Automatic nucleic acid sequence analysis method and device based on micro-fluidic chip

Abstract

The invention relates to the field of nucleic acid sequence analysis, in particular to an automatic nucleic acid sequence analysis method and device based on a micro-fluidic chip. The method comprises the following steps: detecting that nucleic acid sample liquid drops flow into a micro-fluidic chip, and carrying out gradual gradient separation full-period regulation and control to obtain a time-sequenced nucleic acid fragment data stream; carrying out amplification process detection, imaging and amplification kinetics analysis on the nucleic acid sample liquid drops, and constructing a multi-fragment amplification kinetics vector; collecting nucleic acid conductivity parameters based on the confined electrode array, performing multi-scale feature analysis, and constructing a nucleic acid feature marker graph; and performing intelligent nucleic acid sequence analysis on the multi-fragment amplification dynamic vector and the nucleic acid feature marker graph, performing global sequential logic consistency comparison on the nucleic acid sequence splicing optimization graph based on a sequential nucleic acid fragment data stream, and synchronously generating a nucleic acid sequence automatic analysis report. According to the invention, high-precision and high-efficiency nucleic acid sequence analysis is realized, and mismatch and sequence deletion risks are reduced.

Description

Technical Field

[0001] This invention relates to the field of nucleic acid sequence analysis, and in particular to an automatic nucleic acid sequence analysis method and apparatus based on microfluidic chips. Background Technology

[0002] Microfluidic chips, as miniature devices integrating micrometer-scale channels and reaction chambers, offer advantages such as small size, short reaction time, low sample consumption, ease of integration, and automation, thus demonstrating immense potential in life sciences, clinical diagnostics, and genomics research. Microfluidic chips can integrate complex bioanalytical processes into a small chip, achieving rapid, efficient, and automated analysis of nucleic acid samples through precise fluid and reaction control. Particularly in the field of nucleic acid sequence analysis, microfluidic chips can significantly improve analytical throughput and accuracy while reducing sample and reagent consumption.

[0003] However, most existing microfluidic chip-based nucleic acid sequence analysis methods focus on achieving single nucleic acid amplification or single analytical tasks, such as PCR amplification, DNA / RNA hybridization, and genotyping using microfluidic chips. While these methods can provide a certain level of nucleic acid analysis, they often fail to meet the high-efficiency processing requirements of large-scale nucleic acid samples due to a lack of efficient automated sequence assembly, dynamic monitoring, and multi-task parallel processing capabilities. In practical applications, significant manual intervention, manual operation, and subsequent data analysis are often required, leading to low experimental efficiency and a high risk of errors, thus limiting their development potential in high-throughput, large-scale applications. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention proposes an automatic nucleic acid sequence parsing method and apparatus based on microfluidic chips, thereby resolving at least one of the aforementioned technical issues.

[0005] To achieve the above objectives, this invention provides an automated nucleic acid sequence parsing method based on a microfluidic chip. The microfluidic chip embeds a multi-region reaction chamber, a confined electrode array, and a multilayer liquid path network. Pressure valve arrays are integrated at the intersections of the multilayer liquid path network. The method includes the following steps: Step S1: Detect the flow of nucleic acid sample droplets into the microfluidic chip, perform stepwise gradient separation and full-cycle regulation to obtain a time-sequential nucleic acid fragment data stream; Step S2: Detect and image the amplification process of nucleic acid sample droplets and analyze the amplification kinetics to construct a multi-fragment amplification kinetic vector; Step S3: Collect nucleic acid conductivity parameters based on the confined electrode array, perform multi-scale feature analysis, and construct a nucleic acid feature marker map; Step S4: Perform intelligent nucleic acid sequence parsing on the multi-fragment amplification kinetic vector and nucleic acid feature marker map to construct an optimized nucleic acid sequence splicing map; Step S5: Perform a global temporal logical consistency comparison of the nucleic acid sequence splicing optimization map based on the time-series nucleic acid fragment data stream, and simultaneously generate an automated nucleic acid sequence parsing report.

[0006] This specification provides an automated nucleic acid sequence parsing device based on a microfluidic chip, used to execute the automated nucleic acid sequence parsing method based on a microfluidic chip as described above, comprising: The gradient separation module is used to detect the flow of nucleic acid sample droplets into the microfluidic chip, perform stepwise gradient separation and full-cycle regulation, and obtain a time-sequential nucleic acid fragment data stream. The amplification analysis module is used to detect and image the amplification process of nucleic acid sample droplets and analyze the amplification kinetics, and to construct multi-fragment amplification kinetic vectors. The nucleic acid conductivity module is used to collect nucleic acid conductivity parameters based on the confined electrode array, perform multi-scale feature analysis, and construct a nucleic acid feature marker map; The sequence parsing module is used to perform intelligent nucleic acid sequence parsing on multi-fragment amplification kinetic vectors and nucleic acid feature marker maps, and to construct an optimized nucleic acid sequence splicing map. The logical alignment module is used to perform global temporal logical consistency alignment of nucleic acid sequence splicing optimization maps based on time-series nucleic acid fragment data streams, and simultaneously generate automated nucleic acid sequence parsing reports.

[0007] The specific benefits of this invention are as follows: By precisely controlling droplet flow through a microfluidic chip, stepwise gradient separation of nucleic acid fragments is achieved, effectively avoiding interference from impurities in large-volume samples. Full-cycle regulation ensures the continuity and integrity of nucleic acid fragments over time, forming a time-series data stream that provides high-quality, low-noise input for subsequent amplification and sequence analysis. Compared to traditional batch processing methods, droplet microfluidics significantly improves reaction efficiency and fragment resolution, achieving high-throughput detection at the single-molecule level. Real-time imaging of the amplification process allows for the acquisition of kinetic characteristics of different fragments during amplification, avoiding the limitations of traditional endpoint detection. Kinetic analysis not only identifies the presence of target nucleic acids but also distinguishes the amplification efficiency, rate, and inflection point characteristics of different fragments, forming a multi-fragment amplification kinetic vector. A confined electrode array can detect the electrical conductivity characteristics of nucleic acid molecules at the nanoscale, providing another dimension of physical parameters independent of optical imaging. Multi-scale feature analysis correlates electrical conductivity parameters with nucleic acid molecular structural features, further enriching the multimodal characterization of nucleic acid samples. Constructing a nucleic acid feature marker map can serve as a unique "electrical fingerprint," enhancing the discriminative and uniqueness verification capabilities for complex nucleic acid samples. Multimodal fusion of amplification kinetics data and electrical conductivity characteristics improves the robustness of sequence identification and assembly. Based on intelligent parsing algorithms, core correlation information can be extracted from data from different sources, reducing the risk of mismatches and sequence deletions. Constructing an optimized nucleic acid sequence assembly map enables higher-precision sequence assembly globally, improving the usability of automated parsing. Global temporal logical consistency comparison ensures strict consistency between the assembled nucleic acid sequence and the actual fragment flow in time, avoiding global errors caused by local mismatches. Automatic generation of nucleic acid sequence parsing reports reduces manual intervention and improves the objectivity and reproducibility of results. This fully automated process from sample input to sequence parsing significantly improves efficiency, lowers experimental barriers, and promotes the application of microfluidic chips in molecular diagnostics, pathogen detection, and precision medicine. Attached Figure Description

[0008] Figure 1 This is a schematic diagram of the steps of an automatic nucleic acid sequence parsing method based on a microfluidic chip according to the present invention; Figure 2 This is a detailed flowchart illustrating the implementation steps of step S1. Figure 3 This is a flowchart illustrating the detailed implementation steps of step S2. Detailed Implementation

[0009] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.

[0010] This application provides a method and apparatus for automatic nucleic acid sequence parsing based on a microfluidic chip. The execution entities of the method and apparatus include, but are not limited to, mechanical equipment, data processing platforms, cloud server nodes, and network upload devices that can be considered general computing nodes in this application. The data processing platform includes, but is not limited to, at least one of an audio / image management system, an information management system, and a cloud data management system.

[0011] Please see Figures 1 to 3 This invention provides an automatic nucleic acid sequence parsing method based on microfluidic chips, comprising the following steps: Step S1: Detect the flow of nucleic acid sample droplets into the microfluidic chip, perform stepwise gradient separation and full-cycle regulation to obtain a time-sequential nucleic acid fragment data stream; Step S2: Detect and image the amplification process of nucleic acid sample droplets and analyze the amplification kinetics to construct a multi-fragment amplification kinetic vector; Step S3: Collect nucleic acid conductivity parameters based on the confined electrode array, perform multi-scale feature analysis, and construct a nucleic acid feature marker map; Step S4: Perform intelligent nucleic acid sequence parsing on the multi-fragment amplification kinetic vector and nucleic acid feature marker map to construct an optimized nucleic acid sequence splicing map; Step S5: Perform a global temporal logical consistency comparison of the nucleic acid sequence splicing optimization map based on the time-series nucleic acid fragment data stream, and simultaneously generate an automated nucleic acid sequence parsing report.

[0012] In the embodiments of the present invention, see Figure 1 The diagram below illustrates the steps of an automatic nucleic acid sequence parsing method based on a microfluidic chip according to the present invention. In this example, the steps of the automatic nucleic acid sequence parsing method based on a microfluidic chip include: Step S1: Detect the flow of nucleic acid sample droplets into the microfluidic chip, perform stepwise gradient separation and full-cycle regulation to obtain a time-sequential nucleic acid fragment data stream; In this embodiment, the flow of nucleic acid sample droplets into the microfluidic chip is detected. The droplet flow rate is monitored in real time using a pressure sensor array, and an initial flow rate is set within a precise control range of 5-15 μL / min. A stepwise gradient separation cycle is implemented, utilizing a multi-stage T-shaped splitter to construct 6-8 continuous separation cascades, with each stage's separation efficiency controlled between 85% and 95%. Precise separation of nucleic acid fragments of different molecular weights is achieved by adjusting the pressure difference at each stage. A dynamic pressure control system is established, adaptively adjusting the pressure parameters of each separation stage based on the real-time detected fragment concentration distribution, with pressure adjustment accuracy controlled within ±0.1 kPa. A fragment length detection unit is constructed, employing fluorescent labeling combined with optical detection technology to achieve the detection of fragments of 10... Real-time length determination of nucleic acid fragments in the range of 0bp-10kbp; assigning a unique timestamp to each isolated nucleic acid fragment droplet with millisecond-level time accuracy, while simultaneously recording its spatial coordinates within the chip; processing 8-12 sample channels simultaneously through a multi-channel parallel processing architecture, with each channel having a processing capacity of 100-500 droplets / minute; establishing a real-time fragment quality assessment mechanism, judging fragment integrity through fluorescence intensity analysis, and setting a quality threshold of signal-to-noise ratio >20dB; obtaining a time-series nucleic acid fragment data stream, which includes fragment number, timestamp, length information, quality score, and spatial location, providing a standardized input data source for subsequent amplification analysis.

[0013] Step S2: Detect and image the amplification process of nucleic acid sample droplets and analyze the amplification kinetics to construct a multi-fragment amplification kinetic vector; In this embodiment, the entire PCR amplification process of nucleic acid sample droplets is tracked and monitored. A micro-thermal cycling unit is integrated within the microfluidic chip, achieving a temperature control accuracy of ±0.1°C. The amplification cycle parameters are set as a standard three-temperature cycle: 95°C denaturation for 30 seconds, 55-65°C annealing for 30 seconds, and 72°C extension for 60 seconds. A high-resolution CCD imaging system is used for real-time imaging monitoring of the amplification process, with an imaging frame rate set at 10-30fps and a pixel resolution of 1280×1024, ensuring clear capture of minute changes in fluorescence signals within the droplets. An amplification kinetics parameter extraction algorithm is established to calculate the amplification efficiency E value in real time. The normal amplification efficiency is controlled within the range of 90%-110%, while the Ct value of the amplification curve is monitored simultaneously, with a typical Ct value range of 15-35 cycles. A multi-wavelength fluorescence detection system is used to simultaneously monitor FAM and RO. Signal changes from multiple fluorescent labels such as X and Cy5 were observed, with excitation wavelengths set at 495nm, 580nm, and 649nm, respectively, covering a detection spectral range of 500-700nm. An amplification competition analysis model was constructed to quantify the competition relationship between fragments by comparing the differences in amplification rates of different fragments under the same reaction conditions, achieving a competition index calculation accuracy of 0.01. An abnormal amplification identification mechanism was established, automatically identifying amplified droplets by analyzing parameters such as the slope changes, plateau characteristics, and melting temperature of the amplification curve, with an abnormality detection rate >95%. A kinetic feature vector containing 12 key parameters, including amplification efficiency, Ct value, maximum fluorescence intensity, and amplification rate constant, was extracted for each nucleic acid fragment. A multi-fragment amplification kinetic vector matrix was constructed with a matrix dimension of N×12 (N being the number of detected fragments), providing rich kinetic feature data for subsequent intelligent analysis.

[0014] Step S3: Collect nucleic acid conductivity parameters based on the confined electrode array, perform multi-scale feature analysis, and construct a nucleic acid feature marker map; In this embodiment, a nano-confined electrode array system integrated within a microfluidic chip is used, with the electrode spacing precisely controlled within the range of 50-200 nm. A gold-palladium alloy is employed to ensure good conductivity and biocompatibility. Conductivity parameters of nucleic acid molecules are collected under a confined electric field environment. An AC voltage amplitude is applied, controlled within the range of 10-100 mV, and the frequency range is set to 1 kHz-1 MHz. Lock-in amplifier technology is used to improve signal detection accuracy to the femtoampere level. An algorithm for recognizing the conductivity difference between single-stranded and double-stranded nucleic acids is established. The typical conductivity of single-stranded DNA is 10^-8 S / m, while that of double-stranded DNA is 10^-6 S / m. A reliable recognition threshold is established through statistical analysis. Combined with fluorescence signal detection, dual excitation wavelengths of 488 nm and 633 nm are used, with a detection emission spectrum range of 500-750 nm and a photoelectric conversion efficiency >80%, achieving simultaneous acquisition of electrochemical and optical signals. An integrated optical diffraction pattern is also included. The analysis system utilizes the principle of laser diffraction to detect the structural features of nucleic acid molecules. The laser wavelength is set to 532 nm, the diffraction angle detection range is ±30°, and the angular resolution is 0.1°. Multi-scale feature analysis is performed, establishing a cross-scale feature extraction framework from molecular-scale electrical conductivity to nanoscale structural morphology and micrometer-scale spatial distribution. A feature fusion algorithm is developed to standardize and weight the detection data of three modes: conductivity, fluorescence, and diffraction. The weights are 0.4 for conductivity, 0.4 for fluorescence, and 0.2 for diffraction. A 16-dimensional nucleic acid feature vector is constructed, including key parameters such as conductivity, capacitance, fluorescence intensity, emission peak position, diffraction peak intensity, and structure factor. Principal component analysis and clustering algorithms are used to reduce the dimensionality and classify the feature vector, constructing a two-dimensional visualized nucleic acid feature marker map with a resolution of 512×512 pixels, capable of clearly distinguishing nucleic acid molecules with different sequence characteristics.

[0015] Step S4: Perform intelligent nucleic acid sequence parsing on the multi-fragment amplification kinetic vector and nucleic acid feature marker map to construct an optimized nucleic acid sequence splicing map; In this embodiment, data fusion preprocessing is performed on the multi-fragment amplification kinetic vector and nucleic acid feature marker map to establish a unified feature space. Z-score standardization ensures the comparability of data from different modalities. A deep learning neural network model is used for intelligent nucleic acid sequence parsing. The network structure includes 3 convolutional layers, 2 LSTM layers, and 2 fully connected layers, with a total of approximately 1.5 million parameters. The training dataset contains 10,000+ known sequence samples. A sequence-feature mapping relationship learning algorithm is established, which establishes a nonlinear mapping function from multi-dimensional feature vectors to nucleic acid sequences through supervised learning, with a mapping accuracy >92%. A fragment overlap region identification algorithm is designed to identify overlapping sequences between fragments through sequence alignment and similarity analysis. The overlap length threshold is set to 20-50bp, and the similarity threshold is >85%. A graph theory-based fragment splicing algorithm is constructed, treating each nucleic acid fragment as a node in the graph and the overlap relationship between fragments as edge weights. The weight calculation comprehensively considers the overlap length, Sequence similarity and quality scoring were implemented. A multi-objective optimization function was established to simultaneously optimize three objectives: splicing accuracy, sequence coverage, and splicing consistency. A genetic algorithm was used for global optimization, with a population size of 100 and an evolutionary generation of 500. A splicing path evaluation mechanism was designed to calculate a comprehensive score for each candidate splicing path. The scoring function included factors such as sequence integrity (weight 0.3), splicing credibility (weight 0.3), biological rationality (weight 0.2), and computational complexity (weight 0.2). A dynamic splicing parameter adjustment system was established to adaptively adjust the splicing threshold and algorithm parameters based on data quality and fragment characteristics. The uncertainty of the splicing results was quantified using Monte Carlo sampling and Bayesian inference methods, and a confidence interval was assigned to each splicing result. A nucleic acid sequence splicing optimization map containing all candidate splicing paths, scoring information, and uncertainty metrics was constructed. The map adopted a directed acyclic graph structure, with the number of nodes N = the total number of fragments and the maximum number of edges being N(N-1) / 2.

[0016] Step S5: Perform a global temporal logical consistency comparison of the nucleic acid sequence splicing optimization map based on the time-series nucleic acid fragment data stream, and simultaneously generate an automated nucleic acid sequence parsing report.

[0017] In this embodiment, based on the time-series nucleic acid fragment data stream obtained in step S1, the nucleic acid sequence splicing optimization map constructed in step S4 is subjected to global time-series logic consistency comparison and verification; a time-series logic rule base is established, including three major categories of rules: biological time constraints (such as gene transcription order), physical time constraints (such as amplification time sequence), and chemical time constraints (such as reaction kinetic order), with a total of >200 rules; redundant amplification products from the same original fragment are identified through timestamp association analysis, and a fragment family clustering algorithm is established with a clustering accuracy >90%; an amplification artifact detection algorithm is designed to identify abnormal amplification products by comparing the amplification kinetic characteristics of fragments with theoretical expected values, with a detection sensitivity >85%; a time-series abnormal fragment marking system is established to automatically mark fragment combinations that violate time-series logic, with a marking accuracy >88%; a secondary analysis module is constructed to re-analyze the marked abnormal fragments using stricter quality control standards, including improving signal-to-noise ratio. The system employs several methods: increasing the threshold (>25dB), the number of validation experiments (≥3), and the feature dimensions (up to 20). A global consistency scoring mechanism is established, comprehensively considering four dimensions: temporal conformity, fragment integrity, amplification reliability, and sequence rationality, to calculate the overall parsing quality score. An adaptive threshold adjustment algorithm is designed to dynamically adjust the rigor of consistency testing based on sample complexity and data quality. Simultaneously, an automated nucleic acid sequence parsing report is generated, containing sequence parsing results, quality control data, experimental parameter records, and confidence assessments. The report format supports multiple output formats, including PDF, Excel, and XML. A parsing result visualization system is established, generating various charts such as sequence splicing path diagrams, quality distribution heatmaps, and temporal logic validation diagrams, supporting interactive viewing and data drill-down functions. A historical data comparison and analysis module is constructed to compare current parsing results with historical data, identifying repetitive and newly discovered sequences, providing a reference for subsequent research.

[0018] In this embodiment, see Figure 2 The diagram below illustrates the detailed implementation steps of step S1. In this embodiment, the detailed implementation steps of step S1 include: The flow of nucleic acid sample droplets into a microfluidic chip is detected, and the pressure and flow rate of different liquid path networks are defined to obtain fluid control parameters. Based on fluid control parameters, the nucleic acid sample droplets are pushed into the multi-region reaction chamber through the pressure valve array to obtain gradient-separated samples. Based on gradient separation, multi-cavity dynamic enrichment of samples is performed, reaction time points are calculated, and digital droplet conversion is carried out to obtain time-stamped nucleic acid fragments. Calculate the fragment length distribution and concentration gradient of the nucleic acid fragments; Dynamic flow rate regulation is performed based on the fragment length distribution and concentration gradient to generate gradient separation optimization parameters; By optimizing parameters based on gradient separation, droplet separation is controlled throughout its entire lifecycle to obtain a time-sequential nucleic acid fragment data stream.

[0019] In this embodiment, it is ensured that nucleic acid sample droplets can accurately flow into the various fluid pathways of the microfluidic chip. The microfluidic chip consists of multiple microchannels, each responsible for controlling the flow of different samples or reagents. Through pressure sensors, flow meters, and a microfluidic valve array, the flow rate and pressure can be precisely controlled, thereby achieving precise control of droplet flow into the designated reaction area. By adjusting the input pressure of different fluid pathways, flow rate differences can be generated, allowing the droplet flow to achieve the desired effect. During adjustment, pressure difference control ensures that sample droplets can smoothly flow into the target reaction chamber and avoid cross-contamination or unstable flow. Pressure and flow rate settings are predicted and adjusted based on a fluid dynamics model, ultimately ensuring the consistency and stability of the flow. This provides a stable fluid environment for subsequent nucleic acid separation and processing. After obtaining precise fluid control parameters, nucleic acid sample droplets are pushed into multiple reaction chambers in the microfluidic chip through a pressure valve array. Pressure differences in different regions create different flow rates and concentration gradients, allowing nucleic acids in the sample to be separated according to their physicochemical properties (such as size, charge, etc.). Adjusting the pressure in each zone allows for precise control of the droplet inflow sequence and velocity, enabling gradient separation of samples within the reaction chamber. Larger nucleic acid fragments flow into certain zones first, while smaller fragments enter other zones, creating different concentration and size gradients within different reaction chambers. This provides a foundation for subsequent nucleic acid enrichment and analysis. Real-time monitoring of pressure and flow rate is crucial to ensure that the separation effect in each zone meets expectations, ultimately creating optimal conditions for nucleic acid fragment enrichment and analysis.

[0020] By adjusting the flow rate, temperature, and reaction time of each reaction chamber, different nucleic acid fragments are effectively enriched under preset conditions. Nucleic acid fragments within each reaction chamber are enriched at different reaction times based on their characteristics; larger fragments typically require longer enrichment times, while smaller fragments can be enriched more quickly. Digital droplet conversion technology converts each nucleic acid fragment into a digital signal with a time stamp. This ensures that the reaction process for each fragment can be tracked and recorded over time. Precise control of the reaction time and flow rate in each reaction chamber ensures that all nucleic acid fragments are efficiently processed and enriched within the appropriate timeframe. Measuring the length of nucleic acid fragments using appropriate techniques, such as gel electrophoresis, mass spectrometry, or fluorescent probe methods, provides the accurate length distribution of each fragment. Simultaneously, flow meters and pressure sensors monitor changes in sample concentration within the reaction chambers, obtaining concentration gradient data. This data helps analyze the enrichment of samples in different reaction chambers and provides a basis for subsequent flow rate control. The calculation of fragment length distribution and concentration gradient can not only provide data support for optimizing flow rate regulation, but also provide important reference for the separation effect and enrichment efficiency of nucleic acid fragments.

[0021] Based on the nucleic acid fragment length distribution and concentration gradient data obtained in the previous step, the flow rate in the microfluidic system can be further adjusted to optimize the separation effect. The reaction effect of each nucleic acid fragment varies at different flow rates; longer fragments require slower flow rates to achieve sufficient enrichment, while shorter fragments can be processed efficiently at faster flow rates. Therefore, based on the fragment length distribution and concentration gradient, the flow rate and pressure can be adjusted to ensure that fragments in each reaction chamber are processed under suitable conditions. A fluid dynamics model is used to predict the impact of different flow rates on fragment distribution, thereby generating optimal flow rate control parameters. After obtaining the optimized flow rate and pressure parameters, the microfluidic system enters the full-cycle droplet separation control phase. Throughout the process, precise control of the flow rate, pressure, and temperature in each reaction chamber ensures that the nucleic acid fragments maintain a stable flow and processing state throughout the reaction. The flow of droplets within the microfluidic chip is precisely controlled, ensuring that each nucleic acid fragment can be separated under appropriate time and conditions. Finally, all nucleic acid fragment data will be time-seriested and recorded along with time stamps to generate a complete time-series data stream. These data streams contain not only sequence information of nucleic acid fragments, but also key information such as fragment length, concentration gradient, and reaction time.

[0022] In this embodiment, see Figure 3 The diagram below illustrates the detailed implementation steps of step S2. In this embodiment, the detailed implementation steps of step S2 include: The amplification process of nucleic acid sample droplets was detected and imaged using an optical microprobe array, and a nucleic acid amplification cycle image sequence was constructed. The time delay of the nucleic acid amplification cycle image sequence is calculated frame by frame, and the mean delay is optimized to obtain the time delay optimized image frame. The fluorescence intensity of time-delay optimized image frames is detected frame by frame, and multi-wavelength spectral analysis is performed to extract the optical response difference characteristics of different segments; Based on the optical response difference characteristics, inter-fragment amplification competition analysis is performed to generate multi-fragment amplification competition trajectories; Amplification efficiency is evaluated based on the multi-fragment amplification competition trajectory, and amplification kinetics are analyzed to construct a multi-fragment amplification kinetic vector.

[0023] In this embodiment, an optical microprobe array is used to monitor the amplification process of nucleic acid sample droplets in real time. The microprobe array employs high-precision optical probes to capture the amplification reaction of each droplet by focusing them onto the nucleic acid droplets on the microfluidic chip. The amplification process is typically marked by fluorescence signals; nucleic acids exhibit changes in fluorescence intensity over time during amplification. By precisely controlling the wavelength of the light source and the sensitivity of the detection system, continuous imaging of each droplet can be achieved with very high temporal resolution. This process typically relies on high-resolution optical microscopes or laser scanning equipment to record the dynamic changes of nucleic acids at different stages of the amplification reaction. As the amplification reaction proceeds, the fluorescence signal intensity within the droplets exhibits a certain temporal variation pattern. These changes are captured by the optical microprobe array and converted into image data. Using this image data, an image sequence of the nucleic acid amplification cycle can be constructed, clearly showing the change in fluorescence intensity of each droplet over time during the amplification process. The temporal differences in the appearance of fluorescence signals during the amplification process of each droplet are analyzed, and optimization is performed based on the amplification speed of different droplets. By extracting the timestamp data of each frame of the image, the difference in amplification start time between different droplets can be calculated. Since the amplification rate of droplets can be affected by factors such as sample concentration, temperature, and amplification enzyme activity, the time delay of the amplification reaction may vary. Based on this delay information, mean delay optimization can be performed on image frames to adjust the temporal synchronization of each frame in the image sequence, ensuring that the amplification states of all droplets can be compared on the same time scale. This optimization method smooths the time axis of the image sequence, eliminating time deviations caused by differences in reaction rates between droplets, and ensuring that the final generated image frames accurately and realistically reflect the subtle changes in the amplification process.

[0024] Fluorescence intensity detection is performed on each optimized image frame. In this stage, the fluorescence signal in each frame needs to be extracted, and the changes in fluorescence intensity at different time points in the droplet need to be analyzed. Fluorescence intensity is usually related to the progress of the amplification reaction; by analyzing the intensity of the fluorescence signal, the efficiency and progress of nucleic acid amplification can be determined. To further analyze the amplification of different fragments, multi-wavelength spectral analysis of the image frames is required. Nucleic acid fragments within the microfluidic chip may contain different fluorescent labels or different fluorescence response characteristics. Using a multi-wavelength laser source can simultaneously excite signals from different fluorophores and acquire optical responses at multiple wavelengths. In this way, the optical response differences of different nucleic acid fragments during the amplification process can be extracted, thereby analyzing the amplification kinetics of each fragment. This process not only relies on multi-wavelength spectral detection but also requires specialized spectral analysis algorithms to separate fluorescence signals at different wavelengths, ensuring that the response differences of each nucleic acid fragment can be clearly extracted. During nucleic acid amplification, the amplification rate of different fragments may vary due to factors such as fragment length, sequence characteristics, or label concentration. To assess the competition between these fragments, the fluorescence response of each fragment needs to be compared to construct a multi-fragment amplification competition trajectory. This trajectory illustrates the dynamic process of competition between different fragments during amplification, typically represented by curves showing the change in fluorescence signal intensity of different fragments over time. By comparing these curves, it is possible to reveal which fragments amplify faster, which fragments amplify slower, and the interactions between different fragments during amplification.

[0025] By analyzing the competitive trajectories of multiple fragments during amplification, the next step is to evaluate amplification efficiency. The core of this evaluation is analyzing the relationship between fluorescence signal intensity and time for each fragment during amplification, thereby calculating the amplification rate of each fragment. Differences in amplification rates among different fragments reflect their amplification efficiency under specific conditions. Based on changes in amplification rates, parameters of amplification kinetics, such as reaction rate constants and delay times, can be further derived. Amplification kinetic analysis involves establishing mathematical models to describe the physicochemical changes during the amplification process of different fragments. These models typically consider the complexity of nucleic acid amplification reactions, including factors such as enzyme catalysis, template and primer binding efficiency, etc. By establishing and fitting amplification kinetic models, amplification kinetic vector for each fragment can be obtained. These vectors contain key parameters for fragment amplification, such as initial concentration, reaction rate, and final concentration. These analytical results provide a deeper understanding of the behavior of different fragments during nucleic acid amplification.

[0026] In this embodiment, step S3 includes the following steps: Nucleic acid conductivity parameters are acquired based on the confined electrode array; Based on the nucleic acid conductivity parameters, the difference in conductivity between single-stranded and double-stranded nucleic acids is identified, and a nucleic acid identification feature matrix is ​​generated. Optical diffraction analysis is performed on the time-delay optimized image frames to generate nucleic acid optical diffraction features; Multi-scale feature analysis was performed on the optical diffraction characteristics and nucleic acid recognition feature matrix of nucleic acids to construct a nucleic acid feature marker map.

[0027] In this embodiment, a confined electrode array is used to collect conductivity parameters of nucleic acid samples. A confined electrode array is a sensor array composed of multiple microelectrodes that can perform real-time conductivity measurements on passing droplets within a microfluidic chip. Conductivity refers to a substance's ability to conduct electric current. For nucleic acids, their conductivity characteristics are influenced by their molecular structure and morphology. During nucleic acid amplification, the conductivity difference between single-stranded and double-stranded nucleic acids is significant. Single-stranded nucleic acids, due to their high flexibility and small molecular weight, exhibit different conductivity characteristics, while double-stranded nucleic acids possess greater molecular rigidity and structural stability. Therefore, by real-time acquisition of conductivity parameters from nucleic acid samples in droplets, key information about the nucleic acids can be obtained, including their strand state (single-stranded or double-stranded), concentration, and conductivity response. The confined electrode array can not only perform accurate conductivity measurements at different flow rates but also optimize the sensitivity of the conductivity signal by changing the electrode voltage and frequency. The collected nucleic acid conductivity parameters can be used to distinguish between single-stranded and double-stranded nucleic acids. Single-stranded nucleic acids exhibit high flexibility and low conductivity response, while double-stranded nucleic acids are more rigid and have a stronger conductivity response. By comparing the conductivity characteristics of nucleic acids in different states, the conductivity differences between single-stranded and double-stranded nucleic acids can be identified. By analyzing the changing trends of conductivity data, a conductivity feature matrix can be established for each nucleic acid sample in a droplet. This matrix not only includes the conductivity value itself but also incorporates conductivity changes at different time points, helping to further identify the state of the nucleic acid. Through the collaborative work of multiple electrode arrays, high-precision conductivity data can be obtained, thereby helping to determine whether the nucleic acid in each droplet is single-stranded or double-stranded. This feature matrix can be regarded as the conductivity "fingerprint" of each droplet, providing important information for subsequent nucleic acid analysis. In practice, filtering, denoising, and standardizing the conductivity signal can improve the accuracy of identification and avoid data deviations caused by noise and interference.

[0028] Time-delay optimized image frames, by adjusting the time synchronization of different droplets, can more accurately reflect changes during nucleic acid amplification. Based on these optimized image frames, optical diffraction analysis is performed to further extract the optical response characteristics of nucleic acids. Under an optical microscope, the difference in refractive index between nucleic acid molecules and their surrounding medium causes light diffraction. By performing optical diffraction analysis on these image frames, diffraction patterns related to the interaction of nucleic acid molecules can be obtained. These diffraction patterns usually change with the progress of the amplification reaction, especially during the amplification of different nucleic acid fragments, as changes in molecular weight, morphology, and conformation lead to changes in the diffraction patterns. Through high-resolution diffraction image analysis, unique optical diffraction features can be extracted from the nucleic acid amplification reaction. These features not only contain the physical size of the nucleic acid but also reflect its structural changes. To enhance the analysis effect of optical diffraction, multi-angle, multi-wavelength laser irradiation is usually used, combined with highly sensitive detectors to acquire and analyze diffraction signals. These diffraction features provide more dimensions for further nucleic acid analysis and can complement electrical conductivity features, improving the accuracy of nucleic acid sequence resolution.

[0029] Multi-scale analysis can extract key features at different scales to more accurately identify and distinguish different nucleic acid fragments. Optical diffraction features and electrical conductivity feature matrices provide complementary information; electrical conductivity data reflects the chain state of the nucleic acid, while diffraction patterns reflect its physical size and morphology. By performing multi-scale analysis on these two features, valuable nucleic acid information can be extracted at different resolutions, such as the amplification efficiency, conformational changes, and physical states of different nucleic acid fragments. Multi-scale analysis is typically implemented using techniques such as wavelet transform and Fourier transform, which can extract specific features at different scales and frequencies. By combining and weighting these multi-scale features, a nucleic acid feature map can be constructed. This map contains complete information about each droplet nucleic acid sample, including its electrical conductivity state, optical changes during amplification, and their interrelationships. This feature map not only provides accurate reference data for subsequent automatic nucleic acid sequence interpretation but also reveals potential problems in the nucleic acid amplification process, such as amplification inhomogeneity or fragment competition.

[0030] In this embodiment, the specific steps of step S4 are as follows: Intelligent nucleic acid sequence parsing is performed on multi-fragment amplification kinetic vectors and nucleic acid feature marker maps to generate multiple nucleic acid fragment parsing sequences; The system identifies conflicting splicing paths and base error paths in the parsed sequences of multiple nucleic acid fragments and marks abnormal splicing paths. Abnormal splicing paths are removed, and the best splicing segments are selected and marked. The optimal splicing fragments are learned by fragment connection pattern learning and adaptive splicing is performed to construct an optimized nucleic acid sequence splicing map.

[0031] In this embodiment, the amplification path and termination point of each fragment are determined by utilizing information such as amplification rate, delay time, and fluorescence intensity extracted from the amplification kinetics vector. Combined with fragment features in the nucleic acid feature marker map, such as fragment length and sequence complexity, each fragment can be precisely oriented and sorted. The resolved sequences of these fragments are automatically generated using intelligent algorithms such as machine learning and image recognition. Machine learning models (such as convolutional neural networks) can be trained on large amounts of known data to accurately predict and resolve the sequences of unknown nucleic acid fragments. In practice, support vector machines (SVM) or deep learning methods are typically used to construct feature extraction models. The conductivity data, optical diffraction characteristics, and amplification kinetics information of each fragment are input into the model to ultimately obtain the resolved sequence of each nucleic acid fragment. After obtaining the resolved sequences of multiple nucleic acid fragments, the next step is to splice these fragments and identify potential errors during the splicing process. During the splicing process, errors in the amplification process, incomplete sequencing, or repetitive sequences between fragments may cause conflicts in the splicing paths. By comparing and verifying all resolved sequences, splicing algorithms can detect these conflicting paths. For example, if certain sequences cannot be perfectly aligned during assembly, or if positions that do not conform to natural base pairing rules (such as AT, GC) appear, they can be identified as anomalous paths. To identify these conflicting assembly paths, dynamic programming algorithms (such as the Smith-Waterman algorithm) and hash alignment techniques are commonly used. These methods can identify mismatched parts and mark them as anomalous assembly paths through global or local alignment strategies. Simultaneously, base error path identification algorithms, such as Bayesian inference methods based on maximum likelihood estimation, can be used to evaluate the accuracy of each assembly path and identify potential error paths. Through these detection methods, inaccurate assembly paths that may be caused by assembly errors or missing data can be discovered and marked during nucleic acid sequence assembly, thus providing a basis for subsequent error removal and optimization.

[0032] Anomaly removal typically employs quality control (QC) methods, relying on error alignment data and conflict path information encountered during the assembly process to filter potentially problematic fragments. Removal criteria may include base mismatches, abnormal frequency of repetitive sequences, and mismatched assembly regions. By comparing with reference databases or known sequence libraries, anomalies can be compared with standard sequences to further confirm the accuracy of the removal. The removed fragments are then further screened to select the most representative optimal assembly fragment. This process relies on quality assessment criteria for the assembly fragments, such as coverage, sequence consistency, and base correctness. The optimal assembly fragment refers to those that maximally cover the target region without significant errors. Commonly used optimal selection methods include maximum matching algorithms based on coverage, information entropy models, and statistical analysis models. These methods can automatically identify the best assembly result based on the effectiveness and alignment of each fragment.

[0033] The goal of fragment connection pattern learning is to construct an adaptive splicing model by analyzing the splicing patterns of known sequences. This model can automatically adjust the splicing strategy according to different splicing requirements. For example, certain fragments may affect splicing accuracy due to specific sequence characteristics (such as high GC content, complex secondary structures, etc.). Deep learning methods, especially those based on recurrent neural networks (RNNs) and long short-term memory networks (LSTMs), can model the connection patterns of these fragments, enabling the system to learn how to automatically adapt to these special cases. The adaptive splicing algorithm can optimize the splicing path based on the specific sequence information, amplification history, and characteristic parameters (such as length, repetitive sequences, GC content, etc.) of the nucleic acid fragments. During adaptive splicing, the system verifies each splicing result through a real-time feedback mechanism. If splicing problems occur, the splicing strategy is automatically adjusted to improve splicing accuracy. Ultimately, the entire splicing process generates a nucleic acid sequence splicing optimization map, which not only shows the connection relationships of each fragment but also reflects the optimized path and adjustment strategy during the splicing process. This optimized map provides strong support for nucleic acid sequence analysis, effectively improving the integrity and accuracy of the assembly and ensuring that the final analyzed sequence meets the expected goals.

[0034] In this embodiment, the specific steps of step S5 are as follows: Based on the time-series nucleic acid fragment data stream, a global temporal logic consistency comparison is performed on the nucleic acid sequence splicing optimization map, and nucleic acid fragments with temporal logic errors are marked. Amplification artifacts are identified in nucleic acid fragments with temporal logic errors, and multiple rounds of iterative correction are performed to generate iteratively corrected nucleic acid fragments. Sequence consistency assessment was performed on the iteratively corrected nucleic acid fragments, and fusion analysis was conducted based on the optimized nucleic acid sequence splicing map to construct a high-confidence sequence baseline. Identify any missed or unparsed nucleic acid fragments; perform secondary nucleic acid sequence parsing; and simultaneously generate an automated nucleic acid sequence parsing report.

[0035] In this embodiment, during the nucleic acid sequence assembly process, the time-series nucleic acid fragment data stream provides the time progress and related characteristic information of nucleic acid amplification. Based on this time-series data, a global time-series logical consistency comparison is first performed. The purpose of the comparison is to ensure that all nucleic acid fragments conform to a reasonable time sequence during assembly, avoiding confusion in the assembly order caused by time delays or other factors during the amplification process. The assembly order of nucleic acid sequences is usually related to the amplification time sequence, fragment length, and overlapping areas between fragments. Therefore, through time-series logical comparison, the correct positioning of these fragments in the overall sequence can be ensured based on the amplification time, location, and characteristics of each nucleic acid fragment. Time-series logical consistency comparison can be implemented by constructing a time-series graph, where each node represents a nucleic acid fragment, and the edges between nodes represent the order and logical relationship between fragments. Through dynamic programming or graph-based comparison methods, fragments with time-series inconsistencies during the assembly process can be effectively identified. These fragments with time-series logical errors usually manifest as time conflicts or errors in amplification status, and the system automatically marks these fragments and prepares them for further processing. Amplification artifacts refer to erroneous signals or fragments generated during nucleic acid amplification due to sample contamination, uneven amplification reaction, or other experimental errors. These artifacts typically produce false signals in the amplification map, leading to errors in the splicing path. These artifacts can be identified and marked as potential erroneous fragments by comparing them with a reference database or known nucleic acid sequences. Amplification artifact identification can be achieved by analyzing conductance parameters, optical diffraction characteristics, and outliers in amplification time. Artifacts usually manifest as signals that do not conform to conventional amplification patterns. After identifying artifacts, the system performs multiple rounds of iterative correction on these temporally erroneous fragments. The core idea of ​​iterative correction is to reduce the impact of experimental errors by correcting and optimizing erroneous fragments, gradually restoring the true sequence of the fragments. In each round of correction, the system makes feedback adjustments based on various information sources (such as conductance data, optical images, amplification time, etc.) to correct the temporal position and characteristic values ​​of the fragments.

[0036] The corrected nucleic acid fragments undergo sequence consistency assessment. This ensures that each corrected fragment is consistent with other fragments and with the expected nucleic acid sequence standard. Sequence consistency assessment typically relies on comparative analysis techniques, such as local or global alignment, to compare the nucleic acid fragments with known reference sequences and check for errors such as sequence mismatches, deletions, or insertions. This process helps identify and correct inconsistencies caused by errors during amplification or splicing. Based on the assessment results, the system further optimizes the corrected fragments and performs fusion analysis using a nucleic acid sequence splicing optimization map. The splicing optimization map provides global relationships and splicing orders between nucleic acid fragments, allowing for adjustments to the splicing strategy based on the sequence consistency assessment results, further improving splicing accuracy. Finally, the system constructs a high-confidence sequence baseline based on the fusion analysis results. This baseline represents the optimized and corrected nucleic acid sequence, containing the correct splicing order and sequence information of all fragments, and providing a reliable data foundation for subsequent analysis. After constructing the high-confidence sequence baseline, the system also needs to detect any missed unresolved nucleic acid fragments. Missing fragments typically occur because they were not fully resolved or assembled during the initial resolution process due to inefficient amplification or sample contamination. These missing segments can be identified by comparing the resolved sequences with the unresolved fragments. The system automatically marks these unresolved fragments and performs secondary nucleic acid sequence resolution. Secondary resolution usually uses improved algorithms or more sensitive detection techniques, such as higher-resolution conductivity probes or more precise optical imaging techniques, to reanalyze the characteristics of the missing fragments and perform supplementary resolution. During secondary resolution, the system again compares these fragments with the time sequence, diffraction characteristics, and conductivity data from the amplification process to ensure that missing fragments are accurately resolved. After secondary resolution, the system simultaneously generates an automated nucleic acid sequence resolution report. This report includes sequence information for all resolved fragments, splicing maps, the identification and correction process for abnormal fragments, and the final high-confidence sequence baseline. This report provides comprehensive and reliable resolution results for subsequent genomics research and clinical applications.

[0037] In this embodiment, the specific steps for identifying missed unparsed nucleic acid fragments, performing secondary nucleic acid sequence parsing, and simultaneously generating an automated nucleic acid sequence parsing report are as follows: Identifying missed unparsed nucleic acid fragments based on time-sequential nucleic acid fragment data streams; Based on a high-confidence sequence baseline, unresolved nucleic acid fragments are subjected to targeted re-amplification and selective extension, followed by secondary nucleic acid sequence analysis, and an automated nucleic acid sequence analysis report is generated simultaneously. Based on the automated nucleic acid sequence analysis report, the entire process of quality tracking and automatic closed-loop optimization is carried out to build a nucleic acid sequence closed-loop analysis optimization mechanism.

[0038] In this embodiment, during nucleic acid sequence resolution, missed fragments are often caused by incomplete amplification or insufficient sequencing coverage. Therefore, accurately identifying these missed fragments is crucial for ensuring the integrity of the nucleic acid sequence. The time-series nucleic acid fragment data stream provides information such as amplification time, flow rate, and amplification kinetics for each fragment. This information is essential for identifying which fragments have not been correctly resolved. A comprehensive analysis of the time-series data stream can identify fragments that were not fully resolved or failed to reach the expected amplification level within a predetermined time frame. Specifically, the time-series data of resolved fragments is first used as a benchmark to compare the amplification time, flow rate, and other parameters of unresolved fragments to observe for delays or omissions. In the flow control chip, if the amplification signal of certain fragments lags significantly in the time series or fails to be fully resolved within the expected amplification cycle, the system automatically marks these fragments as missed fragments. Dynamic time-series analysis and outlier detection technology can efficiently identify these unresolved nucleic acid fragments. Missed fragments are located using a high-confidence sequence baseline. High-confidence baselines are typically nucleic acid sequences that have undergone multiple rounds of optimization and calibration, containing the most accurate fragment assembly information. Therefore, based on these high-confidence reference sequences, missed fragments can be precisely targeted, ensuring the amplification reaction focuses on these key regions, thereby improving amplification efficiency. During selective extension, the system uses microfluidic chip fluid control technology to precisely guide the sample to specific regions for re-amplification. Using more sensitive amplification techniques and extension reactions further ensures that missed fragments are fully amplified, avoiding omissions due to insufficient amplification or sample bias. The re-amplified fragments will undergo nucleic acid sequence analysis again, typically by increasing the number of amplification cycles or using different amplification strategies to ensure all fragments are covered. The secondary analysis process is similar to the initial analysis, but at this stage, the system optimizes for the characteristics of the missed fragments. For example, a more precise time-matching algorithm is used to monitor the amplification progress of these fragments, or enhanced optical imaging techniques (such as multi-wavelength fluorescence detection) are used to improve signal sensitivity. After the secondary analysis is completed, an automated nucleic acid sequence analysis report is generated simultaneously. The report will detail the re-analyzed fragments, updated splicing information, and corrected sequence content. It will also provide verification results on the re-amplification of missing fragments to ensure that the final sequence has high accuracy and completeness.

[0039] Automated nucleic acid sequence analysis reports not only present the final analysis results but also form the basis for analysis and optimization processes. To ensure high quality in the nucleic acid sequence analysis process, quality tracking and closed-loop optimization of the entire process are essential. End-to-end quality tracking involves precise control and verification of each step, from initial sample preparation, nucleic acid amplification, fragment analysis, and timing alignment to final assembly optimization. By recording data at each step in the report, every parameter throughout the experiment can be retrospectively analyzed to identify potential quality issues. At this point, the quality control system (such as a real-time monitoring system and feedback adjustment mechanisms) can adjust experimental conditions based on real-time data, thereby avoiding result deviations caused by environmental fluctuations or operational errors.

[0040] The automatic closed-loop optimization mechanism relies on a feedback control system. After each parsing report is generated, the system automatically evaluates the accuracy, completeness, and consistency of the results. For example, if quality issues are found in the parsing of certain segments (such as low signal strength or inconsistent amplification progress), the system will adjust the strategy for subsequent parsings based on this feedback. This could involve adjusting the flow rate in the flow control chip, the amplification reaction time, or selecting more suitable amplification conditions. Furthermore, closed-loop optimization also includes adjustments to the algorithm itself, such as optimizing the accuracy of segment recognition and splicing algorithms through deep learning algorithms.

[0041] The system also adaptively optimizes the algorithm based on quality feedback after each iteration, continuously improving the parsing methods and experimental conditions, ultimately forming a stable and efficient nucleic acid sequence parsing mechanism. In this way, any interference from sample quality, amplification conditions, or the sequencing process will be promptly corrected through the system's automated closed-loop adjustments, ensuring that the final nucleic acid sequence parsing results consistently maintain high quality. By establishing this closed-loop optimization mechanism, the reliability and accuracy of nucleic acid sequence parsing can be continuously improved, adapting to constantly changing experimental conditions and technical requirements.

[0042] In this embodiment, a microfluidic chip-based automatic nucleic acid sequence parsing device is provided for executing the microfluidic chip-based automatic nucleic acid sequence parsing method described above, including: The gradient separation module is used to detect the flow of nucleic acid sample droplets into the microfluidic chip, perform stepwise gradient separation and full-cycle regulation, and obtain a time-sequential nucleic acid fragment data stream. The amplification analysis module is used to detect and image the amplification process of nucleic acid sample droplets and analyze the amplification kinetics, and to construct multi-fragment amplification kinetic vectors. The nucleic acid conductivity module is used to collect nucleic acid conductivity parameters based on the confined electrode array, perform multi-scale feature analysis, and construct a nucleic acid feature marker map; The sequence parsing module is used to perform intelligent nucleic acid sequence parsing on multi-fragment amplification kinetic vectors and nucleic acid feature marker maps, and to construct an optimized nucleic acid sequence splicing map. The logical alignment module is used to perform global temporal logical consistency alignment of nucleic acid sequence splicing optimization maps based on time-series nucleic acid fragment data streams, and simultaneously generate automated nucleic acid sequence parsing reports.

[0043] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0044] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement it. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein are implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A method for automatic nucleic acid sequence parsing based on microfluidic chips, characterized in that, The microfluidic chip embeds a multi-region reaction chamber, a confined electrode array, and a multi-layer liquid path network, with pressure valve arrays integrated at the intersections of the multi-layer liquid path network; the process includes the following steps: Step S1: Detect the flow of nucleic acid sample droplets into the microfluidic chip, perform stepwise gradient separation and full-cycle regulation to obtain a time-sequential nucleic acid fragment data stream; Step S2: Detect and image the amplification process of nucleic acid sample droplets and analyze the amplification kinetics to construct a multi-fragment amplification kinetic vector; Step S3: Collect nucleic acid conductivity parameters based on the confined electrode array, perform multi-scale feature analysis, and construct a nucleic acid feature marker map; Step S4: Perform intelligent nucleic acid sequence parsing on the multi-fragment amplification kinetic vector and nucleic acid feature marker map to construct an optimized nucleic acid sequence splicing map; Step S5: Perform a global temporal logical consistency comparison of the nucleic acid sequence splicing optimization map based on the time-series nucleic acid fragment data stream, and simultaneously generate an automated nucleic acid sequence parsing report.

2. The automatic nucleic acid sequence parsing method based on microfluidic chips according to claim 1, characterized in that, The specific steps of step S1 are as follows: The flow of nucleic acid sample droplets into a microfluidic chip is detected, and the pressure and flow rate of different liquid path networks are defined to obtain fluid control parameters. Based on fluid control parameters, the nucleic acid sample droplets are pushed into the multi-region reaction chamber through the pressure valve array to obtain gradient-separated samples. Based on gradient separation, multi-cavity dynamic enrichment of samples is performed, reaction time points are calculated, and digital droplet conversion is carried out to obtain time-stamped nucleic acid fragments. Calculate the fragment length distribution and concentration gradient of the nucleic acid fragments; Dynamic flow rate regulation is performed based on the fragment length distribution and concentration gradient to generate gradient separation optimization parameters; By optimizing parameters based on gradient separation, droplet separation is controlled throughout its entire lifecycle to obtain a time-sequential nucleic acid fragment data stream.

3. The automatic nucleic acid sequence parsing method based on microfluidic chips according to claim 1, characterized in that, The specific steps of step S2 are as follows: The amplification process of nucleic acid sample droplets was detected and imaged using an optical microprobe array, and a nucleic acid amplification cycle image sequence was constructed. The time delay of the nucleic acid amplification cycle image sequence is calculated frame by frame, and the mean delay is optimized to obtain the time delay optimized image frame. The fluorescence intensity of time-delay optimized image frames is detected frame by frame, and multi-wavelength spectral analysis is performed to extract the optical response difference characteristics of different segments; Based on the optical response difference characteristics, inter-fragment amplification competition analysis is performed to generate multi-fragment amplification competition trajectories; Amplification efficiency is evaluated based on the multi-fragment amplification competition trajectory, and amplification kinetics are analyzed to construct a multi-fragment amplification kinetic vector.

4. The automatic nucleic acid sequence parsing method based on microfluidic chips according to claim 1, characterized in that, Step S3 is as follows: Nucleic acid conductivity parameters are acquired based on the confined electrode array; Based on the nucleic acid conductivity parameters, the difference in conductivity between single-stranded and double-stranded nucleic acids is identified, and a nucleic acid identification feature matrix is ​​generated. Optical diffraction analysis is performed on the time-delay optimized image frames to generate nucleic acid optical diffraction features; Multi-scale feature analysis was performed on the optical diffraction characteristics and nucleic acid recognition feature matrix of nucleic acids to construct a nucleic acid feature marker map.

5. The automatic nucleic acid sequence parsing method based on microfluidic chips according to claim 1, characterized in that, The specific steps of step S4 are as follows: Intelligent nucleic acid sequence parsing is performed on multi-fragment amplification kinetic vectors and nucleic acid feature marker maps to generate multiple nucleic acid fragment parsing sequences; The system identifies conflicting splicing paths and base error paths in the parsed sequences of multiple nucleic acid fragments and marks abnormal splicing paths. Abnormal splicing paths are removed, and the best splicing segments are selected and marked. The optimal splicing fragments are learned by fragment connection pattern learning and adaptive splicing is performed to construct an optimized nucleic acid sequence splicing map.

6. The automatic nucleic acid sequence parsing method based on microfluidic chips according to claim 1, characterized in that, The specific steps of step S5 are as follows: Based on the time-series nucleic acid fragment data stream, a global temporal logic consistency comparison is performed on the nucleic acid sequence splicing optimization map, and nucleic acid fragments with temporal logic errors are marked. Amplification artifacts are identified in nucleic acid fragments with temporal logic errors, and multiple rounds of iterative correction are performed to generate iteratively corrected nucleic acid fragments. Sequence consistency assessment was performed on the iteratively corrected nucleic acid fragments, and fusion analysis was conducted based on the optimized nucleic acid sequence splicing map to construct a high-confidence sequence baseline. Identify any missed or unparsed nucleic acid fragments; perform secondary nucleic acid sequence parsing; and simultaneously generate an automated nucleic acid sequence parsing report.

7. The automatic nucleic acid sequence parsing method based on microfluidic chips according to claim 6, characterized in that, The specific steps for identifying missed unparsed nucleic acid fragments, performing secondary nucleic acid sequence parsing, and simultaneously generating an automated nucleic acid sequence parsing report are as follows: Identifying missed unparsed nucleic acid fragments based on time-sequential nucleic acid fragment data streams; Based on a high-confidence sequence baseline, unresolved nucleic acid fragments are subjected to targeted re-amplification and selective extension, followed by secondary nucleic acid sequence analysis, and an automated nucleic acid sequence analysis report is generated simultaneously. Based on the automated nucleic acid sequence analysis report, the entire process of quality tracking and automatic closed-loop optimization is carried out to build a nucleic acid sequence closed-loop analysis optimization mechanism.

8. A microfluidic chip-based automatic nucleic acid sequence parsing device, characterized in that, The method for performing automatic nucleic acid sequence parsing based on a microfluidic chip as described in claim 1 includes: The gradient separation module is used to detect the flow of nucleic acid sample droplets into the microfluidic chip, perform stepwise gradient separation and full-cycle regulation, and obtain a time-sequential nucleic acid fragment data stream. The amplification analysis module is used to detect and image the amplification process of nucleic acid sample droplets and analyze the amplification kinetics, and to construct multi-fragment amplification kinetic vectors. The nucleic acid conductivity module is used to collect nucleic acid conductivity parameters based on the confined electrode array, perform multi-scale feature analysis, and construct a nucleic acid feature marker map; The sequence parsing module is used to perform intelligent nucleic acid sequence parsing on multi-fragment amplification kinetic vectors and nucleic acid feature marker maps, and to construct an optimized nucleic acid sequence splicing map. The logical alignment module is used to perform global temporal logical consistency alignment of nucleic acid sequence splicing optimization maps based on time-series nucleic acid fragment data streams, and simultaneously generate automated nucleic acid sequence parsing reports.

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