Rapid pathogen detection system based on nanopore sequencing

Abstract

The invention relates to the technical field of pathogen detection, in particular to a rapid pathogen detection system based on nanopore sequencing, which comprises an acquisition and pretreatment module, which is used for precipitating and centrifuging serum, carrying out layered monitoring, carrying out supernate separation and concentration, and measuring pH value and temperature. According to the invention, the serum is precipitated and centrifuged, and layered monitoring is combined, so that the layered state of the serum sample in the centrifugation process is visualized, and the stability of precipitated particles and the separation precision of supernate are improved. And in the supernatant concentration link, the dynamic monitoring and optimization of the sample purity are realized by measuring the pH value and the temperature and combining the recording of the protein concentration and the turbidity value. Absorbance and viscosity measurement are combined, so that the change of liquid properties can be captured in real time and compared with a pathogen directory range threshold value, and an abnormal sample can be recognized in an early stage.

Description

Technical Field

[0001] The present invention relates to the technical field of pathogen detection, and in particular, to a rapid pathogen detection system based on nanopore sequencing. Background Art

[0002] The technical field of pathogen detection involves the identification, classification, and quantitative analysis of pathogenic microorganisms, mainly used for clinical diagnosis, public health monitoring, food safety detection, and environmental biological monitoring. By means of biochemistry, molecular biology, immunology, and high-throughput sequencing, pathogens such as viruses, bacteria, fungi, and parasites are accurately detected, and their genomic information, drug-resistant genes, and mutation conditions are analyzed to support disease prevention, control, and treatment decisions.

[0003] The existing technology relies on traditional centrifugation and separation methods in the sample processing stage, which are easily affected by uneven centrifugal force, resulting in uneven distribution of precipitate particles and affecting the subsequent sample purity. The single measurement of absorbance and viscosity lacks a comprehensive assessment of the sample state, making it difficult to effectively identify abnormal samples and affecting the reliability of the detection results. Therefore, improvements are needed. Summary of the Invention

[0004] The object of the present invention is to solve the deficiencies existing in the prior art, and to propose a rapid pathogen detection system based on nanopore sequencing.

[0005] To achieve the above object, the present invention adopts the following technical solution: A rapid pathogen detection system based on nanopore sequencing includes:

[0006] A collection and pretreatment module that precipitates and centrifuges serum and monitors stratification, separates and concentrates the supernatant and measures the pH value and temperature, then detects the protein concentration and records the turbidity value, and subsequently collects precipitate particles and removes impurities to generate an initial extract;

[0007] A quality monitoring module that measures the absorbance and viscosity of the initial extract, captures the transmission value through an optical instrument and records the viscosity change, compares with the pathogen list range threshold to determine whether there is an abnormality and summarizes the pH change information, and then compares with the nanopore sequencing array channel parameters, and at the same time detects the trace ion content to generate monitoring values;

[0008] A sample loading self-adaptive module that, based on the monitoring values, performs target concentration comparison and evaluates the ion content, records the pH fluctuation by reading the concentration difference in the monitoring values, then adjusts the feeding ratio for drug-resistant gene screening and analyzes the evaporation component concentration ratio, and at the same time measures the temperature change curve to generate a sample loading parameter set;

[0009] A signal processing module, based on the above-sampling parameter set, reads the current in the nanopore sequencing channel, collects the instantaneous current and compares it with the channel baseline, monitors the current peak at the same time, detects the current fluctuation difference in the culture medium solution and corrects the signal drift, records the current peak and trims the amplitude in the high-frequency band, records the noise distribution, and generates a denoised sequence packet.

[0010] Preferably, it further includes:

[0011] A sequence splicing module, based on the denoised sequence packet, compares the lengths of the covered fragments and counts the repeated regions, records the binding rate, analyzes the first and last base pairing relationships, unfolds the covered fragments, counts the repeated regions, and merges the full-length sequence segments, then checks the mismatch sites and repairs the base offsets to generate a spliced sequence set;

[0012] An identification and output module, based on the spliced sequence set, matches the pathogen list identifier and confirms the drug-resistant gene sequence, counts the read coverage depth, compares the sequence features and extracts the pathogen list identifier, then screens the pathogenic risk coding region, integrates the typing results and marks the mutation positions, obtains the strain classification information, and generates pathogen identification information.

[0013] Preferably, the collection and pretreatment module includes:

[0014] A precipitation and centrifugation sub-module, by injecting serum into a centrifuge tube for precipitation and controlling the centrifugation time and speed, observing the layering phenomenon and recording the precipitation state, checking the residual liquid level in the centrifuge tube and marking the supernatant area, and finally collecting the precipitation data to generate a centrifugation monitoring result;

[0015] A supernatant concentration sub-module, based on the centrifugation monitoring result, concentrates the separated supernatant and tracks the concentration stage time, checks the liquid level height to measure the PH value and temperature, separately detects the protein concentration and records the turbidity value to generate layering and concentration data;

[0016] An impurity removal sub-module, based on the layering and concentration data, removes impurities by collecting precipitation particles and monitors the precipitation morphology, records the particle density, checks the precipitation color to view the coagulation degree, and then records the attachment state to generate an initial extract.

[0017] Preferably, the quality monitoring module includes:

[0018] An absorbance and viscosity sub-module, based on the initial extract, takes an appropriate amount of liquid and sets the optical path to collect the transmission value, determines the absorbance value and detects the viscosity at the same time to record the change in flow velocity, and separately calculates the flow resistance index to generate optical viscosity monitoring information;

[0019] The threshold comparison sub-module, based on the optical adhesion monitoring information, compares the absorbance numerical viscosity data with the pathogen list range threshold, counts the deviation amplitude, evaluates the viscosity change curve, summarizes the PH change information, and generates a threshold comparison result;

[0020] The ion detection sub-module, based on the threshold comparison result, compares the deviation amplitude with the nanopore sequencing array channel parameters, records the compliance situation, checks the PH fluctuation information, detects the trace ion content, summarizes the observed values, and generates a monitoring value.

[0021] Preferably, the sample loading adaptive module includes:

[0022] The concentration evaluation sub-module, based on the monitoring value, compares the target concentration, evaluates the ion content, reads the concentration difference in the monitoring value, records the PH fluctuation, observes the corresponding ion fluctuation amplitude, and tracks the comparison duration, and generates concentration fluctuation information;

[0023] The ratio adjustment sub-module, based on the concentration fluctuation information, adjusts the drug resistance gene screening feeding ratio, counts the change amount, analyzes the volatile component concentration ratio, records the gasification level, and tracks the volatile process duration, and generates feeding adjustment data;

[0024] The temperature measurement sub-module, based on the feeding adjustment data, observes the temperature curve of the volatile process, records the temperature fluctuation range, analyzes the heating rate compared with the set interval, and compares the influence of the feeding ratio on the temperature, and generates a sample loading parameter set.

[0025] Preferably, the signal processing module includes:

[0026] The current acquisition sub-module, based on the sample loading parameter set, reads the current of the nanopore sequencing channel, acquires the instantaneous current, compares it with the baseline, captures the acquisition duration, records the baseline deviation degree, and counts the acquisition period, and generates current baseline information;

[0027] The peak monitoring sub-module, based on the current baseline information, monitors the current peak, records the occurrence frequency, checks the peak width, identifies the abnormal sudden increase, analyzes the peak shape change range, and records the peak height, and generates peak fluctuation data;

[0028] The signal trimming sub-module, based on the peak fluctuation data, detects the current difference in the culture medium solution, corrects the signal drift, checks the difference distribution interval, records the peak position, and counts the noise distribution, and generates a denoising sequence packet.

[0029] Preferably, the sequence splicing module includes:

[0030] The coverage comparison sub-module, based on the denoised sequence package, generates coverage comparison data by comparing the lengths of the covered segments, checking the boundary differences, and recording the fragment matching rate to mark the overlapping regions;

[0031] The repeated statistics sub-module, based on the coverage comparison data, generates repeated statistics results by counting the repeated regions, recording the repeated frequencies, analyzing the overlapping paragraph structure to calculate the binding rate, and summarizing the successfully paired numbers by analyzing the head and tail base pairs;

[0032] The mismatch repair sub-module, based on the repeated statistics results, generates a spliced sequence set by merging the full-length sequence segments, checking the joints, detecting the mismatch sites, repairing the base offsets to organize the complete sequence, and confirming the fragment binding rate;

[0033] Preferably, the identification output module includes:

[0034] The pathogen matching sub-module, based on the spliced sequence set, generates pathogen matching data by matching the pathogen list identifiers, confirming the drug-resistant gene sequences, recording the identified pathogen types, counting the matching times, monitoring the positions of the drug-resistant genes, and summarizing the distribution;

[0035] The risk screening sub-module, based on the pathogen matching data, generates risk screening results by counting the read coverage depth, recording the missing positions, comparing the sequence features, extracting the pathogen list identifiers, screening the pathogenic risk coding regions, and marking the risk sites;

[0036] The typing marker sub-module, based on the risk screening results, generates pathogen identification information by integrating the typing results, analyzing the sequence branch structure, marking the variant positions to confirm the strain classification information, and then checking the sequence clustering relationship to summarize the drug-resistant gene correlation;

[0037] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0038] In the present invention, through the precipitation and centrifugation operations of the serum, combined with hierarchical monitoring, the stratification state of the serum sample during the centrifugation process is visualized, improving the stability of the precipitated particles and the separation accuracy of the supernatant. In the supernatant concentration step, through the measurement of pH value and temperature, combined with the recording of protein concentration and turbidity values, the dynamic monitoring and optimization of the sample purity are achieved. The combination of absorbance and viscosity measurement enables the real-time capture of changes in the properties of the liquid, and comparison with the threshold range of the pathogen list, enabling the early identification of abnormal samples. Through the detection of trace ion content, the stability of the charge environment during the sequencing process is ensured, reducing the interference of background noise. The combination of target concentration comparison and pH fluctuation monitoring makes the sample loading concentration more accurate, avoiding abnormal fluctuations in the sequencing signal due to concentration deviation. The adjustment of the feeding ratio combined with the analysis of the concentration ratio of volatile components improves the accuracy of drug resistance gene screening. Through the reading of the sequencing channel current, combined with the acquisition of transient current, channel baseline comparison, and current peak monitoring, the control of signal quality is more stable. The detection of the current fluctuation difference and drift correction suppresses the background noise and ensures the reliability of signal analysis. During the sequence splicing process, through the length comparison of the covered fragments, the counting of repeated regions, and the statistical analysis of the combination rate, the splicing accuracy of long sequences is optimized. The repair of mismatch sites combined with the correction of base offset reduces sequencing errors and improves the integrity of the pathogen genome. The combination of pathogen list matching and drug resistance gene screening improves the identification accuracy of pathogens and can effectively identify potential drug resistance mutations. The combination of read coverage depth statistics and risk coding screening enables a more comprehensive detection of pathogenic regions, ensuring the integrity and accuracy of the detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 is the system flow chart of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0040] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. 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.

[0041] Please refer to Figure 1 , the present invention provides a technical solution: A rapid pathogen detection system based on nanopore sequencing includes:

[0042] A collection and pretreatment module, which precipitates and centrifuges the serum and conducts hierarchical monitoring, separates and concentrates the supernatant, measures the pH value and temperature, then detects the protein concentration and records the turbidity value, and subsequently collects the precipitated particles and removes impurities to generate an initial extract;

[0043] Quality monitoring module, which measures the absorbance and viscosity of the initial extraction solution, captures the transmission value through an optical instrument and records the viscosity change, compares with the pathogen list range threshold to determine whether there is an abnormality and summarizes the PH change information, then compares with the nanopore sequencing array channel parameters, and simultaneously detects the trace ion content to generate monitoring values;

[0044] Sample loading adaptive module, based on the monitoring values, conducts target concentration comparison and evaluates the ion content, records the PH fluctuation by reading the concentration difference in the monitoring values, then adjusts the feeding ratio for drug resistance gene screening and analyzes the evaporation component concentration ratio, and simultaneously measures the temperature change curve to generate a set of sample loading parameters;

[0045] Signal processing module, based on the set of sample loading parameters, reads the current in the nanopore sequencing channel, collects the instantaneous current and compares with the channel baseline, simultaneously monitors the current peak, then detects the current fluctuation difference in the culture medium solution and corrects the signal drift, and simultaneously records the current peak and trims the amplitude in the high-frequency band, records the noise distribution to generate a denoised sequence packet.

[0046] Sequence splicing module, based on the denoised sequence packet, conducts length comparison on the covered fragments and counts the repeated regions, records the binding rate, analyzes the head and tail base pairing relationship, unfolds the covered fragments, conducts statistics on the repeated regions, and merges the full-length sequence segments, then checks the mismatch sites and repairs the base offset to generate a set of spliced sequences;

[0047] Identification and output module, based on the set of spliced sequences, matches the pathogen list identifier and confirms the drug resistance gene sequence, counts the read coverage depth, compares the sequence features and extracts the pathogen list identifier, then screens the pathogenic risk coding region, integrates the typing results and marks the mutation positions, obtains the strain classification information to generate pathogen identification information.

[0048] The sample collection and pretreatment module includes:

[0049] Precipitation and centrifugation sub-module, by injecting serum into the centrifuge tube for precipitation and controlling the centrifugation time and speed, observing the layering phenomenon and recording the precipitation state, simultaneously checking the residual liquid level in the centrifuge tube and marking the supernatant area, and finally collecting the precipitation data to generate the centrifugation monitoring result;

[0050] Supernatant concentration sub-module, based on the centrifugation monitoring result, concentrates the separated supernatant and tracks the concentration stage time, checks the liquid level height to measure the PH value and temperature, separately detects the protein concentration and records the turbidity value to generate the layering and concentration data;

[0051] Impurity exclusion sub-module, based on the layering and concentration data, excludes impurities by collecting the precipitation particles and monitors the precipitation morphology, records the particle density, checks the precipitation color to view the coagulation degree, and then records the attachment state to generate the initial extraction solution.

[0052] Specifically, based on the previous document content required for serum centrifugation, extract the setting methods for the serum injection volume and the centrifugation speed range. For example, the serum injection volume can vary from 10 milliliters to 30 milliliters, and the centrifugation speed can range from 1000 revolutions per minute to 3000 revolutions per minute. Based on the previously obtained parameters, set the centrifugation time to range from 30 seconds to 120 seconds and perform centrifugation in batches. During the process, it is necessary to record the height of the stratification interface of the serum in different speed and time intervals and compare it with the pre-established effective stratification range. For example, when the stratification bandwidth is less than or equal to 5 millimeters, it can be regarded as the completion of stratification; otherwise, re-detect after increasing the centrifugation time by 30 seconds. According to the recorded speed and time obtained previously, also pay attention to the deviation between the actual speed and the set value and conduct a numerical comparison. If the deviation is higher than the 0.02 threshold determined through multiple experimental experiences, it is judged as an abnormal state. Subsequently, in combination with the stratification observation results, check whether the color of the serum shows a significant deviation from the reference color range (visible spectrum from 400 nanometers to 700 nanometers). If the color deviation exceeds the 3% standard value obtained based on previous comparison statistics, it is recorded as a special case, and the centrifugation time is extended again or the speed range is reduced to continue tracking. During the process, record the relationship between the speed and the serum stratification speed item by item. Finally, uniformly summarize the interface position and its bandwidth between the precipitate and the supernatant in the serum tube after centrifugation, numerically record the status information obtained for the precipitate in each processing step, and compare it with the temperature (which can be compared item by item in the range of 20°C to 25°C) and other environmental parameters. After corresponding these records to the pre-established precipitate stratification determination criteria one by one, generate specific data. Finally, collect the precipitate data to generate the centrifugation monitoring results.

[0053] Based on the sediment volume and supernatant volume involved in the centrifugation monitoring results, the sedimentation data and bandwidth information generated after the centrifugation are first extracted from the existing records, and the suitability of the test for supernatant concentration is evaluated according to the defined range (such as the sediment volume is between 1 ml and 5 ml). Then, at the beginning of the concentration phase, the concentration time interval is set to 1 minute to 5 minutes and the liquid level height change value is collected in increments of one minute. The height value is compared with the pre-established normal liquid level range (for example, 1 cm to 2 cm). If a measurement result is detected to exceed the upper limit of the range, the concentration operation is suspended for 30 seconds and the record is re-recorded. Before each concentration, the ambient temperature (usually between 20°C and 25°C) and pH value (at 6 .8 to 7.5 range) are tested separately and kept for comparison. For the detection of protein concentration, the established concentration analysis table can be referred to to determine whether the current concentration is between 5 mg / ml and 20 mg / ml. If it is higher than this range, the concentration time will be extended by an additional 1 minute and the latest concentration value will be recorded again. Optical instruments are used to quantify the turbidity values. For example, the turbidity is divided into several levels from 0NTU to 200NTU. The difference between the results and previous test results is compared each time monitoring is performed. If the difference exceeds the 5% range summarized after multiple actual measurements, it is marked as abnormal fluctuation and the record is retained. Finally, all data information such as liquid level, protein concentration, turbidity, etc. in the concentration process are summarized to generate layered concentration data.

[0054] Based on the records of the properties of the precipitated particles in the stratified concentration data, first select the particle size information (for example, the particle size can be in the range of 0.1 mm to 1 mm) and the precipitation density comparison results of the current batch for preliminary screening, and then mark the particles that meet the above size range separately for subsequent impurity screening. During the formal screening process, the observation microscope magnification can be set to 100 times to 400 times to directly compare the surface morphology and color condition of the precipitated particles, and confirm the color deviation one by one according to the color control range collected in advance. If the color of individual precipitated particles deviates from the control range and exceeds the 5% brightness threshold based on experimental experience, it is judged as a special impurity, and then the impurity removal process is carried out in combination with the pre-configured solvent or washing step. At the same time, Record the mass change of particles before and after washing to determine whether there are any residues that have not been eliminated. If the mass reduction of a batch after processing is lower than the 0.1% threshold value obtained from previous experiments, stop the elimination operation. Otherwise, increase the number of washes and continue to record the change. When continuing to check whether there are colloids or flocs attached to the surface of the precipitation, focus on comparing the reference degree of a specific coagulation degree on the colorimetric test strip (such as a level division from 0 to 10). If the value after comparison is greater than 6, it means that the coagulation degree is too high. In order to prevent omissions, wash again and continue to record the color and morphology changes of the particles. After all steps are completed, the obtained particle information will be checked with the previously selected density range, and the final particle state description will be updated to generate the initial extract.

[0055] The quality monitoring module includes:

[0056] The absorbance viscosity sub-module, based on the initial extract, collects the transmittance value by taking an appropriate amount of liquid and setting the optical path, determines the absorbance value, simultaneously detects the viscosity, records the change in flow velocity, calculates the flow resistance index separately, and generates the optical-viscosity monitoring information;

[0057] The threshold comparison sub-module, based on the optical-viscosity monitoring information, compares the absorbance value viscosity data with the threshold range of the pathogen list and statistically calculates the deviation range, and then evaluates the viscosity change curve, summarizes the PH change information, and generates the threshold comparison result;

[0058] The ion detection sub-module, based on the threshold comparison result, compares the deviation range with the nanopore sequencing array channel parameters and records the compliance situation, and then checks the PH fluctuation information, detects the trace ion content, summarizes the observed values, and generates the monitoring value.

[0059] Specifically, based on the liquid characteristics of the initial extract, first mix the sample evenly according to the previously obtained particle density and color information, set a fixed length (such as 1 cm to 5 cm) on the optical path to obtain the transmittance value. When reading the absorbance value measured by the optical instrument, record point by point in the wavelength range of 400 nm to 700 nm and compare it with the pre-configured absorbance reference table. If the absorbance value corresponding to some wavelengths deviates from the reference table by more than the 5% threshold statistically obtained through multiple rounds of experiments, retain the deviation value and mark it as a possible abnormal absorption point. Subsequently, when detecting the viscosity of the sample, set the flow velocity range from 0.1 ml per second to 0.5 ml per second, and use the segmented recording method to obtain the flow resistance. Compare each observed resistance value with the pre-established reference interval (such as 100 mPa to 300 mPa) one by one. If a certain measurement result is higher than 300 mPa, repeat the detection twice again within the current temperature range (20°C to 25°C) and confirm whether it continues to be higher than the reference interval. If it is continuously higher, record the abnormal viscosity value. During this period, it is also necessary to compare the detected flow velocity with the previous velocity standard (such as increasing by 0.05 ml per second successively) and summarize the relationship between the resistance and the velocity. After integrating the optical transmittance and viscosity curves, conduct unified comparison and correction, and finally generate the optical-viscosity monitoring information.

[0060] Based on the absorbance values and viscosity curves obtained from the optical adhesion monitoring information, it is necessary to first perform a point-to-point comparison between each absorbance measurement point and the pathogen list range threshold. Assuming that the absorbance range threshold has been obtained through previous sample comparisons, with its lower limit being 0.02 and the upper limit being 2.0. If the absorbance value at a certain point exceeds 2.0, it is judged as a high absorbance interval and the viscosity curve is continued to be checked. Similarly, if a point is lower than 0.02, it can be regarded as a trace absorbance interval. Immediately afterwards, a comparison of the viscosity data with the pathogen list range is performed. If the viscosity is lower than 100 millipascals or higher than 300 millipascals as statistically given by previous samples, it is recorded as a deviation value, and the deviation amplitude is used to superimpose and evaluate the pH change information. If the pH value is lower than 6.8 or higher than 7.5, this deviation amplitude is specially marked. Then, the key points in the viscosity change curve (such as the positions where it rises to a certain extreme value or drops to a certain extreme value) are sorted out and corresponding to the abnormal points of the absorbance values. If both show deviations and the deviation amplitude exceeds the 3% threshold determined by experience, this position can be marked as a suspicious section. Then, all the deviation statistical information is matched with the previously obtained optical reference table to confirm one by one whether there are repeated deviations. Finally, the above comparison results are summarized with the pH change data to generate the threshold comparison result.

[0061] Based on the absorbance and viscosity deviation information recorded in the threshold comparison result, first compare the deviation values item by item with the main parameters of the nanopore sequencing array channels. These parameters can include the channel width (such as 40 nanometers to 60 nanometers), the channel surface potential (such as in the range of ±0.1 volts), etc. By comparison, determine which groups of deviation data have a higher compliance rate with the channel parameters. If the compliance rate does not reach the pre-set 70% standard (this value is obtained from previous equipment operation statistics), then mark that the coupling between this deviation data and the sequencing channel is incomplete and re-check the pH fluctuation range. The specific operation is to select the stable section between 6.8 and 7.5 from the previously recorded pH curve and observe point by point whether there is an abnormal ion intensity. When the ion intensity fluctuates by more than 5% relative to the initial level, it also needs to be recorded as an abnormal fluctuation. Subsequently, according to the trace ion detection method obtained previously, it is possible to select to perform item-by-item comparisons on sodium ions, potassium ions, etc. For example, compare the sodium ion content with the effective range of 1 millimole to 5 millimoles that has been pre-set. If the result is greater than 5 millimoles, it is necessary to repeat the measurement once to exclude accidental errors. Finally, all the floating situations of the ion contents are combined and summarized with the absorbance and viscosity information to generate the monitoring values.

[0062] The sample loading adaptive module includes:

[0063] A concentration evaluation sub-module, based on the monitoring values, generates concentration fluctuation information by performing a comparison of the target concentration, evaluating the ion content, reading the concentration difference in the monitoring values to record the pH fluctuation, then observing the corresponding ion fluctuation amplitude and tracking the comparison duration.

[0064] Ratio adjustment sub-module, based on the concentration fluctuation information, adjusts the feeding ratio for drug resistance gene screening, counts the change amount, analyzes the evaporation component concentration ratio to record the gasification level, then tracks the evaporation process duration, and generates feeding adjustment data;

[0065] Temperature measurement sub-module, based on the feeding adjustment data, observes the temperature curve of the evaporation process, records the temperature fluctuation range, analyzes the heating rate ratio to the set interval, and then compares the influence of the feeding ratio on the temperature, generating a sample loading parameter set.

[0066] Specifically, based on the monitored values, in order to conduct target concentration comparison and evaluate ion content, it is necessary to first disassemble and extract the ion concentration range and PH detection results from the existing monitored values. For example, the ion concentration is split into several categories such as sodium ions and potassium ions, and their specific content values are recorded separately. Then, each category is compared item by item with the ion content interval (such as 0.5 mmol to 5 mmol) summarized in multiple batches of experiments earlier. If the ion content of a certain category exceeds 5 mmol, it is measured twice again and verified whether it remains at a high level. Through the above method, it is confirmed whether to trigger the subsequent treatment conditions for high ion concentration. At the same time, in terms of PH detection, each measured PH value is compared with the reference interval (6.5 to 7.5) determined by averaging through multiple rounds of experiments. If a certain measured PH value is lower than 6.5, it is marked as acidic, and the time and duration of this data point are recorded. If the duration exceeds the 10-second threshold determined by experience, this situation is registered as an abnormal working condition and summarized together with the ion concentration. Subsequently, the concentration difference distribution shown in the monitored values is retrieved, and the corresponding PH fluctuation amplitude is analyzed. For example, the concentration difference is matched with the synchronous change of the PH value in the way of increasing by 0.1 mg / ml each time. If it is found that the PH value drops by more than 1 PH point after the corresponding difference increases, it means that significant fluctuations occur in this stage, and it is necessary to continue to collect the ion fluctuation data in this section to observe its evolution trend over time. Then, in all sections that meet the recording conditions, the duration is tracked and compared, and it is checked item by item with the pre-established time standard (for example, less than 5 seconds is regarded as short-term fluctuation, and more than 10 seconds is regarded as long-term fluctuation). If the duration of a certain fluctuation exceeds 10 seconds and the amplitude is greater than 1 PH point, it is marked as a key concern. Finally, these concentration differences, PH fluctuations, ion fluctuation amplitudes, and comparison durations are summarized to generate concentration fluctuation information.

[0067] Based on the concentration fluctuation information, in order to adjust the feeding ratio for drug-resistant gene screening, it is necessary to first list all the previously recorded concentration fluctuation ranges and check if there is any situation where the amplitude exceeds the 5% amplitude threshold determined by multi-batch experience statistics. If it is found that the fluctuation amplitude is higher than this threshold, the feeding ratio will be gradually decreased. Each time, the feeding percentage is decreased by 0.5% and the change amount is recorded again. During this period, the concentration ratio of the volatile components also needs to be observed and compared with the set reference range (such as a concentration multiple of 1.2 to 2.0 times). If the comparison result at any time shows that the concentration multiple is higher than 2.0, an additional gasification test operation will be added, and the gas-phase concentration after volatilization will be recorded in categories. The difference between each category can be set to 0.1 times the concentration for step-by-step detection. When the concentration difference between two adjacent gasification data is lower than the 2% threshold obtained in previous tests, it is considered that the concentration ratio is relatively stable. Then, continue to extract the previously identified abnormal pH value section from the concentration fluctuation information and synchronously track it in combination with the volatilization process time. Compare the volatilization duration with the initially set benchmark (such as between 1 minute and 5 minutes). If the volatilization time exceeds 5 minutes without significant attenuation in a certain case, it is regarded as an additional gasification requirement and an additional volatilization process ranging from 0.5 minutes to 1 minute will be added. Finally, after completing the above comparison and tracking, data such as the degree of feeding ratio adjustment and the duration of the volatilization process are obtained and merged and statistically analyzed to generate feeding adjustment data.

[0068] Based on the feeding adjustment data, in order to observe the temperature curve of the volatilization process and record the temperature fluctuation range, it is necessary to first obtain the degree of feeding ratio adjustment and the volatilization duration obtained previously. Then, start temperature acquisition when heating and volatilizing begins, and set the sampling frequency to once every 5 seconds. Compare each collected temperature value with the set interval (such as between 20°C and 60°C) for determination. If the detected temperature value is higher than 60°C at a certain time, temporarily reduce the heating power and measure again. If the temperature still deviates from the set interval within a short time, it will be included in the abnormal statistics. Then, cumulatively summarize the temperature increase between each temperature point and the previous measurement and compare it with the expected range of the heating rate (such as increasing by 0.1°C to 0.3°C per second). If the heating rate within a certain period exceeds 0.3°C per second or is lower than 0.1°C per second, it is marked as an abnormal rate section, and check if there is an excessive or insufficient feeding situation in combination with the feeding ratio. For example, when the feeding ratio is increased by 2% and the measured temperature increase rate is significantly improved, mark this period of time and check again if there is a simultaneous fluctuation in pH or ion concentration. If no supporting abnormality is found, continue to maintain the current operation. If the heating rate deviation and the ion concentration deviation appear simultaneously, the feeding ratio needs to be re-evaluated and may be reduced by 1% to 2%. Finally, a continuous temperature change curve is formed during the entire temperature detection process, recording information such as the temperature fluctuation range and the heating rate, and combining the previously obtained volatilization duration data to generate a sample loading parameter set.

[0069] The signal processing module includes:

[0070] The current acquisition sub-module, based on the sample loading parameter set, reads the current through the nanopore sequencing channel, acquires the instantaneous current to compare with the baseline, captures the acquisition duration to record the degree of baseline deviation, then counts the acquisition period, and generates current baseline information;

[0071] The peak monitoring sub-module, based on the current baseline information, monitors the current peak to record the occurrence frequency and proofread the peak width, identifies abnormal sudden increases and then analyzes the peak shape change range, and then records the peak height to generate peak fluctuation data;

[0072] The signal trimming sub-module, based on the peak fluctuation data, detects the current difference in the culture medium solution to correct signal drift, then checks the difference distribution range, records the peak position, and counts the noise distribution to generate a denoised sequence packet.

[0073] Specifically, based on the sample loading parameter set, in order to perform a current reading operation on the nanopore sequencing channel, it is necessary to first read the previously obtained temperature fluctuation range, feeding ratio, and other values related to the sequencing environment, then set the initial voltage between 0.1 volts and 0.2 volts and gradually monitor the instantaneous current. Real-time current values are obtained by recording once every 100 milliseconds. Compare it with the pre-statistical baseline range (for example, between 0.5 mA and 1.0 mA). If the actual current value is lower than 0.5 mA or higher than 1.0 mA, it is marked as baseline deviation and measured twice again. If the continuous results still maintain the deviation state, it enters the abnormal statistics queue and is analyzed centrally. When counting the acquisition period, the entire monitoring duration from every 1 second to 2 seconds can be used as a batch, and the current mean, maximum, and minimum values within each batch are summarized. It is also necessary to check whether the acquisition duration within each batch is within the defined range (for example, 10 seconds to 20 seconds). If it is found that the acquisition duration of a certain batch is less than 10 seconds or higher than 20 seconds, it is listed as a time anomaly and the corresponding current value is rechecked to see if it fluctuates abnormally. If there is an obvious fluctuation, record the corresponding degree of baseline deviation and make a side-by-side comparison with the channel characteristic parameters. Finally, the baseline current information of all batches is merged and presented to generate current baseline information.

[0074] Based on the current baseline information, in order to monitor the current peak and record the occurrence frequency, it is necessary to first retrieve the points in each batch of current data that are higher than the baseline mean and mark how much amplitude exceeding the mean can be regarded as a peak. If the amplitude exceeding the baseline mean is greater than the 5% threshold statistically obtained from previous multiple sequencing experiments, it is defined as a current peak and the occurrence times of the peak are recorded. Then, for the width measurement of each peak point, it can be detected by extending 2 data points forward and backward on the acquisition time axis. If the peak width is greater than the set interval (such as 0.5 seconds to 1 second), it is classified as a broadened peak and the start and end times are registered. During this period, if there is an abnormal sudden increase, the difference between the rising amplitude and the falling segment of the sudden increase segment is quantitatively statistically analyzed, and whether there is a phenomenon of peak clipping or extension is searched within the range of peak shape changes. If this phenomenon appears, it is measured 3 times again using the same sampling rule as the baseline information and the stability of the sudden increase segment is judged. If the sudden increase peak persists within the measurement interval and the width exceeds 1 second, it is included in the abnormal fluctuation queue and compared with the previous baseline information. Finally, the height of each peak point is recorded one by one and all peak shape feature data are integrated to generate peak fluctuation data.

[0075] Based on the peak fluctuation data, in order to detect the current difference in the culture medium solution and correct the signal drift, it is necessary to first select the time periods with the largest difference from the baseline from the peak fluctuation data, and then observe the change of the current in the solution relative to the previously established control value within the same acquisition time period. For example, the normal baseline current is set between 0.5 mA and 1.0 mA and the current at the peak fluctuation moment is compared with this control value. If the difference is greater than the 3% threshold empirically determined in previous multiple calibration experiments, the drift correction operation is performed and the distribution interval of the difference is checked. If most of the differences are concentrated in the same range (such as between 0.03 mA and 0.05 mA) within a certain time period, it indicates that the drift offset shows a stable trend. At this time, this range is recorded and the peak position is updated. If the difference distribution shows too large a dispersion, the acquisition and comparison are repeated once. When statistically analyzing the noise distribution, the collected current data can be counted by stepping 0.01 mA from low to high. If the frequency in a certain interval is significantly higher than the average level obtained by statistically analyzing multiple batches of sequencing, it is marked as a noise concentration section and stored together with the previously established drift correction information. Finally, these correction and statistical results are summarized to generate a denoised sequence package.

[0076] The sequence splicing module includes:

[0077] The coverage comparison sub-module, based on the denoised sequence package, generates coverage comparison data by comparing the lengths of the covered segments, checking the boundary differences, and recording the segment matching rate to mark the overlapping regions.

[0078] The repeat statistics sub-module, based on the coverage comparison data, generates repeat statistics results by counting the repeat regions, recording the repeat frequency, analyzing the overlapping paragraph structure to calculate the binding rate, and summarizing the number of successfully paired first and last bases.

[0079] The mismatch correction sub-module, based on the repeat statistics results, checks the joints by merging the full-length sequence segments, examines the mismatch sites, repairs the base offsets to organize the complete sequence, confirms the fragment binding rate, and generates a set of spliced sequences.

[0080] Specifically, based on the denoised sequence package, in order to perform length comparison on the covered segments and check for boundary differences, it is necessary to first read the sequence segments one by one from the denoised sequence package and determine their start and end positions. Subsequently, the base length of each segment is counted and compared with the reference interval summarized in previous experiments (for example, 100 bases to 500 bases). If the measured segment length is less than 100 bases or greater than 500 bases, it is marked as abnormal coverage and subjected to secondary verification. Then, the boundary features of the segments at the start and end positions are checked one by one. 10 bases can be intercepted at the head and tail of the segment as the boundary window and aligned with the corresponding regions of other segments. If the boundary position misalignment exceeds the 3-base difference threshold established in previous splicing experiments, it is regarded as a boundary difference and continue to check whether the same deviation occurs multiple times. If there are more than 5 deviations, the segment is marked as a high-risk segment. During the process, the deviation positions detected are centrally summarized with their coverage start and end coordinates, and the relationship between the deviation range and the set allowable floating interval (for example, ±2 bases) is confirmed. If the situation exceeds this interval, the type and degree of the boundary difference are noted in the record, and information is provided for subsequent determination. Next, the overlapping region is judged one by one according to the fragment matching rate threshold (for example, the matching rate should not be less than 80%, and this value is obtained by statistical averaging in multiple batches of sequencing comparisons). When the consistency rate of the base sequences within the overlapping segments of the fragments is less than 80%, it is regarded as an incomplete overlap. Otherwise, it is considered that the overlapping regions can be merged and the position information after merging is retained in a marked manner. All the segments that have completed the comparison are sorted in sequence and the number of overlapping regions and the matching rate distribution are located. After numbering and processing this information, it is collected to generate coverage comparison data.

[0081] Based on the coverage control data, in order to count the repeated regions and record the repetition frequencies, it is necessary to first read all the sequence fragments marked as overlapping in the coverage control data, and then retrieve them one by one according to the start coordinates and end coordinates of their overlapping regions, list the actual number of overlapping bases in each overlapping region, and compare it with the previously determined repetition recognition criteria (for example, a repeated segment of more than 50 bases is regarded as a valid repetition, and this value is determined by referring to the shortest obvious repeated segment length observed in previous experiments). If the length of an overlapping region is greater than or equal to 50 bases, it is counted as one repetition, and the start and end information of the corresponding repeated region is recorded in the repetition list. When counting the repetition frequencies, it is necessary to pay attention to whether there are nested repeated segments. If it is detected that a certain segment is completely contained within another repeated segment, it is counted as a nested repetition and the number of its occurrences is additionally counted. Subsequently, when analyzing the overlapping paragraph structure, each repeated segment can be mapped to the sequence coordinate axis in turn to check whether there is a situation of crossing the fragment boundary and record the total number of times this situation occurs. According to the distribution pattern of these repeated structures, the binding rate can be calculated. For example, the total coverage length of all repeated segments is divided by the total sequence length. If this ratio exceeds the 0.2 threshold obtained from previous database statistics, it indicates that the repetition tendency is relatively significant. In addition, the pairing situation of the first and last bases needs to be summarized separately. For the first and last bases of each repeated segment, by comparing the first and last bases of this segment with the first and last bases of other repeated segments during sequence alignment, the number of successful matches is accumulated and the proportion of the number of successful pairings in all overlapping segments is calculated. If the first and last bases of some segments do not match at all, this abnormal phenomenon is recorded separately. After completing the calculations for all repeated regions, the final statistical information is summarized to generate the repetition statistical results.

[0082] Based on the repeated statistical results, in order to merge the full-length sequence segments and check for mismatched sites at the junctions, it is necessary to first sort all the repeated segments listed in the repeated statistical results, group the adjacent or partially overlapping segments, merge them one by one to obtain several full-length sequence segments and mark their junction positions. Then, a certain range is intercepted near the junctions (for example, 10 bases before and after each junction point) to check for base mismatches. If it is found that some bases are inconsistent at the junction of the two sequences, it is necessary to compare the probabilities of their occurrences in the reference sequence or the common base frequencies one by one. If the difference exceeds the 5% mismatch threshold inferred from the historical sequencing results, it is registered as a mismatched site. If the number of mismatched sites that appear exceeds the 2 thresholds determined through multiple experiments in advance, the region is re-verified. During the verification stage, it is necessary to read the base distribution stored in the previous coverage and repeat information, analyze each base suspected of being mismatched one by one. When the true mismatch is determined, it is repaired according to the context information of the base combined with the established repair rules (for example, if the adjacent base is A with an 80% probability, the mismatched position is temporarily regarded as A). If it is impossible to determine the base, it can be marked as a special pending confirmation status and the sequence position is recorded. Next, scan all the merged segments from left to right or from right to left to further check their base offsets. If the overall offset does not exceed the 1 base upper and lower bounds summarized, the segment is regarded as corrected. Otherwise, it is necessary to check the overlapping region information in the coverage control data again until all the junctions and mismatched points are revised and re-scanned and confirmed. Finally, an integrity check is performed on the formed merged sequence to generate a set of spliced sequences.

[0083] The identification output module includes:

[0084] The pathogen matching sub-module, based on the set of spliced sequences, identifies and confirms the drug-resistant gene sequences by matching the pathogen list identifiers, records the statistical matching times of the identified pathogen species, monitors the positions of the drug-resistant genes, summarizes the distribution, and generates pathogen matching data;

[0085] The risk screening sub-module, based on the pathogen matching data, records the missing positions by statistically analyzing the read coverage depth and compares the sequence features, extracts the pathogen list identifiers, screens the pathogenic risk coding regions, marks the risk sites, and generates risk screening results;

[0086] The typing marker sub-module, based on the risk screening results, integrates the typing results and analyzes the sequence branch structure, marks the variant positions to confirm the strain classification information, and then checks the sequence clustering relationship to summarize the drug-resistant gene correlation degree, and generates pathogen identification information.

[0087] Specifically, based on the assembled sequence set, in order to match the pathogen list identifier and confirm the drug-resistant gene sequence, it is necessary to compare the length and base composition of each sequence in the assembled sequence set with the previously summarized pathogen list item by item. The list usually contains typical sequence information of multiple pathogens, and each pathogen is set with a minimum similarity threshold required for matching (for example, 85% to 95%, and this interval is given by the statistical analysis of known pathogen sequences). If the similarity of a certain assembled sequence to the typical sequence of a certain pathogen is higher than this threshold, it can be regarded as a potential matching object of this pathogen and its species information is marked. When counting the number of matches, it is necessary to confirm whether there is a sequence that matches multiple pathogens. If there are situations where the similarities are all high, multiple species are recorded and the compliance of the drug-resistant gene in the corresponding species is tested separately. If differences are detected at certain specific drug-resistant gene positions (such as key bases or base segments registered in the drug-resistant gene position table), it is necessary to compare in segments and calculate the mapping of specific bases. When the continuous matching degree is lower than the 5% deviation threshold obtained from multiple rounds of data comparison before, it is marked as a doubtful point, and then more reference sequences can be used to further identify at the doubtful point. If it is confirmed as a drug-resistant gene sequence, its distribution position and occurrence frequency in the sequence are summarized and recorded. Finally, the identified pathogen species are numbered and merged into the same index table to generate pathogen matching data.

[0088] Based on the pathogen matching data, in order to count the read coverage depth and record the missing positions, it is necessary to first analyze the marked pathogen species and the distribution coordinates of the drug-resistant genes. After extracting all the sequence fragments related to a certain pathogen, calculate the coverage depth of each fragment item by item. The coverage depth can be defined as how many reads overlap at the same coordinate. The read count can be accumulated at each base position and compared with the coverage benchmark value summarized previously in multiple sequencing (for example, at least 5 reads covering is considered stable, and this value comes from the historical sequencing stability analysis). If the coverage depth at a certain site is lower than 5, it is registered as a missing position, and this missing position is corresponding to the full-length coordinate range of this pathogen. If the missing positions continuously appear in the same section, they are classified as long missing segments. Then, combined with the sequence feature information, check whether there are mismatches or incorrect matches. If it is found that the difference between the base sequence and the characteristic section in the pathogen list is greater than the 5% threshold obtained through statistics, re-check whether this area is affected by sequencing quality or assembly error. During the process, it is also necessary to extract the pathogen list identifier and compare its pathogenic risk coding region. If a deletion or abnormal shift is found in this region, the risk site is further marked. After completing all the missing position and feature comparisons, conduct an overall summary and distinguish by species number or name to generate the risk screening result.

[0089] Based on the risk screening results, in order to integrate the typing results and mark the variant positions, it is necessary to first sequentially read the risk sites and deletion positions in each screening result, map them to the sequence branch structure under the same pathogen list identifier, and determine whether there are cross-branch similar variants by comparing the hierarchical relationship of this branch structure. If a certain variant site appears on multiple branches simultaneously, it is regarded as a widespread variant and its occurrence frequency is counted. When the number of variants on a specific branch is confirmed, then check whether the strain information corresponding to this branch contains the position of the drug-resistant gene and perform parallel mapping of the two. If the overlap degree between the drug-resistant gene position and the variant site exceeds 5% (this value is obtained from the comparative analysis of historical typing data and actual clinical isolation results), it is additionally marked in the strain classification information. Subsequently, all typing results are combined with the variant data information and analyzed to determine whether there are independent variant types in the lower-level branches. If it is detected that some branches repeatedly show similar variants at most nodes, they are classified into the same variant group. After finishing the arrangement, the clustering relationship is verified again. For example, check one by one whether the coincidence rate of variant sites between adjacent branches exceeds the 70% threshold set in the past typing statistics. If it exceeds, the branches are merged; if it is insufficient, the different branches are continued to be retained. After all the information is integrated, the classification attribute of the strain can be confirmed and the drug-resistant gene correlation degree can be obtained, and the pathogen identification information is generated.

Claims

1. A rapid pathogen detection system based on nanopore sequencing, characterized in that, The system includes: A collection and preprocessing module that precipitates and centrifuges the serum and monitors the layering, separates and concentrates the supernatant, measures the pH value and temperature, then detects the protein concentration and records the turbidity value, and subsequently collects the precipitate particles and removes impurities to generate an initial extract; A quality monitoring module that measures the absorbance and viscosity of the initial extract, captures the transmission value through an optical instrument and records the viscosity change, compares the pathogen list range threshold to determine if there is an abnormality and summarizes the pH change information, and then compares it with the nanopore sequencing array channel parameters, while detecting the trace ion content to generate monitoring values; A sample loading adaptive module that, based on the monitoring values, conducts a target concentration comparison and evaluates the ion content, records the pH fluctuation by reading the concentration difference in the monitoring values, then adjusts the feeding ratio for drug resistance gene screening and analyzes the evaporation component concentration ratio, while measuring the temperature change curve to generate a sample loading parameter set; A signal processing module that, based on the sample loading parameter set, reads the current in the nanopore sequencing channel, collects the instantaneous current and compares it with the channel baseline, while monitoring the current peak, then detects the current fluctuation difference in the culture medium solution and corrects the signal drift, and at the same time records the current peak and trims the amplitude in the high-frequency band, records the noise distribution to generate a denoised sequence packet.

2. The rapid pathogen detection system based on nanopore sequencing according to claim 1, characterized in that, It also includes: A sequence splicing module that, based on the denoised sequence packet, conducts a length comparison of the covered fragments and counts the repeated regions, records the binding rate, analyzes the head and tail base pairing relationship, unfolds the covered fragments, statistically analyzes the repeated regions, and merges the full-length sequence segments, then checks the mismatch sites and repairs the base offset to generate a spliced sequence set; An identification and output module that, based on the spliced sequence set, matches the pathogen list identifier and confirms the drug resistance gene sequence, statistically analyzes the read coverage depth, compares the sequence features and extracts the pathogen list identifier, then screens the pathogenic risk coding region, integrates the typing results and marks the mutation positions, obtains the strain classification information to generate pathogen identification information.

3. The rapid pathogen detection system based on nanopore sequencing according to claim 1, wherein The collection and preprocessing module includes: A precipitation and centrifugation sub-module that injects the serum into a centrifuge tube for precipitation, controls the centrifugation time and speed, observes the layering phenomenon and records the precipitation state, while checking the residual liquid level in the centrifuge tube and marking the supernatant area, and finally collects the precipitation data to generate a centrifugation monitoring result; A supernatant concentration sub-module that, based on the centrifugation monitoring result, concentrates the separated supernatant, tracks the concentration stage time, checks the liquid level height to measure the pH value and temperature, separately detects the protein concentration and records the turbidity value to generate layering and concentration data; An impurity removal sub-module that, based on the layering and concentration data, removes impurities by collecting the precipitate particles and monitors the precipitate morphology, records the particle density, checks the precipitate color to view the coagulation degree, and then records the attachment state to generate an initial extract.

4. The rapid pathogen detection system based on nanopore sequencing according to claim 1, characterized in that, The quality monitoring module includes: An absorbance and viscosity sub-module that, based on the initial extract, takes an appropriate amount of liquid, sets the optical path to collect the transmission value, determines the absorbance value, simultaneously detects the viscosity and records the change in the flow rate, and separately calculates the flow resistance index to generate light and viscosity monitoring information; Threshold comparison sub-module, based on the optical adhesion monitoring information, compares the absorbance value viscosity data with the pathogen list range threshold, calculates the deviation amplitude, evaluates the viscosity change curve, summarizes the PH change information, and generates the threshold comparison result; Ion detection sub-module, based on the threshold comparison result, compares the deviation amplitude with the nanopore sequencing array channel parameters, records the compliance situation, checks the PH fluctuation information, detects the trace ion content, summarizes the observed values, and generates the monitoring value; 5. The rapid pathogen detection system based on nanopore sequencing according to claim 1, characterized in that, The sample loading adaptive module includes: Concentration evaluation sub-module, based on the monitoring value, compares the target concentration, evaluates the ion content, reads the concentration difference in the monitoring value, records the PH fluctuation, observes the corresponding ion fluctuation amplitude, and tracks the comparison duration, and generates the concentration fluctuation information; Ratio adjustment sub-module, based on the concentration fluctuation information, adjusts the drug resistance gene screening feeding ratio, calculates the change amount, analyzes the evaporation component concentration ratio, records the gasification level, tracks the evaporation process duration, and generates the feeding adjustment data; Temperature measurement sub-module, based on the feeding adjustment data, observes the temperature curve of the evaporation process, records the temperature fluctuation range, analyzes the heating rate compared with the set interval, compares the influence of the feeding ratio on the temperature, and generates the sample loading parameter set; 6. The rapid pathogen detection system based on nanopore sequencing according to claim 1, wherein The signal processing module includes: Current acquisition sub-module, based on the sample loading parameter set, reads the current in the nanopore sequencing channel, acquires the instantaneous current, compares it with the baseline, captures the acquisition duration, records the baseline deviation degree, and calculates the acquisition cycle, and generates the current baseline information; Peak monitoring sub-module, based on the current baseline information, monitors the current peak, records the occurrence frequency, checks the peak width, analyzes the peak shape change range after identifying the abnormal sudden increase, and records the peak height, and generates the peak fluctuation data; Signal trimming sub-module, based on the peak fluctuation data, detects the current difference in the culture medium solution, corrects the signal drift, checks the difference distribution interval, records the peak position, and calculates the noise distribution, and generates the denoised sequence packet; 7. The rapid pathogen detection system based on nanopore sequencing according to claim 2, wherein The sequence splicing module includes: Coverage comparison sub-module, based on the denoised sequence packet, compares the length of the covered fragments, checks the boundary differences, records the fragment matching rate, marks the overlapping regions, and generates the coverage comparison data; Repeat statistics sub-module, based on the coverage comparison data, counts the repeated regions, records the repetition frequency, analyzes the overlapping paragraph structure, calculates the binding rate, analyzes the first and last base pairing, summarizes the number of successful pairings, and generates the repeat statistics result; Mismatch revision sub-module, based on the repeat statistics result, merges the full-length sequence segments, checks the connection points, checks the mismatch sites, repairs the base offset, arranges the complete sequence, and confirms the fragment binding rate, and generates the spliced sequence set; 8. The rapid pathogen detection system based on nanopore sequencing according to claim 2, wherein The identification output module includes: Pathogen matching sub-module, based on the spliced sequence set, matches the pathogen list identifier, confirms the drug resistance gene sequence, records the identified pathogen types, counts the matching times, monitors the drug resistance gene position, summarizes the distribution, and generates the pathogen matching data; The risk screening sub-module, based on pathogen matching data, records missing positions by statistically analyzing the read depth of coverage, compares sequence features, extracts pathogen list identifiers, screens pathogenic risk coding regions, marks risk sites, and generates risk screening results. The typing marker sub-module, based on the risk screening results, integrates typing results, analyzes the sequence branch structure, marks variant positions to confirm strain classification information, and then checks the sequence clustering relationship to summarize the correlation degree of drug resistance genes, generating pathogen identification information.

Images