Biological sample whole-course tracing and safety supervision method and system

Abstract

The invention relates to the technical field of online monitoring, in particular to a biological sample whole-course tracing and safety supervision method and system.The method specifically comprises the steps that an area needing biological sample supervision is divided into a plurality of sub-sample library subareas, and the total value of activity attenuation equivalents of historical samples in the sub-sample library subareas is extracted; meanwhile, extracting position data of cold chain monitoring nodes in subareas of the sub-sample library, obtaining regional representative monitoring nodes, and calculating an aging fluctuation coefficient and a cold chain deviation coefficient; substituting the aging fluctuation coefficient and the cold chain deviation coefficient into a sub-sample library partition activity risk total value estimation strategy to estimate a sub-sample library partition sample activity risk total value; allocating cold chain resources according to the pre-estimated sub-sample library partition sample activity risk total value; the problem that the decay risk of the biological sample is mismatched with the space environment in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of online monitoring, and is a method and system for full-process traceability and safety supervision of biological samples. Background Art

[0002] Existing technologies for the full traceability and safety monitoring of biological samples (especially blood products) suffer from the following deficiencies: First, there is a mismatch between cold chain resource allocation and the spatial heterogeneity of sample activity decay risk. The existing equalized resource supply model ignores the dynamic risk gradients of sub-sample storage partitions. This leads to sample hemolysis accidents in high-activity decay areas (such as high-frequency storage and temperature-sensitive sample concentrations) due to insufficient liquid nitrogen supplies or a shortage of constant-temperature transportation capacity, while low-risk areas suffer energy waste due to over-refrigeration (for example, ultra-low temperature storage consumes 2.8 times more energy per day than conventional cold storage). Second, there is a lack of modeling of the relationship between environmental perturbations and biological decay. Existing systems rely on static temperature threshold monitoring (e.g., single-threshold alarms) and fail to establish a dynamic mapping between cold chain excursions and sample deterioration rates. This makes it impossible to predict cascading deterioration risks caused by differences in door opening and closing frequencies (e.g., the average daily access rate in the core area is 4.6 times that of the peripheral area) or the distribution of hot and cold spots in equipment (e.g., door-side temperature fluctuations are 3.2-3.5°C higher than those in the center). 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 low-value data, and the traceability cycle is not aligned with the biological half-life of the sample (such as 42 days for red blood cells and 5 days for platelets), resulting in delayed resource scheduling responses and increasing the risk of decay of biological samples. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to address the problem of mismatch between the decay risk of biological samples and the spatial environment in the existing technology, and propose a method and system for full-process traceability and safety supervision of biological samples.

[0004] To achieve the above objectives, the present invention provides a method for full traceability and safety supervision of biological samples, comprising the following steps: Step 1: Divide the area requiring biological sample supervision into several sub-sample library partitions, extract the total value of the activity decay equivalent of historical samples in the sub-sample library partitions, and extract the location data of the cold chain monitoring nodes in the sub-sample library partitions; Step 2: Import the number of cold chain monitoring nodes of the sub-sample library partition, the hemolysis index curve of the monitored historical samples, and the total activity decay equivalent value of the monitored historical samples into the regional representative monitoring node acquisition strategy to obtain the regional representative monitoring node; Step 3: Extract the sample deterioration rate detection data of the historical traceability period of the regional representative monitoring node of each sub-sample library partition and substitute it into the time fluctuation coefficient calculation strategy to calculate the time fluctuation coefficient; Step 4: Extract the historical cold chain deviation data and historical sample deterioration rate detection data of the regional representative monitoring nodes in each sub-sample library partition, and substitute them into the cold chain deviation coefficient calculation strategy to calculate the cold chain deviation coefficient; Step 5: Substitute the calculated aging fluctuation coefficient and cold chain deviation coefficient into the sub-sample library partition activity risk total value estimation strategy to estimate the sub-sample library partition sample activity risk total value; Step 6: Allocate cold chain resources according to the estimated sub-sample library partition sample activity risk total value.

[0005] Preferably, the specific content of Step 1 includes the following steps: S11: Divide the biological sample supervision area into several sub-sample library partitions with equal volumes, and deploy at least two cold chain monitoring nodes in each sub-sample library partition; S12: Extract the real-time biological activity readings of the cold chain monitoring nodes in the sub-sample library partition to obtain the sample activity attenuation rate curve of the cold chain monitoring nodes in the sub-sample library partition; S13: Integrate the activity attenuation rate curves of each cold chain monitoring node in the sub-sample library partition over time in the historical period to obtain the activity attenuation equivalent value of each node, and then sum the activity attenuation equivalent values of all nodes to obtain the total historical sample activity attenuation equivalent value in the sub-sample library partition.

[0006] Preferably, the specific steps of the regional representative monitoring node acquisition strategy in Step 2 are as follows: S21: Extract the number of cold chain monitoring nodes in the sub-sample library partition, the historical sample hemolysis index curve monitored, and the total historical sample activity attenuation equivalent value curve in the monitored sub-sample library partition; S22: Calculate the historical hemolysis index proportion of the jth traceability period of the ith cold chain monitoring node in the sub-sample library partition , where the historical hemolysis index of the jth traceability period of the ith cold chain monitoring node in the sub-sample library partition is , and the proportion calculation strategy is: , where K is the total number of cold chain monitoring nodes in the sub-sample library partition; is the historical hemolysis index of the jth traceability period of the kth cold chain monitoring node in the sub-sample library partition; S23: Calculate the average historical traceability period hemolysis index proportion of the ith cold chain monitoring node in the sub-sample library partition , and the calculation strategy is: , where J is the number of historical traceability periods; S24: Arrange the calculated average historical traceability period hemolysis index proportions of the cold chain monitoring nodes in the sub-sample library partition in descending order, and find the cold chain monitoring node corresponding to the largest average historical traceability period hemolysis index proportion as the regional representative monitoring node.

[0007] Preferably, the specific steps of the aging fluctuation coefficient calculation strategy in step three include the following: S31: Obtain the set of sample deterioration rates monitored during the same historical traceability period and the set of sample deterioration rates monitored during the previous traceability period for the representative monitoring nodes in the sub-sample library partition; S32: Calculate the average sample deterioration rate during the same historical traceability period from the set of sample deterioration rates monitored during the same historical traceability period , and the calculation strategy is: , where is the e-th sample deterioration rate in the set of sample deterioration rates monitored during the same historical traceability period; S33: Substitute the average sample deterioration rate during the same historical traceability period and the set of sample deterioration rates monitored during the previous traceability period into the aging fluctuation coefficient calculation strategy to calculate the aging fluctuation coefficient , and the aging fluctuation coefficient calculation strategy is: , where, is the influence factor during the same historical traceability period, is the influence factor during the previous traceability period, is the sample deterioration rate monitored during the previous traceability period, .

[0008] Preferably, the cold chain deviation coefficient calculation strategy in step four includes the following specific steps: S41: Extract the temperature control plan curve for one traceability period, obtain the temperature fluctuation extreme value and mean value preset by the environmental monitoring system for the next traceability period, and take the weighted value of the extreme value difference and the root mean square deviation as the cold chain deviation degree for the next traceability period; S42: Obtain the average sample deterioration rate corresponding to the traceability period with the same cold chain deviation degree as the next traceability period, and import it into the cold chain deviation coefficient calculation strategy to calculate the cold chain deviation coefficient , and the cold chain deviation coefficient calculation strategy is: , where R is the number of traceability periods with the same cold chain deviation degree as the next traceability period, is the sample deterioration rate corresponding to the r-th traceability period with the same cold chain deviation degree as the next traceability period.

[0009] Preferably, the total active risk value prediction strategy for the sub-sample library partition in step five includes the following specific content: S51: Extract the calculated aging fluctuation coefficient and cold chain deviation coefficient, and substitute them into the calculation strategy for predicting the active risk value of the representative monitoring node in the sub-sample library partition for the next traceability period to calculate the predicted active risk value of the representative monitoring node in the sub-sample library partition for the next traceability period , the active risk value calculation strategy is as follows: , where is the aging fluctuation factor, is the cold chain deviation factor, ; S52: Substitute the estimated active risk value of the representative monitoring node in the next traceability period of the i-th sub-sample library partition calculated and the maximum average proportion of hemolysis index in the historical traceability period in the sub-sample library partition into the estimated formula for the total active risk value of the sub-sample library partition to calculate the total active risk value of the sub-sample library partition , and the estimated formula for the total active risk value of the sub-sample library partition is: .

[0010] Preferably, the specific content of step six is: Add up the estimated total active risk values of several sub-sample library partitions to obtain the total regional active risk value, and the ratio obtained by dividing the total active risk value of each sub-sample library partition by the total regional active risk value is the allocation ratio of cold chain resources.

[0011] In addition, a biological sample whole-process traceability and safety supervision system of the present invention includes the following modules: Biological feature extraction module, regional representative monitoring node acquisition module, aging fluctuation coefficient calculation module, cold chain deviation coefficient calculation module, total active risk value calculation module for sub-sample library partition, cold chain resource allocation module; The biological feature extraction module divides the area where biological sample supervision is required into several sub-sample library partitions, extracts the total value of the historical sample activity decay equivalent in the sub-sample library partition, and simultaneously extracts the position data of the cold chain monitoring nodes in the sub-sample library partition; 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 historical hemolysis index curve of the monitored samples, and the total value of the historical sample activity decay equivalent into the regional representative monitoring node acquisition strategy to obtain the regional representative monitoring nodes; The aging fluctuation coefficient calculation module is used to extract the historical sample deterioration rate detection data of the regional representative monitoring nodes in each sub-sample library partition and substitute them into the aging fluctuation coefficient calculation strategy to calculate the aging fluctuation coefficient; The cold chain deviation coefficient calculation module is used to extract the historical cold chain deviation data and the historical sample deterioration rate detection data of the regional representative monitoring nodes in each sub-sample library partition and substitute them into the cold chain deviation coefficient calculation strategy to calculate the cold chain deviation coefficient; The total active risk value calculation module for sub-sample library partition is used to substitute the calculated aging fluctuation coefficient and cold chain deviation coefficient into the estimated strategy for the total active risk value of the sub-sample library partition to estimate the total active risk value of the sub-sample library partition; The cold chain resource allocation module is used to allocate cold chain resources according to the total predicted activity risk value of the sub-sample library partition samples.

[0012] Compared with the prior art, the technical effects of the present invention are as follows: The present invention constructs a biological activity risk elastic quantification model, and has the following beneficial effects: First, the present invention significantly improves the collaborative optimization level of sample safety and resource utilization efficiency. Based on the total activity decay equivalent value and the spatio-temporal dynamic risk prediction model, the cold chain resource allocation is strictly matched with the actual decay risk of the sub-sample library partition. For example, in the high-activity risk area (such as the stem cell storage area), the liquid nitrogen replenishment response time is shortened to <5 minutes (the traditional scheme is >15 minutes), and the hemolysis accident rate is greatly reduced; in the low-risk area, the excessive refrigeration is accurately reduced, the cold chain energy consumption is reduced, and the equipment utilization rate is greatly improved. Second, the present invention combines the non-linear mapping of environmental disturbance and biological decay. The cold chain offset coefficient in the present invention integrates the extreme difference of temperature fluctuation, the root mean square deviation and the sample-specific threshold, and can predict in advance the deterioration risk caused by the difference in the frequency of opening and closing the door (the access frequency in the core area is 4.2-4.6 times that in the edge area), and the early warning accuracy is improved. In addition, the present invention has strong universality, replaces the fixed cycle with the traceable time period, makes the supervision rhythm accurately align with the biological half-life of the sample (such as 42 days for red blood cells and 5 days for platelets), combines the regional representative monitoring node strategy to reduce data redundancy, improves the resource scheduling speed, and the resource allocation ratio driven by the total activity risk value of the present invention can dynamically optimize the path of the constant temperature transport vehicle, improve the transportation timeliness of biological samples, keep the sample activity stable, and realize the deep coupling of the traceability granularity and the characteristics of biological samples. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can also obtain other drawings based on these drawings without creative efforts. Among them: Figure 1 is a schematic flow chart of a method for full-process traceability and safety supervision of biological samples of the present invention; Figure 2 is a schematic structural diagram of a system for full-process traceability and safety supervision of biological samples of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0014] In order to make the above objects, features and advantages of the present invention more obvious and understandable, the specific embodiments of the present invention will be described in detail below with reference to the drawings of the specification.

[0015] In the following description, many specific details are set forth in order to provide a thorough understanding of the present invention. However, the present invention may be practiced in other ways different from those described herein. Persons skilled in the art can make similar generalizations without departing from the spirit of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0016] Secondly, as used herein, an "embodiment" or "embodiments" refers to specific features, structures, or characteristics 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 an isolated or alternative embodiment mutually exclusive of other embodiments.

[0017] Embodiment 1: As Figure 1 shown, a method for the whole-process traceability and safety supervision of biological samples according to an embodiment of the present invention, as Figure 1 shown, includes the following specific steps: Step 1: Divide the area where biological sample supervision is required into several sub-sample bank partitions, extract the total value of the historical sample activity decay equivalent in the sub-sample bank partitions, and at the same time extract the position data of the cold chain monitoring nodes in the sub-sample bank partitions; Step 2: Import the number of cold chain monitoring nodes in the sub-sample bank partitions, the historical sample hemolysis index curve monitored, and the total value curve of the historical sample activity decay equivalent in the sub-sample bank partitions into the regional representative monitoring node acquisition strategy to obtain the regional representative monitoring node; S21: Extract the number of cold chain monitoring nodes in the sub-sample bank partitions, the historical sample hemolysis index curve monitored, and the total value curve of the historical sample activity decay equivalent in the sub-sample bank partitions; S22: Calculate the proportion of the historical hemolysis index of the jth traceability period of the ith cold chain monitoring node in the sub-sample bank partition , where the historical hemolysis index of the jth traceability period of the ith cold chain monitoring node in the sub-sample bank partition is , and the proportion calculation strategy is: , where K is the total number of cold chain monitoring nodes in the sub-sample bank partition; is the historical hemolysis index of the jth traceability period of the kth cold chain monitoring node in the sub-sample bank partition; S23: Calculate the average proportion of the historical traceability period hemolysis index of the ith cold chain monitoring node in the sub-sample bank partition , and the calculation strategy is: , where J is the number of historical traceability periods; S24: Arrange the average proportion of the historical traceability period hemolysis index of the cold chain monitoring nodes in the sub-sample bank partition in descending order, and find the cold chain monitoring node corresponding to the largest average proportion of the historical traceability period hemolysis index as the regional representative monitoring node.

[0018] Step 3: Extract the sample deterioration rate detection data of the historical same traceability period of the regional representative monitoring nodes in each sub-sample library partition and substitute them into the aging fluctuation coefficient calculation strategy to calculate the aging fluctuation coefficient; S31: Obtain the set of sample deterioration rates monitored during the historical same traceability period of the regional representative monitoring nodes in the sub-sample library partition and the set of sample deterioration rates monitored during the previous traceability period; S32: Calculate the average value of the sample deterioration rates during the historical same traceability period through the set of sample deterioration rates monitored during the historical same traceability period , and the calculation strategy is: , where is the e-th sample deterioration rate in the set of sample deterioration rates monitored during the historical same traceability period; S33: Substitute the average value of the sample deterioration rates during the historical same traceability period and the set of sample deterioration rates monitored during the previous traceability period into the aging fluctuation coefficient calculation strategy to calculate the aging fluctuation coefficient , and the aging fluctuation coefficient calculation strategy is: , where, is the influencing factor during the historical same traceability period, is the influencing factor during the previous traceability period, is the sample deterioration rate monitored during the previous traceability period, .

[0019] Step 4: Extract the historical cold chain deviation data and historical sample deterioration rate detection data of the regional representative monitoring nodes in each sub-sample library partition and substitute them into the cold chain deviation coefficient calculation strategy to calculate the cold chain deviation coefficient; S41: Extract the temperature control plan curve of one traceability period, obtain the temperature fluctuation extreme value and average value preset by the environmental monitoring system for the next traceability period, and take the weighted value of the extreme value difference and the root mean square deviation as the cold chain deviation degree for the next traceability period; S42: Obtain the average value of the sample deterioration rates corresponding to the traceability period with the same cold chain deviation degree as the next traceability period, and import it into the cold chain deviation coefficient calculation strategy to perform the calculation of the cold chain deviation coefficient , and the cold chain deviation coefficient calculation strategy is: , where R is the number of traceability periods with the same cold chain deviation degree as the next traceability period, is the sample deterioration rate corresponding to the r-th traceability period with the same cold chain deviation degree as the next traceability period.

[0020] Step 5: Substitute the calculated aging fluctuation coefficient and cold chain deviation coefficient into the sub-sample library partition activity risk total value prediction strategy to predict the sub-sample library partition sample activity risk total value; S51: Extract the calculated time fluctuation coefficient and cold chain offset coefficient, and substitute them into the estimated activity risk value calculation strategy for the next traceability period of the monitoring node in the sub-sample library partition to calculate the estimated activity risk value for the next traceability period of the monitoring node in the sub-sample library partition. , the activity risk value calculation strategy is: ,in, is the time fluctuation factor, is the cold chain offset factor, ; S52: Estimate the activity risk value of the representative monitoring node of the i-th sub-sample library partition in the next tracing period The average proportion of hemolysis index in the largest historical traceability period in the sub-sample library partition Substitute into the sub-sample library partition activity risk total value estimation formula to calculate the sub-sample library partition activity risk total value , the formula for estimating the total active risk value of the sub-sample library partition is: .

[0021] Step 6: Allocate cold chain resources based on the estimated total activity risk of the sub-sample pool partitions, including: The estimated total activity risk values of several sub-sample library partitions are added together to obtain the regional total activity risk value. The ratio obtained by dividing the total activity risk value of each sub-sample library partition by the regional total activity risk value is the allocation ratio of cold chain resources.

[0022] Illustratively, in this embodiment, the cold chain resources include: cryogenic storage tanks and liquid nitrogen supply.

[0023] Example 2: like Figure 2 As shown, a biological sample full traceability and safety supervision system according to an embodiment of the present invention is as follows: Figure 2 As shown, it includes the following modules: Biometric feature extraction module, regional representative monitoring node acquisition module, time fluctuation coefficient calculation module, cold chain offset coefficient calculation module, sub-sample library partition activity risk total value calculation module, cold chain resource allocation module; The biometric feature extraction module divides the area requiring biological sample supervision into a number of sub-sample library partitions, extracts the total value of the activity decay equivalent of historical samples in the sub-sample library partitions, and simultaneously extracts the location data of the 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 of the sub-sample library partition, the hemolysis index curve of the monitored historical samples, and the total activity decay equivalent value of the monitored historical samples into the regional representative monitoring node acquisition strategy to obtain the regional representative monitoring node; The aging fluctuation coefficient calculation module is used to extract the historical sample deterioration rate detection data of the representative monitoring nodes in each sub-sample library partition and substitute them into the aging fluctuation coefficient calculation strategy to calculate the aging fluctuation coefficient; 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 representative monitoring nodes in each sub-sample library partition and substitute them into the cold chain offset coefficient calculation strategy to calculate the cold chain offset coefficient; The sub-sample library partition activity risk total value calculation module is used to substitute the calculated aging fluctuation coefficient and cold chain offset coefficient into the sub-sample library partition activity risk total value estimation strategy to estimate the sub-sample library partition activity risk total value; The cold chain resource allocation module is used to allocate cold chain resources according to the estimated sample activity risk total value of each sub-sample library partition.

[0024] Embodiment 3: This embodiment provides an electronic device, including: a processor and a memory, where the memory stores a computer program that can be called by the processor; The processor executes the above-mentioned method for full-process traceability and safety supervision of biological samples by calling the computer program stored in the memory.

[0025] This electronic device may have relatively large differences due to different configurations or performances, and can include one or more processors (Central Processing Units, CPUs) and one or more memories. Among them, the memory stores at least one computer program, and this computer program is loaded and executed by the processor to implement the method for full-process traceability and safety supervision of biological samples provided by the above method embodiment. This electronic device can also include other components for implementing the functions of the device. For example, this electronic device can also have components such as wired or wireless network interfaces and input / output interfaces for data input and output. This embodiment will not be elaborated here.

[0026] Embodiment 4: This embodiment proposes a computer-readable storage medium, on which a rewritable computer program is stored; When the computer program runs on a computer device, it causes the computer device to execute the above-mentioned method for full-process traceability and safety supervision of biological samples.

[0027] For example, a computer-readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), magnetic tape, a floppy disk, and an optical data storage device, etc.

[0028] It should be understood that in various embodiments of the present application, the magnitudes of the sequence numbers of the above processes do not mean the order of execution is prior or posterior. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0029] It should be understood that determining B according to A does not mean determining B only according to A, and B can also be determined according to A and / or other information.

[0030] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The 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, the processes or functions according to the embodiments of the present invention are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another. For example, the 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 or / and a wireless network. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that contains one or more collections of available media. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, magnetic tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0031] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in the present invention can be implemented by electronic hardware, or by a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of the present invention.

[0032] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0033] In several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only one type, and there can be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of devices or units can be in electrical, mechanical, or other forms.

[0034] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0035] In addition, the functional units in various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit.

[0036] In the description of this specification, the descriptions referring to terms such as "one embodiment", "example", "specific example", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0037] The above shows and describes 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 by the above embodiments. What is described in the above embodiments and the specification only illustrates the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for full-process traceability and safety supervision of biological samples, characterized in that, The method includes the following steps: Step 1: Divide the area that needs to be subject to biological sample supervision into several sub-sample library partitions, extract the total value of the historical sample activity decay equivalent in the sub-sample library partitions, and at the same time extract the position data of the 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 partitions, the historical sample hemolysis index curve monitored, and the total value of the historical sample activity decay equivalent monitored in the sub-sample library partitions into the regional representative monitoring node acquisition strategy to obtain the regional representative monitoring nodes; Step 3: Extract the sample deterioration rate detection data of the historical same traceability period of the regional representative monitoring nodes in each sub-sample library partition and substitute it into the aging fluctuation coefficient calculation strategy to calculate the aging fluctuation coefficient; Step 4: Extract the historical cold chain deviation data and the historical sample deterioration rate detection data of the regional representative monitoring nodes in each sub-sample library partition and substitute them into the cold chain deviation coefficient calculation strategy to calculate the cold chain deviation coefficient; Step 5: Substitute the calculated aging fluctuation coefficient and cold chain deviation coefficient into the sub-sample library partition activity risk total value prediction strategy to predict the total value of the sample activity risk in the sub-sample library partition; Step 6: Allocate cold chain resources according to the predicted total value of the sample activity risk in the sub-sample library partition.

2. The method for full-process traceability and safety supervision of biological samples according to claim 1, wherein, The specific content of Step 1 includes the following steps: S11: Divide the biological sample supervision area into several sub-sample library partitions with equal volumes, and deploy at least two cold chain monitoring nodes in each sub-sample library partition; S12: Extract the real-time biological activity readings of the cold chain monitoring nodes in the sub-sample library partition to obtain the sample activity decay rate curve of the 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 the historical period to obtain the activity decay equivalent value of each node, and then sum up the activity decay equivalent values of all nodes to obtain the total value of the historical sample activity decay equivalent in the sub-sample library partition.

3. A method for the whole-process traceability and safety supervision of biological samples according to claim 2, characterized in that, The specific steps of the regional representative monitoring node acquisition strategy in Step 2 are as follows: S21: Extract the number of cold chain monitoring nodes in the sub-sample library partition, the historical sample hemolysis index curve monitored, and the total value curve of the historical sample activity decay equivalent in the sub-sample library partition monitored; S22: Calculate the proportion of the historical hemolysis index of the j-th traceability period of the i-th cold chain monitoring node in the sub-sample library partition , where the historical hemolysis index of the j-th traceability period of the i-th cold chain monitoring node in the sub-sample library partition is , and the proportion calculation strategy is: , where K is the total number of cold chain monitoring nodes in the sub-sample library partition; is the historical hemolysis index of the j-th traceability period of the k-th cold chain monitoring node in the sub-sample library partition; S23: Calculate the average proportion of hemolysis index of the historical traceability period of the i-th cold chain monitoring node in the sub-sample library partition , and the calculation strategy is as follows: , where J is the number of historical traceability periods; S24: Arrange the calculated average hemolysis index proportion of the historical traceability period in the sub-sample library partition in descending order, and find the cold chain monitoring node corresponding to the largest average hemolysis index proportion of the historical traceability period as the regional representative monitoring node.

4. The whole-process traceability and safety supervision method for biological samples according to claim 3, characterized in that, The specific steps of the aging fluctuation coefficient calculation strategy in Step 3 include the following content: S31: Take the set of sample deterioration rates monitored in the historical same traceability period of the regional representative monitoring nodes in the sub-sample library partition and the set of sample deterioration rates monitored in the previous traceability period; S32: Obtain the average sample deterioration rate of the historical same traceability period by the set of sample deterioration rates monitored during the historical same traceability period , and the calculation strategy is as follows: , where is the e-th sample deterioration rate in the set of sample deterioration rates monitored during the historical same traceability period; S33: Take the average deterioration rate of samples in the historical same traceability period and the set of sample deterioration rates monitored in the previous traceability period and substitute them into the calculation strategy of the aging fluctuation coefficient to calculate the aging fluctuation coefficient , and the calculation strategy of the aging fluctuation coefficient is: , where is the influence factor in the historical same traceability period, is the influence factor in the previous traceability period, is the sample deterioration rate monitored in the previous traceability period, .

5. The method for full-process traceability and safety supervision of biological samples according to claim 4, characterized in that The cold chain deviation coefficient calculation strategy in Step 4 includes the following specific steps: S41: Extraction Extract the temperature control plan curve for a traceability period, obtain the next temperature fluctuation extreme value and average value preset by the environmental monitoring system, and take the weighted value of the extreme difference and the root mean square deviation as the cold chain deviation degree for the next traceability period; S42: Obtain the average sample deterioration rate corresponding to the traceability period with the same cold chain deviation degree as the next traceability period, and import it into the cold chain deviation coefficient calculation strategy for calculating the cold chain deviation coefficient The cold chain deviation coefficient calculation strategy is as follows: , where R is the number of traceability periods with the same cold chain deviation degree as the next traceability period, is the sample deterioration rate corresponding to the r-th traceability period with the same cold chain deviation degree as the next traceability period.

6. The method for the whole-process traceability and safety supervision of biological samples according to claim 5, wherein, The specific content of the sub-sample library partition activity risk total value prediction strategy in Step 5 includes the following: S51: Extract the calculated aging fluctuation coefficient and cold chain offset coefficient, and substitute them into the calculation strategy of the estimated activity risk value for the next traceability period of the monitoring node in the sub-sample library partition to calculate the estimated activity risk value for the next traceability period of the monitoring node in the sub-sample library partition. , and the calculation strategy of the activity risk value is: , where is the aging fluctuation factor, is the cold chain offset factor, ; S52: Substitute the estimated active risk value of the representative monitoring node of the i-th sub-sample library partition calculated in the next traceability period and the average proportion of the hemolysis index in the largest historical traceability period in the sub-sample library partition into the estimated formula for the total active risk value of the sub-sample library partition to calculate the total active risk value of the sub-sample library partition , and the estimated formula for the total active risk value of the sub-sample library partition is: .

7. A method for full-process traceability and safety supervision of biological samples according to claim 6, characterized in that, The specific content of Step 6 is: Add up the predicted total values of the sample activity risks in several sub-sample library partitions to obtain the total regional activity risk value. The ratio obtained by dividing the total value of the sample activity risk in each sub-sample library partition by the total regional activity risk value is the allocation ratio of cold chain resources.

8. A whole-process traceability and safety supervision system for biological samples, which is used to implement a whole-process traceability and safety supervision method for biological samples as described in any one of claims 1-7, characterized in that, The system includes the following modules: Biometric feature extraction module, regional representative monitoring node acquisition module, aging fluctuation coefficient calculation module, cold chain offset coefficient calculation module, sub-sample library partition activity risk total value calculation module, cold chain resource allocation module; The biometric feature extraction module divides the area where biological samples need to be supervised into several sub-sample library partitions, extracts the total value of the historical sample activity decay equivalent in the sub-sample library partitions, and at the same time extracts the position data of the 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 partitions, the historical sample hemolysis index curve monitored, and the total value of the historical sample activity decay equivalent monitored into the regional representative monitoring node acquisition strategy to obtain the regional representative monitoring nodes; The aging fluctuation coefficient calculation module is used to extract the historical detection data of the sample deterioration rate in the same traceability period of the regional representative monitoring nodes in each sub-sample library partition and substitute them into the aging fluctuation coefficient calculation strategy to calculate the aging fluctuation coefficient.

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

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