813 results about "Infection risk" patented technology

The invention relates to an infection risk assessment method and system for nursing, and the method comprises the steps: collecting multi-source heterogeneous data of a patient, and fusing the multi-source heterogeneous data into a unified original data set; obtaining a standardized risk feature vector based on the original data set; inputting the risk feature vectors into a multi-modal risk assessment model, and outputting risk probabilities of individual infection and group infection; forming a personalized risk index based on the risk probability; according to the personalized risk indexes, the decision support system outputs hierarchical intervention measures matched with the risk levels, and records and feeds back execution conditions and intervention effect data of the hierarchical intervention measures for dynamically updating parameters of the risk assessment model; and integrating the intervention effect data and the early warning signal into feedback information to form a dynamic infection risk assessment system adapted to specific hospital characteristics. The accuracy of infection risk assessment of nursing can be improved.

The invention discloses an infection risk dynamic assessment method based on deep learning, and relates to the technical field of intelligent monitoring. Multi-source heterogeneous data are integrated by calling a pre-trained deep learning model, a basic regeneration number (R0) value set is calculated based on an SEIR infection model, the basic regeneration number (R0) value set serves as a node initial feature of a space-time diagram network, and a double-layer dynamic network is constructed to generate a regional vulnerability scoring matrix. And calculating a global infection risk index through the linear combination vulnerability score and the R0 numerical set, and generating a prediction infection density matrix and a hierarchical prevention and control strategy when the index exceeds a preset threshold. And further comparing a prediction result with actually measured infection data, dynamically adjusting a symptom keyword weight and a medical resource dependency parameter, and triggering online incremental learning of the model to optimize evaluation precision. Dynamic adaptation of multi-source data fusion modeling and prevention and control strategies is achieved, and the timeliness of infection risk assessment and the scientificity of prevention and control decisions are remarkably improved.

The invention relates to the technical field of burn wound treatment, and discloses an intelligent burn wound dynamic monitoring and evaluation system. The system comprises a data acquisition module for acquiring real-time physiological data of a burn wound by using a multi-source sensor; the multi-modal feature fusion module is used for generating wound comprehensive state features based on a deep belief network; the dynamic evaluation module outputs a wound healing level and a risk early warning signal through a pre-trained collaborative evaluation model; the optimization decision generation module is used for constructing a multi-target dynamic optimization model to adjust a treatment scheme according to risk early warning; and the hierarchical execution control module realizes distributed execution of treatment instructions through a hierarchical control architecture. The system can comprehensively monitor the state of the wound surface, accurately evaluate the healing risk, optimize the treatment scheme and accurately execute the treatment scheme, effectively improve the treatment effect of the burn wound surface, reduce the infection risk and shorten the healing period, and has important clinical application value.

The invention discloses a recombinant herpes zoster vaccine as well as a preparation method and application thereof, and particularly relates to the technical field of biological medicines. The recombinant herpes zoster vaccine comprises a recombinant herpes zoster vaccine fusion protein purified product HN101 and a liposome mixture HA201, and the recombinant herpes zoster fusion protein gE-gD is constructed by recombining reconstructed gE protein and reconstructed gD protein. The gE-gD recombinant herpes zoster fusion protein is obtained by reconstructing gE protein of varicella-herpes zoster virus and gD protein of HSV through a gene recombination technology and then recombining the gE protein and the gD protein. After the gE-gD recombinant herpes zoster fusion protein is combined with a liposome mixture HA201, stronger immune response can be induced, the antibody titer and cellular immunity are improved, the vaccine effect is enhanced, and meanwhile, the potential infection risk is avoided.

The invention discloses an infection risk assessment method and system for burn nursing, and the system comprises a long-term trend analysis module and a short-term anomaly detection module, the long-term trend analysis module employs an improved Transform-XL model, and captures the chronic cumulative effect of inflammation indexes within 72 hours through a relative position coding and memory multiplexing mechanism; the short-term anomaly module is based on a bidirectional LSTM reconstruction network, combines a dynamic threshold algorithm to identify sudden physiological anomaly in a window of 6 hours in real time, generates a final infection risk index through a confidence-driven adaptive fusion algorithm and dynamic weighted dual-channel output, triggers clinical response in combination with a hierarchical early warning mechanism, and introduces closed-loop feedback optimization, so as to achieve the early warning of the physiological anomaly. The actual intervention effect feedback model is iterated, the infection recognition time is shortened to 3.2 hours, the ROC-AUC reaches 0.94, and the reasonable utilization rate of antibiotics is increased to 89%.

The invention discloses a medical instrument use supervision system based on image recognition, particularly relates to the field of medical instrument supervision, is used for solving the problems of endoscope pollutant monitoring and use state early warning, and is used for automatically adjusting a time unit and eliminating interference in a non-operation stage through a dynamic time division technology and a system. The method comprises the following steps: calculating repeatability indexes and diffusion regularity indexes of adjacent time unit pollutant areas, and synthesizing the repeatability indexes and the diffusion regularity indexes to generate decision indexes, so as to effectively evaluate the early warning level of pollutant influence; meanwhile, the system can accurately judge whether the use state of the endoscope is in a stable decay or abrupt deterioration stage or not by combining the change trends of the short-term and long-term decision indexes; real-time and fine monitoring of pollutants on the surface of the medical instrument is achieved, sensitivity and accuracy of pollution detection are remarkably improved, and equipment performance reduction and infection risks can be pre-warned in advance, so that cleaning and maintenance strategies are optimized, and safety and accuracy of medical operation are ensured.

The invention provides a hollow electrical stimulation electrode array device and a preparation method thereof, and belongs to the technical field of medical instruments, the hollow electrical stimulation electrode array device comprises a substrate and a microneedle array, the substrate comprises a substrate base body, a first conductive lead, a second conductive lead and a substrate insulating layer, the first conductive lead and the second conductive lead are oppositely arranged and are laid on the first surface of the substrate base body at a certain interval; the microneedle array comprises a microneedle array base body, and a hollow channel used as a drug delivery channel is formed in a microneedle base body of the microneedle array base body. An electroporation electrode is improved to be a hollow electrode, medicine in a liquid storage tank can enter intracellular fluid through a hollow channel, the medicine can be injected into a human body through a medicine feeding opening by adopting the hollow electrode, then electroporation is applied to open nanometer through holes of a cell membrane, the medicine enters the intracellular fluid, the steps are simple, the cost is low, and the efficiency is high. The infection risk and the treatment cost of a user are reduced, and due to the design of the liquid storage pool, the functions of medicine storage and injection buffering can be achieved.

The invention discloses an aerosol cross-regional transmission and infection risk assessment method in a medical building and application, and relates to the technical field of public health, and the method comprises the steps: constructing a medical building model and calculating a human body model; performing grid division on the medical building model and the calculation human body model to obtain a grid medical building model and a grid calculation human body model; performing steady-state simulation based on the gridding medical building model and the real environment data to obtain an initial airflow field in the gridding medical building model; performing transient simulation based on the gridding medical building model, the gridding calculation human body model, the initial airflow field and the personnel motion function to obtain aerosol distribution in the gridding medical building model; and performing infection risk assessment based on aerosol distribution to obtain an assessment result. According to the invention, cross-regional aerosol transmission in a medical building can be accurately simulated, and infection risk assessment is further carried out.

The invention relates to the technical field of health monitoring, in particular to a health monitoring method and device based on the Internet of Things, and the method comprises the steps: obtaining a historical health monitoring data set according to an Internet of Things sensor network; dividing the monitoring area into a multi-scale space-time health state grid; and performing state classification through the historical health monitoring data set according to a density peak clustering algorithm to obtain a health state mode. According to the method, the monitoring area is divided into the multi-scale space-time health state grids, and the mapping relation between the environment parameters and the health indexes is combined, so that the microenvironment-health association which is difficult to find by a traditional monitoring system can be captured, for example, in a ward scene, the monitoring accuracy is improved; the phenomenon that the infection risk of a patient is increased due to poor air circulation in a ward can be accurately identified, so that environmental parameters are optimized in a targeted manner; and through collaborative analysis of a global channel and a local channel, a group trend and individual anomaly can be captured.

The invention relates to the technical field of intelligent building environment control and energy management, and provides a building ventilation regulation and control method and equipment based on multi-target space-time analysis in order to solve the problems of high energy consumption, uncontrollable infection risk and response lag in the prior art. Original data and the spatial-temporal features are combined into a multi-modal fusion data set, an air quality change rule is learned based on an LSTM model, a future air quality state matrix is output, a multi-target dynamic optimization function is constructed, an optimal regulation and control strategy of a fan, fresh air and purification equipment is generated, and the optimal regulation and control strategy of the fan, the fresh air and the purification equipment is obtained. The response speed is improved while energy consumption is reduced and infection risks are controlled, and the system is particularly suitable for public places such as hospitals, schools and nursing homes with high requirements for indoor gas environments.

An infection control tool for a building HVAC system includes processing circuitry configured to receive one or more inputs defining a HVAC system configuration for each of a baseline scenario and one or more other scenarios. The processing circuitry is also configured to estimate infection risk and at least one of energy consumption or operating cost for each of the one or more other scenarios relative to the baseline scenario. The processing circuitry is also configured to operate a display to provide the infection risk and at least one of the energy consumption or operating cost to a user.

The invention provides a lower respiratory tract hospital infection risk prediction method and system based on deep learning, and relates to the field of medical electronic medical record time sequence data analysis processing and deep learning. According to the method, two diagonal masking self-attention mechanism blocks are utilized to carry out missing value filling on electronic medical record medical time sequence data; processing the demographic data in the electronic medical record by using a full-connection layer neural network, and initializing the hidden state of a decoder; constructing an encoder-decoder model based on an attention mechanism, performing model training by using the filled time sequence data and the hidden state of the initialized decoder, and constructing a lower respiratory tract hospital infection risk prediction model; and inputting the processed data into the trained lower respiratory tract hospital infection risk prediction model, and outputting a lower respiratory tract hospital infection risk prediction result of the patient. According to the method, mass data of the electronic medical record can be effectively utilized, the static characteristics and the dynamic time sequence characteristics are fused, the health state of the patient is captured, and the prediction accuracy and effectiveness are improved.

The invention relates to an infection risk digital diagnosis and evaluation method integrated in a medical information system, and belongs to the technical field of risk evaluation. The method comprises the following steps: acquiring three-tube risk screening data, and automatically generating a risk score through a medical information system according to the three-tube risk screening data; a three-management early warning threshold value is preset, and the medical information system obtains continuous prediction data through a monitoring early warning model according to the risk score and the three-management early warning threshold value; obtaining patient infection risk digital diagnosis data through a risk prediction model according to the continuous prediction data; and obtaining a personnel optimization scheduling result through a nursing personnel scheduling model according to the patient infection risk digital diagnosis data. The infection risk assessment integrated in the medical information system is realized, the accuracy of infection risk assessment is improved, and the nursing service is improved.

The invention provides an indoor air purification method, which comprises the following steps of: comparing and analyzing airflow organization characteristics and particulate matter distribution conditions under different ventilation strategies, evaluating the effectiveness of various ventilation modes for controlling infection risks in a hospital, and screening out a target ventilation system design scheme, so that the target ventilation system design scheme meets the requirements of hospital air quality standards; on the basis of the screened target ventilation system design scheme, the arrangement of an air supply outlet, the air speed and the temperature are further optimized, and a large vortex simulation technology is adopted to conduct refined simulation on the airflow turbulence characteristics of a local area so as to verify the stability and reliability of the ventilation effect; and comprehensively evaluating the influence of the optimized ventilation system design scheme on the overall energy consumption of the hospital, carrying out economic analysis, balancing the energy-saving benefit and the sensing control effect, finally determining an enforceable airflow organization optimization strategy and supporting measures, and providing a customized sensing control solution for the hospital.

The invention relates to the technical field of medical instruments, in particular to an intelligent wound nursing system which comprises a dressing body, and the dressing body comprises a skin-friendly layer, a functional layer and a protective layer. A monitoring module, a control module, a medicine leading-in module, a flexible circuit layer and an alarm module are integrated in the functional layer. According to the method, the infection misdiagnosis rate is reduced through multi-sensor fusion monitoring and a preset infection risk threshold algorithm; according to the design, the micro-fluidic chip and the biological probe are integrated in the percolate component sensor, so that the infection detection rate is increased; a temperature-sensitive hydrogel micro-valve array and nanofiber membrane slow release technology is adopted, so that medicine waste or poor treatment effect is reduced; through a multi-sensor data fusion algorithm, the detection accuracy is improved; the layout of the micro-valve array and the annularly distributed independent medicine storage cavities is adopted, so that the release error is reduced; through the ultrathin flexible framework, the snakelike wiring circuit and flip chip packaging, circuit breakage is avoided, and the clinical failure rate is reduced.

The invention discloses a postoperative infection monitoring and early warning method and system, and relates to the technical field of clinical monitoring, and the method comprises the steps: collecting a postoperative infection multi-source alignment data set, building an individual reference model, dynamically extracting postoperative change features, building a behavioral physiological coupling model, recognizing abnormal interaction, and quantifying the infection risk. According to the method, multi-source data are fused, an individualized dynamic baseline model and a behavior-physiological coupling model are established, intelligent recognition and grading early warning of postoperative infection risks are achieved, and the method is high in practicability and easy to popularize. Model parameters are automatically corrected by utilizing a self-learning and feedback recharge mechanism, a closed-loop process of monitoring, prediction, intervention and optimization is formed, infection symptoms can be recognized in advance, manual dependence is reduced, the real-time performance and accuracy of postoperative infection prevention and control are improved, and intelligent decision support is provided for clinic.

The invention discloses an ICU sepsis two-stage early prediction method and device and a storage medium, and the method comprises the steps: continuously collecting a clinical data set after a patient enters an ICU, and carrying out the feature extraction, and obtaining a prediction feature vector; based on the predicted feature vector corresponding to the clinical data set at the current observation moment, determining whether the patient is a non-sepsis sample by using a preset classification model; and if the patient is not determined as a non-sepsis sample by the classification model, predicting whether the patient has a sepsis infection risk in a future preset time period or not by using a preset prediction model according to the current observation moment and the prediction feature vector corresponding to the clinical data set in the previous set time. According to the application, a two-stage mode is adopted, patients are subjected to layered screening, and samples which do not obviously belong to sepsis are excluded; and for the samples which are not excluded, more accurate sepsis risk prediction is carried out by using the prediction model in combination with the prediction feature vectors corresponding to the clinical data of the plurality of time periods, so that the prediction accuracy is improved.

The ureter and urine bag comprises a urine bag body, a spiral pipe, a connecting pipe, a threaded connector pipe and a urine guiding pipe, the threaded connector pipe comprises a pipe body and an insertion pipe, internal threads with the thread pitch being 1.8-2.2 mm are machined on the inner wall of the pipe body, external threads matched with the internal threads are machined on the outer wall of the insertion pipe, and the spiral pipe is connected with the connecting pipe. Two groups of wing plates are symmetrically arranged on the outer wall of the cannula to provide rotating acting points, and a collecting assembly is integrated on the side wall of the urine bag and used for aseptically collecting urine samples. Traditional plug-pull type connection is replaced by a precise thread fit structure, a clear rotating force application point is provided for medical staff, operation is labor-saving and convenient, connector pollution is avoided, the urinary tract infection risk is effectively reduced, and the nursing comfort of a patient is improved.

The invention discloses a nursing communication follow-up visit system for the department of burn and plastic surgery based on artificial intelligence, and belongs to the technical field of medical follow-up visit systems. The system comprises a nursing wound acquisition module used for acquiring physiological data of a patient, wound image data, nursing operation records and text record data constructed by subjective feedback of the patient in real time; according to the method, multi-modal data are integrated, and a dynamic patient data framework is constructed; physiological indexes are monitored in real time, and a basis is provided for early infection early warning. A wound area is accurately segmented, and quantitative evaluation of the wound is achieved; the emotion and compliance of the patient are analyzed, the nursing compliance is improved, the infection risk detection rate is increased, and the false alarm rate is reduced; based on a random forest model and a dynamic threshold value, hierarchical risk management is realized, a personalized follow-up visit strategy is constructed, resource configuration is optimized, a feedback driving mechanism is introduced, a closed loop is formed, model dynamic updating is realized, nursing standard upgrading is promoted, and system model prediction precision is improved.

The preparation method of the rare earth antibacterial glaze comprises the following steps: mixing 30-48 parts by mass of quartz sand, 20-35 parts by mass of borax, 25-35 parts by mass of feldspar powder and 10-25 parts by mass of zinc oxide into a ball mill for ball milling, sieving the ball-milled material, and drying to obtain powder A; mixing 0.5-3 parts of lanthanum oxide, 0.5-2 parts of cerium oxide and 100 parts of powder A into a ball mill for ball milling, and sieving and drying the ball-milled material to obtain powder B; melting the powder B at 1350-1400 DEG C to obtain a glaze melt, taking out the glaze melt, tabletting, and cooling to obtain a glaze frit; and carrying out ball milling on the glaze frit until the particle size is less than 20 microns, slowly heating to 650-700 DEG C, and carrying out heat preservation for 25-30 minutes to obtain the rare earth antibacterial glaze. The ceramic prepared from the rare earth antibacterial glaze has broad-spectrum antibacterial performance, inhibits the breeding of microorganisms such as bacteria and fungi, and reduces the infection risk.

The invention discloses an infection risk grading early warning method and system based on clinical parameter dynamic fusion. The method comprises the following steps: acquiring patient data from a plurality of medical systems to obtain initial data; performing regularization preprocessing on the initial data to obtain a preprocessing result; quantifying the importance of the preprocessing result in combination with clinical expert opinions and a machine learning model, and generating a final risk assessment weight through dynamic weighted fusion; based on the final risk assessment weight and the knowledge graph, factors influencing the risk and weights of the factors are determined, and a dynamic assessment model used for calculating a risk score is established; and performing continuous monitoring by using the dynamic evaluation model, and automatically triggering an alarm when the risk score exceeds a threshold value. By implementing the method provided by the invention, the accuracy and reliability of infection judgment can be effectively improved, false alarms and missing alarms are reduced, and more timely and accurate infection risk early warning is realized.

The invention discloses an intelligent prevention and control and early warning system for children infectious diseases, and relates to the field of data analysis, and the main scheme is as follows: calculating a disease infection risk index # imgabs4 # according to an environmental risk index # imgabs0 #, a physiological risk comprehensive index # imgabs1 #, a disease transmission risk index # imgabs2 # and a comprehensive infection index # imgabs3 #; the children infection risk is judged according to the disease infection risk index # imgabs5 # and the infection risk threshold value, the problem that the physical conditions, living environments and behavioral habits of all children are different is solved, the development trend of infected diseases is judged by calculating the proportion of the number of diseased children, the development trend of the diseases in the whole children group can only be judged from the macroscopic level, and the development trend of the infected diseases in the whole children group is judged. And the method cannot go deep into an individual level and is difficult to meet individual differentiation prevention and control requirements.

The invention relates to the technical field of medical instruments, and discloses an interventional instrument delivery rotating device and conveying system.The interventional instrument delivery rotating device comprises a transmission mechanism, a delivery mechanism and a translation driving component, the transmission mechanism comprises a rotation driving component and a translation driving component, and the delivery mechanism is arranged at the top of the transmission mechanism; the delivery mechanism comprises a delivery driving wheel and a delivery driven wheel, the delivery driving wheel is matched with the rotary driving part and drives the delivery driving wheel to rotate through magnetic force so as to achieve synchronous rotation, and the translation driving part is used for driving the delivery driven wheel to move; compared with the prior art, through non-contact transmission of magnetic coupling, the driving side and the execution side are completely isolated, a pollution path is cut off from the physical structure, and the infection risk is greatly reduced.

This invention relates to the field of nasal aspirator technology, and more specifically, to a nasal aspirator and its method, comprising: a nasal plug suction head unit and a negative pressure suction unit. The nasal plug suction head unit includes: a suction head body, which has an suction port for inserting into the nasal cavity to aspirate nasal secretions and an exhaust port for connecting to the negative pressure suction unit. The nasal aspirator and its method of this invention adjust the suction force of the suction head body through the negative pressure suction unit, making it easier to control the speed or force of aspirating nasal secretions, reducing discomfort inside the nasal cavity. Compared to oral nasal aspirators, this invention provides better suction and reduces the risk of infection. The structural design of the nasal plug suction head unit in this invention effectively prevents the backflow of nasal secretions, preventing secondary infection caused by backflow, and reducing discomfort caused by irritation to the nasal cavity or throat.

The invention relates to a multi-modal data fusion-based infection dynamic visualization evaluation method, which comprises the following steps of: acquiring multi-modal data which comprises a medical image modal, a microbiological modal, a spatio-temporal behavior modal and a physiological and biochemical modal; the multi-modal data is quickly searched through quantum parallel computing, and an optimal feature combination is obtained; carrying out fusion association on the optimal feature combination to obtain fusion features, and constructing a space-time coupling infection risk model at the same time; the space-time coupling infection risk model performs risk field construction based on the fusion features, and generates infection dynamic visualization content; compared with traditional single-mode data, the infection direction can be evaluated more accurately, after quantum calculation is introduced, the operand can be reduced, the calculation efficiency and the accuracy of optimal feature combination obtaining can be improved at the same time, the pathogen propagation path can be visually checked through the infection dynamic visualization content generated through the space-time coupling infection risk model, and the infection risk can be effectively improved. Therefore, blocking can be timely cut off, and disease prevention and control are achieved.

The invention relates to the technical field of network security, and discloses a virus monitoring and early warning method based on network traffic deep analysis, which comprises the following steps: collecting network traffic data in real time through a deep packet inspection technology, extracting traffic features, and constructing a traffic feature sequence based on a time window; establishing an anomaly detection model based on the traffic feature sequence, and identifying an anomaly mode deviating from normal behavior distribution; according to an abnormal behavior detection result, constructing a dynamic model of an equipment infection state, and quantifying an equipment infection risk; and constructing a propagation graph model in combination with an equipment interaction relationship, and simulating a diffusion path and a propagation trend of the virus in the network based on propagation dynamics. According to the method, hidden behaviors of unknown viruses and variant viruses can be accurately monitored, a virus propagation path and an influence range are dynamically predicted, optimal allocation of defense resources is realized in combination with a game theory and linear programming, and the real-time performance of virus monitoring and defense is improved.

The invention relates to the technical field of hydrogel, in particular to mussel mucoprotein loaded extracellular vesicle hydrogel as well as a preparation method and application thereof. The hydrogel is prepared by adopting a mode of combining human umbilical cord mesenchymal stem cell exosome Exo and mussel mucoprotein MAP; the method has the following advantages: 1, the wound repair effect is improved: the biological activity of the exosome Exo and the adhesion performance of the mussel mucin MAP are utilized to jointly promote the wound repair process, so that the repair effect is improved; 2, the adhesion performance is enhanced: the strong adhesion of the mussel adhesive protein MAP is utilized to ensure that the hydrogel can be tightly attached to the wound surface in a wet environment, and the dressing is prevented from falling off and infection risk; and 3, multifunctionality is realized: the composite hydrogel not only has the basic functions of promoting healing, resisting bacteria and the like, but also can transmit genetic information through exosome Exo, so that tissue regeneration and skin barrier repair are further promoted.

The invention discloses a wound healing degree evaluation method and system based on multiple models, and relates to the technical field of medical treatment. According to the method, the wound edge, the wound color, the wound type and the outer layer dressing color of the patient are combined, the multiple neural network models are used for comprehensively detecting the wound, intelligent detection of the wound of the patient is achieved, medical staff are assisted in treatment management of the wound of the patient, the burden of the medical staff is relieved, and the infection risk of the wound of the patient is reduced.

The invention discloses urinary catheterization equipment for urinary surgery. The urinary catheterization equipment comprises a device body, a fixing plate, a mounting assembly, a movable rod, a limiting assembly and a urine bag. The mounting assembly is used for stably fixing the fixing plate to the abdomen of a patient, the movable rod is connected to the fixing plate through the hinge seat, and the limiting assembly is used for fixing the catheter. The urine bag is connected with an electronic monitoring control system, the flow, pressure, temperature and liquid level of urine can be monitored in real time, and urination and alarm can be automatically carried out. Through intelligent monitoring and automatic control functions, the safety, comfort and convenience of the urinary catheterization equipment are improved, the infection risk is reduced, and the urinary catheterization equipment is suitable for urinary catheterization treatment of urinary surgery patients.

The invention relates to the technical field of pathogen detection, and discloses a respiratory system infection viroid sample detection device, which comprises a detection cylinder, a detection card is arranged in the detection cylinder, a one-way air inlet blowing nozzle is arranged at the port of the detection cylinder, and blown air is guided to be stored in an air cavity in the detection cylinder; the detection card comprises a gas production area, the surface is horizontally unfolded opposite to the gas outlet end of the blowing nozzle, and a gap in contact with the gas cavity is reserved in the gas production area; the transparent window is arranged outside the detection cylinder, and a display area is arranged in the middle of the detection card. The handheld portable blowing detection tube is adopted, breath containing droplets is guided to be intensively blown to a detection card sample introduction area, the structure is simple, sampling and detection are efficiently integrated, the infection risk in the use process is greatly reduced, medical staff are not needed for detection, a large amount of manpower and material resources are saved, and the detection efficiency is improved. The method is especially suitable for rapid screening of people closely contacted with a large number of suspected cases and definite cases, and the pressure of detection and diagnosis work can be effectively relieved.