the above objectives, the technical solution of the present invention for a method of full-process traceability and safety supervision of biological samples includes the following steps:

[0005] Step 1: Divide the area requiring biological sample monitoring into several sub-sample library partitions, extract the total value of historical sample activity decay equivalent in the sub-sample library partitions, and extract the location data of cold chain monitoring nodes in the sub-sample library partitions.

[0006] Step 2: Import the number of cold chain monitoring nodes in the sub-sample library, the hemolysis index curve of the monitored historical samples, and the total value of the activity decay equivalent of the monitored historical samples into the regional representative monitoring node acquisition strategy to obtain regional representative monitoring nodes;

[0007] Step 3: Extract the historical sample deterioration rate detection data of the monitoring node representing the region of each subsample library partition and substitute it into the time-effect fluctuation coefficient calculation strategy to calculate the time-effect fluctuation coefficient;

[0008] Step 4: Extract the historical cold chain offset data and historical sample deterioration rate detection data of the regional representative monitoring nodes of each sub-sample library partition, and substitute them into the cold chain offset coefficient calculation strategy to calculate the cold chain offset coefficient;

[0009] Step 5: Substitute the calculated time-varying coefficient and cold chain offset coefficient into the sub-sample library partition activity risk total value prediction strategy to predict the total activity risk value of sub-sample library partition samples;

[0010] Step 6: Allocate cold chain resources according to the estimated total risk value of sample activity in the sub-sample library partitions.

[0011] Preferably, step one includes the following steps:

[0012] S11: Divide the biological sample monitoring area into several sub-sample library partitions of equal volume, and deploy at least two cold chain monitoring nodes in each sub-sample library partition;

[0013] S12: Extract real-time bioactivity readings from cold chain monitoring nodes in the sub-sample library partition, and obtain the sample activity decay rate curves of cold chain monitoring nodes in the sub-sample library partition.

[0014] S13: Integrate the activity decay rate curves of each cold chain monitoring node in the sub-sample library partition over time to obtain the activity decay equivalent value of each node, and then sum the activity decay equivalent values ​​of all nodes to obtain the total activity decay equivalent value of historical samples in the sub-sample library partition.

[0015] Preferably, the specific steps of the strategy for obtaining the regional representative monitoring node in step two are as follows:

[0016] S21: Extract the number of cold chain monitoring nodes in the sub-sample library partition, the hemolysis index curve of the monitored historical samples, and the total value curve of the activity decay equivalent of the monitored historical samples in the sub-sample library partition;

[0017] S22: Calculate the percentage of historical hemolysis index for the i-th cold chain monitoring node during the j-th tracing period in the subsample library partition. Among them, the historical hemolysis index of the i-th cold chain monitoring node in the j-th tracing period in the sub-sample library partition is The percentage calculation strategy is as follows: Where K is the total number of cold chain monitoring nodes in the subsample library partition; The historical hemolysis index of the k-th cold chain monitoring node in the subsample library partition during the j-th traceability period;

[0018] S23: Calculate the average percentage of hemolysis index during the historical tracing period for the i-th cold chain monitoring node in the subsample library partition. The calculation strategy is as follows: Where J represents the number of historical tracing periods;

[0019] S24: Arrange the average percentage of hemolysis index during the historical tracing period of the cold chain monitoring nodes in the calculated subsample library partitions in descending order, and find the cold chain monitoring node corresponding to the largest average percentage of hemolysis index during the historical tracing period as the regional representative monitoring node.

[0020] Preferably, the specific steps of the time-varying coefficient calculation strategy in step three include the following:

[0021] S31: Take the set of sample deterioration rates from the historical source tracing period and the set of sample deterioration rates from the previous source tracing period in the region representing the monitoring node in the subsample library partition;

[0022] S32: Calculate the average value of sample deterioration rates for the same historical period of origin by using the set of sample deterioration rates monitored during the same historical period. The calculation strategy is as follows: ,in The deterioration rate of the e-th sample in the set of sample deterioration rates monitored during the same historical source-tracing period;

[0023] S33: Average the degradation rate of samples from the same historical period. Substituting the sample deterioration rate set monitored in the previous source tracing period into the time-effect fluctuation coefficient calculation strategy, the time-effect fluctuation coefficient is calculated. The time-varying coefficient calculation strategy is as follows: ,in, Influencing factors for historical periods of origin, The influencing factor for the previous source tracing period. This represents the sample deterioration rate monitored during the previous tracing period. .

[0024] Preferably, the cold chain offset coefficient calculation strategy in step four includes the following specific steps:

[0025] S41: Extract The temperature control plan curve for a traceability period is taken from the extreme value and mean value of temperature fluctuation for the next traceability period preset by the environmental monitoring system. The weighted value of the extreme value difference and the root mean square deviation is taken as the cold chain offset for the next traceability period.

[0026] S42: Take the average sample deterioration rate corresponding to the traceability period with the same cold chain offset as the next traceability period, and import it into the cold chain offset coefficient calculation strategy to calculate the cold chain offset coefficient. The calculation strategy for the cold chain offset coefficient is as follows: Where R is the number of traceability periods with the same cold chain offset as the next traceability period. The sample deterioration rate is the same as that of the r-th traceability period, which has the same cold chain offset as the next traceability period.

[0027] Preferably, the strategy for predicting the total activity risk of sub-sample library partitions in step five includes the following specific details:

[0028] S51: Extract the calculated time-varying coefficient and cold chain offset coefficient, and substitute them into the calculation strategy for the estimated activity risk value of the next traceability period representing the monitoring node in the subsample library partition. The strategy for calculating the activity risk value is as follows: ,in, For time-varying factors, This is the cold chain offset factor. ;

[0029] S52: Calculate the estimated activity risk value for the representative monitoring node of the i-th subsample library partition during the next traceability period. The average percentage of hemolysis index during the largest historical period in the sub-sample library partitions Substitute the values ​​into the formula for predicting the total activity risk of a sub-library partition to calculate the total activity risk of the sub-library partition. The formula for predicting the total risk of activity in a sub-sample library partition is: .

[0030] Preferably, step six involves: summing the estimated total activity risk values ​​of several sub-sample library partitions to obtain the total activity risk value of the region, and dividing the total activity risk value of each sub-sample library partition by the total activity risk value of the region, which is the allocation ratio of cold chain resources.

[0031] In addition, the biological sample end-to-end traceability and safety monitoring system of the present invention includes the following modules:

[0032] The module includes: biometric extraction module, regional representative monitoring node acquisition module, time-effect fluctuation coefficient calculation module, cold chain offset coefficient calculation module, sub-sample library partition activity risk total value calculation module, and cold chain resource allocation module.

[0033] The biometric extraction module divides the area requiring biological sample monitoring into several sub-sample library partitions, extracts the total value of historical sample activity decay equivalent in the sub-sample library partitions, and extracts the location data of cold chain monitoring nodes in the sub-sample library partitions.

[0034] The regional representative monitoring node acquisition module is used to import the number of cold chain monitoring nodes in the sub-sample library partition, the hemolysis index curve of the monitored historical samples, and the total value of the activity decay equivalent of the monitored historical samples into the regional representative monitoring node acquisition strategy to obtain regional representative monitoring nodes.

[0035] The time-effect fluctuation coefficient calculation module is used to extract the historical sample deterioration rate detection data of the same traceability period of the monitoring node in each sub-sample library partition and substitute it into the time-effect fluctuation coefficient calculation strategy to calculate the time-effect fluctuation coefficient.

[0036] The cold chain offset coefficient calculation module is used to extract the historical cold chain offset data and historical sample deterioration rate detection data of the regional representative monitoring nodes of each sub-sample library partition and substitute them into the cold chain offset coefficient calculation strategy to calculate the cold chain offset coefficient.

[0037] The subsample library partition activity risk total value calculation module is used to substitute the calculated time fluctuation coefficient and cold chain offset coefficient into the subsample library partition activity risk total value prediction strategy to predict the subsample library partition activity risk total value.

[0038] The cold chain resource allocation module is used to allocate cold chain resources according to the estimated total risk value of the activity of samples in the sub-sample library partition.

[0039] Compared with the prior art, the technical effects of the present invention are as follows:

[0040] This invention, by constructing a bioactivity risk elastic quantification model, has the following beneficial effects: First, it significantly improves the synergistic optimization level of sample safety and resource utilization efficiency. Based on the total equivalent value of bioactivity decay and the spatiotemporal dynamic risk prediction model, it ensures a strict match between cold chain resource allocation and the actual decay risk of sub-sample library partitions. For example, in high-risk areas (such as stem cell storage areas), the liquid nitrogen replenishment response time is shortened to <5 minutes (compared to >15 minutes in traditional methods), significantly reducing the hemolysis accident rate. In low-risk areas, it reduces cold chain energy consumption and significantly improves equipment utilization by precisely reducing excessive refrigeration. Second, this invention combines the nonlinear mapping between environmental disturbances and bioactivity decay. The cold chain offset coefficient in this invention integrates the extreme difference of temperature fluctuations, root mean square deviation, and sample specificity thresholds, which can predict the risk of deterioration caused by differences in door opening and closing frequencies (the core area access frequency is 4.2-4.6 times that of the edge area), improving the accuracy of early warning. Furthermore, this invention is highly universal, replacing fixed cycles with traceability periods, allowing the regulatory rhythm to be precisely aligned with the biological half-life of samples (e.g., 42 days for red blood cells and 5 days for platelets). Combined with a regional representative monitoring node strategy, it reduces data redundancy and improves resource scheduling speed. This invention can dynamically optimize the route of constant temperature transport vehicles by using the resource allocation ratio driven by the total activity risk value, thereby improving the timeliness of biological sample transportation, maintaining stable sample activity, and achieving deep coupling between traceability granularity and biological sample characteristics. Attached Figure Description

[0041] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments 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 drawings can be obtained based on these drawings without creative effort. Wherein:

[0042] Figure 1 This is a flowchart illustrating a method for end-to-end traceability and safety supervision of biological samples according to the present invention.

[0043] Figure 2 This is a schematic diagram of the structure of a biological sample full-process traceability and safety monitoring system according to the present invention. Detailed Implementation

[0044] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0045] 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 those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0046] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0047] Example 1:

[0048] like Figure 1 As shown in the figure, an embodiment of the present invention provides a method for end-to-end traceability and safety supervision of biological samples, such as... Figure 1 As shown, the specific steps include the following:

[0049] Step 1: Divide the area requiring biological sample monitoring into several sub-sample library partitions, extract the total value of historical sample activity decay equivalent in the sub-sample library partitions, and extract the location data of cold chain monitoring nodes in the sub-sample library partitions.

[0050] Step 2: Import the number of cold chain monitoring nodes in the sub-sample library, the hemolysis index curve of the monitored historical samples, and the total value of the activity decay equivalent of the monitored historical samples into the regional representative monitoring node acquisition strategy to obtain regional representative monitoring nodes;

[0051] S21: Extract the number of cold chain monitoring nodes in the sub-sample library partition, the hemolysis index curve of the monitored historical samples, and the total value curve of the activity decay equivalent of the monitored historical samples in the sub-sample library partition;

[0052] S22: Calculate the percentage of historical hemolysis index for the i-th cold chain monitoring node during the j-th tracing period in the subsample library partition. Among them, the historical hemolysis index of the i-th cold chain monitoring node in the j-th tracing period in the sub-sample library partition is The percentage calculation strategy is as follows: Where K is the total number of cold chain monitoring nodes in the subsample library partition; The historical hemolysis index of the k-th cold chain monitoring node in the subsample library partition during the j-th traceability period;

[0053] S23: Calculate the average percentage of hemolysis index during the historical tracing period for the i-th cold chain monitoring node in the subsample library partition. The calculation strategy is as follows: Where J represents the number of historical tracing periods;

[0054] S24: Arrange the average percentage of hemolysis index during the historical tracing period of the cold chain monitoring nodes in the calculated subsample library partitions in descending order, and find the cold chain monitoring node corresponding to the largest average percentage of hemolysis index during the historical tracing period as the regional representative monitoring node.

[0055] Step 3: Extract the historical sample deterioration rate detection data of the monitoring node representing the region of each subsample library partition and substitute it into the time-effect fluctuation coefficient calculation strategy to calculate the time-effect fluctuation coefficient;

[0056] S31: Take the set of sample deterioration rates from the historical source tracing period and the set of sample deterioration rates from the previous source tracing period in the region representing the monitoring node in the subsample library partition;

[0057] S32: Calculate the average value of sample deterioration rates for the same historical period of origin by using the set of sample deterioration rates monitored during the same historical period. The calculation strategy is as follows: ,in The deterioration rate of the e-th sample in the set of sample deterioration rates monitored during the same historical source-tracing period;

[0058] S33: Average the degradation rate of samples from the same historical period. Substituting the sample deterioration rate set monitored in the previous source tracing period into the time-effect fluctuation coefficient calculation strategy, the time-effect fluctuation coefficient is calculated. The time-varying coefficient calculation strategy is as follows: ,in, Influencing factors for historical periods of origin, The influencing factor for the previous source tracing period. This represents the sample deterioration rate monitored during the previous tracing period. .

[0059] Step 4: Extract the historical cold chain offset data and historical sample deterioration rate detection data of the regional representative monitoring nodes of each sub-sample library partition, and substitute them into the cold chain offset coefficient calculation strategy to calculate the cold chain offset coefficient;

[0060] S41: Extract The temperature control plan curve for a traceability period is taken from the extreme value and mean value of temperature fluctuation for the next traceability period preset by the environmental monitoring system. The weighted value of the extreme value difference and the root mean square deviation is taken as the cold chain offset for the next traceability period.

[0061] S42: Take the average sample deterioration rate corresponding to the traceability period with the same cold chain offset as the next traceability period, and import it into the cold chain offset coefficient calculation strategy to calculate the cold chain offset coefficient. The calculation strategy for the cold chain offset coefficient is as follows: Where R is the number of traceability periods with the same cold chain offset as the next traceability period. The sample deterioration rate is the same as that of the r-th traceability period, which has the same cold chain offset as the next traceability period.

[0062] Step 5: Substitute the calculated time-varying coefficient and cold chain offset coefficient into the sub-sample library partition activity risk total value prediction strategy to predict the total activity risk value of sub-sample library partition samples;

[0063] S51: Extract the calculated time-varying coefficient and cold chain offset coefficient, and substitute them into the calculation strategy for the estimated activity risk value of the next traceability period representing the monitoring node in the subsample library partition. The strategy for calculating the activity risk value is as follows: ,in, For time-varying factors, This is the cold chain offset factor. ;

[0064] S52: Calculate the estimated activity risk value for the representative monitoring node of the i-th subsample library partition during the next traceability period. The average percentage of hemolysis index during the largest historical period in the sub-sample library partitions Substitute the values ​​into the formula for predicting the total activity risk of a sub-library partition to calculate the total activity risk of the sub-library partition. The formula for predicting the total risk of activity in a sub-sample library partition is: .

[0065] Step Six: Allocate cold chain resources according to the estimated total activity risk value of samples in the sub-sample library, including:

[0066] The total activity risk value of the region is obtained by summing the estimated total activity risk values ​​of several sub-sample library partitions. The ratio obtained by dividing the total activity risk value of each sub-sample library partition by the total activity risk value of the region is the allocation ratio of cold chain resources.

[0067] For example, in this embodiment, the cold chain resources include: cryogenic storage tanks and liquid nitrogen replenishment.

[0068] Example 2:

[0069] like Figure 2 As shown in the figure, an embodiment of the present invention provides a biological sample end-to-end traceability and safety monitoring system, such as... Figure 2 As shown, it includes the following modules:

[0070] The module includes: biometric extraction module, regional representative monitoring node acquisition module, time-effect fluctuation coefficient calculation module, cold chain offset coefficient calculation module, sub-sample library partition activity risk total value calculation module, and cold chain resource allocation module.

[0071] The biometric extraction module divides the area requiring biological sample monitoring into several sub-sample library partitions, extracts the total value of historical sample activity decay equivalent in the sub-sample library partitions, and extracts the location data of cold chain monitoring nodes in the sub-sample library partitions.

[0072] The regional representative monitoring node acquisition module is used to import the number of cold chain monitoring nodes in the sub-sample library partition, the hemolysis index curve of the monitored historical samples, and the total value of the activity decay equivalent of the monitored historical samples into the regional representative monitoring node acquisition strategy to obtain regional representative monitoring nodes.

[0073] The time-effect fluctuation coefficient calculation module is used to extract the historical sample deterioration rate detection data of the same traceability period of the monitoring node in each sub-sample library partition and substitute it into the time-effect fluctuation coefficient calculation strategy to calculate the time-effect fluctuation coefficient.

[0074] The cold chain offset coefficient calculation module is used to extract the historical cold chain offset data and historical sample deterioration rate detection data of the regional representative monitoring nodes of each sub-sample library partition and substitute them into the cold chain offset coefficient calculation strategy to calculate the cold chain offset coefficient.

[0075] The subsample library partition activity risk total value calculation module is used to substitute the calculated time fluctuation coefficient and cold chain offset coefficient into the subsample library partition activity risk total value prediction strategy to predict the subsample library partition activity risk total value.

[0076] The cold chain resource allocation module is used to allocate cold chain resources according to the estimated total risk value of the activity of samples in the sub-sample library partition.

[0077] Example 3:

[0078] This embodiment provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0079] The processor executes the aforementioned method for end-to-end traceability and safety supervision of biological samples by calling computer programs stored in memory.

[0080] The electronic device can vary considerably depending on its configuration or performance. It may include one or more Central Processing Units (CPUs) and one or more memories, wherein the memory stores at least one computer program, which is loaded and executed by the processor to implement the biological sample end-to-end traceability and safety monitoring method provided in the above-described embodiment. The electronic device may also include other components for implementing its functions; for example, it may have wired or wireless network interfaces and input / output interfaces for data input and output. Details will not be elaborated upon in this embodiment.

[0081] Example 4:

[0082] This embodiment proposes a computer-readable storage medium on which an erasable and rewritable computer program is stored.

[0083] When a computer program runs on a computer device, it enables the computer device to perform the aforementioned method for full traceability and security monitoring of biological samples.

[0084] For example, computer-readable storage media can be read-only memory (ROM), random access memory (RAM), compact disc read-only memory (CD-ROM), magnetic tape, floppy disk, and optical data storage devices.

[0085] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0086] It should be understood that determining B based on A does not mean determining B solely based on A; it also means determining B based on A and / or other information.

[0087] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the flow or function according to the embodiments of the present invention is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired network and / or wireless network. A computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives (SSDs).

[0088] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0089] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0090] In the several embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only one method, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0091] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0092] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0093] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0094] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A method for end-to-end traceability and safety supervision of biological samples, characterized in that, The method includes: Step 1: Divide the area requiring biological sample monitoring into several sub-sample library partitions, extract the total value of historical sample activity decay equivalent in the sub-sample library partitions, and extract the location data of cold chain monitoring nodes in the sub-sample library partitions. Step 2: Import the number of cold chain monitoring nodes in the sub-sample library, the hemolysis index curve of the monitored historical samples, and the total value of the activity decay equivalent of the monitored historical samples into the regional representative monitoring node acquisition strategy to obtain regional representative monitoring nodes; The specific steps for obtaining the regional representative monitoring node in step two are as follows: S21: Extract the number of cold chain monitoring nodes in the sub-sample library partition, the hemolysis index curve of the monitored historical samples, and the total value curve of the activity decay equivalent of the monitored historical samples in the sub-sample library partition; S22: Calculate the percentage of historical hemolysis index for the i-th cold chain monitoring node during the j-th tracing period in the subsample library partition. Among them, the historical hemolysis index of the i-th cold chain monitoring node in the j-th tracing period in the sub-sample library partition is The percentage calculation strategy is as follows: Where K is the total number of cold chain monitoring nodes in the subsample library partition; The historical hemolysis index of the k-th cold chain monitoring node in the subsample library partition during the j-th traceability period; S23: Calculate the average percentage of hemolysis index during the historical tracing period for the i-th cold chain monitoring node in the subsample library partition. The calculation strategy is as follows: Where J represents the number of historical tracing periods; S24: Arrange the average percentage of hemolysis index during the historical tracing period of the cold chain monitoring nodes in the calculated subsample library partitions in descending order, and find the cold chain monitoring node corresponding to the largest average percentage of hemolysis index during the historical tracing period as the regional representative monitoring node. Step 3: Extract the historical sample deterioration rate detection data of the monitoring node representing the region of each subsample library partition and substitute it into the time-effect fluctuation coefficient calculation strategy to calculate the time-effect fluctuation coefficient; Step 4: Extract the historical cold chain offset data and historical sample deterioration rate detection data of the regional representative monitoring nodes of each sub-sample library partition, and substitute them into the cold chain offset coefficient calculation strategy to calculate the cold chain offset coefficient; Step 5: Substitute the calculated time-varying coefficient and cold chain offset coefficient into the sub-sample library partition activity risk total value prediction strategy to predict the total activity risk value of sub-sample library partition samples; Step 6: Allocate cold chain resources according to the estimated total risk value of sample activity in the sub-sample library partitions.

2. The method for full traceability and safety supervision of biological samples according to claim 1, characterized in that, Step one includes the following steps: S11: Divide the biological sample monitoring area into several sub-sample library partitions of equal volume, and deploy at least two cold chain monitoring nodes in each sub-sample library partition; S12: Extract real-time bioactivity readings from cold chain monitoring nodes in the sub-sample library partition, and obtain the sample activity decay rate curves of cold chain monitoring nodes in the sub-sample library partition. S13: Integrate the activity decay rate curves of each cold chain monitoring node in the sub-sample library partition over time to obtain the activity decay equivalent value of each node, and then sum the activity decay equivalent values ​​of all nodes to obtain the total activity decay equivalent value of historical samples in the sub-sample library partition.

3. The method for full traceability and safety supervision of biological samples according to claim 2, characterized in that, The specific steps of the time-varying fluctuation coefficient calculation strategy in step three include the following: S31: Take the set of sample deterioration rates from the historical source tracing period and the set of sample deterioration rates from the previous source tracing period in the region representing the monitoring node in the subsample library partition; S32: Calculate the average value of sample deterioration rates for the same historical period of origin by using the set of sample deterioration rates monitored during the same historical period. The calculation strategy is as follows: ,in The deterioration rate of the e-th sample in the set of sample deterioration rates monitored during the same historical source-tracing period; S33: Average the degradation rate of samples from the same historical period. Substituting the sample deterioration rate set monitored in the previous source tracing period into the time-effect fluctuation coefficient calculation strategy, the time-effect fluctuation coefficient is calculated. The time-varying coefficient calculation strategy is as follows: ,in, Influencing factors for the same historical period of origin, The influencing factor for the previous source tracing period. This represents the sample deterioration rate monitored during the previous tracing period. .

4. The method for full traceability and safety supervision of biological samples according to claim 3, characterized in that, The cold chain offset coefficient calculation strategy in step four includes the following specific steps: S41: Extract The temperature control plan curve for a traceability period is taken from the extreme value and mean value of temperature fluctuation for the next traceability period preset by the environmental monitoring system. The weighted value of the extreme value difference and the root mean square deviation is taken as the cold chain offset for the next traceability period. S42: Take the average sample deterioration rate corresponding to the traceability period with the same cold chain offset as the next traceability period, and import it into the cold chain offset coefficient calculation strategy to calculate the cold chain offset coefficient. The calculation strategy for the cold chain offset coefficient is as follows: Where R is the number of traceability periods with the same cold chain offset as the next traceability period. The sample deterioration rate is the same as that of the r-th traceability period, which has the same cold chain offset as the next traceability period.

5. The method for end-to-end traceability and safety supervision of biological samples according to claim 4, characterized in that, The strategy for estimating the total risk of subsample library partitioning in step five includes the following specific details: S51: Extract the calculated time-varying coefficient and cold chain offset coefficient, and substitute them into the calculation strategy for the estimated activity risk value of the next traceability period representing the monitoring node in the subsample library partition. The strategy for calculating the activity risk value is as follows: ,in, For time-varying factors, This is the cold chain offset factor. ; S52: Calculate the estimated activity risk value for the representative monitoring node of the i-th subsample library partition during the next traceability period. The average percentage of hemolysis index during the largest historical period in the sub-sample library partitions Substitute the values ​​into the formula for predicting the total activity risk of a sub-library partition to calculate the total activity risk of that sub-library partition. The formula for predicting the total risk of activity in a sub-sample library partition is: .

6. The method for full traceability and safety supervision of biological samples according to claim 5, characterized in that, Step six involves summing the estimated total activity risk values ​​of several sub-sample library partitions to obtain the total activity risk value of the region. The ratio obtained by dividing the total activity risk value of each sub-sample library partition by the total activity risk value of the region is the allocation ratio of cold chain resources.

7. A biological sample end-to-end traceability and safety monitoring system, used to implement the biological sample end-to-end traceability and safety monitoring method as described in any one of claims 1-6, characterized in that, The system includes the following modules: The module includes: biometric extraction module, regional representative monitoring node acquisition module, time-effect fluctuation coefficient calculation module, cold chain offset coefficient calculation module, sub-sample library partition activity risk total value calculation module, and cold chain resource allocation module. The biometric extraction module divides the area requiring biological sample monitoring into several sub-sample library partitions, extracts the total value of historical sample activity decay equivalent in the sub-sample library partitions, and extracts the location data of cold chain monitoring nodes in the sub-sample library partitions. The regional representative monitoring node acquisition module is used to import the number of cold chain monitoring nodes in the sub-sample library partition, the hemolysis index curve of the monitored historical samples, and the total value of the activity decay equivalent of the monitored historical samples into the regional representative monitoring node acquisition strategy to obtain regional representative monitoring nodes. The time-effect fluctuation coefficient calculation module is used to extract the historical sample deterioration rate detection data of the regional representative monitoring node of each sub-sample library partition and substitute it into the time-effect fluctuation coefficient calculation strategy to calculate the time-effect fluctuation coefficient.

8. The biological sample end-to-end traceability and safety monitoring system according to claim 7, characterized in that, The cold chain offset coefficient calculation module is used to extract the historical cold chain offset data and historical sample deterioration rate detection data of the regional representative monitoring nodes of each sub-sample library partition and substitute them into the cold chain offset coefficient calculation strategy to calculate the cold chain offset coefficient. The subsample library partition activity risk total value calculation module is used to substitute the calculated time fluctuation coefficient and cold chain offset coefficient into the subsample library partition activity risk total value prediction strategy to predict the subsample library partition activity risk total value. The cold chain resource allocation module is used to allocate cold chain resources according to the estimated total risk value of the activity of samples in the sub-sample library partition.

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