Intelligent molecular diagnosis and analysis method, device and equipment and storage medium

Abstract

The invention relates to the field of DNA amplification, in particular to an intelligent molecular diagnosis and analysis method, device and equipment and a storage medium. The method comprises the following steps: detecting multi-dimensional environmental parameters of the DNA amplification kit in real time based on a multi-dimensional sensing node; performing spatial topology layout mining and environment amplification perception fitting on the multi-dimensional environment parameters, and constructing a real-time amplification perception model; identifying a DNA amplification sample; performing layered temperature control analysis on the DNA amplification sample, and performing dynamic amplification constant-temperature control on the real-time amplification perception model, so as to construct a plurality of dynamic constant-temperature amplification models; carrying out regional DNA reaction degree evaluation on the plurality of dynamic isothermal amplification models one by one, and then carrying out regional flow velocity dynamic regulation, so as to construct a dynamic amplification reaction flow velocity regulation strategy; real-time amplification control is performed based on a dynamic amplification reaction flow rate adjustment strategy to generate a filter optimized amplification reaction signal. According to the method, the accuracy of molecular diagnosis analysis is improved through efficient and convenient DNA isothermal amplification.

Description

Technical Field

[0001] The present invention relates to the field of DNA amplification analysis, and in particular to an intelligent molecular diagnosis and analysis method, device, equipment and storage medium. Background Art

[0002] With the rapid development of molecular biology and genetic technology, DNA testing has been widely used in medical diagnosis, pathogen detection, genetic disease screening and other fields. Traditional PCR (polymerase chain reaction) technology occupies an important position in molecular diagnosis, but its application in on-site rapid detection is limited by the need for complex temperature control equipment and long operation time. DNA isothermal amplification technology has gradually become a molecular diagnostic method with broad application prospects because it can amplify at a constant temperature, does not require complex temperature control equipment, and has a fast reaction speed.

[0003] However, although DNA isothermal amplification technology has shown great potential in many fields, the existing isothermal amplification technology still faces many challenges. Traditional isothermal amplification methods, such as LAMP (loop-mediated isothermal amplification) and RPA (recombinase polymerase amplification), meet the requirements of sensitivity and specificity to a certain extent, but these methods often rely on relatively simple reaction systems and are affected by inhibitors in complex sample environments, resulting in unstable amplification effects. In addition, traditional methods usually rely on external equipment for monitoring and quantitative analysis of amplification products, making it difficult to achieve convenient, real-time, and accurate on-site diagnosis.

[0004] Therefore, with the continuous advancement of molecular diagnostic technology, higher requirements are placed on more intelligent, rapid and efficient DNA isothermal amplification technology. In this context, it is particularly important to develop a new type of DNA isothermal amplification molecular diagnostic analysis method that can overcome the limitations of existing technologies. Summary of the invention

[0005] In order to solve the above technical problems, the present invention proposes an intelligent molecular diagnosis and analysis method, device, equipment and storage medium to solve at least one of the above technical problems.

[0006] To achieve the above object, the present invention provides an intelligent molecular diagnosis and analysis method, comprising the following steps: Step S1: Real-time detection of multi-dimensional environmental parameters of the DNA amplification kit based on multi-dimensional sensor nodes; performing spatial topological layout mining and environmental amplification perception fitting on the multi-dimensional environmental parameters to construct a real-time amplification perception model; Step S2: Identify DNA amplification samples; perform layered temperature control analysis on the DNA amplification samples, and perform dynamic amplification constant temperature control on the real-time amplification perception model, thereby constructing multiple dynamic constant temperature amplification models; Step S3: evaluating the DNA reaction degree of each region of the multiple dynamic constant temperature amplification models, and then dynamically adjusting the regional flow rate, thereby constructing a dynamic amplification reaction flow rate adjustment strategy; Step S4: performing real-time amplification control based on the dynamic amplification reaction flow rate adjustment strategy, and performing dynamic signal filtering to generate a filtered optimized amplification reaction signal; Step S5: performing multi-period amplification reaction analysis on the filtered optimized amplification reaction signal, and performing dynamic amplification timing mining to construct a dynamic amplification curve; Step S6: Perform regional quantitative analysis and comprehensive diagnosis based on the dynamic amplification curve to obtain a sample diagnosis visualization report.

[0007] The present invention also provides an intelligent molecular diagnosis and analysis device, comprising: An amplification perception module is used to detect the multi-dimensional environmental parameters of the DNA amplification kit in real time based on multi-dimensional sensor nodes; perform spatial topological layout mining and environmental amplification perception fitting on the multi-dimensional environmental parameters to build a real-time amplification perception model; The constant temperature control module is used to identify DNA amplification samples; perform layered temperature control analysis on DNA amplification samples, and perform dynamic amplification constant temperature control on the real-time amplification perception model, thereby constructing multiple dynamic constant temperature amplification models; The flow rate adjustment module is used to evaluate the DNA reaction degree of each region of multiple dynamic constant temperature amplification models, and then dynamically adjust the regional flow rate, so as to construct a dynamic amplification reaction flow rate adjustment strategy; An amplification signal processing module, used to perform real-time amplification control based on a dynamic amplification reaction flow rate adjustment strategy, and to perform dynamic signal filtering to generate a filtered optimized amplification reaction signal; The dynamic amplification analysis module is used to perform multi-period amplification reaction analysis on the filtered optimized amplification reaction signal, and to perform dynamic amplification timing mining to construct a dynamic amplification curve; The diagnostic visualization module is used to perform regional quantitative analysis and comprehensive diagnosis based on the dynamic amplification curve to obtain a sample diagnostic visualization report.

[0008] The present invention also provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any of the above-mentioned intelligent molecular diagnosis and analysis methods when executing the computer program.

[0009] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of any of the above-mentioned intelligent molecular diagnosis and analysis methods.

[0010] The beneficial effects of the present invention are as follows: by real-time monitoring of the environmental parameters (such as temperature, pH, ion concentration, etc.) of the DNA amplification kit by multidimensional sensing nodes, the microenvironment changes of each reaction pool can be accurately detected, and high-precision environmental data can be provided. These data provide a reliable basis for the next step of model construction and reaction optimization. By mining the spatial topological layout of environmental parameters, the system can identify and simulate the influence of different environmental parameters on the amplification reaction, thereby forming a dynamic amplification perception model. This model can not only reflect the immediate state of the reaction environment, but also foresee potential reaction trends. Through precise DNA recognition technology, different types of DNA samples can be quickly identified and classified. This provides important hierarchical information for subsequent amplification. Hierarchical temperature control analysis automatically adjusts the reaction temperature according to the sample type, concentration and state to ensure that each type of sample is amplified under the optimal temperature conditions. Through the dynamic feedback of the real-time perception model, the system can automatically adjust the temperature control conditions of each reaction pool according to the actual needs of the sample, and form multiple dynamic constant temperature amplification models that adapt to the reaction needs of different regions. This method can cope with the temperature fluctuation problem encountered in traditional constant temperature amplification equipment and ensure the stability and reliability of the amplification process. The reaction degree of each reaction pool area will be accurately evaluated at each stage. By evaluating multiple dynamic constant temperature amplification models, the reaction process of different areas is adjusted to ensure that the amplification process in each area is in the best state. The dynamic flow rate adjustment strategy can intelligently adjust the liquid flow rate in each area according to the amplification progress of the reaction area, sample concentration and other factors. This flow rate adjustment can effectively avoid uneven reactions caused by too fast or too slow flow rates, thereby improving reaction efficiency and the quality of amplified products. Especially in complex microfluidic environments, optimizing flow rates is the key to ensuring smooth and efficient reactions. Through real-time amplification control based on flow rate adjustment strategies, it is ensured that reaction conditions continue to maintain the optimal state throughout the amplification process. The reaction process of each reaction pool will be precisely adjusted to avoid over-amplification or under-reaction. During the amplification process, signal noise and interference affect the final detection results. Through dynamic signal filtering, background noise can be effectively removed and the purity of the signal can be enhanced. Signal optimization further improves the detection sensitivity of the amplification reaction, so that low-concentration DNA samples can also obtain reliable detection results. By performing signal analysis at different time periods of the amplification reaction, the progress of the amplification reaction can be more comprehensively understood, and the characteristics of each amplification stage can be accurately identified. This provides a solid data foundation for subsequent quantitative analysis and diagnosis. Dynamic time series mining technology can extract key reaction features, such as amplification start time, amplification rate, plateau phase, etc., through real-time analysis of amplification curves. This information is of great significance for dynamically adjusting reaction conditions and predicting amplification results. By analyzing the dynamic amplification curve, regional quantitative analysis can be accurately performed to obtain the amount of DNA amplification in each region. This provides more accurate data support for quantitative analysis of samples and pathogen detection.Combining image processing and data analysis technology, the generated sample diagnosis visualization report will provide users with an intuitive and clear display of results. Both clinicians and researchers can quickly understand the sample detection and amplification results through the report, so as to make timely diagnosis and decision. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 A schematic diagram of the steps of an intelligent molecular diagnosis and analysis method of the present invention; Figure 2 Detailed implementation flow chart of step S1; Figure 3 Detailed implementation flow chart of step S2; Figure 4 Detailed implementation flow chart of step S3. DETAILED DESCRIPTION

[0012] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0013] The present application example provides an intelligent molecular diagnosis and analysis method, device, equipment and storage medium. The execution subjects of the intelligent molecular diagnosis and analysis method, device, equipment and storage medium include but are not limited to the following: mechanical equipment, data processing platform, cloud server node, network upload equipment, etc. equipped with the system can be regarded as the general computing node of the present application, and 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.

[0014] See also Figures 1 to 4 The present invention provides an intelligent molecular diagnosis and analysis method, which comprises the following steps: Step S1: Real-time detection of multi-dimensional environmental parameters of the DNA amplification kit based on multi-dimensional sensor nodes; performing spatial topological layout mining and environmental amplification perception fitting on the multi-dimensional environmental parameters to construct a real-time amplification perception model; Step S2: Identify DNA amplification samples; perform layered temperature control analysis on the DNA amplification samples, and perform dynamic amplification constant temperature control on the real-time amplification perception model, thereby constructing multiple dynamic constant temperature amplification models; Step S3: evaluating the DNA reaction degree of each region of the multiple dynamic constant temperature amplification models, and then dynamically adjusting the regional flow rate, thereby constructing a dynamic amplification reaction flow rate adjustment strategy; Step S4: performing real-time amplification control based on the dynamic amplification reaction flow rate adjustment strategy, and performing dynamic signal filtering to generate a filtered optimized amplification reaction signal; Step S5: performing multi-period amplification reaction analysis on the filtered optimized amplification reaction signal, and performing dynamic amplification timing mining to construct a dynamic amplification curve; Step S6: Perform regional quantitative analysis and comprehensive diagnosis based on the dynamic amplification curve to obtain a sample diagnosis visualization report.

[0015] In the embodiment of the present invention, refer to Figure 1 , is a schematic flow chart of the steps of an intelligent molecular diagnosis and analysis method of the present invention. In this example, the steps of the intelligent molecular diagnosis and analysis method include: Step S1: Real-time detection of multi-dimensional environmental parameters of the DNA amplification kit based on multi-dimensional sensor nodes; performing spatial topological layout mining and environmental amplification perception fitting on the multi-dimensional environmental parameters to construct a real-time amplification perception model; In this embodiment, multidimensional sensors suitable for monitoring the DNA amplification process are selected, including temperature sensors, humidity sensors, pH sensors, fluorescence intensity sensors, etc. These sensors can monitor the environmental parameters that affect the DNA amplification reaction in real time. The selection of each sensor should be optimized according to its measurement range and accuracy. The temperature sensor should have an accuracy of ±0.1°C, and the humidity sensor should have an accuracy of ±2% RH. Reasonably arrange the sensor nodes in the laboratory or reaction area to ensure that each sensor is evenly distributed to obtain multidimensional environmental data. A mesh layout is adopted to ensure that each area is covered by sensors. Determine the number and location of the sensor nodes, for example, arrange at least 5 sensor nodes around the reactor to facilitate real-time monitoring of environmental parameters in different spatial positions. Select a suitable data acquisition system to ensure that data from multiple sensor nodes can be received simultaneously. Use a microcontroller (such as Arduino or Raspberry Pi) as a central control unit to connect all sensors and realize real-time data collection. Configure a data transmission protocol (such as MQTT or HTTP) to ensure the transmission and storage of real-time data. Set the frequency of data acquisition, for example, collect data once per second to track the dynamic changes of environmental parameters. Start the data acquisition system to monitor the environmental parameters obtained by each sensor in real time. Record the output data of each sensor, including temperature (°C), humidity (%), pH value, fluorescence intensity (RFU), etc. Automated data recording process by writing programs to ensure the timeliness and accuracy of data. Use Python to write scripts to save sensor data in real time to a database or CSV file. Preprocess the real-time collected data to remove outliers and noise. Set a threshold range (such as the temperature range is set to 20-40°C) to identify and remove data points that are out of range. Use data interpolation methods to handle missing values, such as using linear interpolation to fill in missing environmental parameter data to ensure data continuity. Use spatial analysis models (such as Kriging interpolation or inverse distance weighted method) to mine the spatial topological layout of multidimensional environmental parameters. Determine the spatial distribution characteristics of parameters by analyzing sensor data. Use GIS (geographic information system) software (such as ArcGIS) for spatial visualization to display the spatial distribution of each environmental parameter and help identify environmental factors that affect the amplification reaction. Construct a spatial topological model of multidimensional environmental parameters to identify the spatial relationship between each parameter. Analyze the relationship between temperature and fluorescence intensity to determine the impact of temperature changes on the amplification reaction. Use data visualization tools (such as Matplotlib or Seaborn) to display the spatial layout of multidimensional environmental parameters for further analysis. According to the characteristics of multidimensional environmental parameters, select appropriate fitting models, such as multivariate linear regression, support vector machine or neural network model, to establish the relationship between environmental parameters and amplification reactions.Conduct model training and use historical data sets (including known environmental parameters and amplification results) to fit the model to ensure the accuracy of the model. Apply the trained model to real-time detection data to predict the effect of the amplification reaction in real time. The model should be able to output the expected results of the amplification reaction in real time based on the current environmental parameters. Record the comparison between the model's predicted results and the actual results to evaluate the accuracy and reliability of the model, and optimize and adjust the model based on feedback. Use visualization tools to display the output results of the real-time amplification perception model, including a relationship diagram between environmental parameters and amplification reactions, to help researchers adjust experimental conditions in a timely manner. Set up a feedback mechanism to automatically adjust experimental conditions (such as temperature, time, etc.) to optimize the amplification reaction when there is a large deviation between the model's predicted results and the actual reaction.

[0016] Step S2: Identify DNA amplification samples; perform layered temperature control analysis on the DNA amplification samples, and perform dynamic amplification constant temperature control on the real-time amplification perception model, thereby constructing multiple dynamic constant temperature amplification models; In this embodiment, DNA samples to be amplified are collected to ensure representativeness and consistency of the samples. The samples are from different experimental groups or sample types, such as blood, tissue or cell culture. The samples are preliminarily processed, such as extracting DNA and quantifying its concentration, to ensure that the concentration of the samples to be amplified is appropriate (usually within the range of 10-100 ng / μL) for subsequent amplification reactions. Each sample is labeled with a barcode or QR code technology to ensure that the relevant information of each sample can be accurately tracked during the experiment. The real-time PCR (qPCR) technology is used to preliminarily identify the samples, and specific primers and probes are combined to quickly determine whether the sample contains the target DNA sequence. A sample database is established to record the basic information of each sample, including the sample number, source, extraction concentration, etc., to ensure that the database has a clear structure for subsequent query and analysis. The sample information is maintained through experimental management software (such as LabArchives or Benchling) to ensure the data Completeness and accuracy. Design a tiered temperature control scheme based on the characteristics of the sample and the requirements of amplification. Generally speaking, DNA amplification reactions include multiple stages: denaturation (95°C), annealing (50-65°C), and extension (72°C). Determine the time and temperature parameters for each stage and design a multi-layer temperature control system. For example, use a multi-channel PCR instrument that can simultaneously control the temperature of different areas. Before the experiment begins, calibrate the temperature control system to ensure the accuracy of each temperature sensor. The temperature accuracy is usually required to be ±0.1°C. Start the tiered temperature control system, monitor and adjust the temperature in real time, and use a data acquisition system (such as LabJack or National Instruments). Instruments) record the actual temperature of each stage to ensure that it is consistent with the set temperature, analyze the real-time temperature control data to ensure that the temperature changes at different stages are in line with expectations, record the temperature fluctuation range, and analyze whether it affects the amplification effect. Build a feedback mechanism. When the temperature control data deviates from the set value, automatically adjust the temperature control parameters to maintain the stability of the reaction. On the basis of the real-time amplification perception model established in step S1, integrate the temperature control data and amplification reaction data. The model should be able to predict the amplification effect under different temperature conditions in real time. Use machine learning algorithms (such as random forests or neural networks) to train historical amplification data and build a dynamic Dynamic amplification constant temperature control model, according to the real-time monitoring data, set the dynamic constant temperature control strategy, when the temperature is detected to be too high, the system should lower the temperature to prevent DNA degradation; when the temperature is too low, the system should increase the temperature to ensure amplification efficiency, determine the control parameters, such as the temperature change rate (such as no more than 1 ° C per minute) and the adjustment frequency (such as monitoring the temperature every 10 seconds), according to different experimental conditions and sample types, build multiple dynamic constant temperature amplification models, each model should be optimized for specific samples and reaction conditions, each model is verified, and amplification experiments are performed using standard samples of known concentrations to evaluate the accuracy and reliability of the model.

[0017] Step S3: evaluating the DNA reaction degree of each region of the multiple dynamic constant temperature amplification models, and then dynamically adjusting the regional flow rate, thereby constructing a dynamic amplification reaction flow rate adjustment strategy; In this embodiment, a real-time fluorescence quantitative PCR (qPCR) method is used to monitor the DNA amplification reaction in each region. The fluorescence signal intensity and the corresponding Ct value of each region are recorded to evaluate the reaction degree. Evaluation parameters such as reaction start time, maximum fluorescence intensity and Ct value are set to determine the amplification efficiency of each region. Generally, a Ct value below 30 is set as good, 30-35 is set as medium, and above 35 is set as poor. During the experiment, the amplification data of each region, including the fluorescence signal, Ct value and timestamp, are recorded in real time. To ensure the integrity and accuracy of the data, the results of at least three repeated experiments should usually be recorded for each region. The evaluation results are organized into a data table to form an overview of the DNA reaction degree of each region for subsequent analysis. According to the evaluation results of the regional DNA reaction degree, a dynamic flow rate adjustment strategy is designed. The optimal flow rate for each region is determined to optimize the reaction conditions. The flow rate is appropriately increased in the region with good reaction, while the flow rate needs to be reduced in the region with poor reaction. The range and step size of the flow rate adjustment are set, for example, the flow rate range is set to 0.1-1.0 mL / min, and the step size is 0.1 mL / min, to ensure the meticulousness and accuracy of the flow rate adjustment. Use automated fluid control systems (such as pumps and valves) to achieve real-time adjustment of flow rates in different areas. The system should be able to automatically adjust the flow rate based on feedback data to achieve dynamic response. Start the flow rate adjustment system to monitor the flow rate and reaction status of each area in real time. Record the reaction results after each flow rate adjustment through the data acquisition system to ensure the effectiveness of the adjustment. After implementing the dynamic flow rate adjustment strategy, re-evaluate the amplification effect of each area. Record the trend of fluorescence signal changes in each area and analyze the effect of flow rate on the amplification effect. Use statistical analysis methods (such as variance analysis) to evaluate the effectiveness of the flow rate adjustment strategy and determine the difference in amplification efficiency under different flow rates. According to the evaluation results, iteratively optimize the flow rate adjustment strategy. If a certain flow rate is found to perform best in a specific area, set it as the standard flow rate and further optimize the flow rate in other areas. Record the optimized flow rate adjustment strategy in the experimental document and update the dynamic constant temperature amplification model to reflect the latest experimental conditions and results. Organize the implementation results of the dynamic amplification reaction flow rate adjustment strategy to form a detailed experimental report. The report should include comparative data before and after flow rate adjustment, the amplification effect of each area and its statistical analysis results. Use visualization tools (such as Excel or GraPHPad Prism) to generate charts to intuitively show the impact of flow rate adjustment strategies on DNA amplification reactions, facilitating subsequent discussions and decision-making.

[0018] Step S4: performing real-time amplification control based on the dynamic amplification reaction flow rate adjustment strategy, and performing dynamic signal filtering to generate a filtered optimized amplification reaction signal; In this embodiment, a real-time monitoring system is configured to ensure that key parameters of the amplification reaction, such as fluorescence signal intensity, temperature, flow rate, etc., can be obtained in real time. A highly sensitive fluorescence detection instrument (such as a qPCR instrument) is used for real-time data acquisition. The data acquisition frequency is set, for example, a fluorescence signal is collected once per second, so as to effectively capture the rapid changes in the amplification process. According to the flow rate adjustment strategy established in step S3, the flow rate of each region is adjusted in real time. An automated fluid control system (such as a precision pump) is used to accurately control the liquid flow rate to ensure smooth and accurate flow rate changes. The range and response time of the flow rate adjustment are set. When the amplification signal intensity of a certain area is detected to be lower than a preset threshold (such as 70 RFU), the system should adjust the flow rate to the optimal value within 5 seconds. A feedback mechanism is established through the data acquisition system to monitor the state of the amplification reaction in real time. A threshold is set, and when the fluorescence signal does not meet expectations, the system automatically triggers flow rate adjustment or temperature correction. The reaction data of each adjustment is recorded to form a real-time feedback log for subsequent analysis and optimization. During real-time amplification, the noise component in the signal is identified. Typically, the source of the noise is determined by comparing the background noise level (such as the average value of the fluorescence signal in the absence of DNA). Use statistical analysis methods (such as standard deviation or coefficient of variation) to evaluate the stability of the signal and set a noise threshold. For example, when the signal fluctuation exceeds 5%, it is considered that there is noise interference. Select a suitable filtering algorithm for signal processing, such as Kalman filtering or sliding average filtering. Kalman filtering can effectively process dynamically changing signals, while sliding average filtering is simple and easy to use and suitable for smoothing signals. Set filtering parameters according to experimental requirements, such as the covariance matrix of process noise and measurement noise of Kalman filtering, or the sliding average window size (such as 5 data points). Perform dynamic filtering on the real-time fluorescent signal. Automated processing is achieved through programming to ensure that each data point is processed by the filtering algorithm. Record the original signal and the filtered signal for subsequent comparative analysis. Ensure that each step of processing has a detailed log record, including information such as processing time and signal intensity before and after processing. Perform quality assessment on the filtered amplification reaction signal. Evaluation indicators include signal-to-noise ratio (SNR), signal stability and contrast (such as the ratio of signal intensity to background noise). Set quality assessment standards, such as a signal-to-noise ratio greater than 10 is good, 5-10 is medium, and less than 5 is poor. Organize the amplification reaction signal data after filtering optimization and form a data table for further analysis. Each data table should include information such as timestamp, original signal, filtered signal and its signal-to-noise ratio. Use visualization tools (such as Matplotlib or GraPHPad Prism) to draw signal change curves to intuitively display the comparison between original signal and filtered signal to help researchers understand the effect of signal processing. Generate a report to summarize the results of the amplification reaction and the effect of filtering optimization in detail. The report should include the background, methods, results and discussion of the experiment to ensure the comprehensiveness of the information.Format the report into a shareable document (such as PDF) to facilitate subsequent discussion and reference.

[0019] Step S5: performing multi-period amplification reaction analysis on the filtered optimized amplification reaction signal, and performing dynamic amplification timing mining to construct a dynamic amplification curve; In this embodiment, the amplification reaction signal data processed by dynamic filtering are collected. These data should include information such as fluorescence signal intensity, Ct value, and rate of change at different time points. Ensure that the data of each time period is complete and mark the timestamp. These data are organized into a structured data table containing signal data at each time point for subsequent analysis. The data table should include "time (seconds)", "fluorescence intensity (RFU)", "Ct value", etc. The amplification reaction process is divided into multiple time periods, for example, each time period is set to 1 minute, 2 minutes or 5 minutes, and the selection of specific time periods can be determined according to the actual situation of the experiment and the speed of the amplification reaction. Statistical analysis methods (such as mean, standard deviation and variance analysis) are used to analyze the fluorescence signal of each time period. By calculating the statistical indicators of each time period, the stability and consistency of the amplification reaction are evaluated. The data of each time period are analyzed, the mean and standard deviation of the fluorescence signal are calculated, and the change trend of the signal in each time period is identified. Compare the signal intensity at the 3rd minute and the 5th minute to observe the acceleration of amplification. Record the analysis results of each time period and form an analysis report, including the signal intensity, change amplitude and statistical characteristics of each time period. Use time series data mining algorithms (such as dynamic time warping (DTW) or autoregressive moving average (ARIMA) models) to conduct in-depth analysis of amplification reaction signals. These algorithms can effectively capture the laws and trends of signal changes. Select appropriate mining methods based on needs and data characteristics. DTW is suitable for analyzing the similarities between different time series, while ARIMA is suitable for time series prediction. Use the selected mining method to analyze the sorted time period data and extract the time series characteristics of the dynamic amplification reaction. Record the signal change pattern of each time period and identify the key stages of the reaction (such as start, acceleration, and stability). Compare the results of time series mining with the original signal to analyze the relationship between the mined time series characteristics and the amplification efficiency and reaction conditions. Observe whether the changes in fluorescence signals show similar patterns under specific temperatures and flow rates. Organize the results of time series mining to form a time series feature report, recording important time series features and findings. This report should describe the characteristics of each time period and its relationship with the amplification reaction in detail. Use visualization tools (such as Matplotlib or Seaborn) to draw a dynamic amplification time series diagram to show the trend of fluorescence signal changes at different time points, which helps to intuitively understand the dynamic characteristics of the amplification process. Based on the results of multi-period analysis and time series mining, a dynamic amplification curve is constructed. The fluorescence signal intensity of each time period is plotted in the same coordinate system to form a continuous amplification curve. The key indicators of the curve are determined, including the amplification starting point, logarithmic growth phase, and plateau phase, and these key points are marked in the curve graph for subsequent analysis. The generated dynamic amplification curve is analyzed to evaluate the efficiency and consistency of the amplification reaction. Indicators such as the stability of the curve, the slope of the logarithmic growth, and the stability of the plateau phase are identified.Set evaluation criteria, such as the slope of the logarithmic growth phase should be greater than 0.1, the change in the plateau phase should be less than 5%, etc. Evaluate the effect of the amplification reaction according to these criteria. Organize the dynamic amplification curves and related analysis results to form a comprehensive report. The report should include amplification curve graphs, key indicator analysis, and time series mining findings to ensure comprehensive information. Format the report into a shareable document (such as PDF) for subsequent discussion and reference.

[0020] Step S6: Perform regional quantitative analysis and comprehensive diagnosis based on the dynamic amplification curve to obtain a sample diagnosis visualization report.

[0021] In this embodiment, first, key features are extracted from the generated dynamic amplification curve, including Ct value (threshold cycle number), amplification efficiency, maximum fluorescence intensity, etc. The Ct value refers to the number of cycles at which the amplification curve reaches the set fluorescence threshold, and is usually accurately located using an automated algorithm. The dynamic amplification curve is analyzed using a specific software tool (such as Bio-Rad's CFX Manager or Applied Biosystems' QuantStudio) to extract characteristic parameters for each region. To ensure that the data extracted from the curve in each region is accurate, the results of at least three repeated experiments are usually required. Relative quantitative (such as ΔΔCt method) or absolute quantitative (standard curve method) analysis methods are used. The relative quantitative method is suitable for comparing the relative expression levels between different samples, while the standard curve method is used to determine the absolute concentration of the target DNA in the sample. When selecting a method, ensure that appropriate internal reference genes (such as GAPDH or β-actin) are used for relative quantification to improve the accuracy of the results. The extracted quantitative data are organized into a table, listing the Ct value, amplification efficiency, relative concentration, and other information for each region. Ensure that the format of the data table is clear for subsequent analysis. Calculate the amplification efficiency of each region, and usually expect the amplification efficiency to be between 90% and 110%. Calculate the DNA concentration of each region based on the Ct value and known standard samples and record it in the data table. Set comprehensive diagnostic criteria based on the quantitative analysis results. A Ct value below 30 indicates good amplification, 30-35 indicates moderate amplification, and more than 35 indicates amplification failure or no amplification. Combine other indicators (such as amplification efficiency and relative concentration) for comprehensive scoring. If the amplification efficiency is less than 80%, even if the Ct value is below 30, it should be judged as poor amplification. Classify each region according to the set diagnostic criteria. Record the diagnostic results of each region, such as "good", "moderate", and "poor". Ensure that the diagnostic process is automated to reduce human errors. Use programming tools (such as Python or R) to write scripts to automatically process quantitative data and diagnostic results and generate diagnostic status reports for each region. Organize the comprehensive diagnostic results to form a final data table, including information such as region number, Ct value, amplification efficiency, relative concentration, and diagnostic status. Ensure the integrity and accuracy of the information to facilitate subsequent report generation. Summarize the results of all regions and calculate the proportion of each type of diagnostic result to identify areas with good amplification effects and areas that need improvement. Design the structure of the sample diagnostic visualization report, including introduction, methods, results, discussion, and conclusions. Each section should be concise and easy for readers to understand. Determine the visualization content, such as amplification results of each region, distribution map of diagnostic results, statistical charts, etc., to ensure the comprehensiveness and readability of the report. Choose appropriate data visualization tools (such as Tableau, Power BI, or Python's Matplotlib, Seaborn library) for intelligent data visualization.Make sure the chosen tool can handle large data volumes and produce high-quality graphs. Design visualizations, but make sure the content is combined with textual content to form a complete diagnostic report.

[0022] In this embodiment, refer to Figure 2 , is a flowchart of detailed implementation steps of step S1. In this embodiment, the detailed implementation steps of step S1 include: Step S11: Real-time detection of multi-dimensional environmental parameters of the DNA amplification kit based on the multi-dimensional sensing node; Step S12: performing a spatial temperature distribution analysis of the test kit on the multi-dimensional environmental parameters to generate a spatial temperature distribution feature; Step S13: performing pH value fluctuation calculation on the multi-dimensional environmental parameters to obtain pH value fluctuation data of the test kit; Step S14: performing ion concentration dynamic change detection on the multi-dimensional environmental parameters to generate a dynamic change characteristic of ion concentration of the test kit; Step S15: performing spatial topological layout mining according to the multi-dimensional sensor nodes to generate a reagent kit topological layout structure; Step S16: Perform amplification perception fitting on the spatial temperature distribution characteristics, the pH value fluctuation data of the reagent kit and the dynamic change characteristics of the ion concentration of the reagent kit according to the topological layout structure of the reagent kit to construct a real-time amplification perception model.

[0023] In this embodiment, suitable multi-dimensional sensor nodes are selected, including temperature sensors, pH sensors, ion concentration sensors, etc. These sensors should have high sensitivity and response speed to ensure the effectiveness of real-time monitoring. Sensor nodes are arranged at different positions of the test kit to ensure comprehensive coverage of the spatial environment. The layout of the sensor is designed according to the size and shape of the test kit so that it can collect environmental data at different positions. The environmental parameter data of each sensor node is collected in real time through the set data acquisition frequency (such as once per second). Ensure that the data acquisition system can operate efficiently and stably. The collected data is transmitted to the central processing system or cloud database in real time using wireless communication technology (such as Wi-Fi or Bluetooth) for subsequent data analysis and processing. The real-time collected data is preprocessed, including noise removal, outlier filtering, etc. Methods such as mean filtering or median filtering are used to improve data quality. The processed data is stored in the database to ensure the integrity and traceability of the data for subsequent analysis. The temperature data of each sensor node is extracted from the database to ensure that the data contains information such as timestamps and node positions for subsequent spatial analysis. Integrate the temperature data at different locations and calculate the average temperature, maximum temperature, and minimum temperature of each node to form a representative temperature feature data set. Use interpolation methods (such as Kriging interpolation or 3D reconstruction technology) to build a spatial temperature distribution model. This process can effectively convert discrete temperature data into a continuous spatial distribution map. In the process of model construction, consider the impact of environmental factors (such as the location of the reagent box, ambient temperature changes, etc.) on the temperature distribution to ensure the accuracy of the model. Use heat maps or 3D graphics to visualize the temperature distribution characteristics and intuitively display the temperature changes in different areas of the reagent box. Use tools such as Matplotlib or Plotly for visualization. Analyze the obtained temperature distribution characteristics, identify areas with large temperature changes, and evaluate their impact on the DNA amplification reaction. Extract pH data from each sensor node, and ensure that the data record contains information such as timestamps and node locations for subsequent analysis. Integrate the pH data and calculate the average pH value, maximum value, and minimum value of each node to obtain the overall pH characteristics. Use statistical methods such as standard deviation and coefficient of variation to calculate the fluctuation of pH value. The standard deviation reflects the degree of dispersion of pH value, and the coefficient of variation can reflect the relative change of fluctuation. Record pH fluctuation data and compare the fluctuations in different time periods to analyze the causes of fluctuations (such as temperature changes, reagent reactions, etc.). Use line charts or bar charts to visualize pH fluctuation data and show the changes in pH values ​​in different time periods. Visualization helps to intuitively understand the stability of pH values. Analyze the visualization results, identify the time periods with large pH fluctuations, and conduct in-depth analysis in combination with other environmental parameters. Extract ion concentration data from sensor nodes, and ensure that the records contain timestamps and node location information for subsequent analysis.The ion concentration data are integrated, and the average concentration and change rate of each node are calculated to form an ion concentration feature data set. The dynamic change of ion concentration is modeled using time series analysis methods (such as the autoregressive model ARIMA) to extract the trend and periodic characteristics of concentration change. The change of ion concentration at each node, including the rate of concentration increase and decrease, is recorded to analyze its impact on the DNA amplification reaction. The location information and environmental parameters of each node are extracted from the sensor nodes to construct a data set containing the location and sensor type. The two-dimensional or three-dimensional coordinates of the sensor layout are converted into a topological structure for subsequent analysis and mining. Select a suitable topological analysis method, such as graph analysis or network analysis, to mine the relationship and mutual influence between sensor nodes. Determine the key indicators of the analysis, such as the connectivity and importance of the nodes, to evaluate the complexity of the topological structure. Apply the selected topological analysis method to analyze the layout structure of the sensor nodes and generate a topological layout diagram of the reagent kit. Analyze the topological structure, identify important nodes and potential bottlenecks, and optimize the sensor layout. Integrate the temperature distribution characteristics, pH fluctuation data, and ion concentration change characteristics in steps S12, S13, and S14 to form a multidimensional feature data set. Determine the model input variables, including spatial location, timestamp, temperature, pH value, and ion concentration, to facilitate the construction of a real-time amplification perception model. Select a suitable machine learning model, such as support vector machine (SVM), random forest, or neural network, for amplification perception fitting. These models are capable of processing multidimensional data and capturing complex nonlinear relationships. Determine the model's training parameters and hyperparameters, such as learning rate, number of iterations, etc., to optimize model performance. Train the selected model using a collated multidimensional feature dataset, and use a cross-validation method to evaluate the model's generalization ability. Record the model's prediction accuracy, recall rate, and F1 score to ensure that the model can effectively predict the situation of the DNA amplification reaction. Integrate the constructed real-time amplification perception model into the data acquisition system to ensure that the system can analyze environmental parameters in real time and predict the amplification status. According to the real-time monitoring results, adjust the experimental conditions and parameters to optimize the effect of the DNA amplification reaction.

[0024] In this embodiment, refer to Figure 3 , is a flowchart of detailed implementation steps of step S2. In this embodiment, the detailed implementation steps of step S2 include: Step S21: Identify the DNA amplification sample; perform multi-sequence amplification simulation on the DNA amplification sample, and extract amplification simulation data of different sequences; Step S22: Calculating the optimal amplification temperature of each sequence according to the amplification simulation data of different sequences, thereby obtaining the optimal amplification temperatures of multiple DNA sequences; Step S23: performing a hierarchical temperature control analysis based on the optimal amplification temperatures of multiple DNA sequences, thereby obtaining hierarchical amplification temperature control data; Step S24: dividing the real-time amplification perception model into amplification reaction regions, and extracting a plurality of amplification reaction region models; Step S25: Dynamically amplify and control the constant temperature of multiple constant temperature amplification reaction area models according to the layered amplification temperature control data, thereby constructing multiple dynamic constant temperature amplification models.

[0025] In this embodiment, the DNA sample to be amplified is identified to ensure that the source, quality and concentration of the sample meet the experimental requirements. A standard PCR (polymerase chain reaction) kit and appropriate primer design are used to ensure efficient amplification. The sample is pretreated, such as removing impurities and performing appropriate dilution to achieve the best amplification effect. The DNA concentration is measured using a spectrophotometer to ensure that the concentration is within the recommended range (such as 10-100 ng / μL). Multiple amplification sequences are designed, each of which should have similar GC content and length for comparison. Ensure that the primers are highly specific to reduce nonspecific amplification. Primers are designed using computing software (such as Primer3 or OligoCalc), and the specificity and effectiveness of the primers are verified by bioinformatics tools (such as BLAST). PCR amplification simulation is performed according to the designed sequence. Different experimental conditions (such as the number of cycles, denaturation temperature, annealing temperature, and extension temperature) are set, and the parameter settings for each experiment are recorded. A real-time fluorescence PCR instrument is used to monitor the amplification process, record the changes in the fluorescence signal of each sequence under different conditions, and extract amplification simulation data (such as Ct value, amplification curve, etc.). Extract amplification simulation data of different sequences and calculate the amplification efficiency and specificity of each sequence. Use relative quantitative analysis methods (such as ΔΔCt method) to evaluate the amplification performance of different sequences. Organize the simulation data to form a data set, including sequence information, amplification efficiency, Ct value, etc., for subsequent analysis. Organize the amplification efficiency data at different temperatures for each DNA sequence to calculate the optimal amplification temperature. Ensure that each sequence has amplification data under multiple temperature conditions for comparison and analysis. Select an appropriate algorithm to calculate the optimal amplification temperature, such as linear regression analysis or curve fitting method. Use software tools (such as GraPHPad Prism) for data analysis. Determine the criteria for the optimal amplification temperature, such as selecting the temperature with the highest amplification efficiency, or selecting the temperature with the smallest change in amplification efficiency within a specific temperature range (such as 60-70°C). Analyze each DNA sequence segment by segment and calculate the optimal amplification temperature according to the selected method. Record the optimal temperature and its corresponding amplification efficiency for each sequence. Analyze the calculation results and identify the differences in the optimal amplification temperatures of different sequences due to sequence characteristics (such as GC content). Organize the optimal amplification temperature data for each sequence to form a data table that clearly shows the temperature of each sequence and the corresponding amplification efficiency. Use charting tools (such as Excel or Matplotlib) to visualize the optimal amplification temperature results and intuitively display the amplification temperature comparison of different sequences. According to the optimal amplification temperature calculated in step S22, construct a hierarchical temperature control analysis model. The model should include different temperature levels, time periods, and corresponding amplification sequences. Determine the temperature control parameters of each layer, such as the temperature range (such as ±1°C) and time setting of each layer, to ensure that each sequence is amplified under its optimal temperature conditions. Design a hierarchical temperature control experiment to ensure that each layer of temperature control conditions can cover the optimal amplification temperature.Set up multiple temperature control levels (such as low temperature layer, medium temperature layer and high temperature layer). Record the temperature control settings of each layer, including temperature, time and DNA sequence to be amplified, for subsequent analysis. Apply the layered temperature control scheme in the experiment, monitor the temperature changes of each layer, and record the real-time data of the amplification reaction. Use a real-time fluorescence PCR instrument to collect the amplification data of each layer, including fluorescence intensity, Ct value, etc., and record the amplification effect at different levels. Analyze the collected layered temperature control data to evaluate the amplification efficiency under the temperature control conditions of each layer. Calculate the average amplification efficiency and standard deviation of each layer to evaluate the temperature control effect. Arrange the analysis results to form a layered temperature control data table to show the changes in amplification efficiency at different levels. Define the amplification reaction area based on the data of the real-time amplification perception model. The reaction area is divided according to factors such as temperature, time and chemical concentration. Determine the characteristic parameters of each reaction area, such as temperature range, pH value and ion concentration, for subsequent model extraction. Use cluster analysis, layered model or grid division to divide the amplification reaction area. Select a suitable algorithm (such as K-means clustering or DBSCAN) to ensure the accuracy of the area division. Determine the partitioning parameters, such as the minimum and maximum boundary values ​​of the region, so as to accurately identify the reaction region. Apply the selected method to divide the amplification reaction region and extract the models of different regions. Record the characteristic parameters and corresponding amplification efficiency of each region to mark each divided region to form an amplification reaction region model data set for subsequent analysis. Arrange the extracted amplification reaction region model to form a data table, including information such as region number, characteristic parameters and amplification efficiency. Use visualization tools to display the distribution of the amplification reaction region to intuitively display the characteristics of different regions. Design a dynamic constant temperature control model based on the hierarchical amplification temperature control data in step S23. The model should include temperature control strategies for different amplification reaction regions, such as temperature setting, time control, etc. Determine the control parameters of each region, such as temperature adjustment rate and holding time, to ensure that it can be flexibly adjusted during the amplification process. Select a suitable dynamic temperature control algorithm, such as PID control (proportional-integral-differential control) or fuzzy control algorithm, to achieve precise control of amplification temperature control. Set the parameters of the algorithm, such as the proportional, integral and differential coefficients of PID control, to optimize the temperature control response. The dynamic constant temperature control model is applied to the actual amplification experiment, real-time temperature changes are monitored and adjusted according to the set temperature control strategy. The temperature changes and amplification effects of each amplification reaction area are recorded to evaluate the effectiveness of dynamic constant temperature control.

[0026] In this embodiment, refer to Figure 4 , is a flowchart of detailed implementation steps of step S3. In this embodiment, the detailed implementation steps of step S3 include: Step S31: Calculating the reaction rate and flow rate of each region of multiple dynamic constant temperature amplification models to generate the reaction rate and flow rate of each region; Step S32: dynamically evolve the reaction velocity of each region in a time series manner, and extract the reaction velocity variation characteristics of each region; Step S33: evaluating the DNA reaction degree of each region of the multiple dynamic constant temperature amplification models to generate amplification reaction efficiencies of different regions; Step S34: dynamically adjust the regional flow rate according to the amplification reaction efficiency of different regions and the reactant flow rate change characteristics of each region, thereby constructing a dynamic amplification reaction flow rate adjustment strategy.

[0027] In this embodiment, the reaction rate flow rate is defined as the number of DNA molecules passing through a specific reaction area per unit time. The calculation of this rate depends on multiple factors, such as the volume, concentration and reaction time of the reaction system. Ensure the experimental conditions and parameters of each region collected, such as the volume of the reaction liquid (usually 20-50 μL), the DNA concentration (such as 10 ng / μL) and the volume distribution of each region. Reaction rate flow rate = DNA concentration × reaction volume / reaction time, determine the reaction time, usually set as the time of each cycle in the PCR reaction, and select the duration of each amplification cycle (such as 30 seconds to 1 minute). Detailed reaction rate flow rate calculations are performed for different regions of each dynamic constant temperature amplification model to obtain the reaction rate flow rate data for each region. Record the reaction rate flow rate of each region and form a data table to ensure the integrity and accuracy of the data. Arrange the calculation results and generate a reaction rate flow rate data set for each region. Analyze the flow rate differences between different regions and identify regions with higher or lower reaction rate flow rates. Visualize the results, such as using a bar chart to display the reaction rate flow rates of different regions, intuitively display the reaction rate characteristics of each region, and provide a basis for subsequent analysis. During the reaction process, monitor the change of reaction rate flow rate in each area in real time. Record the flow rate data at different time points to ensure the time series of the data. Use a real-time data acquisition system to set the data acquisition frequency (for example, once every 5 seconds) to capture dynamic changes. Use time series analysis methods to process the collected flow rate data. Use sliding average or exponential smoothing to remove noise and ensure the accuracy of data analysis. Calculate the rate of change of reaction rate flow rate in each area, for example, using the difference method (the flow rate at the next time point minus the flow rate at the previous time point). Perform dynamic time series change analysis on the flow rate data of each area and extract flow rate change characteristics, such as maximum value, minimum value, average value and change amplitude. Record the analysis results to form a flow rate change characteristic data set for each area, including change trend, rate and stability. Define the degree of DNA reaction as the efficiency of DNA amplification reaction within a specific time, usually evaluated by relative fluorescence intensity (such as Ct value) or relative amplification amount. Collect real-time PCR data, including the change of fluorescence signal and Ct value in each area, to ensure the timeliness and accuracy of the data. Relative quantitative analysis methods (such as ΔΔCt method) are used to calculate the amplification reaction efficiency of each region. Amplification efficiency = , determine the control sample and calculate the difference so as to compare with the sample to be tested and evaluate the amplification efficiency of each region. The DNA reaction degree is evaluated for each region of the dynamic constant temperature amplification model, the amplification efficiency is calculated and the results are recorded. A data set of amplification reaction efficiency in different regions is formed to ensure the integrity and accuracy of the data. According to the amplification reaction efficiency evaluated in step S33 and the flow rate change characteristics extracted in step S32, a regional flow rate dynamic adjustment strategy is designed. The strategy should consider how to adjust the flow rate between different regions to optimize the amplification effect. Determine the key parameters of the adjustment, such as the target amplification efficiency, the flow rate change amplitude and the adjustment frequency, to achieve dynamic adjustment. Select a suitable dynamic adjustment algorithm, such as PID control, fuzzy logic control or adaptive control algorithm, to achieve real-time adjustment of the flow rate. Determine the parameters of the algorithm, such as the proportional, integral and differential coefficients in PID control, to ensure the accuracy of the flow rate adjustment. Apply the dynamic adjustment strategy to the actual amplification reaction, monitor the amplification effect of each region in real time, and adjust the reaction flow rate according to the preset strategy. Record the real-time data of the flow rate adjustment, monitor the amplification effect after adjustment, and ensure the stability and efficiency of the reaction process.

[0028] In this embodiment, step S4 includes the following steps: Step S41: performing real-time amplification control based on a dynamic amplification reaction flow rate adjustment strategy, and collecting dynamic amplification reaction signals of each region using a fluorescent molecular probe; Step S42: performing low-concentration sample signal identification on the dynamic amplification reaction signal of each region, and marking the low-concentration sample region; Step S43: performing adaptive enhancement processing of the fluorescence signal according to the low-concentration sample area to obtain an enhanced signal in the low-concentration area; Step S44: performing dynamic signal filtering on the low-concentration region enhancement signal and the dynamic amplification reaction signal of each region to generate a filtered optimized amplification reaction signal.

[0029] In this embodiment, according to the dynamic amplification reaction flow rate adjustment strategy formulated in step S34, the flow rate and temperature parameters of the PCR instrument are configured. Ensure that the PCR instrument can adjust the flow rate in real time to adapt to the amplification requirements of different regions. Use a real-time monitoring system to set the data acquisition frequency (such as once every 10 seconds) to record the amplification reaction signal of each region in real time. Select a suitable fluorescent molecular probe (such as TaqMan probe or SYBR Green) to ensure that the probe can specifically bind to the target DNA sequence. The selection of the probe should take into account its fluorescence properties and sensitivity. Add a fluorescent molecular probe to the reaction system to ensure that its concentration is appropriate (such as 200 nM) to ensure that the fluorescent signal can be effectively emitted during the amplification process. Start the PCR amplification reaction and monitor the changes in the fluorescent signal in real time. The fluorescence intensity of each region is recorded by a fluorescence detector, and the data is transmitted to the data processing system. Record the fluorescence signal data of each region, including timestamp, region number and fluorescence intensity, for subsequent analysis. Set the recognition standard for low-concentration sample signals, such as setting a fluorescence signal intensity threshold (such as less than 50 RFU, RFU is a relative fluorescence unit). The threshold should be adjusted based on previous experimental data and background signals. Ensure that background noise measurements are included in the data set to more accurately identify low-concentration signals. Use data processing software (such as Matlab or Python) to analyze real-time fluorescence signal data. Write an algorithm to analyze the signal intensity region by region and identify signals below the threshold. Mark each low-concentration sample region to form a marking result data set, including the region number and the corresponding signal intensity. Organize the marked low-concentration sample region data to form a data table, including the region number, signal intensity, and marking status. Use visualization tools (such as GraPHPad Prism or Excel) to draw a distribution map of low-concentration sample regions to intuitively display the characteristics of low-concentration regions. Choose an appropriate signal enhancement method, such as adaptive filtering based on background noise or signal averaging of multiple repeated measurements. Ensure that the enhancement method can effectively improve the detectability of low-concentration signals. Determine the enhancement parameters, such as the enhancement factor (for example, 2x or 3x) and the calculation method of background noise. Enhance the fluorescence signal of the marked low-concentration sample region. Use the written algorithm to enhance the signal of each region and calculate the signal intensity after enhancement. Record the enhancement results in the data set, including the comparison of signal intensity before and after enhancement, to ensure the accuracy of the data. Verify the effectiveness of the enhanced signal and evaluate the accuracy of the enhancement process by comparing it with the signal intensity of samples with known concentrations. Analyze the changes in the signal after enhancement and identify the impact of the enhancement process on the signal of low-concentration samples. Select appropriate signal filtering methods, such as moving average filtering, Kalman filtering, or wavelet transform, to remove noise and optimize signal quality. Set filtering parameters, such as window size, filtering frequency, etc., to ensure the best filtering effect.Apply the filtering algorithm to the enhanced low-concentration area signal and the dynamic amplification reaction signal in all areas. Apply the filtering process to each area one by one to ensure smooth signals. Record the filtered signal data to form a new data set, including the filtered signal intensity in each area. Analyze the signal data after filtering optimization to evaluate the clarity and stability of the signal. Compare the signals before and after filtering to verify the filtering effect. Based on the analysis results, further optimize the filtering parameters to improve the signal quality and ensure that the final amplification reaction signal has high reliability and accuracy.

[0030] In this embodiment, the specific steps of step S5 are: Step S51: performing multi-period amplification reaction analysis on the filtered optimized amplification reaction signal, and extracting amplification reaction signals of multiple time periods; Step S52: performing amplification stage identification based on amplification reaction signals of multiple time periods to obtain an amplification reaction stage of each time period; Step S53: performing dynamic evolution analysis on the amplification reaction stage of each time period to obtain the dynamic amplification reaction characteristics of each stage; Step S54: Perform dynamic amplification timing mining according to the dynamic amplification reaction characteristics of each stage to construct a dynamic amplification curve.

[0031] In this embodiment, the time range of the analysis is set. The entire amplification reaction process is divided into multiple time periods, such as the initial heating stage (0-1 minute), the annealing stage (1-2 minutes), the extension stage (2-4 minutes), etc. From the filtered optimized amplification reaction signal data extracted in step S44, the data integrity of each time period is ensured according to the set time period division. The amplification reaction signal in each time period is extracted. Use programming tools (such as Python) to write scripts to automate the data extraction process to ensure efficiency and accuracy. Arrange the extracted data to form a data set, including information such as signal intensity and timestamps for each time period for subsequent analysis. Perform a preliminary analysis on the extracted amplification reaction signals of multiple time periods, and calculate statistical indicators such as the average signal intensity and standard deviation of each time period. Use visualization tools (such as Matplotlib) to draw the signal change curve of each time period, intuitively display the characteristics of the amplification reaction in different time periods, and help identify the key time periods. According to the theory of amplification reaction, define each amplification stage, such as the startup stage, exponential amplification stage, and platform stage. Determine the characteristics of each stage with reference to existing literature or experimental data. Set threshold criteria for phase identification, such as when the fluorescence signal reaches a certain intensity (such as the threshold is set to 200 RFU), it is marked as entering the exponential amplification phase. Select an appropriate algorithm for amplification phase identification. Use a simple threshold-based classification method or a more complex machine learning algorithm (such as a support vector machine) for signal classification. Write a program to automatically identify the amplification phase of each time period to ensure efficiency and accuracy. According to the set standards and algorithms, perform phase identification on the extracted signals of multiple time periods. Analyze the signal intensity of each time period one by one to determine its corresponding amplification phase. Record the amplification phase of each time period to form a data set, including the time period number and the corresponding amplification phase. Define dynamic amplification reaction characteristics, such as amplification rate, stability, and amplification efficiency. These characteristics are obtained by analyzing parameters such as the slope, peak value, and duration of the fluorescence signal changes in each stage. Determine the key parameters of each stage, such as the signal growth rate (rate of change of RFU time) and stability (standard deviation of the signal). Select appropriate data analysis methods, such as linear regression analysis, curve fitting, or derivative calculation, to extract dynamic amplification reaction characteristics. Use programming tools (such as R or Python) to write algorithms to analyze the signal data of each stage and extract dynamic features. Perform dynamic evolution analysis on the signal data of each amplification stage, calculate various characteristic parameters, and form a characteristic data set. Record the analysis results, including the dynamic amplification reaction characteristics of each stage, such as amplification rate, signal stability, etc. Organize the dynamic amplification reaction characteristic data, generate a data table, and display the characteristic parameters of different stages. Use visualization tools to draw dynamic characteristic change diagrams to intuitively display the characteristics of amplification reactions at different stages and help identify key influencing factors. Design a dynamic amplification curve model based on the extracted dynamic amplification reaction characteristics. The curve should be able to accurately reflect the amplification characteristics of different stages.Determine the curve shape, such as Sigmoid curve or exponential curve, to describe the different stages of the amplification process. Select appropriate time series mining algorithms, such as time series analysis, curve fitting, or machine learning classifiers, to extract the key features of the dynamic amplification curve. Determine the key parameters of mining, such as time window size, fitting error range, etc., to ensure the accuracy of the mining results. According to the dynamic feature data, apply the selected time series mining method to construct the dynamic amplification curve. Gradually adjust the model parameters to optimize the fitting effect. Record the key parameters and final curves during the construction process to ensure the repeatability of the results. Organize the dynamic amplification curve results and generate data tables and charts to show the amplification curves at different stages. Use visualization tools to draw the final dynamic amplification curve graph to intuitively display the overall trend of the amplification reaction and help analyze the amplification efficiency and stability.

[0032] In this embodiment, the specific steps of step S6 are: Step S61: performing regional quantitative analysis according to the dynamic amplification curve to generate DNA amplification quantitative analysis data for each region; Step S62: Performing a comprehensive diagnosis on the DNA amplification quantitative analysis data of each region, thereby generating a DNA amplification diagnosis result; Step S63: Perform intelligent data visualization on the DNA amplification diagnosis results to obtain a sample diagnosis visualization report.

[0033] In this embodiment, the accuracy and integrity of the dynamic amplification curve are ensured, and the curve should be able to clearly show the signal intensity changes in each amplification stage. Generally, the amplification curve presents an S-shaped curve, which includes three main stages: a delay phase, a logarithmic phase, and a plateau phase. When preparing the data set, the dynamic amplification curve data of each region is extracted, including the fluorescence intensity at each moment and the corresponding time value. A relative quantitative analysis method (such as the ΔΔCt method or the standard curve method) is used for regional quantitative analysis. Relative quantitative analysis is based on the Ct value (threshold cycle number), and the standard curve method generates a standard curve for quantitative analysis by using a standard substance of known concentration. Determine the key parameters of the analysis, such as the threshold setting of the Ct value (usually set to a signal intensity of 3 times the background noise), so as to accurately identify the amplification starting point. The dynamic amplification curve of each region is quantitatively analyzed and its Ct value is recorded. Use a programming tool (such as Python or R) to write a script to automatically calculate the Ct value and the corresponding amplification amount of each region. Generate DNA amplification quantitative analysis data for each region, and the data should include the region number, Ct value, relative amplification amount, and corresponding DNA concentration. The DNA amplification quantitative analysis data obtained in step S61 are integrated. Ensure the integrity of the data, including the Ct value, amplification amount, and relative concentration information of each region. Set the standard for comprehensive diagnosis, for example: Ct value below 30 indicates good amplification effect, Ct value between 30-35 indicates moderate amplification effect, and Ct value above 35 indicates poor amplification effect or no amplification. Perform comprehensive diagnosis on the quantitative analysis data of each region and classify each region using the set standard. Use simple conditional judgment statements to automatically generate the diagnostic results of each region. Record the diagnostic results of each region to form a data set, including the region number and the corresponding diagnostic status (such as "good", "moderate", "poor"). Organize the comprehensive diagnostic results to form a data table to display the diagnostic status of each region and the corresponding Ct value and amplification amount. Perform statistical analysis, calculate the proportion of each type of diagnostic results, identify areas with good amplification effect and areas that need improvement, and provide data support for subsequent decision-making. Use visualization tools to display the comprehensive diagnostic results and generate diagnostic result charts, such as pie charts or bar charts, to intuitively display the distribution of diagnostic status in different regions. Ensure that the results are clearly displayed to facilitate further analysis and decision-making. Determine the structure of the sample diagnostic visualization report, including introduction, methods, results, discussion, and conclusions. Each section should be concise and easy for readers to understand. Determine the visualization content, such as amplification results of each region, distribution map of diagnostic results, statistical charts, etc., to ensure the comprehensiveness and readability of the report. Choose appropriate data visualization tools (such as Tableau, Power BI or Python's Matplotlib, Seaborn library) for intelligent data visualization. Make sure the selected tool can handle large data volumes and generate high-quality charts.Ensure that the visualization tool supports interactive features, such as dynamic charts, hover prompts, etc., to improve the user experience of the report. Import comprehensive diagnostic results and quantitative analysis data into the visualization tool to generate various charts and visualizations. Draw amplification efficiency bar charts and diagnostic status charts for different regions. Design report templates to ensure that the visualization content is combined with the text content to form a complete diagnostic report. Generate the final sample diagnosis visualization report to ensure that the report content is accurate, complete, and clearly formatted. Export the report to PDF or HTML format for easy sharing and archiving. Review the report to ensure the accuracy of the data and the beauty of the visualization, and finally submit the report for decision-making and subsequent analysis.

[0034] The present invention also provides an intelligent molecular diagnosis and analysis device, comprising: An amplification perception module is used to detect the multi-dimensional environmental parameters of the DNA amplification kit in real time based on multi-dimensional sensor nodes; perform spatial topological layout mining and environmental amplification perception fitting on the multi-dimensional environmental parameters to build a real-time amplification perception model; The constant temperature control module is used to identify DNA amplification samples; perform layered temperature control analysis on DNA amplification samples, and perform dynamic amplification constant temperature control on the real-time amplification perception model, thereby constructing multiple dynamic constant temperature amplification models; The flow rate adjustment module is used to evaluate the DNA reaction degree of each region of multiple dynamic constant temperature amplification models, and then dynamically adjust the regional flow rate, so as to construct a dynamic amplification reaction flow rate adjustment strategy; An amplification signal processing module, used to perform real-time amplification control based on a dynamic amplification reaction flow rate adjustment strategy, and to perform dynamic signal filtering to generate a filtered optimized amplification reaction signal; The dynamic amplification analysis module is used to perform multi-period amplification reaction analysis on the filtered optimized amplification reaction signal, and to perform dynamic amplification timing mining to construct a dynamic amplification curve; The diagnostic visualization module is used to perform regional quantitative analysis and comprehensive diagnosis based on the dynamic amplification curve to obtain a sample diagnostic visualization report.

[0035] The present invention also provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any of the above-mentioned intelligent molecular diagnosis and analysis methods when executing the computer program.

[0036] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of any of the above-mentioned intelligent molecular diagnosis and analysis methods.

[0037] Those skilled in the art clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0038] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it is stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or partly or all or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions for a computer device (a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media for storing program codes such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks or optical disks. Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0039] The above is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. An intelligent molecular diagnosis and analysis method, characterized in that: The following steps are involved: Step S1: Real-time detection of multi-dimensional environmental parameters of the DNA amplification kit based on multi-dimensional sensor nodes; performing spatial topological layout mining and environmental amplification perception fitting on the multi-dimensional environmental parameters to construct a real-time amplification perception model; Step S2: Identify DNA amplification samples; perform layered temperature control analysis on the DNA amplification samples, and perform dynamic amplification constant temperature control on the real-time amplification perception model, thereby constructing multiple dynamic constant temperature amplification models; Step S3: evaluating the DNA reaction degree of each region of the multiple dynamic constant temperature amplification models, and then dynamically adjusting the regional flow rate, thereby constructing a dynamic amplification reaction flow rate adjustment strategy; Step S4: performing real-time amplification control based on the dynamic amplification reaction flow rate adjustment strategy, and performing dynamic signal filtering to generate a filtered optimized amplification reaction signal; Step S5: performing multi-period amplification reaction analysis on the filtered optimized amplification reaction signal, and performing dynamic amplification timing mining to construct a dynamic amplification curve; Step S6: Perform regional quantitative analysis and comprehensive diagnosis based on the dynamic amplification curve to obtain a sample diagnosis visualization report.

2. The intelligent molecular diagnosis and analysis method according to claim 1, characterized in that: The specific steps of step S1 are: Step S11: Real-time detection of multi-dimensional environmental parameters of the DNA amplification kit based on the multi-dimensional sensing node; Step S12: performing a spatial temperature distribution analysis of the test kit on the multi-dimensional environmental parameters to generate a spatial temperature distribution feature; Step S13: performing pH value fluctuation calculation on the multi-dimensional environmental parameters to obtain pH value fluctuation data of the test kit; Step S14: performing ion concentration dynamic change detection on the multi-dimensional environmental parameters to generate a dynamic change characteristic of ion concentration of the test kit; Step S15: performing spatial topological layout mining according to the multi-dimensional sensor nodes to generate a reagent kit topological layout structure; Step S16: Perform amplification perception fitting on the spatial temperature distribution characteristics, the pH value fluctuation data of the reagent kit and the dynamic change characteristics of the ion concentration of the reagent kit according to the topological layout structure of the reagent kit to construct a real-time amplification perception model.

3. The intelligent molecular diagnosis and analysis method according to claim 1, characterized in that: The specific steps of step S2 are: Step S21: Identify the DNA amplification sample; perform multi-sequence amplification simulation on the DNA amplification sample, and extract amplification simulation data of different sequences; Step S22: Calculating the optimal amplification temperature of each sequence according to the amplification simulation data of different sequences, thereby obtaining the optimal amplification temperatures of multiple DNA sequences; Step S23: performing a hierarchical temperature control analysis based on the optimal amplification temperatures of multiple DNA sequences, thereby obtaining hierarchical amplification temperature control data; Step S24: dividing the real-time amplification perception model into amplification reaction regions, and extracting a plurality of amplification reaction region models; Step S25: Dynamically amplify and control the constant temperature of multiple constant temperature amplification reaction area models according to the layered amplification temperature control data, thereby constructing multiple dynamic constant temperature amplification models.

4. The intelligent molecular diagnosis and analysis method according to claim 1, characterized in that: The specific steps of step S3 are: Step S31: Calculating the reaction rate and flow rate of each region of multiple dynamic constant temperature amplification models to generate the reaction rate and flow rate of each region; Step S32: dynamically evolve the reaction velocity of each region in a time series manner, and extract the reaction velocity variation characteristics of each region; Step S33: evaluating the DNA reaction degree of each region of the multiple dynamic constant temperature amplification models to generate amplification reaction efficiencies of different regions; Step S34: dynamically adjust the regional flow rate according to the amplification reaction efficiency of different regions and the reactant flow rate change characteristics of each region, thereby constructing a dynamic amplification reaction flow rate adjustment strategy.

5. The intelligent molecular diagnosis and analysis method according to claim 1, characterized in that: The specific steps of step S4 are: Step S41: performing real-time amplification control based on a dynamic amplification reaction flow rate adjustment strategy, and collecting dynamic amplification reaction signals of each region using a fluorescent molecular probe; Step S42: performing low-concentration sample signal identification on the dynamic amplification reaction signal of each region, and marking the low-concentration sample region; Step S43: performing adaptive enhancement processing of the fluorescence signal according to the low-concentration sample area to obtain an enhanced signal in the low-concentration area; Step S44: performing dynamic signal filtering on the low-concentration region enhancement signal and the dynamic amplification reaction signal of each region to generate a filtered optimized amplification reaction signal.

6. The intelligent molecular diagnosis and analysis method according to claim 1, characterized in that: The specific steps of step S5 are: Step S51: performing multi-period amplification reaction analysis on the filtered optimized amplification reaction signal, and extracting amplification reaction signals of multiple time periods; Step S52: performing amplification stage identification based on amplification reaction signals of multiple time periods to obtain an amplification reaction stage of each time period; Step S53: performing dynamic evolution analysis on the amplification reaction stage of each time period to obtain the dynamic amplification reaction characteristics of each stage; Step S54: Perform dynamic amplification timing mining according to the dynamic amplification reaction characteristics of each stage to construct a dynamic amplification curve.

7. The intelligent molecular diagnosis and analysis method according to claim 1, characterized in that: The specific steps of step S6 are: Step S61: performing regional quantitative analysis according to the dynamic amplification curve to generate DNA amplification quantitative analysis data for each region; Step S62: Performing a comprehensive diagnosis on the DNA amplification quantitative analysis data of each region, thereby generating a DNA amplification diagnosis result; Step S63: Perform intelligent data visualization on the DNA amplification diagnosis results to obtain a sample diagnosis visualization report.

8. An intelligent molecular diagnosis and analysis device, characterized in that: Used to perform the intelligent molecular diagnosis and analysis method as claimed in claim 1, comprising: An amplification perception module is used to detect the multi-dimensional environmental parameters of the DNA amplification kit in real time based on multi-dimensional sensor nodes; perform spatial topological layout mining and environmental amplification perception fitting on the multi-dimensional environmental parameters to build a real-time amplification perception model; The constant temperature control module is used to identify DNA amplification samples; perform layered temperature control analysis on DNA amplification samples, and perform dynamic amplification constant temperature control on the real-time amplification perception model, thereby constructing multiple dynamic constant temperature amplification models; The flow rate adjustment module is used to evaluate the DNA reaction degree of each region of multiple dynamic constant temperature amplification models, and then dynamically adjust the regional flow rate, so as to construct a dynamic amplification reaction flow rate adjustment strategy; An amplification signal processing module, used to perform real-time amplification control based on a dynamic amplification reaction flow rate adjustment strategy, and to perform dynamic signal filtering to generate a filtered optimized amplification reaction signal; The dynamic amplification analysis module is used to perform multi-period amplification reaction analysis on the filtered optimized amplification reaction signal, and to perform dynamic amplification timing mining to construct a dynamic amplification curve; The diagnostic visualization module is used to perform regional quantitative analysis and comprehensive diagnosis based on the dynamic amplification curve to obtain a sample diagnostic visualization report.

9. A computer device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the computer program, the steps of the intelligent molecular diagnosis and analysis method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the intelligent molecular diagnosis and analysis method according to any one of claims 1 to 7 are implemented.

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