Automatic linkage execution method for gene detection process control and sample bank management

Abstract

The application discloses a kind of gene detection process control and the automatic linkage execution method of sample library management, it is related to sample library management field, first for each sample generates the sample execution image containing allow exposure length, target path and the like information, as its space-time constraint benchmark of whole life cycle.Meanwhile, the admission capacity of downstream target station is assessed in real time, and the station readiness vector is formed.The key is that only when the time limit requirement in sample image can completely cover the estimated transfer and waiting time, and leave safety margin, the space-time constraint token with uniqueness will be issued, to drive sample out of warehouse.This mechanism fundamentally eliminates the risk of sample being trapped in blind state due to downstream congestion or equipment abnormality, ensures its completion within the biological activity safety window, and solves the technical problem of sample degradation caused by space-time resource mismatch.

Description

Technical Field

[0001] This application relates to the field of sample bank management, and more specifically, to an automated linkage method for gene detection process control and sample bank management. Background Technology

[0002] With the rapid development of precision medicine and high-throughput gene sequencing technologies, clinical laboratories have placed almost stringent demands on the throughput and compliance of biological sample testing processes. The core purpose of constructing an automated, interconnected execution solution for gene testing process control and sample bank management is to break down the physical and informational barriers between cryogenic storage environments and room-temperature testing pipelines. This involves achieving standardized operations across the entire sample lifecycle, from sample release and transport to instrumental testing, through millisecond-level spatiotemporal coordination. This is not only a key requirement for improving laboratory operational efficiency but also essential for ensuring the molecular structural integrity of highly bioactive samples in complex process pathways, guaranteeing the authenticity and clinical validity of test results.

[0003] However, existing automated gene testing solutions are often limited to a linear execution mode driven by instructions, which is essentially a crude scheduling mechanism lacking biological attribute perception. In practical applications, although the sample bank and the testing pipeline can complete the handshake at the instruction level, the system monitoring dimension only stays at the physical level of whether the action is completed or not, failing to deeply embed the thermodynamic characteristics of the sample's dynamic decay with environmental temperature and exposure time into the scheduling closed loop. This limitation results in the system lacking global spatiotemporal feedback. Once downstream testing stations frequently exceed biosafety thresholds due to mechanical disturbances or instantaneous high loads, triggering nucleic acid degradation or biochemical drift, the resulting experimental deviations may even lead to misdiagnosis or test failure, greatly restricting the quality and reliability of gene testing in complex industrial scenarios. Alternatively, temporary shortages of consumables may cause delays, and the execution end cannot adjust the outbound cycle or optimize the transport trajectory based on the real-time execution profile of the sample. This makes gene samples with extremely high environmental sensitivity easily fall into an uncontrollable blind waiting state in the transport area or buffer position, resulting in prolonged environmental exposure.

[0004] Therefore, we hope to develop an automated, interconnected execution method that can deeply couple the biological activity of a sample with the execution status of the physical world, thereby avoiding the risk of sample degradation caused by the mismatch of spatiotemporal resources at the source. Summary of the Invention

[0005] To address the aforementioned problems in existing technologies, this application provides an automated, interconnected execution method for gene detection process control and sample bank management, comprising: Step 1, based on biostability mapping rules and process topology tables, parsing, converting, and path matching the acquired initial sample metadata to determine the maximum permissible exposure time corresponding to the sample barcode, target workstation coordinates, transport baseline time, and sample identifier binding relationship, thereby obtaining a sample execution profile; Step 2, based on the sample execution profile, retrieving the pipeline status data corresponding to the target workstation, and performing time-series extrapolation and acceptance analysis on the pipeline status data to obtain the expected release time, predicted waiting time, and health status combination results of the target workstation, generating a characterization of the workstation's acceptance capacity. Step 3: Issue spatiotemporal constraint tokens based on safety margin judgment to the sample execution profile and the workstation ready vector to obtain spatiotemporal constraint tokens that drive the collaborative execution of the sample library and the detection pipeline; Step 4: Start the sample library outbound control based on the spatiotemporal constraint tokens and retrieve the on-site motion data, and use token verification and trajectory compensation to continuously compare and correct anomalies in the on-site motion data to obtain a transfer execution log reflecting the actual sample transfer process and time limit health; Step 5: Based on the transfer execution logs and spatiotemporal constraint tokens, confirm the handover of the target workstation receiving results, and associate the transfer execution logs, sample execution profiles, and the actual response time of the target workstation into the sample library record to form the sample library record.

[0006] Compared with existing technologies, this application provides an automated linkage execution method for gene detection process control and sample bank management, which deeply binds the biological time-sensitivity characteristics of the sample itself with the physical state of the automated pipeline, constructing a predictive closed-loop scheduling mode. It first generates a sample execution profile for each sample, containing information such as the allowed exposure duration and target path, serving as a spatiotemporal constraint benchmark for its entire lifecycle. Simultaneously, it assesses the acceptance capacity of downstream target workstations in real time, forming a workstation readiness vector. Crucially, a unique spatiotemporal constraint token is only issued to drive sample release when the time limit requirements in the sample profile fully cover the estimated transport and waiting time, with a safety margin. This mechanism upgrades traditional command-driven processes to token authorization, shifting from post-event traceability to pre-event prediction, fundamentally eliminating the risk of samples falling into a blind waiting state due to downstream congestion or equipment malfunction, ensuring that handover is completed within the bioactivity safety window, and solving the technical problem of sample degradation caused by spatiotemporal resource mismatch. Attached Figure Description

[0007] The above and other objects, features and advantages of this application will become more apparent from the more detailed description of the embodiments of this application in conjunction with the accompanying drawings.

[0008] Figure 1This is a flowchart of an automated linkage execution method for gene detection process control and sample bank management according to an embodiment of this application.

[0009] Figure 2 This is a schematic diagram of the data flow in an automated linkage execution method for gene detection process control and sample bank management according to an embodiment of this application.

[0010] Figure 3 This is a flowchart of step two in the automated linkage execution method for gene detection process control and sample bank management according to an embodiment of this application.

[0011] Figure 4 This is a flowchart of step four in the automated linkage execution method for gene detection process control and sample bank management according to an embodiment of this application.

[0012] Figure 5 This is a flowchart of step five in the automated linkage execution method for gene detection process control and sample bank management according to an embodiment of this application. Detailed Implementation

[0013] Embodiments of this application will now be described in more detail with reference to the accompanying drawings.

[0014] To address the shortcomings in the aforementioned technical fields, this application proposes an automated, interconnected method for gene detection process control and sample bank management. For example... Figure 1 and Figure 2 As shown, Figure 1 This is a flowchart of an automated linkage execution method for gene detection process control and sample bank management according to an embodiment of this application. Figure 2 This is a schematic diagram of the data flow in an automated linkage execution method for gene detection process control and sample bank management according to an embodiment of this application.

[0015] Step one involves parsing, converting, and matching the acquired initial sample metadata based on biological stability mapping rules and process topology tables to determine the maximum allowable exposure time corresponding to the sample barcode, target workstation coordinates, transport baseline time, and sample identifier binding relationship, thus obtaining a sample execution profile. It should be understood that in modern gene testing processes, the transfer of samples from cryogenic storage environments to automated testing lines at room temperature, while seemingly simple, actually carries extremely high quality risks. The molecular structural activity of each biological sample, especially nucleic acid samples which are highly sensitive to environmental changes, irreversibly decays with prolonged exposure time and rising ambient temperature. If a unique identifier containing biological activity time limits and physical spatial paths cannot be tailored for each sample at the source of scheduling, the automated system can only perform indiscriminate, command-based, and crude management. Therefore, this step aims to generate a unique, high-dimensional sample execution profile for each sample by performing in-depth analysis and thermodynamic conversion of sample metadata, combined with a pre-set process knowledge base.

[0016] In one achievable embodiment of this application, step one includes: performing field-level segmentation and core element extraction processing on the initial sample metadata to obtain a sample basic identifier package; performing thermodynamic numerical deduction calculations on the sample basic identifier package and biological stability mapping rules to obtain sample time-limited close-up data with additional maximum allowable time-series limit parameters; and performing spatial feature extraction and identifier fusion on the sample time-limited close-up data and process topology table to obtain a sample execution profile.

[0017] The execution process is detailed as follows: The first step is to perform field-level segmentation and core element extraction on the initial sample metadata to obtain the sample basic identifier package. The initial sample metadata is a relatively complex raw data stream sent from the upstream Laboratory Information Management System (LIMS) to the linkage control core. This metadata not only contains key information necessary for executing the automation process, but also includes a large amount of redundant text that is meaningless to the robotic arm or transmission unit, such as clinical medical record summaries, subject privacy information, and detailed test lineage descriptions. In order to achieve data lightweighting in the automation process and to follow the principle of information desensitization, the parsing gateway of the control unit will immediately start the field-level segmentation and extraction program after receiving this metadata. Based on preset rules, the program precisely strips away all descriptive fields that are not strongly related to the automated physical flow, extracting only three core objects from the data stream: a sample barcode that uniquely identifies the sample, such as the string "SA20260325-001"; a stability rating characterizing the sample's biological characteristics, such as a classification label defined as Level-3 High-Sensitivity; and a target process code pointing to subsequent testing procedures, such as the code P-NGS-004-LibPrep representing the NGS automated library construction scheme -04. After extraction, these three core objects are aligned and serialized at the underlying data type level, encapsulating them into a structured, standardized data unit, namely the sample basic identification package.

[0018] Next, based on the previously generated sample basic identification package, thermodynamic numerical extrapolation calculations are performed on the sample basic identification package and the biological stability mapping rules. The biological stability mapping rules are a key knowledge base pre-installed in a local cache; essentially, they are a parameter mapping table calibrated by experts and validated by experimental data. The table's structure uses a stability rating as the primary key, and its corresponding values ​​contain the core thermodynamic parameters required to calculate sample activity decay. These parameters are derived by experienced experimenters based on extensive temperature-controlled exposure experimental data for different sample types, such as DNA, RNA, protein, and preservation solution formulations. In this step, the Level-3-High-Sensitivity stability rating is first extracted from the sample basic identification package and used as a search pointer to query the biological stability mapping rules. Matching records return a specific set of thermodynamic parameters, such as a baseline half-life of 1800 seconds and a temperature sensitivity coefficient of 0.08. Simultaneously, the processing unit retrieves the current ambient temperature value of 25 degrees Celsius from a calibrated environmental temperature sensor deployed in the automated workshop. Based on all the parameters obtained above, the actuator invokes the Arrhenius-improved thermodynamic decay logic model and performs numerical deduction using the following formula to calculate the maximum allowable exposure time for the sample under the current environment. In one feasible embodiment of this application, the thermodynamic numerical deduction calculation of the sample basic identification package and biological stability mapping rules includes: performing thermodynamic numerical deduction calculation using the following formula, where the formula is:

[0019] in, For the maximum permissible exposure time, The baseline half-life, derived from the biological stability mapping rules, represents the active half-life of the sample at near-ideal laboratory standard temperatures, such as 22 degrees Celsius; in this example, it is 1800 seconds. is the base of the natural logarithm. This is the temperature sensitivity coefficient, a dimensionless parameter that characterizes the sensitivity of sample activity to temperature changes. A higher value indicates a more severe attenuation; in this example, it is 0.08. The current workshop ambient temperature is obtained in real time from sensors. The standard cold storage temperature is a fixed system constant that represents the baseline low temperature for sample storage, set at 4 degrees Celsius. This is a physical transfer safety buffer time, a global safety margin preset to handle minor physical disturbances such as robotic arm start-up delays and barcode scanning retries, for example, set to 60 seconds. Analysis of the formula shows that its core lies in the exponent, which is a decay coefficient between 0 and 1. Ambient temperature. Compared to standard cold storage temperature The greater the deviation, the smaller the value of the exponential term, thus making the baseline half-life... The sample was drastically reduced, accurately simulating the biological reality of high temperatures accelerating sample degradation. Substituting the specific values ​​above: Seconds. After the calculation is completed, this dynamically generated maximum allowable exposure time, accurate to the second, will be forcibly bound to the sample barcode and target process code in the original sample basic identification package, together forming a new data object containing time dimension constraints, namely, sample time limit close-up data.

[0020] Finally, spatial features are extracted and fused from the sample time-limit close-up data and the process topology table. The process topology table is a digital twin map of the automation system. It uses a multi-way tree or directed graph data structure to accurately describe the physical layout of all workstations, equipment, and buffer points within the laboratory, as well as the feasible transport paths between them. This is pre-built by engineers during the system deployment phase through equipment calibration and path planning. Each node stores the physical three-dimensional coordinates of that workstation, and each edge records the baseline time taken to move along that path. In this step, the actuator extracts the target process code P-NGS-004-LibPrep from the sample time-limit close-up data and uses this code as the addressing primary key to traverse and search the process topology table. Once the process starting node corresponding to the process code, such as the automated pipetting workstation-02, is matched, its precise physical space parameters and estimated mechanical motion parameters are extracted from the node's data records: the target station's three-dimensional coordinates, specifically (X:1500, Y:3500, Z:900) millimeters; and the baseline transfer time from the sample library outlet to the station, for example, 25 seconds. These two extracted spatial and baseline time parameters are then fully fused and encapsulated with the existing identification identifier in the sample time limit close-up data, namely the sample barcode SA20260325-001, and the time constraint, namely the maximum allowable exposure time of 275.3 seconds, to form a comprehensive data object. To ensure the integrity and tamper-proof nature of this highly integrated core data during subsequent transfers, a checksum based on a secure hash algorithm such as SHA-256 is generated based on the encapsulated full data and appended to it. Ultimately, this comprehensive data structure, which includes sample identification, biological time limit, physical target coordinates, and estimated transportation time and has undergone integrity verification, is formally instantiated in the memory pool to generate a sample execution profile.

[0021] Step two involves retrieving the pipeline status data corresponding to the target workstation based on the sample's execution profile, and performing time-series calculations and acceptance analysis on the pipeline status data to obtain the expected release time, predicted waiting time, and health status combination results of the target workstation, generating a workstation readiness vector representing the workstation's acceptance capacity. Correspondingly, after accurately identifying the spatiotemporal attributes of a single sample, a more critical question arises: the sample's lifespan has been activated, but does its intended destination—the target workstation on the automated pipeline—have the capacity to immediately accept it? If physical transport is blindly initiated based solely on the sample's own profile, it is highly likely that the sample will be forced into an uncontrollable, blind waiting state before arriving at a busy or malfunctioning workstation, thus rapidly consuming its precious biological activity time. Therefore, this step actively retrieves and analyzes the real-time physical and task status of the target workstation to perform accurate time-series calculations. This step aims to generate a dynamic, multi-dimensional workstation readiness vector for each sample to be transferred, thereby providing a basis for decision-making for subsequent safety margin judgment and token issuance, ensuring that each sample's outbound action is based on a reliable prediction of the future state.

[0022] Figure 3 This is a flowchart of step two in the automated linkage execution method for gene detection process control and sample bank management according to an embodiment of this application. Figure 3 As shown, in an embodiment achievable in this application, step two includes: Step two-one, using the underlying hardware address index of the industrial equipment, performing target workstation load status analysis on the sample execution profile and pipeline status data to obtain a real-time load image of the target workstation reflecting the immediate execution pressure of the physical workstation; Step two-two, based on the heat loss frequency reduction compensation coefficient and the standard instruction cycle time, estimating the expected release time of the real-time load image of the target workstation based on the task residual to obtain the workstation availability timestamp that the equipment can be released globally at a future time as the expected release time; Step two-three, performing an interference comparison between the motion prediction arrival time and the workstation availability boundary on the sample execution profile and the workstation availability timestamp to obtain the predicted waiting time; Step two-four, performing parallel up-dimensional splicing processing of the predicted waiting time and the sample execution profile with the delay attribute and the equipment health alarm baseline to obtain the workstation readiness vector.

[0023] The execution process is detailed as follows: First, the target workstation load status is analyzed based on the sample execution profile and pipeline status data using the underlying hardware address index of the industrial equipment. The pipeline status data is a low-level data stream transmitted in real-time to the central control unit via industrial buses such as EtherCAT or PROFINET, from programmable logic controllers (PLCs), sensors, and actuators deployed throughout the automation system. This data stream contains complete status information for each hardware unit in the form of data packets, such as device address, current task queue, currently executing instruction pointer, motor load current, and sensor readings. In this step, the unique identifier of the target workstation, i.e., its underlying hardware address index (e.g., PLC-LIQ-02-Station), is extracted from the sample execution profile generated in the previous step. This address code is the crucial bridge connecting the logical flow and the physical entity. Using this address code as a precise filter, all status packets related to PLC-LIQ-02-Station are captured and separated in real-time from the massive, global pipeline status data stream. Subsequently, these messages were deeply analyzed to extract several key dynamic parameters that accurately characterize their real-time load status: the total number of remaining unexecuted step instructions in the current task queue, for example, 150; for cyclically executed tasks, the number of robot arm cycles completed; and the preset operating voltage fluctuation value reflecting the equipment's health and power consumption status, for example, a current fluctuation of +0.5%. These multi-dimensional dynamic parameters were then structurally integrated to generate a standardized data object defined as the real-time load mirror of the target workstation.

[0024] Next, based on the heat loss frequency reduction compensation coefficient and the standard instruction cycle time, the expected release time of the target station's real-time load image is estimated based on the task residual. The standard instruction cycle time is a pre-calibrated key parameter representing the basic time required for a specific station to execute a standard motion or logic instruction. This value is statistically derived after extensive no-load and load testing during the equipment commissioning phase and stored in the equipment's configuration file. For example, for the pipetting operation of the PLC-LIQ-02-Station, its standard instruction cycle time might be set to 50 milliseconds. However, under continuous high-load operation, industrial equipment may experience slightly slower actual execution speeds than ideal due to factors such as motor heating and decreased driver efficiency. This phenomenon is quantified and compensated for using the heat loss frequency reduction compensation coefficient. This coefficient can be a dynamic value based on the total equipment operating time or real-time temperature monitoring, or a static conservative value set based on experience, such as 0.05, representing a 5% performance decrease. In this step, the actuator calls the following formula to accurately calculate when the target station can complete its current task and fully release: ,in, It is the workstation availability timestamp, which represents the earliest time when the workstation is expected to be able to accept new tasks; It is a high-precision current system timestamp obtained from the central control unit; This is the total number of remaining step instructions extracted from the real-time load image of the target workstation generated previously; in this example, it is 150. This is the standard instruction cycle time obtained from the device configuration file, which is 50 milliseconds in this example; This is the preset or dynamically obtained thermal loss frequency reduction compensation coefficient, which is 0.05 in this example. The residual task time under ideal conditions is calculated by multiplying the remaining number of instructions by the time consumed per instruction, and then multiplied by... This compensation item corrects for the frequency reduction caused by physical losses, resulting in a more realistic and buffered estimated time. Adding this estimated time to the current time yields the future available timestamp for the workstation. Substituting the values ​​above, the calculation is as follows: This means that, from the current moment, the workstation needs another 7.875 seconds to complete its current task.

[0025] Subsequently, an interference comparison is performed between the predicted arrival time of the sample execution profile and the available timestamps of the workstations, and the available workstation limits. In this step, the linkage control core will conduct a virtual simulation. First, the transfer baseline time is extracted from the sample execution profile; this value was determined in step one based on the process topology table, for example, 25 seconds. Based on this, the estimated arrival time of the sample at the target workstation if it departs immediately from the sample library can be calculated: In this example, it's 25 seconds after the current time. Next, we'll discuss this predicted arrival time. Compared with the available timestamps of the workstations calculated in the previous step The two are compared. The difference between the two is the waiting time the sample may experience. The judgment logic is: if... Later This indicates that the workstation was still busy when the sample arrived, and the difference ( This represents a positive prediction of the waiting time. Conversely, if... Earlier than or equal to This indicates that the workstation was already free before the sample arrived, meaning the sample did not need to wait, and the prediction waiting time was forcibly recorded as zero. This process avoids negative waiting time caused by the workstation becoming free in advance interfering with subsequent calculations. In this example, (Current time + 7.875 seconds) is much earlier than (Current time + 25 seconds), therefore, the calculated predicted waiting time is 0 seconds.

[0026] Finally, the predicted waiting time and sample execution profile are subjected to parallel upsizing and stitching with the equipment health alarm baseline. At this point, the time-dimensional prediction for the target workstation is complete, but a comprehensive acceptance capacity assessment also needs to include the workstation's own health status. The equipment health alarm baseline is a real-time status source provided by the equipment maintenance and monitoring module. It compares various key operating parameters of the equipment, such as motor temperature, vibration frequency, and error codes, with preset health thresholds, outputting a discrete, graded status representing the current equipment health, such as: NORMAL, WARNING (warning, e.g., temperature too high but still operational), or ERROR (fault, out of service). In this step, the time-dimensional information obtained in the previous step (the expected release time of the target workstation) is used... The data is then aggregated with the predicted waiting time and the current device health status (such as NORMAL) obtained by querying the device hardware address index in the sample execution profile. This aggregation process can be understood as concatenating information from multiple different dimensions into a high-dimensional vector. This final result, which includes the expected release time, predicted waiting time, and real-time health status, is formally defined as the workstation readiness vector, representing the workstation's acceptance capacity. For example, for sample SA20260325-001, the workstation readiness vector of its target workstation PLC-LIQ-02-Station is specifically represented as: {Expected Release Time: +7.875s, predicted waiting time: 0s, health status: NORMAL}.

[0027] Step three involves issuing spatiotemporal constraint tokens based on safety margin judgments to the sample execution profile and workstation readiness vector to obtain the spatiotemporal constraint tokens that drive the collaborative execution of the sample library and the detection pipeline. Understandably, after constructing the sample's own spatiotemporal profile and the target workstation's acceptance vector, the automated linkage decision-making core possesses all the key information about both sides in the game: one side is the sample carrying a biological activity countdown, and the other side is the physical equipment with its future load and availability predicted. However, simply knowing these two states is insufficient to directly trigger costly and irreversible physical transport. Before initiating any action, a rigorous and decisive risk assessment is required, determining whether the sample's lifespan budget is sufficient to cover the entire process costs, including transport and potential waiting, and whether there is a safety margin to cope with sudden disturbances. Therefore, this step is implemented by performing difference calculations and safety margin judgments on the sample's maximum survival time and the estimated total process time to make the most critical decision.

[0028] In an implementation of this application, step three includes: performing global planning time deduction and ultimate lifetime interference assessment on the sample execution profile and workstation readiness vector to obtain a safety margin judgment state indicating the current scheduling risk level and remaining survivability; performing virtual logic slot locking and structured anti-tampering encapsulation preprocessing on the safety margin judgment state and sample execution profile to obtain hash resource metadata with embedded session feature markers; and performing asymmetric instantiation and issuance of hash resource metadata and sample execution profile using an asymmetric encryption algorithm and a signing private key to obtain a spatiotemporal constraint token.

[0029] The execution process is detailed as follows: First, a global planned time deduction and ultimate lifespan interference assessment are performed on the sample execution profile and workstation readiness vector. This step is the core of the decision-making process, directly colliding the sample's time dimension with the workstation's time dimension. First, the key time parameter, the predicted waiting time, is extracted from the workstation readiness vector generated in the previous step; in this embodiment, this value is 0 seconds. Simultaneously, the biological time boundary and physical movement time of the sample are extracted from the sample execution profile generated in step one, namely, the maximum permissible exposure time of 275.3 seconds and the transport baseline time of 25 seconds. Based on these three core time parameters, the execution mechanism initiates a safety margin evaluation algorithm, the core of which is to assess whether the sample's biological activity still retains sufficient safety redundancy after completing the entire predetermined process. The benchmark for judging this redundancy is a key parameter called the preset system safety redundancy constant. This constant is a conservative time value set by process engineers during the system design and verification phase, based on historical data analysis, process capability index (Cpk), and assessment of potential risks (such as slight robotic arm tremors or instantaneous network command delays). It represents the minimum safety buffer reserved to cope with unforeseen minor disturbances; for example, it can be set to 30 seconds. The calculation and judgment logic of the safety margin is as follows: First, calculate the total planned time, which is the sum of transfer and waiting times: In this application, it is: =25s + 0s = 25s. Next, calculate the remaining survival rate of the samples after completing the task under ideal conditions: In this application, it is: =275.3s - 25s = 250.3s. Finally, this remaining survivability is compared with the preset system safety redundancy constant: if If 250.3s > 30s, then it is determined to be safe. In this embodiment, 250.3s > 30s, so the determination result is safe. Based on this determination result, a structured output is generated, namely the safety margin determination state. This state is a data object containing explicit instructions and quantitative indicators. In this example, since it is determined to be safe, this state will be marked as allowing issuance, along with the calculated net safety margin value, i.e. =250.3s - 30s = 220.3s. In another scenario, the calculated... If the time is less than or equal to 30 seconds, the safety margin judgment status will be marked as rejected or suspended, and this linkage request will be temporarily put on hold until the workstation status is updated in the next scheduling cycle for reassessment.

[0030] Upon receiving a safety margin judgment status signal marked as allowing issuance, the linkage control logic immediately enters the second stage: pre-processing of virtual logical slot locking and structured anti-tampering encapsulation for the safety margin judgment status and sample execution profile. This stage aims to reserve resources for the upcoming physical action and generate a secure session credential. First, based on the target workstation identifier PLC-LIQ-02-Station in the sample execution profile, a logical locking operation is performed on the virtual buffer bit or receiving entry of that workstation in the central resource scheduling and allocation table. The central resource scheduling and allocation table is a real-time memory data structure initialized according to the preset process topology layout at system startup. It is dynamically maintained by the central control unit during task execution to track and manage the occupancy, idle, and locked status of all automated workstations. This locking behavior ensures that no other samples will be scheduled to the same target workstation before the completion of this linkage task, thereby completely avoiding the risks of race conditions and conflicts that may occur in multi-threaded task scheduling. Next, to give this collaborative task a unique identifier, a cryptographically secure pseudo-random number generator is invoked to generate a high-entropy, unpredictable session random number, such as SESSION-A8E4-11EE-B962-0242AC120002, serving as the sole tracking credential for this task from beginning to end. Subsequently, this session random number, the high-precision system absolute timestamp used during locking, and the locked slot index number are structurally encapsulated. Finally, to ensure the absolute integrity of this encapsulated object, a standard cryptographic hash algorithm such as SHA-256 is used to process the object, generating a fixed-length hash digest sequence with strong collision and tamper-resistance properties. The final generated hash value is the hash resource metadata that embeds the session feature, for example, 5E884898DA28047151D0E56F8DC6292773603D0D6AABBDD62A11EF721D1542D8.

[0031] The final step involves asymmetric instantiation and issuance of the hash resource metadata and sample execution profile using an asymmetric encryption algorithm and a signing private key. First, all core information requiring legal validity and transmission to the execution unit is gathered from the sample execution profile, security margin judgment status, and previously generated hash resource metadata. This information constitutes a complete dataset, containing at least: an identity identifier (sample barcode SA20260325-001); spatial coordinates (target workstation 3D coordinates: X:1500, Y:3500, Z:900); an expiration time (a dynamically calculated absolute timestamp indicating the token's validity period, calculated by adding the net security margin of 220.3 seconds from the current system time, representing the time before which the token authorization action must be completed); a task random number (SESSION-A8E4-11EE-B962-0242AC120002); and an integrity check code (hash resource metadata). Subsequently, the signing private key stored in the Hardware Security Module (HSM) is invoked to digitally sign the entire dataset using a mature asymmetric encryption algorithm such as Elliptic Curve Digital Signature Algorithm (ECDSA). This signing process ensures that the token's origin is trustworthy (only the central control core possesses the private key) and that its content is immutable (any modification to the plaintext information will cause signature verification to fail). Finally, the original dataset containing the plaintext information is encapsulated together with the generated digital signature to instantiate a structured, self-contained, cryptographically secure digital credential—the Spatiotemporal Constraint Token. This token serves as the unique, legitimate, and time-limited pass for the sample library and the detection pipeline to collaborate.

[0032] In a preferred embodiment of this application, step three includes: evaluating the sample execution profile and workstation readiness vector based on normal distribution and confidence level to obtain a safety margin judgment state indicating the current scheduling risk level and remaining survivability; performing virtual logic slot locking and structured tamper-proof encapsulation preprocessing on the safety margin judgment state and sample execution profile to obtain hash resource metadata with embedded session feature markers; and performing asymmetric instantiation and issuance of the hash resource metadata and sample execution profile using an asymmetric encryption algorithm and a signing private key to obtain a spatiotemporal constraint token. The implementation of the latter two processes is consistent with the preceding description and will not be repeated here. The implementation of evaluating the sample execution profile and workstation readiness vector based on normal distribution and confidence level to obtain a safety margin judgment state indicating the current scheduling risk level and remaining survivability will now be described in detail.

[0033] Specifically, in the process of performing global planning time deduction and ultimate lifespan interference assessment on sample execution profiles and workstation readiness vectors to generate a safety margin judgment state, the core task is to predict the risks of the upcoming automated process. However, in real industrial environments, the execution time of automated equipment, such as the transfer of robotic arms and the response of workstations, is not a constant theoretical value. It exhibits slight random fluctuations due to physical factors such as microsecond-level delays in motor startup, positional jitter in sensor recognition, and minor wear of transmission components. If the predicted waiting time and the baseline transfer time are simply considered fixed and precise values ​​for evaluation, the system will determine safety and issue a spatiotemporal constraint token when the predicted total time is just slightly below the critical point of the maximum allowable exposure time. However, at this point, any small negative fluctuation—that is, the actual time being slightly longer than predicted—will directly lead to the exposure of highly environmentally sensitive gene samples exceeding the timeout, causing irreversible loss of bioactivity. This evaluation method based on deterministic values ​​lacks the ability to quantitatively perceive potential execution risks. To fundamentally enhance the robustness of decision-making and avoid the potential risk of sample degradation caused by random fluctuations in execution time, it is preferable to upgrade the calculation of the safety margin to a risk assessment model based on normal distribution and confidence level.

[0034] In one feasible implementation of this application, the sample execution profile and workstation readiness vector are evaluated based on normal distribution and confidence level to obtain a safety margin judgment state indicating the current scheduling risk permissibility and remaining survivability, including: Based on historical operational data, normal distributions for actual transfer time and actual waiting time are established. First, the invisible random fluctuations in the physical world are transformed into calculable and analyzable mathematical objects. Specifically, through long-term statistical analysis of historical task execution data for the target workstation, the two core time-consuming parameters, actual transfer time and actual waiting time, are modeled as random variables following a normal distribution. Their mathematical expression is: Actual Transfer Time Actual waiting time .here, and These represent the actual, random transfer and waiting times, respectively. Mean parameter. and This refers to the baseline transit time and predicted waiting time calculated in the previous steps for this task. These are the best expected estimates of the transit time, i.e. For the above , For the above And the variance parameter and This quantifies the stability of the transfer and waiting processes. This value needs to be continuously learned and dynamically updated by the system from the long-term historical operating data of the equipment. The larger the variance, the more drastic the fluctuation in the execution time of the corresponding process.

[0035] The total exposure time is obtained based on the normal distributions of the actual transport time and the actual waiting time. Since the total exposure time of the sample is a direct superposition of the transport and waiting processes, these two independent sources of uncertainty need to be merged to assess the overall risk from a global perspective. According to the additivity principle of normal distributions, the sum of two or more independent normally distributed random variables still follows a normal distribution. Therefore, the random variable representing the total exposure time of the sample... Their joint distribution is This step integrates two independent stochastic processes into a single, complete probabilistic model that can characterize the uncertainty of the total exposure time. The new mean is the sum of the original means, and the new variance is also the sum of the original variances.

[0036] The probabilistic conditions for risk control of the total exposure time are determined to obtain the safety margin assessment state. Ultimately, the abstract risk control requirements are transformed into a clear and actionable calculation criterion. First, based on the rigor of the process and quality control requirements, a system-acceptable risk tolerance is preset. For example, for high-precision clinical testing, this value might be cautiously set at 1%. The essential requirement for this decision is the total exposure time. Exceeding the maximum permissible exposure time for the sample The probability must be strictly less than the preset risk tolerance. ,Right now In engineering practice, this probability problem is efficiently transformed into a discriminant inequality based on confidence intervals:

[0037] In this formula, It is a standard normal distribution Quantiles are determined by risk tolerance. The uniquely determined constant represents the level of safety confidence sought in the decision-making process. This represents the standard deviation of the total exposure time. This automatically incorporates historical execution volatility into the decision-making safety buffer. Even if the predicted average execution time is very close to the critical point, as long as the system detects significant fluctuations in the execution process (i.e., large variance) through historical data, the probabilistic safety buffer term on the right side of the inequality will... This will automatically increase accordingly, making it more likely to lead to judgment failure and rejection of token issuance. This greatly enhances the system's risk avoidance capabilities and decision robustness under complex operating conditions. Taking sample SA20260325-001 in this application as an example: the preliminary steps have determined its maximum permissible exposure time. The transit time was 275.3 seconds, which is the baseline transit time. The predicted waiting time is 25 seconds. The time is 0 seconds. If the system continuously learns from the historical operational data of the transfer robot, it can obtain the variance of its transfer time. The variance of the waiting time is 2.25 (i.e., the standard deviation is 1.5 seconds). Since the current workstation is idle and there is no queuing uncertainty, the variance of the waiting time is... The set risk tolerance is 0. The value is 0.01. Referring to the standard normal distribution table, the corresponding confidence quantile is... Approximately 2.33. At this point, a safety margin judgment is performed: First, calculate the mean of the total exposure time = 25 + 0 = 25 seconds, and the total variance = 2.25 + 0 = 2.25. Next, calculate the right side of the discrimination inequality, which contains the worst-case estimated total time with a 99% confidence level: 25 + 2.33 × sqrt(2.25) = 28.495 seconds. Finally, make a judgment: determine the sample's maximum lifetime. This is compared to the worst-case estimated time. Since 275.3 seconds is much larger than 28.495 seconds, the discriminant inequality is satisfied. Therefore, the final safety margin judgment state generated by the system will be marked as allowing issuance. Based on this preferred implementation, the system's decision boundary becomes dynamic and intelligent. For example, in another scenario, the sample's ultimate lifetime... It takes only 30 seconds. At this point, although the average planned time of 25 seconds seems to leave a 5-second margin, the system's worst-case prediction, based on a 99% confidence level, is 28.495 seconds. Since 30 seconds is still greater than this value, the token will still be issued. However, if the wear and tear on the transport equipment worsens, causing the system to learn from historical data an increase in operational volatility, such as variance... If the value is increased to 4 (standard deviation of 2 seconds), then the worst-case estimated time will become 25 + 2.33 × 2 = 29.66 seconds. In this case, for For a 29-second sample, its status will be marked as rejected or suspended, thus effectively avoiding potential failure risks caused by suboptimal equipment status.

[0038] Step four involves initiating sample library outbound control based on a spatiotemporal constraint token and retrieving on-site motion data. Token verification and trajectory compensation are then used to continuously compare and correct anomalies in the on-site motion data to obtain a transport execution log reflecting the actual sample transport process and its time-bound health. It should be understood that after issuing a unique token with spatiotemporal constraints, the scheduling plan transitions from purely numerical deduction to a preparatory stage that will impact the physical world. However, an unpredictable gap exists between planning and actual execution. Minor vibrations of the robotic arm, unexpected dust on the track causing additional friction, and even momentary voltage fluctuations in the drive motor can all cause deviations between the actual trajectory and the preset path, thus wasting valuable time reserved for sample biological activity. Therefore, this step transforms the digital token into controlled physical action and establishes a real-time dynamic calibration and recording system throughout the entire transport process, providing tamper-proof evidence for subsequent handover confirmation and end-to-end quality control.

[0039] Figure 4 This is a flowchart of step four in the automated linkage execution method for gene detection process control and sample bank management according to an embodiment of this application. Figure 4 As shown, in an embodiment achievable in this application, step four includes: step four-one, performing encryption signature legality stripping and priority level decoding on the spatiotemporal constraint token to obtain the physical trigger command stream; step four-two, performing dynamic gain correction based on real-time time limit constraints on the physical trigger command stream and the field motion data to obtain the speed gain adjustment factor; step four-three, performing mechanical trajectory smoothing compensation control on the speed gain adjustment factor and the field motion data, and performing multi-dimensional data aggregation and storage at the stream processing level to obtain the transfer execution log.

[0040] The execution process is detailed as follows: First, the spatiotemporal constraint token undergoes cryptographic signature verification and priority level decoding. The warehouse controller, deployed at the exit of the automated sample library—a high-performance programmable logic controller (PLC)—does not immediately execute the token after receiving it from the central control unit via the industrial bus. It first activates its built-in cryptographic verification module. Using a pre-configured public key paired with the central control unit's private key, it decrypts and verifies the digital signature attached to the token. This step verifies that the token originates from the central control unit and that the token's content has not been tampered with during transmission. Simultaneously, the controller verifies the unique task random number SESSION-A8E4-11EE-B962-0242AC120002 within the token to ensure that the task is currently active and valid, thus preventing any form of replay attack. Only after successful dual verification does the controller trust the token and begin decoding the plaintext instructions it contains. It extracts key execution parameters, such as the target workstation coordinates (X:1500, Y:3500, Z:900) and potential outbound priorities like "High". Based on these high-level instructions, the motion planning engine inside the warehouse controller translates them into a series of specific motor drive instructions that the underlying execution units can understand and execute, such as driving the robotic arm to grasp a specified sample and planning a vector sequence of motion paths. As the instruction set is generated, the controller synchronously sends an electrical signal to unlock the electromagnetic interlock of the physical access gate of the sample warehouse and activate the initial path planning algorithm of the robot responsible for transfer (such as an AGV or a multi-axis robotic arm). Finally, this serialized instruction data packet, containing precise path vectors, initial speed expectations, and motor control parameters, is formally encapsulated into a physical trigger instruction stream and sent to the lowest-level servo driver, ready to initiate physical actions.

[0041] Next, dynamic gain correction based on real-time time constraints is applied to the physical trigger command stream and the on-site motion data. The on-site motion data is a stream of physical world state information collected in real time by various sensors installed on the transfer robot or track and transmitted back to the monitoring unit. These data sources are diverse, with core components including: high-precision rotary encoders installed at each joint of the robotic arm, capable of reporting real-time position coordinates with sub-millimeter accuracy; and multiple photoelectric switches or position verification points deployed along the predetermined track, triggering a precise timestamp event each time the sample passes a point. In this step, a dynamic, closed-loop feedback control logic is activated. After issuing the initial physical trigger command stream, the monitoring unit begins continuously ingesting on-site motion data at millisecond-level frequencies. It extracts the current position fed back by the robotic arm encoders in real time and calculates the real-time remaining path length of the sample from the target workstation through vector operations. Simultaneously, it identifies the planned nominal running speed from the initial physical trigger command stream and reads the absolute, insurmountable latest arrival time limit from the spatiotemporal constraint token, i.e., the token's expiration time. Based on this real-time data, a predictive dynamic feedback algorithm continuously runs. Its core logic is to predict the final arrival time of the sample at the target workstation if it continues to move in its current state, using the current actual position, remaining path length, and nominal running speed. If this predicted arrival time exceeds the token's expiration time due to physical disturbances such as slight slippage or increased load, a correction mechanism intervenes. At this point, a speed gain adjustment factor needs to be calculated to dynamically increase the motor's execution frequency, thereby offsetting the physical delay that has occurred. The formula for calculating this factor is as follows:

[0042] in, This is the calculated speed gain adjustment factor, which is a dimensionless multiplier. If its value is greater than 1, it means that acceleration is required; if it is less than 1, deceleration is required. It is the real-time remaining path length calculated from on-site motion data, in meters. It is the token's real-time remaining validity period, obtained by subtracting the current system timestamp from the token's absolute expiration timestamp, in seconds. This is the time window within which the sample must complete the remaining journey. This is the nominal operating speed set in the physical trigger command stream, measured in meters per second. The denominator represents the theoretical maximum distance the robot can travel within the remaining time window if it operates at the nominal speed. The numerator is the actual distance that needs to be traveled. The ratio of the two precisely quantifies the rate at which the speed needs to be adjusted. For example, during transport, the monitoring unit calculates the remaining path... The distance is 1.5 meters, and the remaining validity time of the token. Only 5 seconds, nominal speed The speed is 0.25 m / s. Theoretically, the maximum distance that can be covered in the remaining time is 5s × 0.25 m / s = 1.25 m. Since 1.5 m > 1.25 m, it's clear that continuing at the original speed will exceed the time limit. Substituting into the formula... =1.5m / (5s×0.25m / s)=1.2. This calculated speed gain adjustment factor of 1.2 will be issued as an instruction, meaning that the current motor speed needs to be increased by 20% to ensure timely arrival before the token expires.

[0043] Finally, mechanical trajectory smoothing compensation control is performed on the speed gain adjustment factor and on-site motion data, and multi-dimensional data aggregation and storage are carried out at the stream processing level. The calculated speed gain adjustment factor of 1.2 is immediately applied to the servo drive of the transfer robot. After receiving this gain multiplier, the servo drive adjusts the pulse frequency output to the motor in real time, achieving online speed correction of the physical trajectory while ensuring smooth motion and avoiding impact. This monitoring-calculation-correction closed loop continues at a very high frequency until the sample reaches the destination. Throughout the correction process, all relevant state data are continuously recorded. The monitoring unit captures and stores the absolute timestamp of each track verification point (e.g., photoelectric switch) the sample passes, the real-time position feedback from the encoder, the deviation between the instantaneous speed and the nominal speed, and the speed gain adjustment factor and the real-time remaining lifetime of the token calculated at each time point. These multi-dimensional data streams are aggregated and solidified in real time according to the time series using a structured log model (e.g., writing to a time-series database or generating a JSON log stream). The entire transfer process ends the moment the sample finally enters the target station buffer area and triggers the destination sensor. At this point, the system performs a final consistency check on the motion data throughout the entire process, confirming that it is within the time limit constraints of the token and that the final location is accurate. Subsequently, the high-fidelity serialized data recorded throughout the process, including all timestamps, locations, speed deviations, and correction instructions, is encapsulated and generated into a complete and tamper-proof electronic record—the transport execution log. It meticulously reproduces every physical detail of the sample's journey from the warehouse to its arrival at the workstation, serving as the gold standard for evaluating the quality and timeliness of the transport process.

[0044] Step five involves confirming the handover of the receiving result at the target workstation based on the transfer execution log and spatiotemporal constraint token. The transfer execution log, sample execution profile, and actual response time of the target workstation are then linked and written into the sample database record to form the sample database record. In other words, after the sample safely arrives at the target workstation, the entire automated linkage process reaches its final and crucial closed-loop point. Physical arrival merely represents the end of one displacement, but for a rigorous, traceable, end-to-end quality control system, are these arrivals effectively confirmed by the workstation? How does the actual time taken for the entire process deviate from the initial bioactivity budget? When can the valuable workstation resources locked for this task be safely released to serve the next sample? Without a standardized process for final handover confirmation, resource release, and information archiving, all the previous precise calculations and dynamic controls will lose their long-term value due to the lack of final summary and solidification. Therefore, this step triggers the cancellation of the token and the release of resources through the final verification of the arrival event, and associates, compresses and permanently stores all the key data throughout the process, ultimately forming an immutable, high-dimensional sample database record.

[0045] Figure 5 This is a flowchart of step five in the automated linkage execution method for gene detection process control and sample bank management according to an embodiment of this application. Figure 5 As shown, in an implementation of this application, step five includes: Step five-one, based on the endpoint physical pulse matching benchmark value and the original expected constant of the flow time, extracting the last sensor action and quantizing the extreme value drift of the actual environment exposure in the transfer execution log to obtain the handover verification features; Step five-two, based on resource cycle slicing, performing forced cancellation of credential validity and deep stripping of target position exclusive mutual exclusion permissions on the handover verification features and spatiotemporal constraint tokens to generate a system resource release trigger in the form of an instruction set to guide the queue update and turnover; Step five-three, performing data dimensionality reduction and packaging of the system resource release trigger, transfer execution log and sample execution profile with associated fingerprint fixation and ultra-high-dimensional feature attributes to form a sample library record.

[0046] The execution process is detailed as follows: First, based on the endpoint physical pulse matching benchmark value and the expected constant of the original flow time, the transfer execution log is subjected to final sensor action extraction and extreme value drift quantification of actual environmental exposure. The core task of this step is to perform final analysis and quantitative evaluation of the transfer execution log generated in the previous step to confirm the fact of successful arrival. The endpoint physical pulse matching benchmark value is a unique sensor trigger signal preset for the sample receiving buffer of each workstation during the hardware configuration stage of the automation system. For example, when the sample falls precisely into the slot of the workstation (X:1500, Y:3500, Z:900), the fiber optic sensor at its bottom will generate a specific high-level pulse signal, and this signal pattern is defined as the benchmark value of that workstation. The expected constant of the original flow time is the transfer baseline time extracted from the process topology table and fixed in the sample execution profile in step one, which is 25 seconds in this embodiment. In this step, the processing unit scans the end of the transport execution log, searching for sensor event records that perfectly match the benchmark value, and extracts the absolute timestamp of the event. This timestamp is confirmed as the actual arrival time of the sample. Simultaneously, the actual outbound time is extracted from the beginning of the log. The difference between these two timestamps calculates the actual transport time; for example, due to dynamic speed compensation during transport, the actual time is 24.8 seconds. Next, the extreme value drift quantification calculation for actual environmental exposure is performed. This includes two levels of quantification: First, the transport time drift is calculated, i.e., the difference between the actual transport time of 24.8 seconds and the originally planned constant transport time of 25 seconds, resulting in -0.2 seconds, indicating that the transport was 0.2 seconds ahead of schedule. Second, the total exposure time is calculated, i.e., the sum of the actual transport time and the predicted waiting time of 0 seconds in the workstation readiness vector, totaling 24.8 seconds. This total duration represents the entire ambient temperature exposure time from the sample leaving the cryogenic environment to being received by the workstation. Finally, this set of structured data, including the successful matching of the received confirmation signal, the actual arrival time, the transfer time drift of -0.2 seconds, and the total environmental exposure time of 24.8 seconds, is encapsulated into a new data object, namely the handover verification feature.

[0047] After successfully generating the handover verification feature that marks the physical end of the task, the process immediately moves to the second stage: based on the resource cycle slice, the handover verification feature and the spatiotemporal constraint token undergo forced cancellation of credential validity and deep stripping of exclusive mutual exclusion permissions for the target bit. A resource cycle slice is not a physical entity, but rather refers to the trigger point of a logical event. In this scenario, the successful generation of the handover verification feature is defined as the end slice of the current task's resource occupation cycle. This event immediately triggers the subsequent resource cleanup and state transition process. First, the central control unit, based on the unique task random number SESSION-A8E4-11EE-B962-0242AC120002 in the spatiotemporal constraint token, forcibly updates the session's state from ACTIVE to COMPLETED in its internal session management table and records the completion time. This operation, called forced cancellation of credential validity, makes the spatiotemporal constraint token logically invalid immediately, preventing it from being used again for authorizing any instructions and ensuring the uniqueness of the task. Next, the control unit retrieves the target workstation identifier PLC-LIQ-02-Station associated with the session and accesses the central resource scheduling and allocation table operated on in step three. It changes the workstation's status from LOCKED to IDLE, an operation known as deep stripping of the target position's exclusive mutual exclusion permission. This means that from this moment on, the workstation can be reassigned by the scheduling system to other queued sample tasks. These two actions—deregistering the token and unlocking the workstation—are packaged into a set of internal control instructions, forming a signal called a system resource release trigger. This trigger immediately broadcasts to the scheduling queue management module, notifying it that it can begin evaluating and planning the next task entering the workstation, thereby ensuring the smooth and efficient turnaround of the entire automated pipeline.

[0048] The final step involves fixing the correlation fingerprints of the system resource release triggers, transfer execution logs, and sample execution profiles, and packaging and writing the data into a sample library record by reducing the dimensionality of ultra-high-dimensional feature attributes. After the system resource release triggers a signal, signifying the completion of resource-level cleanup, data archiving officially begins. First, a key indicator generated during the handover process needs to be captured: the actual response time of the target workstation. The starting point for this measurement is the actual arrival time recorded in the handover verification features, and the ending point is the moment when the PLC of the target workstation PLC-LIQ-02-Station first sends back a signal that it has begun performing operations on the sample, such as the mechanical gripper closing or the barcode scanner starting. The time difference between these two moments is the response time, for example, 0.5 seconds. Next, correlation fingerprint fixing is performed. The processing unit gathers three core data objects from the entire lifecycle of this task: the original sample execution profile, which defines the sample attributes and the target; the transfer execution log, which records all the motion details of the physical world; and the just-captured actual response time of the target workstation. The unique identifiers or core content of these three data objects are concatenated and a unique, fixed-length association fingerprint is generated using a strong hash function such as SHA-256. This fingerprint ensures that these three logically related data from different sources are permanently and indivisibly bound together. Subsequently, the data is packaged and written after dimensionality reduction of the ultra-high-dimensional feature attributes. Considering that the original transport execution log may contain tens of thousands of millisecond-level sensor readings, direct storage is both space-consuming and inefficient for fast retrieval. Therefore, a dimensionality reduction program is invoked, which extracts a series of key statistical and process indicators from the original log, such as average running speed, maximum speed deviation, number of speed gain adjustment factor triggers, and overall run smoothness variance, using these limited key indicators to replace the massive original data stream. Finally, the dimensionality-reduced and compressed transport log summary, the complete sample execution profile, the actual response time of the target workstation, and the unique association fingerprint are packaged together into a structured, information-rich final record. This record is written into the sample database and linked to the sample's unique barcode SA20260325-001 as a primary key, thus formally forming a permanent sample database record that can be audited and analyzed at any time.

[0049] In summary, the automated linkage execution method for gene detection process control and sample bank management based on the embodiments of this application is elucidated. It deeply binds the biological time-sensitivity characteristics of the sample itself with the physical state of the automated pipeline, constructing a predictive closed-loop scheduling mode. First, it generates a sample execution profile for each sample, containing information such as the allowed exposure duration and target path, serving as a spatiotemporal constraint benchmark for its entire lifecycle. Simultaneously, it assesses the acceptance capacity of downstream target workstations in real time, forming a workstation readiness vector. Crucially, a unique spatiotemporal constraint token is issued only when the time limit requirements in the sample profile fully cover the estimated transport and waiting time, with a safety margin, driving the sample out of the bank. This mechanism upgrades traditional command-driven processes to token authorization, shifting from post-event traceability to pre-event prediction. It fundamentally eliminates the risk of samples falling into a blind waiting state due to downstream congestion or equipment malfunction, ensuring that the transfer is completed within the bioactivity safety window and solving the technical problem of sample degradation caused by spatiotemporal resource mismatch.

Claims

1. An automated, interconnected method for gene detection process control and sample bank management, characterized in that, include: Step 1: Based on the biological stability mapping rules and process topology table, the obtained initial sample metadata is parsed, converted and path matched to determine the maximum allowable exposure time, target workstation coordinates, transfer baseline time and sample identifier binding relationship corresponding to the sample barcode, so as to obtain the sample execution profile; Step 2: Based on the sample execution profile, retrieve the pipeline status data corresponding to the target workstation, and perform time-series calculation and acceptance analysis on the pipeline status data to obtain the expected release time, predicted waiting time and health status combination results of the target workstation, and generate a workstation readiness vector that represents the workstation's acceptance capacity. Step 3: Issue spatiotemporal constraint tokens based on safety margin judgment to the sample execution profile and workstation ready vector to obtain spatiotemporal constraint tokens that drive the collaborative execution of the sample library and the detection pipeline. Step 4: Based on the spatiotemporal constraint token, start the sample library outbound control and retrieve the on-site motion data. Then, use token verification and trajectory compensation to continuously compare and correct anomalies in the on-site motion data to obtain a transfer execution log that reflects the actual sample transfer process and time limit health. Step 5: Based on the transfer execution log and the spatiotemporal constraint token, confirm the handover of the receiving result at the target workstation, and associate the transfer execution log, sample execution profile, and the actual response time of the target workstation into the sample database record to form the sample database record.

2. The automated linkage execution method for gene detection process control and sample bank management according to claim 1, characterized in that, Step one includes: The initial sample metadata is processed by field-level segmentation and core element extraction to obtain the sample basic identifier package; Thermodynamic numerical deduction and calculation were performed on the basic identification package of the sample and the biological stability mapping rule to obtain close-up data of the sample time limit with additional maximum allowable time limit parameter; Spatial features are extracted and fused from the sample time-limit close-up data and process topology table to obtain the sample execution profile.

3. The automated linkage execution method for gene detection process control and sample bank management according to claim 2, characterized in that, Thermodynamic numerical derivation calculations are performed on the sample basic identifier package and biological stability mapping rules, including: thermodynamic numerical derivation calculations using the following formula, wherein the formula is: ;in, For the maximum permissible exposure time, Based on the baseline half-life, is the base of the natural logarithm. For temperature sensitivity coefficient, The current workshop ambient temperature, Standard cold storage temperature, This provides a safe buffer time for physical transfer.

4. The automated linkage execution method for gene detection process control and sample bank management according to claim 1, characterized in that, Step two includes: By using the underlying hardware address index of industrial equipment, the target workstation load status is parsed from the sample execution profile and pipeline status data to obtain the real-time load image of the target workstation that reflects the real-time execution pressure of the physical workstation. Based on the thermal loss frequency reduction compensation coefficient and the standard instruction cycle time, the expected release time of the target workstation real-time load image is estimated based on the task residual degree to obtain the workstation available timestamps that the equipment can be released in the future as the expected release time. The motion prediction arrival time of the sample is compared with the available timestamp of the workstation by performing motion profiling and workstation availability time interfering with the workstation availability limit to obtain the predicted waiting time; The predicted waiting time and sample execution profile are subjected to parallel up-dimensional splicing of delay attributes and equipment health alarm baseline to obtain the workstation readiness vector.

5. The automated linkage execution method for gene detection process control and sample bank management according to claim 1, characterized in that, Step three includes: Global planning time deduction and ultimate lifespan intervention assessment are performed on the sample execution profile and workstation readiness vector to obtain a safety margin judgment state indicating the current scheduling risk level and remaining survival capacity; Virtual logical slot locking and structured anti-tampering encapsulation preprocessing are performed on the security margin discrimination status and sample execution profile to obtain the hash resource metadata of the embedded session feature mark; A time-space constrained token is obtained by asymmetric instantiation and issuance of hash resource metadata and sample execution profiles using an asymmetric encryption algorithm and a signing private key.

6. The automated linkage execution method for gene detection process control and sample bank management according to claim 1, characterized in that, Step three includes: The sample execution profile and workstation readiness vector are evaluated based on normal distribution and confidence level to obtain a safety margin judgment state indicating the current scheduling risk level and remaining survivability. Virtual logical slot locking and structured anti-tampering encapsulation preprocessing are performed on the security margin discrimination status and sample execution profile to obtain the hash resource metadata of the embedded session feature mark; A time-space constrained token is obtained by asymmetric instantiation and issuance of hash resource metadata and sample execution profiles using an asymmetric encryption algorithm and a signing private key.

7. The automated linkage execution method for gene detection process control and sample bank management according to claim 1, characterized in that, The sample execution profile and workstation readiness vector are evaluated based on normal distribution and confidence level to obtain a safety margin judgment state indicating the current scheduling risk level and remaining survivability, including: Based on historical operational data, establish normal distributions for actual transfer time and actual waiting time; The total exposure time was obtained based on the normal distribution of actual transfer time and the normal distribution of actual waiting time; The probability condition for risk control of total exposure time is determined to obtain the safety margin discrimination state.

8. The automated linkage execution method for gene detection process control and sample bank management according to claim 1, characterized in that, Step four includes: The spatiotemporal constraint token is subjected to cryptographic signature verification and priority level decoding to obtain the physical trigger instruction stream; The velocity gain adjustment factor is obtained by performing dynamic gain correction based on real-time time limit constraints on the physical trigger command stream and field motion data. The speed gain adjustment factor and on-site motion data are subjected to mechanical trajectory smoothing compensation control, and multi-dimensional data collection and storage at the stream processing level are performed to obtain the transfer execution log.

9. The automated linkage execution method for gene detection process control and sample bank management according to claim 1, characterized in that, Step five includes: Based on the endpoint physical pulse matching benchmark value and the expected constant of the original flow time, the last sensor action extraction and the extreme value drift quantification of the actual environmental exposure of the transfer execution log are performed to obtain the handover verification characteristics. Based on resource cycle slicing, the handover verification features and spatiotemporal constraint tokens are subject to forced cancellation of credential validity and deep stripping of exclusive mutual exclusion permissions of target bits to generate a set of instructions to guide the release trigger of system resources for queue update and turnover. The data of system resource release triggers, transfer execution logs and sample execution profiles are associated with fingerprint fixation and ultra-high-dimensional feature attributes are packaged and written to form a sample library record.

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