Animal tissue sampling tube whole-process traceability management method based on internet of things coding

Abstract

This invention discloses a method for full-process traceability management of animal tissue sampling tubes based on Internet of Things (IoT) coding, belonging to the field of traceability management technology. It addresses the issue of reduced security in traceability management by detecting interactive trigger data during the filling and sampling period of the sampling tubes to be tested, generating a sampling type, obtaining the repackaged sampling tubes after the tubes are repackaged, recording the repackaging time and counting the number of seal changes, screening and marking the repackaged sampling tubes, and triggering a recall mechanism when the accumulated risk reaches a certain condition. This involves retrieving the transport path information of the marked repackaged sampling tubes, identifying sampling tubes along the same path and evaluating transport association characteristics, and performing a recall verification judgment on sampling tubes along the same path based on the sampling type. This achieves risk identification and precise recall control of animal tissue sampling tubes throughout the sampling, repackaging, and transport process, improving the security and reliability of traceability management.

Description

Technical Field

[0001] This invention relates to the field of traceability management technology, and more specifically, to a method for full-process traceability management of animal tissue sampling tubes based on Internet of Things (IoT) coding. Background Technology

[0002] Animal tissue sampling is widely used in scenarios such as livestock and poultry disease monitoring, animal-derived food safety testing, biosafety supervision, and scientific research experiments. To ensure the authenticity, traceability, and biosafety of the sampling results, animal tissue samples are usually packaged in disposable sampling tubes and undergo multiple operational steps such as dispensing, sealing, transportation, and warehousing after sampling.

[0003] The existing technology has the following shortcomings: Currently, existing technologies mostly use barcodes or RFID tags for static identification of animal tissue sampling tubes and rely on manual or semi-automatic recording of operational information. They lack the ability to dynamically collect and quantitatively analyze data on key stages such as filling, dispensing, and changes in sealing status, leading to reduced security in traceability management and difficulty in implementing precise recall and verification control based on transport path relationships. Therefore, this paper proposes a full-process traceability management method for animal tissue sampling tubes based on IoT coding.

[0004] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a method for full-process traceability management of animal tissue sampling tubes based on Internet of Things coding. This method addresses the problems mentioned in the background art by employing a risk identification and precise recall control mechanism that combines multi-stage interactive data perception and path association analysis.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for full-process traceability management of animal tissue sampling tubes based on Internet of Things (IoT) coding, comprising the following steps: Step S1: Before the sampling tube under test is dispensed, the filling and sampling period of the sampling tube under test is read. During the filling and sampling period, the interactive trigger data of the sampling tube under test is collected, and the sampling type of the sampling tube under test is generated based on the interactive trigger data. Step S2: After the sampling tube to be tested is repackaged, the repackaged sampling tube is obtained. The repackaging dwell time of the repackaged sampling tube is detected and the repackaging loss coefficient is calculated. The evaluation cycle is set and the number of times the seal of the repackaged sampling tube is changed is collected. Step S3: Analyze the sealing stability based on the number of seal changes, screen and mark the sub-packaging sampling tubes by combining the sealing stability with the sub-packaging loss coefficient, and determine whether to trigger the recall mechanism after counting the number of marked sub-packaging sampling tubes. Step S4: Retrieve the transport path information of the marked and packaged sampling tubes in the recall mechanism, identify the sampling tubes along the same path based on the transport path information and evaluate the transport association characteristics, and determine whether to perform the recall kernel for the sampling tubes along the same path based on the sampling type.

[0007] In a preferred embodiment, in step S1, before the sampling tube to be tested is dispensed, the filling and sampling period corresponding to the sampling tube to be tested is read through the timestamp recording interface. During the filling and sampling period, the sampling interaction trigger data of the sampling tube under test is collected through the RFID interaction unit, including the trigger duration of the trigger event and the trigger source identifier; The number of different source identifiers is counted to obtain the number of subject categories. The number of subject categories is compared with a preset subject number threshold. When the number of subject categories is greater than the preset subject number threshold, the sampling type of the sampling tube to be tested is determined to be passive sampling. When the number of subject categories is less than or equal to the preset subject number threshold, the trigger duration ratio will be further compared with the preset duration ratio threshold.

[0008] In a preferred embodiment, in step S1, triggering events whose duration exceeds a preset duration triggering threshold are marked, and the ratio of the number of marked triggering events to the total number of triggering events is used as the triggering duration ratio. When the trigger duration ratio is greater than or equal to the preset duration ratio threshold, the sampling type is determined to be active sampling; when the trigger duration ratio is less than the preset duration ratio threshold, the sampling type is determined to be passive sampling.

[0009] In a preferred embodiment, in step S2, the sampling tube to be tested is repackaged to obtain each repackaged sampling tube, the repackaged sampling tube is sealed independently, and the sealing status of the repackaged sampling tube is recorded. The time spent filling the sampling tubes is obtained by recording the filling operation log. Access the historical database to retrieve the historical dispensing dwell time corresponding to the historical dispensing sampling tube with the same sampling type as the sampling tube under test and in normal dispensing state, and use the average of the historical dispensing dwell time as the dispensing baseline time. The ratio of the dispensing dwell time to the dispensing baseline time is used as the dispensing loss coefficient.

[0010] In a preferred embodiment, in step S2, an evaluation period is preset, and within the preset evaluation period, the seal change event of the dispensing sampling tube is collected through the seal status record log. The seal change event refers to the event in which the seal status of the dispensing sampling tube changes after the sealing is completed.

[0011] In a preferred embodiment, in step S3, a stable sealing state is obtained by normalizing and reverse mapping the number of seal changes: ; in, For a sealed and stable state, To assess the number of seal changes recorded during the assessment period, This is the preset maximum number of times the seal can be changed; Using the sealing stability state as a positive indicator reflecting the integrity of the sealing structure and the sub-packaging loss coefficient as a negative indicator reflecting the risk of exposure during the sub-packaging stage, a weighted fusion formula is introduced to comprehensively calculate the two, resulting in the marking judgment index. The calculation formula is as follows: ; in, To mark the judgment index, For a sealed and stable state, This is the repackaging loss coefficient. and These are preset weighting coefficients.

[0012] In a preferred embodiment, in step S3, when the marking judgment index is greater than or equal to the preset marking judgment threshold, it is determined that the corresponding sample tube has a high risk of contamination during the packaging and sealing process, and screening and marking processing is performed. When the marking judgment index is less than the preset marking judgment threshold, it is determined that the sealing status and dispensing process of the corresponding dispensing sampling tube are within a controllable range, and no screening marking process is performed; All sample tubes in the evaluation period are traversed, and sample tubes with a marking judgment index greater than or equal to the preset marking judgment threshold are included in the risk marking set. The total number of sample tubes in the risk marking set is counted to obtain the number of marked sample tubes. The recall mechanism is triggered when the number of labeled sampling tubes is greater than or equal to the preset recall trigger threshold; otherwise, the recall mechanism is not triggered.

[0013] In a preferred embodiment, in step S4, after the recall mechanism is triggered, the corresponding transport path information is retrieved for the marked and packaged sampling tube. The transport path information consists of the reading results of the IoT code at each transport node and is represented as a node path sequence arranged in chronological order. The transport path information of the marked sample tubes is used as a reference path. The transport path information of other sample tubes within the evaluation period is compared and analyzed to identify sample tubes that overlap with the reference path, i.e., sample tubes on the same path. Based on the transit route information, the number of overlapping nodes and the time of node dwell overlap are obtained, and transit association features are constructed: ; in, This refers to the transport correlation characteristics of sampling tubes along the same path. This represents the number of overlapping nodes. is the time the nodes stay in an overlapping state, and m is the index value of the overlapping nodes.

[0014] In a preferred embodiment, in step S4, a sampling type weighting factor is introduced, and the transport association feature is multiplied by the sampling type weighting factor corresponding to the sampling type to obtain the recall kernel determination index of the same path sampling tube. When the recall review judgment index is greater than or equal to the recall review threshold, it is determined that the corresponding sampling tube along the same path has a high risk of associated contamination during transportation, and recall review is performed. When the recall review index is less than the recall review threshold, it is determined that the risk of the corresponding sampling tube being affected by the marked and repackaged sampling tube is within a controllable range. No recall review process is performed, and only the path association is recorded for subsequent source tracing analysis.

[0015] The technical effects and advantages of this invention are as follows: This invention detects interactive trigger data during the filling and sampling period of the sample tubes to be tested, generates a sampling type, obtains the repackaged sample tubes after the sample tubes are repackaged, records the repackaging time and counts the number of seal changes, filters and marks the repackaged sample tubes, and triggers a recall mechanism when the risk accumulation reaches a certain condition. It retrieves the transport path information of the marked repackaged sample tubes, identifies sample tubes along the same path and evaluates the transport association characteristics, and performs a recall verification judgment on sample tubes along the same path in combination with the sampling type. This enables risk identification and precise recall control of animal tissue sample tubes throughout the entire process of sampling, repackaging and transport, and improves the safety and reliability of traceability management. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the implementation of the IoT-based coding-based full-process traceability management method for animal tissue sampling tubes according to the present invention.

[0017] Figure 2 This is a schematic diagram illustrating the steps of the animal tissue sampling tube traceability management method based on Internet of Things coding according to the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] This invention detects interactive trigger data during the filling and sampling period of the sample tube to be tested, generates a sampling type, obtains the repackaged sample tubes after the sample tubes are repackaged, records the repackaging time and counts the number of seal changes, filters and marks the repackaged sample tubes, and triggers a recall mechanism when the risk accumulation reaches a certain condition. It retrieves the transport path information of the marked repackaged sample tubes, identifies sample tubes along the same path and evaluates the transport association characteristics, and performs a recall verification judgment on sample tubes along the same path in combination with the sampling type. This enables risk identification and precise recall control of animal tissue sample tubes throughout the entire process of sampling, repackaging and transport.

[0020] Example 1, as Figures 1 to 2 As shown, the method for full-process traceability management of animal tissue sampling tubes based on IoT coding includes the following steps: Step S1: Before the sampling tube under test is dispensed, the filling and sampling period of the sampling tube under test is read. During the filling and sampling period, the interactive trigger data of the sampling tube under test is collected, and the sampling type of the sampling tube under test is generated based on the interactive trigger data. Step S2: After the sampling tube to be tested is repackaged, the repackaged sampling tube is obtained. The repackaging dwell time of the repackaged sampling tube is detected and the repackaging loss coefficient is calculated. The evaluation cycle is set and the number of times the seal of the repackaged sampling tube is changed is collected. Step S3: Analyze the sealing stability based on the number of seal changes, screen and mark the sub-packaging sampling tubes by combining the sealing stability with the sub-packaging loss coefficient, and determine whether to trigger the recall mechanism after counting the number of marked sub-packaging sampling tubes. Step S4: Retrieve the transport path information of the marked and packaged sampling tubes in the recall mechanism, identify the sampling tubes along the same path based on the transport path information and evaluate the transport association characteristics, and determine whether to perform the recall kernel for the sampling tubes along the same path based on the sampling type.

[0021] The specific implementation is as follows: In step S1, before the sampling tube to be tested is dispensed, the filling sampling period corresponding to the sampling tube to be tested is read through the timestamp recording interface. The filling sampling period is the time interval from when the sampling tube to be tested completes the filling of animal tissue samples to when it enters the dispensing process. During the filling and sampling period, the sampling interaction trigger data of the sampling tube under test is collected through the RFID interaction unit, including the trigger duration of the trigger event and the trigger source identifier; Among them, the trigger source identifier refers to the identifier of the entity that interacts with the sampling tube under test; By statistically analyzing the sampling interaction trigger data generated by the sampling tube under test during the filling and sampling period, the sampling tube under test is classified into the corresponding sampling type. The number of different source identifiers is counted to obtain the number of subject categories. The number of subject categories is compared with the preset subject number threshold. When the number of subject categories is greater than the preset subject number threshold, it is determined that there is multi-subject interaction behavior in the sampling tube under test during the filling and sampling period. If the sampling interaction behavior shows non-single fixed subject characteristics, the sampling type of the sampling tube under test is determined to be passive sampling. When the number of subject categories is less than or equal to the preset subject number threshold, the trigger duration ratio is compared with the preset duration ratio threshold. Triggering events with a trigger duration greater than the preset continuous trigger threshold are marked. The ratio of the number of marked triggering events to the total number of triggering events is used as the trigger duration ratio. The larger the value, the more long the interaction exists in the sampling tube under test during the filling and sampling period. When the trigger duration ratio is greater than or equal to the preset duration ratio threshold, it indicates that there is a continuous interactive operation performed by a fixed subject during the filling and sampling period of the sample tube under test, and the sampling type is determined to be active sampling; when the trigger duration ratio is less than the preset duration ratio threshold, it is determined that the sampling interaction behavior is mainly triggered by short time, and the sampling type is determined to be passive sampling.

[0022] It should be noted that the timestamp recording interface is used to obtain the timestamp information of events such as animal tissue sample filling in the sampling tube under test; the RFID interaction unit generates interaction trigger events by reading the electronic tag information carried by the sampling tube under test; the preset subject number threshold can be set according to the number of subjects involved in historical sampling operations; the setting rule for the preset continuous proportion threshold is as follows: first, in the historical normal sampling data, the proportion of continuous interaction events of each packaged sampling tube is statistically analyzed according to the evaluation cycle to form a historical proportion sample set; then, the distribution statistics of the historical proportion sample set are performed to obtain the proportion distribution interval; finally, the upper quantile value of the proportion distribution interval is used as the preset continuous proportion threshold, where the upper quantile value can be selected as a percentile threshold to characterize the high boundary of the proportion of continuous interaction events in the historical normal sampling data.

[0023] In step S2, the sampling tube to be tested is repackaged. The repackaging process refers to the repackaging station distributing the animal tissue sample in the sampling tube to be tested into multiple repackaged sampling tubes. Each repackaged sampling tube is sealed independently, and the sealing status of the repackaged sampling tube is recorded. The sealing status is the current state of the sealing of the repackaged sampling tube. For the dispensing of sampling tubes, the dispensing dwell time of the sampling tubes is obtained through the dispensing operation log, which reflects the length of time the sampling tubes are exposed to the external environment during the dispensing process; Access the historical database to retrieve the historical dispensing dwell time corresponding to the historical dispensing sampling tube with the same sampling type as the sampling tube under test and in normal dispensing state. Use the average of the historical dispensing dwell time as the dispensing benchmark time to reflect the reference time required to complete the dispensing process under normal dispensing conditions. The ratio of the dispensing dwell time to the dispensing baseline time is used as the dispensing loss coefficient. The larger the value, the higher the risk of exposure to the external environment during the dispensing process. A preset evaluation period is set. Within the preset evaluation period, the seal change event of the repackaged sampling tube is collected through the seal status log. The seal change event refers to the event in which the seal status of the repackaged sampling tube changes after the seal is completed. By counting the seal change events one by one during the evaluation period, the number of seal changes of the repackaged sampling tubes is obtained. The larger the number, the more frequently the seal is opened or abnormal intervention occurs after the repackaged sampling tube is repackaged, and the lower its sealing stability.

[0024] It should be noted that the packaging operation log is the process record data generated by the packaging operation system during the packaging process; the historical database is a traceability data storage unit used to store historical packaging operation data; the setting rule for the preset evaluation period is as follows: first, determine the transportation observation duration based on the expected transportation completion time in the transportation path corresponding to the packaging sampling tube, then determine the storage observation duration based on the storage duration requirement corresponding to the packaging sampling tube, and add the transportation observation duration and the storage observation duration as the preset evaluation period. Here, the transportation observation duration is the time length between the packaging completion time and the expected transportation completion time; the storage observation duration is the time length from the expected transportation completion time to the cutoff time corresponding to meeting the storage duration requirement; the seal status log refers to the log generated by the electronic seal or seal status sensor when a change in the seal status is detected.

[0025] In step S3, after completing the statistics on the number of times the seals of the sub-packaged sampling tubes were changed within the evaluation period, the number of seal changes was used as an input quantity reflecting the degree of disturbance to the sealing structure of the sampling tubes. The maximum allowable number of seal changes was introduced to quantitatively characterize the sealing stability of the sub-packaged sampling tubes.

[0026] Specifically, the stable sealing state is obtained by normalizing and inversely mapping the number of seal changes. The calculation formula is as follows: ; in, For a sealed and stable state, To assess the number of seal changes recorded during the assessment period, This is the preset maximum number of permissible seal changes, used to limit the upper limit of disturbances that the seal structure can withstand under normal process conditions.

[0027] The above formula maps the number of seal changes to a dimensionless parameter with a value range of [0,1] to characterize the sealing stability state, making the sampling tubes packaged under different batches and different circulation scales comparable.

[0028] The sealing stability value characterizes the ability of the sealing structure of the repackaged sampling tube to maintain its sealing integrity after multiple sealing operations. The higher the value, the less disturbance the sealing structure is subjected to during repackaging and transfer, the higher the degree of sealing integrity maintenance, and the lower the risk of micro-cracks, leakage, or external contamination in the sampling tube. The lower the value, the more frequent the sealing changes, the lower the stability of the sealing structure, and the greater the potential risk of biological contamination during subsequent transportation and storage of the repackaged sampling tube.

[0029] It should be noted that the maximum permissible number of seal changes is used to characterize the maximum seal disturbance intensity that the sealing structure of the repackaged sampling tube can withstand without significant deterioration of its sealing performance under the constraints of a given repackaging process, seal material performance, and cold chain circulation conditions. Specifically, based on historical repackaging operation data and quality control records, the distribution range of seal change frequency under conditions where no leakage, seepage, or abnormal microbial detection occurs is statistically analyzed, and the upper limit of this distribution range is used as the maximum permissible number of seal changes.

[0030] After obtaining the sealing stability state corresponding to the sub-packaged sampling tube, the sealing stability state and the sub-packaged loss coefficient are further calculated together to form a label judgment index for characterizing the comprehensive pollution risk level of the sub-packaged sampling tube.

[0031] Specifically, the sealing stability state is used as a positive indicator reflecting the integrity of the sealing structure, and the sub-packaging loss coefficient is used as a negative indicator reflecting the risk of exposure during the sub-packaging stage. A weighted fusion formula is introduced to comprehensively calculate the two to obtain the marking judgment index, the calculation formula of which is as follows: ; in, To mark the judgment index, For a sealed and stable state, This is the repackaging loss coefficient. and The preset weighting coefficient is used to adjust the influence ratio of sealing stability factors and packaging process loss factors in the comprehensive judgment.

[0032] By reversing the process of the sealing stability state, the decrease in sealing stability and the abnormal increase in dispensing time are pushed up the marking judgment index in the same direction, thereby ensuring the consistency and interpretability of the judgment results.

[0033] The labeling index comprehensively characterizes the overall contamination risk level accumulated by the repackaged sampling tubes during the repackaging stage and the sealing maintenance process. The higher the value, the greater the degree of decline in the stability of the seal structure and the more significant the risk of abnormal retention or exposure during the repackaging process. The greater the potential probability of micro-crack leakage, external contamination intrusion, or contamination diffusion in the subsequent transportation, storage, and testing stages. Conversely, the lower the value, the more stable the repackaged sampling tubes are in terms of sealing maintenance and repackaging process control, with a low overall contamination risk level, and they can be used as normal circulation objects without priority intervention.

[0034] After calculating the labeling index, the labeling index is compared with the preset labeling threshold: When the marking judgment index is greater than or equal to the preset marking judgment threshold, it is determined that the corresponding sample tube has a high risk of contamination during the packaging and sealing process. Screening and marking are performed on it, and it is included in the subsequent recall quantity statistics. When the marking judgment index is less than the preset marking judgment threshold, it is determined that the sealing status and dispensing process of the corresponding dispensing sampling tube are within a controllable range. No screening marking process is performed, and only the judgment result is recorded for process traceability.

[0035] It should be noted that the preset weighting coefficients and the marking judgment thresholds are set based on the statistical analysis results of historical packaging operation data and quality results. Specifically, under established repackaging processes and cold chain conditions, repackaging sampling tubes from multiple historical evaluation periods are selected as sample sets. The corresponding sealing stability, repackaging loss coefficient, and subsequent abnormal results such as leakage, seepage, or microbial detection anomalies are calculated. Abnormal results are used as binary labels, and the reverse term of sealing stability and the repackaging loss coefficient are used as regression variables. After normalization of the regression variables, regression analysis is performed. Based on the absolute values ​​of the regression coefficients for each regression variable, the influence of the reverse term of sealing stability and the repackaging loss coefficient on the probability of abnormal results is quantified. Each quantified influence value is then divided by the sum of all quantified influence values ​​to obtain the corresponding preset weight coefficients. This ensures that the preset weight coefficients correspond to the relative strength of each factor's influence on the probability of abnormal results, and the sum of all preset weight coefficients is one. Furthermore, statistical distribution analysis is performed on the calculated labeling judgment index, and the lower bound of the labeling judgment index interval for samples with abnormal results is used as the labeling judgment threshold.

[0036] The number of sampled tubes that have completed risk screening and marking is counted to determine whether a recall mechanism has been triggered. Specifically, all sampled tubes within the evaluation period are traversed, and sampled tubes with a marking judgment index greater than or equal to a preset marking judgment threshold are added to the risk marking set. The total number of sampled tubes in the risk marking set is then counted to obtain the number of marked sampled tubes.

[0037] The number of labeled and packaged sampling tubes obtained from the statistics is compared with the preset recall trigger threshold: When the number of labeled and packaged sampling tubes is greater than or equal to the preset recall trigger threshold, it is determined that the risk accumulation level has reached the condition for triggering recall, and the recall mechanism is triggered. When the number of labeled and packaged sampling tubes is less than the preset recall trigger threshold, it is determined that the current risk level has not reached the recall trigger condition. Only the number of labeled and packaged sampling tubes and the corresponding packaged sampling tube information are recorded for subsequent circulation traceability, without triggering the recall mechanism.

[0038] It should be noted that the recall trigger threshold is objectively and quantitatively set based on historical repackaging operation data and batch size. For example, the average doubling method of the frequency of historical anomalies can be used as the recall trigger threshold to ensure that the recall operation is executed in a timely manner when the risk accumulation reaches a significant level, thereby achieving dynamic monitoring and effective control of the risk of the repackaging sampling tube group.

[0039] In step S4, after the recall mechanism is triggered, the process enters the stage of tracing the source of diffusion risk and reviewing the recall based on the transfer path. The specific implementation process is as follows: For the tagged and packaged sampling tubes, the corresponding transport path information is retrieved. This transport path information consists of the reading results of IoT codes at each transport node, represented as a node path sequence arranged in chronological order, including the transport node identifier and the timestamp of the tagged and packaged sampling tube arriving at the corresponding transport node. Through the node path sequence, the actual flow trajectory of the packaged sampling tubes during the cold chain packaging, temporary storage, loading, and transport processes is fully depicted.

[0040] Based on this, the transport path information of the marked sample tubes is used as a reference path. The transport path information of other sample tubes within the evaluation period is compared and analyzed to identify sample tubes that overlap with the reference path, i.e., sample tubes on the same path.

[0041] Specifically, when the node path sequence of any sample tube meets the preset path overlap condition with the reference path in both the node and time dimensions, it is determined to be a sample tube on the same path. The path overlap condition is quantified by the path overlap rate, expressed as follows: ; in, This refers to the path overlap rate. To mark the transport path information of the dispensing sampling tubes, This provides information on the transport path for other sample tubes.

[0042] When the path overlap rate is greater than or equal to the preset path overlap threshold, it is determined that the corresponding sample tube and the marked sample tube belong to the same path.

[0043] It should be noted that the preset path overlap threshold is used to limit the minimum path similarity at which different packaged sampling tubes can be identified as following the same path during transportation. Its setting is based on historical transportation path data and statistical results related to contamination events. Specifically, under a given cold chain transportation network structure, packaged sampling tubes that have been confirmed to have associated contamination or require joint handling within the historical assessment period are selected. Their corresponding path overlap rate distribution intervals are calculated, and the minimum overlap rate within this distribution interval that stably covers the vast majority of known associated contamination cases is used as the path overlap threshold.

[0044] After identifying the sampling tubes along the same route, the transport association characteristics between these tubes and the labeled, dispensed sampling tubes are further evaluated based on the transport route information. Specifically, the number of overlapping nodes and the node dwell time are obtained based on the transport route information, and transport association characteristics are constructed to characterize the possibility of potential pollution diffusion between the two in the same transport environment. The calculation expression for the transport association characteristics is as follows: ; in, This refers to the transport correlation characteristics of sampling tubes along the same path. This represents the number of overlapping nodes. is the time the nodes stay in an overlapping state, and m is the index value of the overlapping nodes.

[0045] Furthermore, the transport correlation characteristics are jointly judged with the sampling type, and a sampling type weighting factor is introduced to reflect the sensitivity of pollution leakage or diffusion under different sampling types. Specifically, the transport correlation characteristics are multiplied by the sampling type weighting factor corresponding to the sampling type to obtain the recall kernel judgment index of sampling tubes along the same route.

[0046] It should be noted that the sampling type weighting factor is used to quantify the potential impact of different sampling types on surrounding sampling tubes when sealing anomalies or pollution leaks occur. Its setting is based on the statistical correlation between sampling type and pollution diffusion results. Specifically, based on historical sampling and detection data, the frequency of detection anomalies under different sampling types is statistically analyzed, and its amplification factor relative to the overall average anomaly incidence rate is calculated. This amplification factor, after normalization, is used as the corresponding sampling type weighting factor. This ensures that the magnitude of the sampling type weighting factor objectively reflects the differences in the sensitivity of different sampling types to pollution diffusion.

[0047] The recall review determination index is compared with the preset recall review threshold: When the recall review judgment index is greater than or equal to the recall review threshold, it is determined that the corresponding sampling tube along the same path has a high risk of associated contamination during the transportation process, and a recall review is performed on it. When the recall review index is less than the recall review threshold, it is determined that the risk of the corresponding sampling tube being affected by the marked and repackaged sampling tube is within a controllable range. No recall review process is performed, and only the path association is recorded for subsequent source tracing analysis.

[0048] It should be noted that the recall review threshold is used to determine whether sampling tubes along the same route need to be included in the recall review scope. Its setting is based on the correspondence between transport association characteristics and actual associated contamination results. Specifically, samples of sampling tubes along the same route that have completed recall review within the historical assessment period are selected, and their corresponding recall review judgment index distribution is calculated. The lower limit of the index interval where samples that actually show associated contamination or require joint review are located is used as the recall review threshold. Recall review processing refers to the risk control and verification operations performed on repackaged sampling tubes after determining that there is a high risk of associated contamination with sampling tubes along the same route. This includes freezing the circulation status of repackaged sampling tubes, re-verifying transport nodes and time information, and repackaging, key testing, or biosafety review of the repackaged sampling tubes.

[0049] Through the above steps, the risk diffusion identification and precise recall control from a single risk sampling tube to sampling tubes along the same route are realized after the recall mechanism is triggered. This ensures that the recall scope matches the actual transportation-related risks, and that potentially contaminated sampling tubes are effectively identified and isolated before entering subsequent testing or use stages. This reduces the risk of contamination diffusion and improves the safety and reliability of the entire process of traceability management for animal tissue sampling.

[0050] Finally, it should be noted that in this paper, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0051] Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0052] In this document, the singular forms “a,” “an,” and “the” may also include the plural forms unless the context clearly indicates otherwise. It should also be understood that terms such as “comprising / including” or “having” specify the presence of the stated features, integrals, steps, operations, components, parts, or combinations thereof, but do not preclude the possibility of the presence or addition of one or more other features, integrals, steps, operations, components, parts, or combinations thereof. Meanwhile, the term “and / or” as used in this specification includes any and all combinations of the associated listed items.

[0053] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can be referred to each other.

[0054] The above description of the disclosed embodiments will enable those skilled in the art to make or use various modifications to these embodiments. It will be readily apparent to those skilled in the art that the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for full-process traceability management of animal tissue sampling tubes based on Internet of Things (IoT) coding, characterized by: Includes the following steps: Step S1: Before the sampling tube under test is dispensed, the filling and sampling period of the sampling tube under test is read. During the filling and sampling period, the interactive trigger data of the sampling tube under test is collected, and the sampling type of the sampling tube under test is generated based on the interactive trigger data. Step S2: After the sampling tube to be tested is repackaged, the repackaged sampling tube is obtained. The repackaging dwell time of the repackaged sampling tube is detected and the repackaging loss coefficient is calculated. The evaluation cycle is set and the number of times the seal of the repackaged sampling tube is changed is collected. Step S3: Analyze the sealing stability based on the number of seal changes, screen and mark the sub-packaging sampling tubes by combining the sealing stability with the sub-packaging loss coefficient, and determine whether to trigger the recall mechanism after counting the number of marked sub-packaging sampling tubes. Step S4: Retrieve the transport path information of the marked and packaged sampling tubes in the recall mechanism, identify the sampling tubes along the same path based on the transport path information and evaluate the transport association characteristics, and determine whether to perform the recall kernel for the sampling tubes along the same path based on the sampling type.

2. The method for full-process traceability management of animal tissue sampling tubes based on IoT coding according to claim 1, characterized in that: In step S1, before the sampling tube to be tested is dispensed, the filling and sampling period corresponding to the sampling tube to be tested is read through the timestamp recording interface; During the filling and sampling period, the sampling interaction trigger data of the sampling tube under test is collected through the RFID interaction unit, including the trigger duration of the trigger event and the trigger source identifier; The number of different source identifiers is counted to obtain the number of subject categories. The number of subject categories is compared with a preset subject number threshold. When the number of subject categories is greater than the preset subject number threshold, the sampling type of the sampling tube to be tested is determined to be passive sampling. When the number of subject categories is less than or equal to the preset subject number threshold, the trigger duration ratio will be further compared with the preset duration ratio threshold.

3. The method for full-process traceability management of animal tissue sampling tubes based on IoT coding according to claim 2, characterized in that: In step S1, trigger events whose duration exceeds a preset trigger duration threshold are marked, and the ratio of the number of marked trigger events to the total number of trigger events is used as the trigger duration ratio. When the trigger duration ratio is greater than or equal to the preset duration ratio threshold, the sampling type is determined to be active sampling; When the trigger duration ratio is less than the preset duration ratio threshold, the sampling type is determined to be passive sampling.

4. The method for full-process traceability management of animal tissue sampling tubes based on IoT coding according to claim 1, characterized in that: In step S2, the sampling tubes to be tested are repackaged to obtain each repackaged sampling tube. The repackaged sampling tubes are sealed independently, and the sealing status of the repackaged sampling tubes is recorded. The time spent filling the sampling tubes is obtained by recording the filling operation log. Access the historical database to retrieve the historical dispensing dwell time corresponding to the historical dispensing sampling tube with the same sampling type as the sampling tube under test and in normal dispensing state, and use the average of the historical dispensing dwell time as the dispensing baseline time. The ratio of the dispensing dwell time to the dispensing baseline time is used as the dispensing loss coefficient.

5. The method for full-process traceability management of animal tissue sampling tubes based on IoT coding according to claim 1, characterized in that: In step S2, an evaluation period is preset. Within the preset evaluation period, the seal change event of the dispensing sampling tube is collected through the seal status record log. The seal change event refers to the event in which the seal status of the dispensing sampling tube changes after the sealing is completed.

6. The method for full-process traceability management of animal tissue sampling tubes based on IoT coding according to claim 5, characterized in that: In step S3, the stable sealing state is obtained by normalizing and inversely mapping the number of seal changes: ; in, For a sealed and stable state, To assess the number of seal changes recorded during the assessment period, This is the preset maximum number of times the seal can be changed; Using the sealing stability state as a positive indicator reflecting the integrity of the sealing structure and the sub-packaging loss coefficient as a negative indicator reflecting the risk of exposure during the sub-packaging stage, a weighted fusion formula is introduced to comprehensively calculate the two, resulting in the marking judgment index. The calculation formula is as follows: ; in, To mark the judgment index, For a sealed and stable state, This is the repackaging loss coefficient. and These are preset weighting coefficients.

7. The method for full-process traceability management of animal tissue sampling tubes based on IoT coding according to claim 6, characterized in that: In step S3, when the marking judgment index is greater than or equal to the preset marking judgment threshold, it is determined that the corresponding sample tube has a high risk of contamination during the packaging and sealing process, and screening and marking processing is performed. When the marking judgment index is less than the preset marking judgment threshold, it is determined that the sealing status and dispensing process of the corresponding dispensing sampling tube are within a controllable range, and no screening marking process is performed; All sample tubes in the evaluation period are traversed, and sample tubes with a marking judgment index greater than or equal to the preset marking judgment threshold are included in the risk marking set. The total number of sample tubes in the risk marking set is counted to obtain the number of marked sample tubes. When the number of labeled sampling tubes is greater than or equal to the preset recall trigger threshold, the recall mechanism is triggered. Conversely, the recall mechanism will not be triggered.

8. The method for full-process traceability management of animal tissue sampling tubes based on IoT coding according to claim 1, characterized in that: In step S4, after the recall mechanism is triggered, the corresponding transport path information is retrieved for the marked and packaged sampling tubes. The transport path information consists of the reading results of the IoT code at each transport node, and is represented as a node path sequence arranged in chronological order. The transport path information of the marked sample tubes is used as a reference path. The transport path information of other sample tubes within the evaluation period is compared and analyzed to identify sample tubes that overlap with the reference path, i.e., sample tubes on the same path. Based on the transit route information, the number of overlapping nodes and the time of node dwell overlap are obtained, and transit association features are constructed: ; in, This refers to the transport correlation characteristics of sampling tubes along the same path. This represents the number of overlapping nodes. is the time the nodes stay in an overlapping state, and m is the index value of the overlapping nodes.

9. The method for full-process traceability management of animal tissue sampling tubes based on IoT coding according to claim 8, characterized in that: In step S4, a sampling type weighting factor is introduced, and the transport association feature is multiplied by the sampling type weighting factor corresponding to the sampling type to obtain the recall kernel determination index of the sampling tubes along the same path. When the recall review judgment index is greater than or equal to the recall review threshold, it is determined that the corresponding sampling tube along the same path has a high risk of associated contamination during transportation, and recall review is performed. When the recall review index is less than the recall review threshold, it is determined that the risk of the corresponding sampling tube being affected by the marked and repackaged sampling tube is within a controllable range. No recall review process is performed, and only the path association is recorded for subsequent source tracing analysis.

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