62results about How to "Small sample size" patented technology

The invention belongs to the technical field of biological feature recognition, and relates to a Transform-based gene data mining method, which comprises the following steps of: loading gene expression data from a file, and preprocessing; inputting the data into a designed model for training and evaluation; the designed model comprises a CNN (Convolutional Neural Network) and an improved Transform model; the improved Transform model comprises an encoder and a classifier; the encoder adopts a multi-head self-attention mechanism, a convolutional self-attention mechanism and a feedforward neural network; processing the data by using the trained model, and storing the fusion attention weight obtained by the coding layer in the data processing process; and finally, averaging the fusion attention weights of all batches, calculating the comprehensive attention weight of each gene at different time points, and identifying the most important gene by calculating and analyzing the attention score of each gene. According to the method, the problems of scarcity and imbalance of gene data can be solved, the risk of overfitting is reduced, and the precision of gene data analysis is improved.

The invention relates to the technical field of biological detection, and provides an inflammatory factor detection kit based on a liquid-based chip technology, and the inflammatory factor detection kit comprises six fluorescent microspheres coupled with capture antibodies, and six detection antibodies labeled by report fluorescence; in the six fluorescent microspheres containing the captured antibodies, the fluorescence intensities of all the fluorescent microspheres are different; according to the detection kit provided by the invention, on the basis of a liquid-based chip technology, fluorescein is adopted to carry out fluorescence coding on microspheres, and a captured antibody is connected to the fluorescent microspheres through a chemical reaction; a detection target is captured through the fluorescent microspheres, and then a signal is displayed through the detection antibody, so that the detection of the inflammatory factors is completed, the defects of large demand quantity of soluble inflammatory factor detection samples, long detection time, tedious detection steps and the like in the prior art are overcome, and great convenience is brought to scientific researchers at a hospital end and an industrial end.

The invention relates to a patient diagnosis and treatment method based on a vector database and a medium, and relates to the technical field of intelligent medical treatment, and the method comprises the steps: collecting patient medical data, vectorizing the medical data comprising digital information, text information and image information, carrying out the feature fusion of the vectorized data, and obtaining a first fusion vector; calculating the similarity between the first fusion vector and a second fusion vector corresponding to each historical patient to obtain a preset number of second fusion vectors with the highest similarity; and obtaining an optimal treatment scheme based on a preset number of second fusion vectors with the highest similarity. Compared with the prior art, dependence on the diagnosis and treatment level of doctors can be reduced with the help of previous excellent diagnosis and treatment cases, a better treatment scheme can be quickly obtained, and the diagnosis and treatment level of existing doctors can be quickly improved.

The invention provides a surface roughness monitoring method of an embedded physical prediction model. According to the method, in a model training stage, cutting force signals and three-phase current signals of a main shaft servo motor in the machining process are collected through a dynamometer and a current sensor, the signals are preprocessed and then input into a cutting force calculation module, cutting force under the corresponding current signals is output, and the obtained cutting force is used for calculating a surface roughness theoretical value; and the current signal, a current signal and a processing parameter are input into a surface roughness calculation module, and a surface roughness monitoring model is trained. In the machining stage, the roughness value of the machined surface of the workpiece at the moment is calculated in real time by collecting the three-phase current signals of the main shaft servo motor in the machining process, and it is avoided that sensors such as a vibration sensor or an acoustic emission sensor or a dynamometer are installed in a machine tool in the actual machining stage.

The invention relates to the technical field of mechanical cutting machining, and discloses a method for constructing a milling force prediction model for milling a variable-curvature part based on RIME-SVM, and the method comprises the following steps: S1, obtaining prediction model data; s2, building an RIME-SVM milling force prediction model based on a support vector machine; s3, constructing a cutting force prediction model for machining a conventional straight surface by the end mill; s4, verifying the precision of the cutting force prediction model of the end mill for processing the conventional straight surface; s5, constructing a cutting force prediction model for machining different geometrical characteristics of the end mill; and S6, verifying the precision of the cutting force prediction model for processing different geometrical characteristics by the end mill. The RIME-SVM model combines the advantages of SVM processing nonlinear regression and the high efficiency of the RIME optimization process. According to the RIME-SVM model, the prediction precision is improved, the experiment cost is reduced, the generalization ability is high, unseen data can be accurately predicted, and the method is suitable for practical engineering application.

The invention provides a dry chemical reagent tablet as well as a preparation method and application thereof, and relates to the field of dry chemical reagent tablets. The dry chemical reagent piece sequentially comprises an upper outer frame, a diffusion layer, a reagent layer, a light-transmitting layer and a lower outer frame from top to bottom, the upper outer frame is provided with a sample adding hole, the lower outer frame is provided with a light-transmitting hole, the reagent layer comprises a reagent layer reagent or is formed by the reagent layer reagent, the diffusion layer comprises a diffusion layer reagent or is formed by the diffusion layer reagent, and the light-transmitting layer is formed by the diffusion layer reagent. The diffusion layer reagent comprises at least one of a hydrophilic high-molecular polymer and an adhesive, the diffusion layer reagent further comprises particulate matters, and the particulate matters comprise first particulate matters and second particulate matters; the number ratio of the first particulate matters to the second particulate matters is 1: (0.25-4); the ratio D2 / D1 of the particle size D2 of the second particulate matter to the particle size D1 of the first particulate matter is 0.2-0.8. The dry chemical reagent sheet provided by the invention is simple and convenient to operate, and can rapidly, accurately and quantitatively detect the contents of various substances in a liquid sample.

The invention discloses a method for detecting the contents of VMA, HVA and 5-HIAA in urine by using liquid chromatography-tandem mass spectrometry. The method comprises the following steps: S1, removing VMA, HVA and 5-HIAA in urine to obtain a blank urine matrix; s2, weighing VMA, HVA and 5-HIAA solid powder, fixing the volume with methanol, mixing the dissolved liquids, diluting with methanol-water to obtain a VMA, HVA and 5-HIAA working solution, and finally diluting step by step with a blank urine matrix to obtain a calibration product; s3, respectively taking a urine sample to be detected, the calibration substance and the quality control substance, adding the mixed internal standard working solution of VMA, HVA and 5-HIAA isotopes, uniformly mixing, centrifuging, and taking supernate; s4, separating the supernate by using liquid chromatography; s5, the separated supernate enters a mass spectrum to be detected, and the content of VMA, HVA and 5-HIAA in the urine sample to be detected is obtained. The detection method disclosed by the invention is simple and rapid in pretreatment, high in specificity, low in reagent consumption cost, free of environmental pollution, small in required sample size, easy in high-throughput operation and suitable for being developed into a kit for conventional clinical detection.

The invention discloses a fluorescent quantitative PCR (polymerase chain reaction) detection method and a detection kit for detecting differentiation potential of induced pluripotent stem cells. The detection method comprises the following steps: S1, preparing an embryoid body by utilizing iPSC (induced pluripotent stem cells); s2, extracting RNA (Ribonucleic Acid) of the iPSC and the embryoid body; s3, respectively carrying out reverse transcription on the iPSC and RNA of an embryoid body to obtain cDNA; s4, establishing a fluorescent quantitative PCR reaction system by adopting the primer composition in claim 1; s5, carrying out fluorescent quantitative PCR (Polymerase Chain Reaction) detection on the cDNA in S3; and S6, analyzing a result. The detection kit disclosed by the invention can be used for rapidly and accurately evaluating the differentiation potential of the induced pluripotent stem cells, and is suitable for large-scale cell differentiation research and clinical application.

The invention relates to the field of food detection, in particular to a method for detecting dish food quality, which comprises the following steps: acquiring a package three-dimensional image and a food three-dimensional image of a dish food to be detected, and respectively processing the package three-dimensional image and the food three-dimensional image to obtain a corresponding package contour map and a food contour map; analyzing contour lines of the package contour diagram and the food contour diagram, and calculating the curvature difference of each corresponding point; according to the curvature difference threshold value and the abnormal corresponding point threshold value, judging whether the to-be-detected dish food has an abnormal area or not; performing spectrum detection on the to-be-determined abnormal food sample to obtain the nutrient content, and determining whether the to-be-determined abnormal food goes bad according to the nutrient content; and storing the to-be-determined qualified product, and detecting the residual nutrient content of the to-be-determined qualified product when the rated storage time is up. According to the method, potential abnormal samples are rapidly screened out by analyzing the contour lines of the package and the food contour, the number of samples needing spectrum detection is reduced, and the overall detection efficiency is improved.

The invention discloses a text semantic classification method and device, computer equipment and a storage medium. The method comprises the steps of obtaining input text information; performing primary classification on the text information through a first classification engine; judging whether primary classification succeeds or not; if the primary classification is successful, outputting a corresponding primary classification result; if the primary classification is not successful, carrying out secondary classification on the text information through a second classification engine; judging whether the secondary classification is successful or not; and if the secondary classification is successful, outputting a corresponding secondary classification result. Through the text semantic classification method provided by the embodiment of the invention, the classification flexibility and the classification efficiency can be improved, and meanwhile, the method can be better adapted to different types of text contents, so that the semantic classification accuracy and the classification success possibility are improved, the number of samples which cannot be classified is remarkably reduced, and the user experience is improved.

The invention belongs to the technical field of endocrine urine sampling, and particularly relates to an endocrine metabolism urine sampling device and method. Comprising a workbench and a liquid collecting container, a rotary control distance adjusting unit for adjusting the working height of the liquid collecting container is arranged on the workbench; the rotary control distance adjusting unit comprises a rotary screw rod, and the rotary screw rod is arranged at the top of the workbench; the rotating screw rod is in threaded connection with a rotating square cylinder, the bottom of the rotating square cylinder is provided with an opening, and the end, away from the workbench, of the rotating screw rod is located in the opening of the rotating square cylinder; a rotating base is mounted at the top of the rotating square barrel; by means of the locking swing-stopping mechanism, the situation that urine collides with the inner wall of the liquid collecting container more irregularly to generate more rebound and splashing due to the fact that the liquid collecting container shakes in the urine collecting process is avoided, and therefore the probability that the urine splashes out is reduced, the sample size of collected urine is guaranteed, the situation that the surrounding environment is polluted by the splashed urine is avoided, and the service life of the urine collecting container is prolonged. And the limitation of the device in use is reduced.

The embodiment of the invention discloses a high-throughput fluorescence detection method and system, relates to the field of molecular detection and artificial intelligence, and particularly relates to a high-throughput fluorescence detection method and system capable of simultaneously detecting and analyzing multiple target nucleic acids in a single reaction system. The multi-color code comprises a combination of different colors of the same molecular probe, and the multi-order code comprises a combination of fluorescence intensity of the same color; the coded molecular probes are distributed into at least two reaction units; in the transcoding link, carrying out super-multiple amplification on the molecular probe in each reaction unit, and carrying out multi-interval amplification on each reaction unit; in the decoding link, the reaction units passing through the transcoding link are used for decoding, the number of fluorescence coding positive reaction units corresponding to all the detection targets is obtained, and the actual content of the detection targets in the sample is obtained through calculation; according to the method, the detection efficiency is greatly improved, and the sample size required by detection is reduced.

The present invention relates to the field of biotechnology. The present invention provides a method and a kit for simultaneously detecting 16 pathogenic microorganisms in raw milk using a liquid chip, and respectively designs specific upstream primers, specific downstream primers, probes and microspheres for different strain sequences, and then uses the multiple PCR products obtained after multiple PCR amplification to mix and hybridize with the liquid chip working solution with microsphere probes, and then incubate with SAPE, and then use the machine to determine the type of pathogenic microorganisms and their detection amount based on the detected microsphere fluorescence code and the MFI value of the fluorescence intensity of the reporter molecule on the microsphere. The detection method of the present invention requires a small amount of samples, and can achieve rapid, high-sensitivity, high-specificity, and high-throughput detection of multiple pathogenic microorganisms; it can provide a new method selection for laboratory testing of raw milk quality and safety, and better ensure the quality and safety of products and people's food safety.

The present invention provides a microfluidic chip magnetic nanoparticle detection system based on spin exchange relaxation-free. The biomacromolecules to be detected are fixed on the chip substrate by a microfluidic chip device and marked with magnetic nanoparticles. The biomacromolecules to be detected include tumor-associated antigens, tumor-associated glycolipids and glycoproteins, and hormone peptides. The size of the magnetic nanoparticles used is 10nm-10μm. The modification and fixation process of the biomacromolecules can be controlled by a fast dwelling technology. The magnetic nanoparticles in the microfluidic chip are uniformly magnetized by a magnet placement device, and then the microfluidic chip containing the molecules to be detected is mounted on a linear scanning sample stage, and the spatial magnetic field distribution on the surface of the microfluidic chip is obtained by scanning magnetic imaging. The spatial magnetic field distribution is fitted according to the dipole magnetic field distribution, and the number and spatial information of the magnetic particles are obtained by inversion. The detection system has strong specificity and high sensitivity, and the reading results are fast, intuitive, and quantifiable.

The invention relates to a tower air temperature correction method and device based on weather station forecast and computer equipment. The method comprises the following steps: acquiring monitored air temperature data and air temperature forecast data of a target power grid in a set historical time period, and acquiring a to-be-trained air temperature prediction model related to a power grid tower at the same time; using the monitored air temperature data and the forecast data as a training set input model, training the model, and generating a trained air temperature prediction model; and evaluating the model in the aspects of a low-temperature interval, a whole-section temperature interval and low-temperature forecasting performance so as to obtain a low-temperature interval index, a whole-section temperature index and a low-temperature forecasting performance index. Based on the evaluation results, different indexes of the model are endowed with combination weights, and various adjusted air temperature prediction models are generated. And selecting the adjustment model with the maximum weight index total amount in all the weight combinations as a final target air temperature prediction model applied to the power grid tower. By adopting the method, the accuracy of predicting the icing of the power grid can be effectively improved.

The invention relates to the technical field of biological detection, in particular to a kit and method for detecting NfL, the kit comprises a magnetic bead coated with a capture antibody, a non-functional auxiliary magnetic bead, an NfL detection antibody coupled with a first marker, beta-galactosidase coupled with a second marker, an enzyme reaction luminescent substrate and an NfL standard substance; the capture antibody in the magnetic bead coated with the capture antibody is used for being specifically combined with an NfL antigen; the first marker in the NfL detection antibody coupled with the first marker is connected with the second marker in the beta-galactosidase coupled with the second marker, and the NfL detection antibody is suitable for being specifically combined with an NfL antigen. The kit provided by the invention has the advantages of higher sensitivity, less background signal interference, wider linear range, convenience for full-automatic detection instruments and the like.

This invention discloses a method for evaluating the storage lifetime of a missile-borne fiber optic inertial navigation system, comprising: determining the acceleration temperature value T of the test. k The acceleration temperature value T was obtained. k Under these conditions, the acceleration coefficient A of the fiber optic inertial navigation system F According to the acceleration coefficient A of the fiber optic inertial navigation system F Storage time t required for fiber optic inertial navigation systems 贮存时间 The acceleration temperature value T was obtained. k Under these conditions, the time t required to conduct accelerated testing 加速时间 According to t 加速时间 Determine several sampling times; at the accelerating temperature value T k Under certain conditions, accelerated testing was conducted on the fiber optic inertial navigation system (INS), and degradation-sensitive parameters of the INS were acquired at each sampling time. A relationship between these degradation-sensitive parameters and time was constructed. Based on the relationship between these parameters and time under storage temperature conditions, and the out-of-tolerance criterion for the degradation-sensitive parameters, the storage lifetime of the INS under storage temperature conditions was obtained. This invention achieves the assessment of the storage lifetime of fiber optic INS, with advantages such as short testing time, scientific evaluation method, and reasonable evaluation results.

The invention belongs to the technical field of avian reticuloendotheliosis virus detection, and particularly relates to an avian reticuloendotheliosis virus detection primer, a probe, a kit and a detection method. Comprising a primer and a probe, the primer sequence is shown as SEQ ID NO: 1 and SEQ ID NO: 2, and the probe sequence is shown as SEQ ID NO: 3. The primer, the probe and the detection method provided by the invention have the advantages of high sensitivity and specificity, good repeatability, simplicity and convenience in operation, short detection time, small required sample size, minimum detection concentration of 1.77 * 10 copies / mu L, low pollution, cost saving and the like, can be used for directly detecting DNA extracted from an unknown sample, can be complementary with a traditional detection scheme, and can be used for detecting the DNA in the unknown sample. The kit has a high application value in rapid detection of viruses, and provides a basis for clinical diagnosis of REV.

This invention discloses an adaptive PC-Kriging reliability analysis method and system based on active learning. The method includes: using weighted clustering to obtain uniformly distributed first candidate samples from a pre-generated MC sample pool and constructing an initial PC-Kriging model; using interval reduction to select samples and construct a new sample pool, dividing the new sample pool into two sub-sample pools: a safe domain and a failure domain; for each sub-sample pool, weighted clustering is used again to select uniformly distributed second candidate samples; using the second candidate samples distributed in the failure domain and the safe domain, crossing points are constructed; the constructed crossing points are used as new experimental points to iteratively update the PC-Kriging model; convergence is determined and the results are output. The technical solution of this invention focuses on key regions through interval reduction and updates the model based on crossing points, which can more accurately approximate the limit state surface, reduce the number of experimental points, improve prediction accuracy, and is more efficient in utilizing computational resources.

The invention relates to an unmanned autonomous system performance mode analysis method and device based on multilayer clustering. The method comprises the following steps: performing initial clustering on a sample set according to a mean shift clustering technology to obtain a plurality of initial class clusters; the initial class cluster is a performance mode set of the unmanned aerial vehicle autonomous system in different scenes; clustering the initial class cluster by using a DBSCAN clustering method to generate a sub-cluster set about the boundary region; calculating two class clusters with different performance mode labels in the sub-cluster set by using a KNN algorithm to generate a scene boundary; and performing unmanned autonomous system performance mode distinguishing according to the scene boundary. By adopting the method, the performance analysis efficiency of the unmanned autonomous system can be improved.

The invention belongs to the technical field of simulation optimization, and particularly relates to a centrifugal pump impeller multi-physics field coupling simulation optimization method which comprises the following steps: constructing a geometric model of a centrifugal pump by adopting three-dimensional drawing software according to an actual structure of the centrifugal pump; finite element modeling is conducted, and a finite element analysis model of the centrifugal pump is obtained; hexahedral grids are divided for the impeller, a shape variable space of the impeller is built, and the impeller deformation serves as a design variable; solving the finite element analysis model after grid division through a solver; an experimental design sample is generated by adopting a Latin hypercube sampling method, a global response surface model is constructed through quadratic polynomial regression fitting, an optimal impeller deformation amount is iteratively solved by adopting a global response surface optimization algorithm, a finite element analysis model is adjusted, and an optimization model is formed. According to the method, fluid-solid coupling simulation, a global response surface method and an automatic iteration process are fused, and the efficiency of the centrifugal pump impeller is maximized and optimized on the basis of accurately matching actual working conditions.

The application discloses a kind of agricultural non-point source pollution load evaluation method, device and medium based on water quality monitoring, and is related to agricultural non-point source detection field, wherein, method includes: according to hydrology water quality monitoring data, the sampling days of each target pollutant are estimated, the daily load data corresponding to the estimated minimum sampling days is posterior stratification, and the annual load of each target pollutant in each stratification is estimated by applying ratio method, and finally the annual total load of each target pollutant is obtained.In the application, the minimum sampling days is obtained by estimating the sampling days, which reduces the number of samples involved in pollution load calculation, improves the calculation efficiency, and uses ratio method to estimate pollution load, which ensures the accuracy of load estimation value under the premise of relatively high efficiency.

The invention relates to the technical field of data protection, in particular to a user data protection method and system for a human resource management platform. The method comprises the following steps: acquiring behavior data of a user of the human resource management platform; according to the behavior data of the user, establishing a user portrait of the user, and obtaining a portrait label of the user; performing similarity classification on the user portraits of all the users according to the portrait labels to obtain a plurality of types of users; obtaining a model training matrix according to the multiple types of users; according to the model training matrix, training to obtain a target random forest model; and performing user abnormal behavior monitoring by using the target random forest model to complete protection of user data of the human resource management platform. The user abnormal behavior detection efficiency can be improved, the potential safety hazard of the system is reduced, and the system safety coefficient is improved.

The application discloses a digital PCR kit for thyroid cancer detection, and is characterized in that the digital PCR kit is used for detecting TERT-C250T, TERT-C228T and / or BRAF V600E mutations, a buffer system in the kit comprises Tris-HCl, glycerol, dNTPs, KCl, MgCl2, Taq DNA polymerase, UNG enzyme and betaine, the final concentration of the betaine is 0.25-0.7 M, and the final concentration of the Taq DNA polymerase is more than 3 U. The application provides a high-sensitivity, simple-to-operate auxiliary molecular diagnostic kit for thyroid cancer based on a digital PCR platform, combined with fluorescence quantification and multiplex PCR technology. The kit has high sensitivity and high specificity through optimization of the final concentration of the betaine, the final concentration of the Taq DNA polymerase and primers and probes for detecting three mutation sites, and the sensitivity of the kit for detecting target sequences in a complex background can reach 0.2 %.

The invention discloses a method and system for correcting multiple time-width pulse signals of radar-specific test equipment. The method for correcting multiple time-width pulse signals of radar-specific test equipment can transform a continuous sampling problem into a discrete sampling problem by performing discrete transformation on a sampling sequence, thereby reducing the number of samples. Then, amplitude calculation and phase calculation are performed on the sample sequence after discrete transformation, so that the sample data after discrete transformation can be sampled and quantized at equal intervals according to the amplitude calculation results and the phase calculation results, so as to further reduce the number of samples and enhance the representativeness of the samples. Finally, a pulse correction signal is generated by using the data obtained after the equal-interval sampling and quantization processing, so as to ensure the accuracy of sample processing, thereby solving the problem of large amount of calculation when the traditional pulse signal correction method corrects multiple time-width linear frequency modulation signals.

The application discloses a kind of primitive object and remote sensing classification method of graph neural network. Method includes: the adjacent pixel of pixel value similar in remote sensing image is formed into pixel set and vectorization is obtained primitive object, constructs the graph feature of primitive object, image feature and image common edge feature, generates the sample graph structure of primitive object in sample area in remote sensing image, to obtain node feature matrix, node adjacency list, edge feature matrix and node label matrix as sample training set;Local subgraph is established according to containing depth, to input sample training set in graph neural network in batches training;Based on final graph neural network, the classification result of each primitive object in remote sensing image is obtained.The method of the application improves the representativeness of sample, improves the generalization ability, and constructs features from the physical mechanism level to improve the spatial feature expression ability, effectively reduces the influence of feature scale, effectively improves the remote sensing classification precision, and can be widely applied in crop remote sensing information extraction and the like scene.

The present invention discloses the use of peripheral blood leukocyte RNA combined markers in the preparation of a kit for detecting or diagnosing early primary gallbladder cancer. The present invention is based on peripheral blood leukocyte RNA markers for detection. The marker is a liquid biopsy biomarker that is more sensitive, stable, non-invasive, and requires less sample volume, which is more acceptable to patients. Its successful development will help the diagnosis of gallbladder cancer. The etiology of primary gallbladder cancer is complex. Compared with a single molecular marker, the present invention evaluates the accuracy of the test sample from two dimensions of abnormally high expression and low expression, which can reduce the error caused by individual expression differences of a single indicator to a certain extent, making the result more accurate.

The invention relates to a monthly electricity consumption prediction method considering new installation capacity increasing and return and supplement factors, which adopts a monthly electricity quantity report statistical value after purchase and sale as sample initial data to avoid the influence of inconsistent statistical periods on the electricity consumption prediction accuracy, and meanwhile, the data is data in recent two years, can better reflect the electricity consumption habits and rules of users, and can improve the electricity consumption prediction accuracy. The prediction stability is higher; according to the method, the initial value of the electric quantity in the corresponding month is corrected according to the non-government refund and supplement details of the user and the actual error month of the refund and supplement electric quantity, and the accuracy of electric quantity prediction is ensured from the data source; meanwhile, the corrected monthly electric quantity data is decomposed into stock user electric quantity and increment user electric quantity, the stock user electric quantity is predicted on the basis of analyzing the electric quantity change trend, and the influence of air temperature and holidays on the electric quantity is considered on the basis of the seasonal and continuous electric quantity change; and the electric quantity is predicted by combining the user research condition and the electricity consumption statistical condition of similar users, so that the prediction accuracy and stability are improved.

The application provides a high-speed high-precision analog-digital converter and a performance improvement method of the analog-digital converter, the analog-digital converter comprises an input configuration module, an analog-digital conversion module, an adaptive parameter extraction module and a whole cycle data recovery module, in a parameter extraction working mode, the modified parameters are extracted through the cooperation among the input configuration module, the analog-digital conversion module, the adaptive parameter extraction module and the whole cycle data recovery module; in a normal working mode, the digital calibration and correction of the digital signal are realized through the analog-digital conversion module and the modified parameters, the modified parameters comprise a jitter correction parameter, a gain correction parameter and a mismatch error correction parameter, the signal-to-noise ratio and the linearity of the analog-digital converter are improved, the influence of the performance of the analog-digital converter caused by the smaller and smaller intrinsic gain and the larger and larger mismatch error with the reduction of the process size is avoided, the injected random jitter is completely eliminated, and the influence of the signal-to-noise ratio of the analog-digital converter caused by the injected random jitter is avoided.

The application discloses a microfluidic device and a method for introducing microparticles into cells. The method comprises injecting a microparticle solution and a cell suspension into an inlet channel, and the microparticle solution and the cell suspension are mixed and flow into a main channel. When flowing through the main channel, gas in a microstructure and mixed liquid in the main channel form a gas-liquid interface at a first gap. A piezoelectric transducer is controlled to be turned on, and the piezoelectric transducer generates sound waves. The sound waves make the gas-liquid interface vibrate and generate acoustic vortices near the gas-liquid interface. The acoustic vortices drive the cells and the microparticles in the mixed liquid to gather and rotate. The cells reversibly deform under stress to generate micropores on the cell membrane. The microparticles enter the cells through the micropores on the cell membrane, and the process of loading the cells with the microparticles is completed. The device and the method disclosed by the application realize higher microparticle loading efficiency and solve the problem of uneven microparticle loading effect while protecting the integrity and functionality of the cells.