Quality control and sample tracking management method for whole process of gene detection

Abstract

The application discloses a quality control and sample tracking management method for a whole process of gene detection, and relates to the field of sample tracking management. First, the reagent flow parameter is converted into a digital activity vector by using the attenuation integral technology. Then, a collaborative feature matrix coupled across dimensions is constructed by fusing the features of the activity vector and the library quality control data. On this basis, the calibration instruction set executable by the device is reversely solved based on the precision loss gradient and combined with the hardware parameter mapping table, so that the sequencer can automatically adjust the underlying operation parameters according to the real-time performance of the reagent. Finally, the quality control tracking report is generated through the whole-link correlation audit, the whole-process transparency control from the underlying biochemical fluctuation to the macro quality state is realized, and the sequencing deviation caused by the dynamic biochemical attenuation is fundamentally eliminated.

Description

Technical Field

[0001] This application relates to the field of sample tracking and management, and more specifically, to a quality control and sample tracking and management method for the entire process of gene testing. Background Technology

[0002] With the industrial-scale popularization of high-throughput sequencing technology, gene testing has shifted from laboratory research to large-scale, high-throughput clinical and scientific applications. This necessitates rigorous quality monitoring and sample tracking across the entire process, from reagent warehousing and library construction to sequencing. Developing a comprehensive quality control solution aims to eliminate management blind spots between production stages through digital means, enabling real-time monitoring of microscopic changes in the biochemical reaction environment, and ensuring the integrity of biological information and the reliability of sequencing output data for each sample during complex transit processes. This provides irreplaceable support for reducing testing costs and enhancing the scientific rigor of precision medicine decision-making.

[0003] However, existing gene sequencing quality management solutions often focus on segmented, static monitoring, which shows significant limitations in addressing reagent degradation and sequencing quality mismatch across time and space. On one hand, the gene sequencing industry commonly suffers from data silos, with reagent management systems (recording expiration dates and batches) and sequencing analysis servers (recording quality scores and cluster densities) in a state of both physical and logical isolation. This makes it impossible for management to predict the specific impact of reagent status on the execution results. On the other hand, existing quality control methods largely rely on static quality checks after library construction, such as judging pass / fail status solely by library concentration or fragment peak values. This approach cannot characterize dynamic chain elongation rate changes caused by decreased enzyme activity in reagents, increased cumulative time spent unsealed, or frequent freeze-thaw cycles. Due to the lack of a digital mirror model of reagent activity loss, sequencing equipment assumes that all biochemical components involved in the reaction are in their nominal optimal activity state when receiving samples. This leads to uncompensated deviations between the sequencing algorithm and the actual biochemical reaction, thereby inducing increased off-target noise or decreased sequencing accuracy. This disruption in the feedback chain from reagent status to hardware execution parameters means that existing tracking solutions can only perform post-event tracing, and cannot achieve pre-event warnings and in-event calibration based on changes in reagent efficacy, which severely restricts the deep integration of automation and intelligence in the entire gene testing process.

[0004] Therefore, it is desirable to provide a quality control solution that can deeply integrate reagent dynamic activity characterization and real-time hardware parameter calibration, so as to achieve accurate quality pre-control and transparent, digital sample tracking management covering the entire process. Summary of the Invention

[0005] To address the aforementioned problems in the existing technology, according to one aspect of this application, a method for quality control and sample tracking management throughout the entire gene testing process is provided, comprising: Step 1: Perform attenuation integral and normalization characterization on the raw reagent stream, which includes reagent batches, cumulative opening time, storage temperature sequence and number of freeze-thaw cycles, to obtain a reagent activity vector that reflects the current state of biochemical efficiency loss. Step 2: Perform time-series alignment and matrix fusion on the reagent activity vector and library quality control data to obtain a collaborative feature matrix characterizing the coupling relationship between reagent state and library quality. The library quality control data includes library concentration, fragment peak value, and linker ligation efficiency. Step 3: Perform loss gradient quantization on the cooperating feature matrix to calculate the degree of imbalance between the target sequencing efficiency and the standard sequencing efficiency, and obtain the accuracy loss gradient that characterizes the strength of the influence of reagent attenuation on sequencing accuracy. Step 4: Based on the hardware parameter mapping table, the accuracy loss gradient is solved in reverse and compensated to obtain a set of calibration instructions that can be directly executed by the sequencing equipment. Step 5: Send the calibration instruction set to the sequencer and collect the compensated quality indicators. Then, perform correlation auditing and tracking summary on the reagent activity vector, library quality control data and co-operation feature matrix to obtain a quality control tracking report covering the sample flow link and quality status changes.

[0006] Compared with existing technologies, this application provides a quality control and sample tracking management method for the entire gene sequencing process, aiming to solve the challenges of reagent attenuation and algorithm mismatch in the gene sequencing process. First, attenuation integral technology is used to convert reagent flow parameters into digital activity vectors, solving the problem of the inability to quantitatively model biochemical losses. Then, by fusing the activity vectors with library quality control data, the silos between reagent management and quality monitoring are broken down, constructing a cross-dimensional coupled collaborative feature matrix. Based on this, a calibration instruction set executable by the device is inversely solved using the precision loss gradient and a hardware parameter mapping table, enabling the sequencer to automatically adjust its underlying operating parameters according to the real-time performance of the reagents, transforming traditional passive quality inspection into an active, dynamic self-compensating mode. Finally, a quality control tracking report is generated through end-to-end correlation auditing, achieving full transparency and control from underlying biochemical fluctuations to macroscopic quality status, fundamentally eliminating sequencing bias caused by dynamic biochemical attenuation. 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. The drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the embodiments of this application to explain this application and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0008] Figure 1This is a flowchart of a quality control and sample tracking management method for the entire gene testing process according to an embodiment of this application.

[0009] Figure 2 This is a schematic diagram of the data flow in a quality control and sample tracking management method for the entire gene testing process according to an embodiment of this application.

[0010] Figure 3 This is a flowchart of step 1 in the quality control and sample tracking management method for the entire gene testing process according to an embodiment of this application.

[0011] Figure 4 This is a schematic diagram of the data flow in step 3 of the quality control and sample tracking management method for the entire gene testing process according to an embodiment of this application.

[0012] Figure 5 This is a flowchart of step 5 in the quality control and sample tracking management method for the entire gene testing process according to an embodiment of this application. Detailed Implementation

[0013] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0014] To address the problems mentioned in the background section, this application proposes a quality control and sample tracking management method for the entire gene testing process. Figure 1 This is a flowchart of a quality control and sample tracking management method for the entire gene testing process according to an embodiment of this application. Figure 2 This is a schematic diagram of the data flow in a quality control and sample tracking management method for the entire gene testing process according to an embodiment of this application. Figure 1 and Figure 2As shown, the quality control and sample tracking management method for the entire gene detection process according to an embodiment of this application includes: Step 1, performing attenuation integral and normalization characterization on the raw reagent stream containing reagent batches, cumulative opening time, storage temperature sequence, and freeze-thaw cycles to obtain a reagent activity vector reflecting the current biochemical efficiency loss state; Step 2, performing time-series alignment and matrix fusion on the reagent activity vector and library quality control data to obtain a collaborative feature matrix characterizing the coupling relationship between reagent state and library quality, wherein the library quality control data includes library concentration, fragment peak value, and adapter ligation efficiency; Step 3, performing time-series alignment and matrix fusion on the collaborative feature vector and library quality control data; The feature matrix is ​​quantized for loss gradient to calculate the degree of imbalance between the target sequencing efficiency and the standard sequencing efficiency, and the accuracy loss gradient, which characterizes the strength of the impact of reagent attenuation on sequencing accuracy, is obtained. Step 4: Based on the hardware parameter mapping table, the accuracy loss gradient is solved in reverse and compensated to obtain a calibration instruction set that can be directly executed by the sequencing equipment. Step 5: The calibration instruction set is sent to the sequencer and the compensated quality indicators are collected. The reagent activity vector, library quality control data and co-feature matrix are correlated, audited and tracked to obtain a quality control tracking report covering the sample flow link and quality status changes.

[0015] In step 1, the raw reagent stream, including reagent batches, cumulative opening time, storage temperature sequence, and freeze-thaw cycles, is characterized by decay integral and normalization to obtain a reagent activity vector reflecting the current state of biochemical performance loss. It should be understood that in the industrial production environment of high-throughput sequencing, reagents, as the core carriers of biochemical reactions, directly determine the accuracy of base recognition and the stability of signal generation based on their activity state. Traditional quality management models often treat reagents as static inventory items, only recording batch numbers and expiration dates, neglecting the microscopic biochemical degradation caused by frequent reagent entry and exit, multiple openings, and environmental temperature fluctuations during actual flow. This degradation is insidious and irreversible; without quantitative characterization, sequencing equipment cannot perceive the true activity loss of reagents, leading to imbalances in chain extension rates or increased off-target noise during sequencing. Step 1 aims to construct a digital mirror that maps the biochemical state of the physical world. By transforming discrete flow records into structured performance indicators, it provides a crucial decision-making benchmark for subsequent dynamic calibration of hardware parameters, thereby eliminating sequencing accuracy fluctuations caused by unclear reagent states.

[0016] Figure 3 This is a flowchart of step 1 in the quality control and sample tracking management method for the entire gene testing process according to an embodiment of this application. Figure 3As shown, in one possible implementation, step 1 includes: step 11, performing structured field decomposition on the original reagent stream to obtain a subset of thermal time parameters containing the cumulative duration of opening the lid and the storage temperature sequence, and a subset of discrete events containing the number of freeze-thaw cycles; step 12, performing environmental stress-induced thermal time nonlinear decay integral on the subset of thermal time parameters to obtain a thermal time decay scalar; step 13, performing discrete mechanical penalty fusion and high-dimensional tensor mapping on the thermal time decay scalar and the subset of discrete events to obtain a reagent activity vector.

[0017] The processing steps are as follows: First, it is necessary to obtain the raw reagent stream, which contains the entire lifecycle history of the reagent from warehousing to application. This raw reagent stream is collected in real-time via sensor links integrated into the cold chain equipment and pipetting workstations. Its data structure is an unstructured log stream, containing the reagent's unique identification code, batch attributes, the specific timestamp of each opening, the real-time temperature monitoring sequence of each storage slice, and discrete freeze-thaw cycles. Data masking and structured field decomposition are performed. Using regular expressions and a pre-defined data dictionary, these raw logs are physically decoupled and separated, extracting fields representing continuous environmental variables, such as the cumulative duration of opening and the corresponding storage temperature sequence, and encapsulating them into a subset of thermal time parameters reflecting dynamic environmental stress. Simultaneously, fields representing physically destructive events, such as the number of freeze-thaw cycles and the initial nominal active capacity of the reagent, are extracted and packaged and output as a discrete event subset. For example, if the original reagent flow record of a batch of DNA polymerase shows three key periods of heat exposure during its use: the first exposure was for 2.0 hours at room temperature (25°C), the second for 0.5 hours at refrigerated conditions (4°C), and the third for 0.2 hours at a cryogenic operating table (-10°C). Furthermore, if the reagent underwent two freeze-thaw cycles, the above disassembly process will extract the corresponding duration, temperature sequence, and discrete number of freeze-thaw cycles.

[0018] Subsequently, the aforementioned subset of thermal time parameters is received, and the recorded discrete time segments and their corresponding transient storage temperatures are read. Based on the deviation of the actual temperature from the ideal temperature of standard sequencing, the dynamic degradation rate under each time slice is solved using environmental stress-induced thermal nonlinear decay integral logic. During the processing, considering the biochemical characteristic that enzyme activity decreases exponentially with increasing temperature, a thermal decay calculation formula is adopted, the specific form of which is:

[0019] In this formula, This represents the final thermal decay scalar. Represents the duration of the k-th time slice, in hours. Set a preset safety reference temperature, for example, -20.0 degrees Celsius. This represents the instantaneous monitored temperature within that time slice. The thermosensitivity index reflects the sensitivity of a specific reagent component to temperature fluctuations. Its value is obtained by fitting the Arrhenius equation to previous accelerated aging experiments, for example, for a certain high-fidelity polymerase. It is 0.085. In this example, =3, calculated by substituting specific values: =[2.0×exp(0.085×(25-(-20)))]+[0.5×exp(0.085×(4-(-20)))]+[0.2×exp(0.085×(-10-(-20)))]=95.98. By amplifying the destructive impact of high-temperature environments on reagent activity through an exponential term and discretely summing the weighted losses across all exposure periods, the complex temperature control curve is reduced in dimension and converged to a scalar value of approximately 96, quantitatively characterizing the degree of irreversible degradation of biochemical molecules due to thermodynamic instability.

[0020] Based on this, the calculated thermal decay scalar and discrete event subset are read concurrently. The discrete event subset contains the number of reagent freeze-thaw cycles. and the initial nominal active capacity constant As a crucial input for discrete mechanical penalty fusion, the mechanical breakage of enzyme protein spatial structures caused by repeated expansion and contraction of microscopic ice crystals differs from thermodynamic degradation and requires the introduction of specific penalty operators. By performing high-dimensional tensor mapping, the relative thermal decay obtained from the pre-integration is jointly reduced and calculated with the physical freeze-thaw damage ratio, and then normalized to a standardized range to ultimately generate the reagent activity vector. The specific calculation formula is as follows:

[0021] Analysis of the above formula reveals that the vector consists of three dimensions. The first dimension reflects the proportion of remaining reagent activity purely due to thermal stress, where... The first dimension represents the initial nominal activity capacity constant of the reagent at the time of manufacture, for example, set to 1200 units. The second dimension reflects the retention rate of mechanical damage caused by freeze-thaw cycles. The discrete penalty coefficient is set between 0.1 and 0.2; in this example, we take [value missing]. A value of 0.1 reflects the average probability of disruption to the protein's tertiary structure during each freeze-thaw cycle. The third dimension is the cross-coupling loss term, used to characterize the synergistic negative effects of thermal degradation and physical damage in biochemical reactions. The coupling weighting factor is obtained by collecting a large amount of historical quality control data and training a multiple linear regression model. In this example... Approximately 0.00084, used to balance the strength of the interaction between the two stress sources. If the calculated... for This indicates that although the reagent maintains good thermal stability, frequent freeze-thaw cycles significantly reduce its overall performance.

[0022] In a possible preferred embodiment, step 1 includes: decomposing the original reagent stream into structured fields to obtain a subset of thermal time parameters containing the cumulative duration of opening the lid and the storage temperature sequence, and a subset of discrete events containing the number of freeze-thaw cycles; performing state-iteration-based autocatalytic decay on the subset of thermal time parameters to obtain a thermal time decay scalar; and performing discrete mechanical penalty fusion and high-dimensional tensor mapping on the thermal time decay scalar and the subset of discrete events to obtain a reagent activity vector. Since the implementations of the first and third steps have been explained above, they will not be repeated here. Instead, the focus will be on the specific implementation of performing state-iteration-based autocatalytic decay on the subset of thermal time parameters to obtain the thermal time decay scalar.

[0023] It is understandable that one of the core objectives of quality monitoring throughout the entire lifecycle of gene testing reagents is to accurately digitally characterize the degradation state of their biochemical activity. A basic approach is to obtain a quantified thermal degradation scalar by integrating the environmental stress-induced thermal nonlinear degradation of a subset of thermal parameters. This scalar is not only an abstract summary of the reagent's historical experiences but also the logical starting point of the entire quality pre-control and compensation chain. The accuracy of its modeling directly determines the success or failure of subsequent dynamic calibration of hardware parameters. However, when a simplified model of linearly accumulating damage stages is adopted, its underlying logic essentially assumes that the molecular damage caused by thermal stress in each time period is isolated and unaffected by each other, and the final total damage is merely the arithmetic summation of these independent damage events. But this assumption has significant simplification biases in the complex field of biochemistry, especially for structure-sensitive macromolecules such as enzymes and proteins. The actual degradation process often exhibits strong path-dependent and state-dependent characteristics. Therefore, even if a reagent that has undergone severe high-temperature shock is subsequently rapidly cooled to the standard storage temperature, the three-dimensional conformation of some of its proteins may have already undergone irreversible denaturation. Such historical trauma leads to a decrease in overall thermodynamic stability, causing a sharp decline in tolerance to subsequent small temperature fluctuations and exhibiting a faster degradation rate—something linear models cannot capture. Furthermore, linear summative models implicitly ignore potential autocatalytic or saturation effects during the damage process. In some cases, denatured protein molecules can disrupt the stability of the microenvironment, acting as a catalyst to accelerate the denaturation of surrounding normal protein molecules, resulting in a snowballing decay—something simple summative models also fail to reflect. Therefore, to overcome the inherent limitations of linear summative models and more realistically simulate the complex degradation kinetics of biomolecules such as enzymes and proteins, this application preferably employs a state-iterative autocatalytic decay process on a subset of thermal parameters to obtain a thermal decay scalar. This approach introduces a mechanism capable of remembering historical damage states and dynamically adjusting the accumulation rate of subsequent damage based on the currently accumulated damage level. This process provides a deeper understanding of the reagent's true activity state, distinguishing subtle differences between reagents with the same total exposure time but vastly different temperature change paths, thereby significantly improving the scene resolution and physical realism of the quality assessment model.

[0024] The specific implementation of this approach involves constructing the calculation of the thermal decay scalar as an iterative and dynamic process, rather than a one-time static integration. In each discrete time step, the system calculates and adds the new damage generated in the current time step based on the current environmental stress (i.e., temperature and duration) and the total damage accumulated up to the previous time step. Its iterative expression is shown below:

[0025] Analysis of this formula reveals that it incorporates the state from the previous time step. This is used to correct the damage generation rate at the current moment. The exponential term in the formula contains... This is the mathematical expression of the autocatalytic effect, which directly uses the accumulated damage level (after normalization) as an influencing factor. When the accumulated damage... The larger the value, the larger the value of this term, resulting in a higher growth rate of the exponential function and more newly generated damage, perfectly simulating the physical process by which historical trauma exacerbates current damage. Specifically, This represents the cumulative thermal decay scalar after the k-th time segment, and is the output of the model at the k-th step. This represents the accumulated thermal decay scalar up to the end of the previous time segment k minus 1, and is the historical state input of the model. This represents the duration of the k-th anomalous exposure time segment, in hours. The thermal sensitivity index represents a specific reagent and is a dimensionless empirical constant that characterizes the reagent's sensitivity to temperature degradation. Let be the instantaneous monitored temperature actually captured by the sensor during the k-th exposure time segment. This refers to the factory-specified safe reference temperature required for this batch of reagents. It is the autocatalytic decay coefficient, a dimensionless hyperparameter used to describe the intensity of the catalytic or accelerating effect of existing damage on new damage. This is the initial nominal activity capacity constant of the reagent obtained from the discrete event subset, used to normalize cumulative damage. The following is a sequential explanation using the example mentioned in the preceding steps. For instance, a subset of the thermal time parameters of a batch of DNA polymerase contains three key thermal exposure histories: the first exposure at 25°C for 2.0 hours, the second at 4°C for 0.5 hours, and the third at -10°C for 0.2 hours. Its physicochemical constants are set as follows: thermal sensitivity index... The value is 0.085, which is the safe reference temperature. -20.0 degrees Celsius, initial nominal total reaction capacity It is 1200 units. Regarding the autocatalytic decay coefficient... Its value needs to be determined by fitting nonlinear kinetic curves to a large amount of accelerated aging experimental data of reagents under different pre-set damage levels. Here, the fitting value is set to 0.5. The calculation process is as follows: Initial state (k=0): The reagent has not been exposed to heat, and its cumulative damage is zero, that is... =0. First iteration (k=1): Calculate the first segment of heat exposure ( =2.0 hours, Damage caused at 25 degrees Celsius. At this point, the damage from the previous moment... Substituting 0 into the formula, we get: Second iteration (k=2): Calculate the second segment of thermal exposure ( =0.5 hours, Damage generated at 4 degrees Celsius. At this point, the damage accumulated in the previous time step must be substituted. =91.66: Third iteration (k=3): Calculate the third segment of thermal exposure ( =0.2 hours, Damage generated at -10 degrees Celsius, substituted with the cumulative damage from the previous moment. =95.66: Ultimately, the thermal decay scalar obtained using this state-iteration-based autocatalytic decay model is 96.15. This value is slightly higher than the result calculated by the linear accumulation model that does not consider historical states (approximately 95.98). This increase accurately reflects the slight acceleration effect of the cumulative damage caused by the initial high-temperature shock on the decay process under subsequent mild conditions. This makes the final quantification result closer to physical reality and provides a more solid data foundation for accurate compensation calculations in subsequent steps.

[0026] In step 2, the reagent activity vector and library quality control data are time-aligned and matrix-fused to obtain a co-functional feature matrix characterizing the coupling relationship between reagent state and library quality. The library quality control data includes library concentration, fragment peak value, and adapter ligation efficiency. Correspondingly, in the entire gene detection process, library construction is a crucial biochemical bridge connecting the original sample and sequencing data. Its actual quality is not only limited by the integrity of the sample itself but also deeply dependent on the immediate activity of the enzymes and buffers used during construction. Although step 1 achieves digital modeling of reagent loss through analysis of the original reagent flow, in actual production, the negative impact of the same degree of reagent attenuation on libraries of different concentrations and fragmentation levels is not linearly consistent. For example, for low-starting-volume clinical sample libraries, a slight decrease in ligase activity may lead to a precipitous drop in adapter ligation efficiency, while the impact is less severe for high-quality standard libraries. This quality fluctuation caused by the cross-coupling of reagent efficacy and sample physical state, without deep feature correlation, will leave quality assessment superficial. In step 2, through cross-dimensional feature integration, the implicit association between reagents under specific decay states and libraries of specific physical indicators is identified and quantified, which can transform isolated biochemical background data into decision support information that can guide hardware to make personalized modifications.

[0027] In one possible implementation, step 2 includes: performing topological tracing and feature alignment on reagent activity vectors and library quality control data based on sample unique identifiers and timestamps to obtain an aligned heterogeneous feature set; performing cross-dimensional feature projection and nonlinear cross-cascading on the aligned heterogeneous feature set to obtain a hierarchical projection fusion array; and performing orthogonal mapping and collinear pruning on the hierarchical projection fusion array to obtain a cooperative feature matrix.

[0028] The processing procedure is as follows: First, it is necessary to receive the reagent activity vector characterizing the loss of biochemical efficacy generated by the pretreatment, to carry over the value of the first example in step 1. This vector can be represented as... These are mapped to residual activity during heating, freeze-thaw survival rate, and cross-coupling potential, respectively. Simultaneously, library quality control data needs to be obtained from the quality inspection stage. This data is a set of indicators obtained from physical testing of the constructed library entity. Specifically, library quality control data includes library molar concentration, fragment distribution peaks, and adapter ligation efficiency. These indicators are obtained using quantitative fluorescence detectors such as Qubit, automated electrophoresis instruments such as TapeStation, and quantitative polymerase chain reaction (PCR) analysis. In terms of data architecture, library quality control data is encapsulated as a multidimensional vector. To achieve precise correlation, each library entity is assigned a globally unique sample identification code upon entry into the library. This code is represented by a barcode or QR code containing information about the sample's origin, testing items, and batch number. During laboratory workflow, the scanning device records the sample's operation timestamps at each experimental node in real time. When performing topological tracing, the backtracking matching logic retrieves reagent requisition logs and library construction logs from the database. Based on the sample's unique identification code, it identifies the specific batch of reagents consumed during library construction and compares the timestamps with the topological coordinates of the spatial device nodes. For example, if a sample with identification code SN20260327001 used reagent batch REAG-B2 at 10:30, the topological tracing logic will use a hard link to bind the corresponding reagent activity vector at that moment to the library quality control data generated at 14:00, eliminating mismatched orphan data due to missing manual records or misaligned scanning. The final output is a logically closed-loop and strictly paired aligned heterogeneous feature set.

[0029] After obtaining the aligned heterogeneous feature set, since the values ​​of reagent activity vectors are usually between 0 and 1, and the library molar concentration (e.g., 15.5 nM) and fragment peak value (e.g., 320 bp) have significant physical differences in terms of dimensions, direct fusion would cause high-dimensional features to mask low-dimensional biochemical details. Therefore, targeted diagonal adaptive weight matrices are introduced during the implementation process. and Spatial projection is then performed on the data. The parameters of these weight matrices are obtained by statistically analyzing the variance distribution of indicators from tens of thousands of historical samples, aiming to map heterogeneous data to a unified dimensionless scale. To fully capture the deep implicit coupling between reagent aging effects and library connector efficiency, the Kronecker product is used to calculate the nonlinear interaction terms between the two normalized vector sequences. The Kronecker product can generate higher-order terms covering all combinations of variable interactions, simulating the superposition effect of reagent decay and library physical defects. Subsequently, the initial reagent features, library entity features, and their nonlinear adjoint features derived through tensor multiplication after linear projection scaling are vertically concatenated in the column direction to construct a hierarchical projection fusion array. Its specific mathematical expression is as follows:

[0030] In this formula, It is a hierarchical projection fusion array. It is a 3×3 reagent weight diagonal matrix. It is a 3×3 diagonal matrix of quality control weights. The preset interaction scaling factor is used to control the contribution weight of the nonlinear term to the overall feature; in this application, it is set to 0.15. This represents a vectorization operator. Representing the Kronecker product operator, it expands a 3-dimensional activity vector and a 3-dimensional quality control vector into a 9-dimensional interaction feature. This formula, by layering and stacking linear mappings and nonlinear interactions, enables the array to simultaneously characterize macroscopic indicators and microscopic coupled information.

[0031] Next, the generated hierarchical projection fusion array is received, and orthogonal mapping and collinearity pruning are performed to purify the core quality control features. Due to the large number of cross terms introduced in the previous step, feature redundancy and environmental noise interference inevitably appear in the array. During implementation, the overall covariance state distribution corresponding to this array set is calculated, and its eigenvalue distribution spectrum is solved using the singular value decomposition (SVD) algorithm. By calculating the energy proportion corresponding to each eigenvector, i.e., the proportion of the eigenvalue to the total sum, the top k principal component orthogonal eigenvectors are selected according to a preset energy proportion threshold, such as when the cumulative variance explained reaches 98%. At this point, due to the previously stitched hierarchical projection fusion array... The original physical feature space is 15 dimensions, composed of 3D reagent activity features, 3D library physical features, and 9D joint cross features concatenated together. Within these 15-dimensional vectors, inherent collinearity and information overlap exist among higher-order cross terms. To effectively eliminate redundant variables and standardize input specifications, the truncation dimension k is preset to 8 in this embodiment. These vectors constitute a dynamic dimensionality reduction projection transformation matrix. This process can map high-order fusion arrays to a low-dimensional manifold space. While preserving the core mapping topology of reagent decay and library quality, it effectively removes collinear interference terms. The final solidified output is a cooperative feature matrix. The calculation formula is as follows: In this formula, This is the collaborative feature matrix. It is an orthogonal dimension-reduced projection matrix. This is a preset bias vector used to correct for drift in the laboratory environment baseline. Its value is obtained through a periodically run standard calibration process, and can be set as a set of minute constant vectors reflecting the background noise of the detection accuracy. If a sample has an extremely high library concentration but extremely low cross-coupling terms in the reagent activity vector, after orthogonal mapping, the collaborative feature matrix will keenly capture potential problems with the quality of the connector connections, rather than simply giving a judgment that the concentration is acceptable.

[0032] In step 3, the loss gradient quantization of the co-signal feature matrix is ​​performed to calculate the degree of imbalance between the target sequencing efficiency and the standard sequencing efficiency, obtaining the accuracy loss gradient that characterizes the strength of the impact of reagent attenuation on sequencing accuracy. It is understandable that while the co-signal feature matrix captures the complex coupling relationship between reagent attenuation and library quality through cross-dimensional feature projection, these high-dimensional abstract features cannot be directly mapped to specific physical control commands. Since the sequencing process is essentially a synergistic effect of laser excitation signals, chemical reagent replacement, and photoelectric signal acquisition, any small loss of biochemical activity will cause nonlinear fluctuations in the signal-to-noise ratio of the sequencing signal. Without a quantitative evaluation center from abstract features to physical performance loss, it is impossible to know at which cycle node and to what extent the current biochemical state will cause the sequencing accuracy to collapse. In step 3, the state fluctuations at the biochemical level are transformed into corrective potential energy at the physical level. By establishing a gradient transfer chain from feature to quality to hardware, a quantitative analytical benchmark is provided for the subsequent generation of accurate executable calibration instruction sets, thereby ensuring that the compensation actions have scientific guidance and dimensional consistency.

[0033] Figure 4 This is a schematic diagram of the data flow in step 3 of the quality control and sample tracking management method for the entire gene testing process according to an embodiment of this application. Figure 4 As shown, in one possible implementation, step 3 includes: inputting the collaborative feature matrix into the quality regression network for hierarchical decoding and forward inference to obtain a sequencing quality prediction vector characterizing multi-dimensional expected quality indicators; calculating the efficiency deviation scalar between the sequencing quality prediction vector and the standard performance benchmark vector; and performing multi-directional derivative decomposition on the efficiency deviation scalar based on the hardware-specific Jacobian sensitivity matrix to obtain the accuracy loss gradient used to guide the hardware to make directional corrections.

[0034] The process is as follows: First, the collaborative feature matrix generated in the previous step needs to be processed... The input is fed into a pre-built and trained quality regression network. This network employs an architecture combining a deep multilayer perceptron and residual connections, aiming to simulate the nonlinear degradation trajectory of complex biochemical reactions under varying degrees of decay. In the specific network topology design, to more comprehensively capture dynamic changes, the 8-dimensional core feature matrix output from the preceding steps undergoes feature engineering, such as by introducing time windows or aggregating features from multiple sequencing clusters, expanding and flattening it. The input layer receives a d-dimensional collaborative feature matrix, such as a flattened vector with a dimension of 128, followed by four fully connected hidden layers with the number of neurons in each layer set to 512, 256, 128, and 64, respectively. To prevent overfitting during training, a random deactivation layer with a dropout rate of 0.2 is attached after each hidden layer, and a linear rectified function is used as the activation function to enhance the network's ability to extract nonlinear features. The training process of this network is based on massive historical sequencing records, using the reagent history and quality inspection indicators of historical samples as input, and the actual base identification confidence and cyclic signal-to-noise ratio after sequencing as annotation labels. Using mean squared error as the loss function, an error backpropagation algorithm combined with an adaptive moment estimation optimizer is employed to continuously adjust the weight matrices and bias terms between layers over tens of thousands of iterations. When the processing logic executes the forward inference algorithm, the quality regression network adjusts the input... Hierarchical decoding is performed to map abstract features into spatial distributions representing the performance of the entire sequencing cycle, ultimately outputting a sequencing quality prediction vector. This vector It contains the predicted quality score for each of the expected 100 sequencing cycles. For example, if the input co-feature matrix shows a 15% decrease in reagent activity, the quality regression network may predict that the base confidence in the early cycles is damaged and can only be maintained at Q35, and that the base confidence after cycle 50 will further decline to Q28 as the biochemical reaction is exhausted, thus forming a vector sequence reflecting the trend of quality decline.

[0035] Subsequently, the computing unit extracts the standard performance benchmark vector corresponding to the current sequencing mode from the underlying configuration library. This standard performance benchmark vector is based on the theoretical upper limit of the mass distribution achievable by the most ideal, lossless reagents under standard library conditions. In practical applications, A constant high-scoring sequence is set, for example, the score for each cycle is set to Q40, which represents a base identification error rate of one in ten thousand. Next, a sequencing quality prediction vector is calculated. Compared with standard performance benchmark vector scalar of efficiency deviation between This is used to assess the absolute degree to which the current sample deviates from the ideal performance. The calculation process uses the L2 Euclidean distance formula:

[0036] In this formula, This represents a scalar measure of efficiency deviation. This represents the total number of sequencing cycles, such as 100. and These represent the quality scores of the predicted vector and the baseline vector in the i-th cycle, respectively. The magnitude of this scalar value directly reflects the severity of the sequencing efficiency loss. Since the ideal baseline score set by the system is Q40 throughout the entire process, when the device detects that the quality of the first 49 cycles out of 100 cycles is only Q35 (single-point bias of 5), and the quality of the last 51 cycles drops significantly to Q28 (single-point bias of 12), the sum of squared errors between the two will increase dramatically with the increase of the loss dimension. For example, the efficiency deviation scalar value calculated based on the entire sequence set of this quality decline curve is approximately... 92.57. This value precisely represents the total spatial distance in multiple dimensions between the current biochemical state and the ideal state throughout the entire sequencing lifecycle. The larger the value, the more significant the potential threat of reagent decay to accuracy.

[0037] Finally, based on the hardware-specific Jacobian sensitivity matrix Scalar for efficiency deviation Perform multi-directional derivative decomposition to obtain the precision loss gradient. Hardware-specific Jacobian sensitivity matrix It is a core transformation operator that measures the sensitivity of the impact of changes in hardware physical parameters on sequencing quality. Its specific architecture is an M×D matrix, where M represents the number of hardware control channels, such as laser power, pump flow rate, and exposure time, and D represents the dimension of the quality metric. Each element in this matrix... This represents the partial derivative of the i-th mass dimension caused by a one-unit change in the j-th physical parameter. The method for obtaining this matrix is ​​based on hardware micro-perturbation testing under controlled experimental conditions. By incrementally changing the laser power or liquid replenishment pressure within a calibrated range, the slope of the resulting mass fluctuations is recorded, thus solidifying the matrix parameters. The generated accuracy loss gradient... The solution is obtained using the following formula:

[0038] In this formula, The precision loss gradient is essentially a column vector containing the correction direction and magnitude of each control variable. This represents the gradient direction of the efficiency deviation scalar relative to each quality dimension. Through this matrix multiplication operation, the computational unit can analyze which biochemical performance deficiency is primarily causing the current efficiency deviation and decompose it into the pulling force on specific physical variables. For example, if... The signal strength was significantly weakened, as obtained after Jacobian matrix decomposition. The laser power gradient might exhibit a positive gradient, such as +0.15, while the exposure time gradient might show a positive gradient, such as +0.08. This indicates that subsequent hardware needs to increase the excitation energy or extend the signal acquisition time to compensate for the weakness of the biochemical signal. By decomposing the derivative of the Jacobian matrix, the precision loss gradient can accurately indicate the direction and extent of parameter deviation that each hardware actuator should make to offset the loss of reagent activity.

[0039] In step 4, based on the hardware parameter mapping table, the precision loss gradient is inversely solved and compensated to obtain a calibration instruction set that can be directly executed by the sequencing equipment. It should be understood that in the underlying execution logic of high-throughput sequencing, although the precision loss gradient mathematically indicates the ideal direction and magnitude of hardware correction, as a high-dimensional gradient component, it does not possess the ability to directly control the physical execution mechanism. The operation of the sequencer relies on complex electromechanical control, such as the drive current of the laser driver, the pulse frequency of the stepper motor, and the gain voltage of the photomultiplier tube. If only gradient information is used without precise dimensional transformation and safety constraints, it is highly likely that the hardware will experience overload losses during compensation, or even induce accelerated laser decay or fluid system pressure imbalance. Therefore, step 4 transforms the precision correction dynamics calculated at the upper layer into discrete control instructions that conform to the underlying physical characteristics and communication protocols. By establishing an inverse mapping link from gradient to microcode, it ensures that biochemical compensation actions can safely, accurately, and orderly act on every hardware execution dimension, thereby mitigating the quality decline caused by reagent decay to the greatest extent possible while protecting the hardware's lifespan.

[0040] In one possible implementation, step 4 includes: performing dimensional analysis and dimensional transformation on the accuracy loss gradient based on the hardware parameter mapping table to obtain the hardware parameter correction increment containing the correction values ​​corresponding to each physical control channel; performing smoothing constraint and inverse compensation calculation on the hardware parameter correction increment to obtain a calibration control scalar sequence for guiding the action of the underlying driver; and performing binary microcode synthesis and timing encapsulation on the calibration control scalar sequence based on the underlying device communication protocol to obtain a calibration instruction set.

[0041] The process is as follows: First, receive the precision loss gradient generated in step 3. Taking the numerical example from the previous step, the resulting gradient vector has a component of +0.15 in the laser power dimension and +0.08 in the exposure time dimension. To transform these abstract gradient values ​​into physical increments that the sequencer can resolve, a pre-defined hardware parameter mapping table needs to be consulted during implementation. The hardware parameter mapping table is a multidimensional lookup table or a set of linear mapping operators, whose core architecture contains a matrix of transformation coefficients from the mathematical gradient space to the physical dimension space. This mapping table is obtained through calibration experiments before the hardware leaves the factory and records the upper limit and baseline step size of the physical change of each control channel (such as excitation source energy, microfluidic pump valve flow rate control, and photodetector integration shutter time) under a unit gradient. In the specific process of performing dimensionality resolution, the hardware parameter mapping table is defined as an association matrix containing the sensitivity coefficients and safety threshold boundaries of each physical channel. The computation unit first linearly weights the precision loss gradient with the corresponding transformation coefficients in the mapping table through matrix multiplication. Specifically, for each physical control dimension... Through formula Calculate the initial increment, where This is the initial increment. The weighted slope, which characterizes the transformation of a unit gradient into a physical quantity, has a numerical value that determines the gain strength of the gradient transformation into the physical step size. A preset zero-point correction intercept is used to compensate for the inherent lag in hardware response during static startup. Subsequently, the linear weighted algorithm retrieves the physical security adjustment range from the mapping table in real time. The calculation results are subjected to amplitude locking. If the calculated initial increment exceeds the specified range, it is forcibly truncated to a safe boundary value, thus ensuring that each physical correction is within the linear response range of the device. For example, the laser power component +0.15, after being converted by the mapping table, is resolved to a physical step value of +1.5 milliwatts, with the weight slope set to 10 and the zero-intercept set to 0; the exposure duration component +0.08 is resolved to a physical adjustment value of +0.8 milliseconds (with the weight slope set to 10 and the zero-intercept set to 0). These generated structured arrays containing correction values ​​for different control channels constitute the hardware parameter correction increments.

[0042] Subsequently, the aforementioned hardware parameter correction increments are received and subjected to smoothing constraints and inverse compensation calculations. Considering the physical limits of hardware response and the potential for system instability due to overcompensation, a hyperbolic tangent saturation function (tanh) is introduced as the core constraint operator to smooth the hardware parameter correction increments. This calculation process aims to ensure that the compensation amplitude, while offsetting the accuracy loss caused by biochemical decay, never exceeds the safe threshold of the hardware's physical response. The specific calibration control scalar sequence... The solution is obtained by following the reverse compensation formula:

[0043] In this formula, This represents the final calibration control scalar corresponding to the j-th physical control channel, i.e., the j-th scalar in the calibration control scalar sequence. These are the basic control parameter values ​​for the channel under standard conditions, such as setting the laser's reference power to 20.0 milliwatts and the exposure reference to 10.0 milliseconds. This represents the incremental correction of the hardware parameters for the corresponding channel as previously analyzed. This is a preset sensitivity attenuation factor used to adjust the entry slope of the saturation curve and prevent excessive fluctuations in the correction amount. To ensure that the physical increment can be mapped well within the normal correction range, this parameter is set here in conjunction with the upper limit of the maximum compensation gain, for example, set to 0.2. The maximum compensation gain is preset to a limit, the value of which is determined by the physical safety boundary of the hardware. For example, it is set to 5.0 for a laser to ensure that the maximum increase in a single calibration does not exceed 5 milliwatts. Through the nonlinear constraint of this formula, even if the accuracy loss gradient oscillates significantly, the generated calibration control scalar sequence can approach the compensation target in a smooth manner that is constrained by the physical hardware limits. For example, for a laser power correction increment of +1.5 milliwatts derived from forward analysis. ,go through After calculating the constraints of the function and the upper limit of gain, the actual hardware compensation increment is 5.0·tanh(0.2·1.5) = 1.455 milliwatts. This entire calculation process accurately demonstrates the data damping value of the saturation constraint algorithm: the system does not mechanically accept the absolute linear command of 1.5 milliwatts, but rather combines the set hardware safety tolerance characteristics to perform a slight flexible smoothing, ultimately outputting a safety constraint compensation value of approximately 1.46 milliwatts. Subsequently, the system safely increases the reference power of the underlying driver from 20.0 milliwatts to 21.46 milliwatts, thus outputting a precise physical quantity to guide the underlying workstation in performing effective photoelectric signal compensation while strictly adhering to the physical boundary.

[0044] Finally, the generated calibration control scalar sequence is received and converted into the final calibration instruction set based on the underlying device communication protocol. The underlying device communication protocol defines the data exchange format between the sequencer's internal control motherboard and various execution subsystems such as the fluidic plate and optical plate. Its specific architecture includes frame header definition, load length control, instruction function code, and cyclic redundancy check field. In this implementation phase, the computation unit groups the decimal calibration control scalars and converts them into binary microcode format according to the data bit width agreed upon in the protocol. Specifically, during binary microcode synthesis, the computation unit first calls the protocol register mapping table to convert the calibration control scalars of each dimension... A hard link is established with the underlying hardware register address. Subsequently, the floating-point physical scalar undergoes fixed-point quantization. Using a quantization factor, a physical quantity such as a compensated 21.5 milliwatt laser power is converted into a 16-bit or 32-bit integer value compatible with the underlying processor's processing bit width. This integer value is then embedded into the data payload field of a specific instruction frame through bit shifting and masking logic. To ensure signal integrity during long-distance bus transmission, the assembled frame header, function code, and data payload are scanned in real-time using the CRC-16-CCITT algorithm, generating a binary check sequence and appending it to the end of the data frame. In the timing encapsulation stage, the encapsulation logic establishes an asynchronous execution queue with priority weights based on the time constraints of the sequencing chemical reaction. Following the physical logic sequence of "reagent pump pressure stabilization - laser excitation start-photodetector signal sampling," each group of microcode instructions is assigned an execution offset marker accurate to the microsecond level. Finally, the encapsulation logic concatenates the metadata header segment, which includes the sample's unique identifier such as SN20260327001 and the current compensation round sequence number, with the synthesized binary instruction stream to form a binary data packet that is logically highly aggregated and atomic in execution, which is the calibration instruction set.

[0045] In step 5, the calibration command set is sent to the sequencer and the compensated quality indicators are collected. The reagent activity vector, library quality control data, and co-operation feature matrix are then correlated, audited, and tracked to obtain a quality control tracking report covering the sample flow path and changes in quality status. In other words, while hardware-level compensation commands can perform precise calibration for biochemical degradation, a verification vacuum still exists between their actual execution effect and the final output quality of the biochemical reaction. Because gene detection involves complex random errors and irreversible biochemical damage, simply issuing commands does not equate to meeting quality standards. Without real-time monitoring and end-to-end traceability of the compensated data, management cannot determine whether the current physical intervention is sufficient to offset reagent loss, thus affecting data release decisions. Step 5 aims to verify the effectiveness of the hardware compensation action, quantify the remaining quality defects even after physical compensation, and integrate the discrete fragmented information from the reagent source to the execution terminal into an immutable chain of evidence, thereby providing a definitive quantitative basis and archive for the output quality of each sample.

[0046] Figure 5 This is a flowchart of step 5 in the quality control and sample tracking management method for the entire gene testing process according to an embodiment of this application. Figure 5As shown, in one possible implementation, step 5 includes: step 51, sending the calibration instruction set to the sequencer and simultaneously removing off-target noise from the real-time sequencing signal stream to obtain a compensated quality assessment matrix; step 52, performing full-dimensional topological correlation and defect residual quantification on the compensated quality assessment matrix to obtain a full-link audit log containing irreversible quality defect quantification records; step 53, performing serialization processing and visualization rendering on the full-link audit log, and automatically judging based on a preset release threshold to generate a quality control tracking report.

[0047] The process is as follows: First, the calibration instruction set, generated in step 4 and carrying customized correction microcode, is injected into the hardware execution queue via the high-speed low-level control interface provided by the sequencer. Taking the numerical example following the previous steps, for the sample with identification code SN20260327001, its calibration instruction set includes an opcode to increase the laser power from the baseline 20.0 mW to 21.5 mW. During the hardware execution of the corrected excitation power, flow rate, and exposure parameters, the real-time sequencing signal stream output by the photosensitive element in each sequencing cycle is captured simultaneously. The real-time sequencing signal stream is a series of high-depth fluorescence image bitmap data. The processing unit analyzes these real-time captured raw images, locates each sequencing cluster through spatial topological mapping, and executes an off-target noise stripping algorithm to filter out background noise caused by chemical crosstalk and optical diffusion. In the specific implementation of generating the compensated quality assessment matrix, the processing unit inputs the purified fluorescence intensity signal after off-target noise removal into the quality assessment operator. For each sequencing cluster, based on its performance in each sequencing cycle, it calculates the peak signal-to-noise ratio (SNR) and Phred quality score for each base channel (A / T / C / G) in real time. Subsequently, the calculation module structures these multi-dimensional quality indicators according to the tensor dimension of [cycle number, spatial coordinates, indicator type], forming a compensated quality assessment matrix that comprehensively characterizes the signal performance after hardware intervention. For example, after increasing the laser power by 1.5 milliwatts, the matrix shows that the SNR after the 50th cycle improved from the predicted 12 to 18, a significant improvement in data output quality.

[0048] Subsequently, the compensated quality assessment matrix is ​​received, and the data for each monitoring dimension are matched against the sample's unique identifier throughout its lifecycle using a full-dimensional loopback correlation analysis. The core of this step lies in performing a full-dimensional topological correlation and defect residual quantification between the compensated actual quality and the theoretical perfect quality. The processing unit retrieves the reagent activity vector from step 1, such as... The collaborative feature matrix from step 2 and the precision loss gradient from step 3 are used as background causal variables and calculated together with the compensated evaluation matrix generated in step 5 to assess whether irreversible quality defects caused by excessive reagent attenuation still exist after physical compensation. This process calculates the defect residual index. In one possible implementation, the compensated quality assessment matrix is ​​subjected to full-dimensional topological correlation and defect residual quantification to obtain a full-link audit log containing irreversible quality defect quantification records. This includes performing full-dimensional topological correlation and defect residual quantification on the compensated quality assessment matrix using the following formula:

[0049] in, The defect residual index reflects the severity of irreversible quality damage. This represents the total number of sequencing cycles, for example, set to 100. To determine the ideal Q40 score for the t-th sequencing cycle, based on the standard expected quality score threshold achievable with the nominally most ideal reagents. This represents the actual detection quality score exhibited by the sequencing device in the t-th sequencing cycle, as shown in the compensated quality assessment matrix. This is the error tolerance weighting coefficient. The introduction of the operator ensures that only quality defects below the ideal value are accumulated, while This assigns different penalty weights to sequencing quality at different stages. Typically, as the number of cycles t increases, the natural chemical degradation effect... This will gradually decrease, allowing the formula to focus more on the core accuracy of early sequencing. If the calculated... A high value indicates that even with hardware compensation, the initial damage to the reagent still resulted in irreversible data loss. To solidify this audit process, the processing module associates the identification code and defect residual index. The flow status data of each biochemical node (including physical quantities such as reagent batches, storage time, and library concentration) and the parameter comparison records before and after hardware compensation are encrypted and packaged. During the encryption process, the SHA-256 digest algorithm is used to generate a data fingerprint, and digital signature is performed in combination with the laboratory private key, thereby ensuring that the packaged end-to-end audit log has tamper resistance and legal traceability.

[0050] Finally, the system receives the end-to-end audit logs and performs JSON-based serialization and formatting on the intermediate computational quantities contained within, from the initial reagent vector distribution to the hardware loss gradient and residual features. This process transforms the complex structured tensors and binary logs into a human-readable and easily parsed standardized text stream. In the specific serialization process, the system calls the structured conversion module to map the unstructured binary audit logs to fields according to a preset architectural logic such as JSON or XML. This transforms the reagent activity history, co-factor matrix, hardware compensation increment, and residual calculation results for each sample into a uniformly encoded text object, facilitating cross-platform data interaction and indexing. Subsequently, the visualization rendering engine reads these serialized data streams and projects them onto a chart component on the web. This engine displays the reagent decay trend line and the quality gain curve after hardware compensation by drawing a dual-axis time series graph, and simultaneously visualizes the entire lifecycle flow of the sample from reagent warehousing to final qualification through a topology tree diagram, allowing managers to intuitively view the quality status of each stage. In this implementation phase, the sequencing results need to be automatically judged based on preset release thresholds. The preset release threshold is the quality red line that distinguishes between qualified and unqualified data. It is obtained by statistical distribution analysis of tens of thousands of batches of high-standard operation data in history, and by taking the defect residual index. The 95th percentile is used as the baseline limit. For example, for whole-genome sequencing (WGS) applications, the allowance threshold is set to 1.5. The processing unit will generate the current sample... The value, such as 1.12, is compared with the threshold. Since 1.12 < 1.5, the judgment logic automatically concludes that the sample is qualified. Finally, by integrating compensation records, traceability details, and judgment conclusions, a quality control tracking report covering the entire sample flow and quality status changes is generated. This report is archived and solidified as an electronic record, achieving traceable management of the entire gene testing process.

[0051] In summary, a quality control and sample tracking management method for the entire gene sequencing process, based on embodiments of this application, is presented, aiming to solve the challenges of reagent attenuation and algorithm mismatch in the gene sequencing process. First, attenuation integral technology is used to convert reagent flow parameters into digital activity vectors, solving the problem of unquantifiable modeling of biochemical losses. Then, by fusing the activity vectors with library quality control data, the silos between reagent management and quality monitoring are broken down, constructing a cross-dimensional coupled collaborative feature matrix. Based on this, a calibration instruction set executable by the device is inversely solved using the precision loss gradient and a hardware parameter mapping table, enabling the sequencer to automatically adjust its underlying operating parameters according to the real-time performance of the reagents, transforming traditional passive quality inspection into an active, dynamic self-compensation mode. Finally, a quality control tracking report is generated through end-to-end correlation auditing, achieving full transparency and control from underlying biochemical fluctuations to macroscopic quality status, fundamentally eliminating sequencing bias caused by dynamic biochemical attenuation.

Claims

1. A method for quality control and sample tracking management throughout the entire gene testing process, characterized in that, include: Step 1: Perform attenuation integral and normalization characterization on the raw reagent stream, which includes reagent batches, cumulative opening time, storage temperature sequence and number of freeze-thaw cycles, to obtain a reagent activity vector that reflects the current state of biochemical efficiency loss. Step 2: Perform time-series alignment and matrix fusion on the reagent activity vector and library quality control data to obtain a collaborative feature matrix characterizing the coupling relationship between reagent state and library quality. The library quality control data includes library concentration, fragment peak value, and linker ligation efficiency. Step 3: Perform loss gradient quantization on the cooperating feature matrix to calculate the degree of imbalance between the target sequencing efficiency and the standard sequencing efficiency, and obtain the accuracy loss gradient that characterizes the strength of the influence of reagent attenuation on sequencing accuracy. Step 4: Based on the hardware parameter mapping table, the accuracy loss gradient is solved in reverse and compensated to obtain a set of calibration instructions that can be directly executed by the sequencing equipment. Step 5: Send the calibration instruction set to the sequencer and collect the compensated quality indicators. Then, perform correlation auditing and tracking summary on the reagent activity vector, library quality control data and co-operation feature matrix to obtain a quality control tracking report covering the sample flow link and quality status changes.

2. The quality control and sample tracking management method for the entire gene testing process according to claim 1, characterized in that, Step 1 includes: The original reagent stream was decomposed into structured fields to obtain a subset of thermal time parameters containing the cumulative duration of opening the lid and the storage temperature sequence, and a subset of discrete events containing the number of freeze-thaw cycles; A thermal-time nonlinear decay integral induced by environmental stress is performed on a subset of thermal-time parameters to obtain a thermal-time decay scalar. The reagent activity vector is obtained by fusing discrete mechanical penalties and high-dimensional tensor mapping between the thermal decay scalar and the discrete event subset.

3. The quality control and sample tracking management method for the entire gene testing process according to claim 1, characterized in that, Step 1 includes: The original reagent stream was decomposed into structured fields to obtain a subset of thermal time parameters containing the cumulative duration of opening the lid and the storage temperature sequence, and a subset of discrete events containing the number of freeze-thaw cycles; A state-iteration-based autocatalytic decay of a subset of thermal time parameters is performed to obtain a thermal time decay scalar. The reagent activity vector is obtained by fusing discrete mechanical penalties and high-dimensional tensor mapping between the thermal decay scalar and the discrete event subset.

4. The quality control and sample tracking management method for the entire gene testing process according to claim 1, characterized in that, Step 2 includes: Based on the unique identifier and timestamp of the sample, topological tracing and feature alignment are performed on the reagent activity vector and library quality control data to obtain an aligned heterogeneous feature set. A hierarchical projection fusion array is obtained by performing cross-dimensional feature projection and nonlinear cross-concatenation on the aligned heterogeneous feature set. Orthogonal mapping and collinear pruning are performed on the hierarchical projection fusion array to obtain the cooperative feature matrix.

5. The quality control and sample tracking management method for the entire gene testing process according to claim 1, characterized in that, Step 3 includes: The collaborative feature matrix is ​​input into the quality regression network for hierarchical decoding and forward inference to obtain a sequencing quality prediction vector that represents multi-dimensional expected quality indicators; Calculate the efficiency deviation scalar between the sequencing quality prediction vector and the standard performance benchmark vector; Based on the hardware-specific Jacobian sensitivity matrix, the efficiency deviation scalar is decomposed into multi-directional derivatives to obtain the accuracy loss gradient that guides the hardware to make directional corrections.

6. The quality control and sample tracking management method for the entire gene testing process according to claim 1, characterized in that, Step 4 includes: Based on the hardware parameter mapping table, the precision loss gradient is analyzed in dimension and transformed in dimensionality to obtain the hardware parameter correction increment containing the correction values ​​of each physical control channel. Smoothing constraints and inverse compensation calculations are performed on the hardware parameter correction increments to obtain a calibration control scalar sequence to guide the action of the underlying driver. Based on the underlying device communication protocol, the calibration control scalar sequence is synthesized into binary microcode and time-series encapsulated to obtain the calibration instruction set.

7. The quality control and sample tracking management method for the entire gene testing process according to claim 1, characterized in that, Step 5 includes: The calibration instruction set is sent to the sequencer and off-target noise in the real-time sequencing signal stream is simultaneously removed to obtain the compensated quality assessment matrix. Perform full-dimensional topological correlation and defect residual quantification on the compensated quality assessment matrix to obtain a full-link audit log containing irreversible quality defect quantification records; The system serializes and visualizes the audit logs across the entire supply chain, and automatically determines the quality control tracking report based on preset release thresholds.

8. The quality control and sample tracking management method for the entire gene testing process according to claim 7, characterized in that, The compensated quality assessment matrix is ​​subjected to full-dimensional topological correlation and defect residual quantification to obtain a full-link audit log containing irreversible quality defect quantification records. This includes performing full-dimensional topological correlation and defect residual quantification on the compensated quality assessment matrix using the following formula: ;in, The defect residual index, This represents the total number of sequencing cycles. To determine the standard expected quality score threshold achievable with the nominally ideal reagent in the t-th sequencing cycle, This represents the actual detection quality score exhibited by the sequencing device in the t-th sequencing cycle, as shown in the compensated quality assessment matrix. This is the error tolerance weighting coefficient.

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