626 results about "Respiration" patented technology

The invention belongs to the technical field of artificial intelligence and natural language processing, and relates to a respiratory anesthesia knowledge retrieval type question answering method and system, and the method comprises the steps: receiving a respiratory anesthesia related question text; performing segmentation, vectorization and text embedding processing on the problem text to obtain corresponding vectors; inputting a pre-established and trained respiratory anesthesia professional knowledge model, generating a related text by the respiratory anesthesia professional knowledge model through vector search of a retrieval type question and answer engine, generating question-related answer cues through context integration, and calculating and outputting answers to the questions according to similarity; the respiratory anesthesia professional knowledge model is obtained by fusing respiratory anesthesia professional knowledge to carry out fine adjustment on a basic big language model, and the respiratory anesthesia professional knowledge model is based on an enhanced Transform network structure, adopts a multi-head dynamic attention mechanism, and adds domain semantic adaptation, bidirectional interaction and retrieval enhancement.

The invention relates to a food fresh-keeping method, device and equipment and a storage medium, and the method comprises the steps: collecting respiration intensity data and decay gas concentration of target food in a cold chain carriage, carrying out fresh-keeping threshold matching, and generating food fresh-keeping information; real-time environment parameters of the cold chain compartment are collected, a fresh-keeping instruction is generated according to the food fresh-keeping information and the real-time environment parameters, and an air adjusting valve opening instruction and a compressor frequency instruction are obtained; dynamic regulation and control operation is executed based on the air regulating valve opening instruction and the compressor frequency instruction, and putrefying bacterium comprehensive evaluation indexes and compressor power consumption data are obtained in real time; and according to the putrefying bacterium comprehensive evaluation index and the compressor power consumption data, instruction feedback analysis is carried out, strategy construction is carried out, and a fresh-keeping regulation and control strategy is output. The air conditioning and refrigeration strategy can be dynamically adjusted according to the breathing characteristics of different foods and the growth dynamic state of putrefying bacteria.

The invention discloses a method and equipment for predicting a tumor position under respiratory movement based on LSTM (Long Short Term Memory), and the method comprises the steps: collecting a 4DCT (4 Discrete Cosine Transform) image sequence of a tumor part of a plurality of patients under free respiration; based on the acquired 4DCT image sequence, extracting a movement track of a tumor centroid point position along with time change; sequentially inputting all tumor motion data points in each fixed time window on the motion trail into a CNN-Attention-LSTM model for offline training, wherein the CNN-Attention-LSTM model is used for predicting the tumor position at the next time; and in a radiotherapy stage, performing online real-time updating on the trained CNN-Attention-LSTM model by using the latest respiratory movement data of the current patient, and predicting the tumor position at the next time. The real-time position of the tumor is provided for the radiotherapy robot, the time delay of the whole system is compensated, and the tumor position tracking is accurately realized in real time.

The invention provides a life sleep sign monitoring method, and relates to the technical field of sign data monitoring, and the method comprises the following steps: collecting an original echo signal of a monitoring area, and extracting a respiration signal, a heartbeat signal and a body movement signal corresponding to a body movement event from the original echo signal; obtaining relevance characteristics of the respiratory signal and the heartbeat signal and adjusting characteristics of the body movement event on the respiratory signal and / or the heartbeat signal; according to the relevance features and the adjustment features, biological features representing individual physiological differences are generated; based on the biological characteristics, target identity probability distribution is obtained; and performing interference suppression processing on the original echo signal according to the target identity probability distribution so as to separate a target respiration waveform and a target heartbeat waveform of the target individual from the original echo signal. The method is suitable for practical application scenes such as multi-person same-bed monitoring and family bedroom monitoring, and the technical bottleneck that the monitoring precision is reduced along with the increase of the target number in the prior art is broken through.

The invention provides a lightweight non-contact multi-parameter physiological detection method and system, a storage medium and electronic equipment, and relates to the field of non-contact multifunctional physiological detection. According to the method, double-spectrum information of thermal infrared and visible light is fully utilized, respiratory wave signals are extracted from a thermal infrared spectrum by adopting affine transformation and double-light fusion technologies, and iPPG signals are extracted from a double-light video based on a time-frequency domain feature analysis method. Meanwhile, a lightweight signal noise reduction network is designed, automatic noise reduction processing is carried out on the respiration signals and the iPPG signals in a multi-task learning mode, and the incidence relation between different physiological parameter estimation tasks is excavated, so that accurate estimation of the three physiological parameters including the respiration rate, the heart rate and the heart rate variability can be achieved under the scene that computing resources are limited, and the accuracy of estimation of the three physiological parameters including the respiration rate, the heart rate and the heart rate variability is improved. The physical health condition of an individual is analyzed in real time, and objective and accurate physiological perception information is provided for mental health screening.

The invention discloses a radiofrequency ablation navigation device based on multi-modal image fusion. The device comprises a respiration sensor, an image reconstruction server, an interaction terminal and a processor. The respiration sensor is worn on a target object and used for collecting respiration data of the target object. The image reconstruction server is used for receiving the multi-modal image of the target object and reconstructing the multi-modal image into a three-dimensional model; the three-dimensional model comprises an organ geometric model, nerve distribution and a blood vessel network. And the interaction terminal is connected with the image reconstruction server and is used for marking a starting point and an ending point for the three-dimensional model. The processor is connected with the respiration sensor, the image reconstruction server and the interaction terminal; and the processor generates a predicted path output according to the respiration data and the three-dimensional model marking the starting point and the ending point. The method has the advantages that respiratory movement errors are accurately compensated, the nerve mistaken injury risk is reduced, and the operation safety is improved.

The invention belongs to the technical field of medical imaging and digital twinning, and discloses a thoracic cavity whole organ system level lung four-dimensional EIT detection digital motif and a library building method, and the method comprises the following steps: S1) segmenting multiple organs such as double lungs, heart, sternum, rib and thoracic vertebra central axis skeleton, main bronchus and esophagus based on an individual CT image, and building a three-dimensional geometric model integrated with an EIT electrode array; s2) simulating breathing dynamic deformation by using a solid mechanical model; s3) establishing a coupling relationship between deformation and conductivity, and generating dynamic three-dimensional conductivity distribution; s4) solving an electromagnetic field positive problem, and generating a virtual EIT voltage database synchronous with respiration; the invention also covers pathological model integration and data enhancement strategies. According to the method, a high-fidelity, large-scale and diversified EIT virtual data set can be generated, core data support is provided for training and verifying an AI intelligent reconstruction algorithm, and a model foundation is laid for developing a lung function dynamic digital twinning system.

The invention discloses a fresh commodity after-ripening regulation and control planning method oriented to maturity grading, and relates to the technical field of food science and biologication.The method comprises the steps that physiological parameters of fresh commodities are collected in real time based on a multi-modal sensor, and the physiological parameters comprise the epidermis pigment content, the ethylene release rate, the fruit hardness and the respiration intensity; the collected physiological parameters are input into a multi-objective optimization model based on deep learning, the model fuses a metabolic kinetic equation and an LSTM time sequence prediction algorithm, and a regulation and control parameter combination adaptive to the current mature stage is output; according to the regulation and control parameter combination, the temperature, the relative humidity and the O2 / CO2 concentration gradient of the controlled atmosphere storage equipment are synchronously regulated through a distributed control system, and a plant hormone antagonist is injected through an ultrasonic atomization device; and constructing a supply chain collaborative network based on a block chain technology, binding a grading result with logistics path planning, and adjusting a temperature control strategy and a shelf life countdown parameter of the cold chain transport vehicle in real time through an edge computing node.

The invention belongs to the technical field of sleep respiratory disease detection, and relates to a sleep apnea identification method based on a hybrid neural network, which comprises the following steps of: sensing human sleep in a bed surrounding environment by using a millimeter wave radar, and positioning a thoracic cavity position by adopting spatial two-dimensional analysis; the problem that interference exists in radar sign signal extraction of different targets at the same distance is solved. And a respiration signal Shannon entropy is constructed as an iterative index of the whale optimization algorithm, and the index is minimized to realize iterative optimization of variational mode extraction parameters, so that different respiration states are matched with different parameters, and thus a dominant frequency narrowband respiration signal is accurately extracted. A breathing time-frequency energy diagram obtained by data processing is used as input, and a CNN network and a bidirectional long-short-term memory module are used together to construct a feature extraction module, so that the method can recognize the change trend of frequency along with time, capture more comprehensive features, and recognize target sleep apnea more accurately. In addition, the hybrid network enables accurate classification of unmanned scenarios, apnea, hypopnea, and normal respiration.

The invention discloses a respiratory training quantitative evaluation method, which comprises the following steps of: acquiring physiological parameter characteristics related to heart rate, respiration and brain function of a human body through a human body physiological data acquisition platform, and performing dimensionality reduction, dimensionless processing and weighted summation on the characteristics to obtain a respiratory training quantitative evaluation result. The method comprises the following specific steps: firstly, collecting human heart rate, respiration and brain function related physiological features through a physiological parameter collection platform, then calculating feature classification weight of each dimension through random forest and cross validation and removing redundant feature dimensions, and then removing dimensions of features in a feature subset through a dimensionless method; and finally, performing weighted summation on the feature data through the feature weight to obtain a respiratory training quantitative evaluation result. Quantitative evaluation is carried out on respiratory training, so that people can be helped to formulate more scientific respiratory training strategies and methods.

The invention provides an intelligent identification and alarm method for respiratory suppression events in an anesthesia revival period, which comprises the following steps of: continuously acquiring high-frequency physiological data such as respiration, blood oxygen and electrocardio of a patient through multi-channel equipment, and establishing a dynamic causal network model fusing medical priori knowledge and clinical guidelines after standardized processing and feature extraction; a Granger causal test and a dynamic time warping algorithm are combined, a significant causal relationship among key physiological parameters is dynamically identified, a causal network structure is updated in real time, a causal analysis result is further input into a time sequence Bayesian network, calculation of a respiratory suppression event occurrence probability and reasoning path tracing are realized, and the probability of occurrence of a respiratory suppression event is calculated. According to the method and the system, the probability score is calculated, an interpretable medical logic evidence chain and thermodynamic diagram visualization are automatically generated, and if the probability score exceeds the limit, multi-mode alarm and data locking are synchronously triggered, so that the timeliness, intelligence and interpretability of respiratory suppression detection are improved, and clinical precise intervention is facilitated.

The invention discloses a method for identifying the stability of granular sludge based on microbial activity change characteristics. The method comprises the following steps: taking a certain amount of sludge at the aeration tail end of an AGS device as a sample; the method comprises the following steps: putting a sample into a transparent measuring cylinder, judging the settling performance of granular sludge, quantitatively detecting the cell activity of microorganisms in the sludge by using a flow cytometer, and meanwhile, carrying out online monitoring on the sludge sample of an aerobic granular sludge system by using a respiration graph measuring device (LB-WBM-450PI type smart workstation). Through multi-dimensional analysis of sedimentation characteristics, microbial metabolic activity and flow cytometry activity of the aerobic granular sludge, characteristic information such as particle morphological structure integrity, dynamic change between the microbial metabolic activity and the flow cytometry activity and the like can be provided, and data support is provided for stability regulation and control research of the aerobic granular sludge.

The utility model discloses a respiration and metabolism oxygen consumption measuring device which comprises an experiment cabin, an air inlet assembly and a differential pressure sensor, the experiment module is internally provided with an adsorption material for absorbing water and carbon dioxide; the air inlet assembly comprises an electromagnetic valve, one end of the air inlet assembly is communicated with the experiment module through a second pipeline, the other end of the air inlet assembly is communicated with the atmosphere or a normal pressure air storage bag, a positive pressure interface of a differential pressure sensor is communicated with the experiment module through a first pipeline, and a negative pressure interface of the differential pressure sensor is communicated with the atmosphere; the differential pressure sensor is used for collecting air pressure changes in the experiment module and obtaining respiration data of a detected object according to the air pressure changes, and the respiration data comprises respiration frequency and / or lung ventilation capacity. The respiratory data such as the respiratory rate and the lung gas energy change of a detected object can be obtained without an oxygen sensor, the device is used for measuring the metabolic oxygen consumption, the service life of equipment is prolonged, and the cost is reduced.

The invention discloses a multi-factor coupling ganoderma lucidum environment digital twinborn regulation and control method oriented to high-yield requirements, particularly relates to the technical field of intelligent cultivation of edible mushrooms, and is used for solving the problems of equipment action oscillation and recessive physiological stress caused by physiological response hysteresis of ganoderma lucidum in an existing environment regulation and control system. Carbon dioxide concentration and illumination intensity predicted values are generated in real time by constructing ganoderma lucidum growth digital twins, and an oxidative stress mark is activated by combining hypha respiratory entropy matching degree analysis and an active oxygen accumulation curve; when the matching degree exceeds a threshold value and oxidative stress exists, a metabolic overload signal is generated, and whether the carbon dioxide predicted value is in a physiological hysteresis interval or not is judged; delay compensation and metabolic overload suppression coefficient adjustment are performed on the carbon dioxide predicted value in the delay interval; and finally, inputting the compensated carbon dioxide value and the illumination prediction value into an environment factor coordination rule base, generating a ventilation opening degree and light supplementing power instruction to drive execution equipment, and ensuring the morphological development stability of the sporocarp and the synthesis efficiency of the active component.

The invention discloses a vital sign monitoring method based on multilayer variational mode decomposition and reconstruction, which comprises the following steps: sending a radar microwave signal to a target human body, receiving a path of returned radar microwave reflection signal, and extracting frequency domain characteristics of the radar microwave reflection signal; judging whether the current human body is in a resting state or not according to the radar microwave reflection signals, and if so, extracting the radar microwave reflection signals in the resting state; carrying out respiratory signal and heart rate signal separation reconstruction on the radar microwave reflection signal in the resting state to obtain a reconstructed respiratory signal and a reconstructed heart rate signal; phase data of the reconstructed respiration signals and the reconstructed heart rate signals are extracted respectively, wavelet transformation is conducted on the phase data of the respiration signals and the phase data of the heart rate signals respectively, respiration phase characteristics and heart rate phase characteristics are obtained, and the respiration frequency and the heart rate are calculated according to the respiration phase characteristics and the heart rate phase characteristics.

The invention discloses a non-contact respiration detection method. First, an access point (AP), a base station (STA) and a capture device are arranged in a target area, and channel state information is obtained by capturing a beamforming feedback frame (BFI) periodically sent from the STA to the AP. And then, decompressing and extracting amplitude signals and time information in the BFI frame, performing phase-space reconstruction on the extracted representative subcarrier signals of the antenna combination, and extracting and evaluating the optimal antenna combination through dynamic characteristics. Thirdly, performing Hampel filtering on the subcarrier amplitude signals of the optimal antenna combination to remove abnormal values, performing phase-space reconstruction and dynamic feature extraction on all subcarriers, and selecting the optimal subcarrier by using a K-means clustering algorithm; and then, Savitzky-Golay filtering and band-pass filtering are carried out on the selected optimal subcarrier signal, and linear interpolation is carried out through principal component analysis (PCA) dimension reduction to obtain an equally-spaced sampling signal. And finally, performing fast Fourier transform (FFT) on the processed signal, and estimating the respiratory rate through an innovative peak detection algorithm. The invention provides a non-contact respiration detection method by utilizing a phase space reconstruction and dynamic feature extraction technology, and compared with a traditional method, the method can better capture nonlinear dynamic features of signals and more comprehensively evaluate dynamic features of each subcarrier, so that the subcarrier most suitable for respiration detection is selected, and the detection accuracy is improved. The breathing signal and the interference signal are effectively distinguished, and the accuracy and robustness of non-contact breathing detection based on the Wi-Fi signal are remarkably improved.

The invention discloses a PET / MR (positron emission tomography / magnetic resonance) all-in-one machine-based dynamic attenuation correction and respiratory and cardiac dual motion correction method. The method comprises the following steps of: generating a dynamic mu graph of a respiratory cycle; mR cardiac function movie sequence scanning of the cardiac cycle; accurately segmenting the cardiac muscle; 6, performing tissue segmentation on the dynamic mu graph; clustering the PET data; pET data space-time registration is carried out; merging PET data of the same time phase; performing frame-by-frame dynamic attenuation correction; respiratory movement correction; correcting a partial volume effect; carrying out myocardial MR image space standardization on different time phases of the cardiac cycle; and carrying out spatial standardization on the myocardial PET image of different time phases in the cardiac cycle. According to the method, the mu graph of the respiration and cardiac cycles is reconstructed through time-phase sharing and is accurately registered with the PET data, so that the high-quality attenuation correction of the heart PET image is realized and the motion correction is completed at the same time.

The utility model discloses a respiration and metabolism oxygen consumption measuring device which comprises an experiment cabin, an air inlet assembly and a differential pressure sensor, the experiment module is internally provided with an adsorption material for absorbing water and carbon dioxide; the air inlet assembly comprises a resistance valve, one end of the air inlet assembly is communicated with the experiment module through a second pipeline, the other end of the air inlet assembly is communicated with the atmosphere or a normal pressure air storage bag, a positive pressure interface of a differential pressure sensor is communicated with the experiment module through a first pipeline, and a negative pressure interface of the differential pressure sensor is communicated with the atmosphere; the differential pressure sensor is used for collecting air pressure changes in the experiment module and obtaining respiration data of a detected object according to the air pressure changes, and the respiration data comprises respiration frequency and / or lung ventilation capacity. The respiratory data such as the respiratory rate and the lung gas energy change of a detected object can be obtained without an oxygen sensor, the device is used for measuring the metabolic oxygen consumption, the service life of equipment is prolonged, and the cost is reduced.

The present technology relates to an electroporation based antimicrobial apparatus The electroporation based antimicrobial apparatus includes a negative electrode and a positive electrode powered by an energy source, operatively coupled to an air inlet structure, such that upon applying 10 an external electric field across the negative electrode and the positive electrode, a lightning-rod effect is generated that causes permanent damage to outer structure of microbes passing along with air through the air inlet structure, thus achieving instant disinfection. The energy source includes a 15 respiration driven triboelectric Nanogenerator (R-TENG) for harvesting wasted biomechanical energy from the periodic motion of respiration. The R-TENG includes a pair of nanofiber films comprising a first nanofiber film composed of a tribopositive material and a second nanofiber film 20 composed of a tribonegative material. The tribopositive and tribonegative materials are with opposite polarity and the R-TENG serves as a filtration system concurrently. Most illustrative diagram:

The invention is a system for continuous monitoring of respiratory and gastric functions using wearable devices. Each device includes a three-axis accelerometer and optionally a gyroscopic sensor. They feature wireless communication for data transmission and means for attachment to the skin. A computation device analyzes the data, using a machine learning model to detect coughing, swallowing and respiration. The method involves placing sensors on the skin of the cricoid region and epigastric region, filtering and transmitting data, training the model, and plotting coughing frequency. This system enhances detection accuracy and facilitates early medical interventions, benefiting chronic condition management and postoperative care.

The invention provides a regional ecosystem carbon sink function evaluation system, which is based on remote sensing data and meteorological data, estimates vegetation net primary productivity (NPP) and soil heterotrophic organism respiration consumption (Rh) through a CASA model and a regression equation model respectively, and further calculates net ecosystem productivity (NEP) to evaluate the carbon sink function of a regional ecosystem. The system considers a plurality of parameters such as photosynthetically active radiation (APAR), actual light energy utilization rate (epsilon), a temperature stress factor, a water stress factor and the like, and ensures the accuracy and scientificity of estimation. The system can be widely applied to the fields of regional carbon sink monitoring, ecological environment evaluation, climate change research and the like, and provides a scientific basis for regional sustainable development.

The invention discloses a rice large-scale metabolism regulator DGP1 and crop metabolism quality design. The invention finds that the structural domain of the rice DGP1 family protein TIGR01589 targets a plurality of metabolic enzymes and promotes the metabolic enzymes to be transferred to a cell nucleus; the structural domain also has an intervention effect on a light breathing pathway by changing the composition of a glycine decarboxylase compound in mitochondria. Based on a molecular mechanism of DGP1, a chimeric protein ST containing a conservative structural domain and coupled with a chloroplast positioning signal is designed, and genetic engineering modification of pakchoi is realized. The modification strategy not only can guide substrate flow of a central glycolysis pathway to flavonoid biosynthesis, but also can promote accumulation of saccharides derived from photosynthetic products in leaves.

The invention discloses an intimate contact person traceability device based on droplet detection and a traceability method thereof, and belongs to the circuit system field, the human respiration field and the respiratory tract disease traceability technical field, the intimate contact person traceability device comprises a droplet detection module, an intimate contact person traceability module and a rear end system which are electrically connected with each other, the spittle detection module detects spittle signals to judge physical contact conditions among individuals, the close contact person traceability module records geographic positions and time when the spittle signals are obtained, spittle spreading conditions of main spreading modes of respiratory diseases can be obviously embodied, and a rear-end system has a function of filtering a large amount of redundant information. According to the method, the search range can be accurately limited at a specific time point and a space position, close contact persons and secondary close contact persons can be found out more quickly, and an epidemic prevention department can lock high-risk crowds and regions in the shortest time and effectively control the illness state within the minimum range.

The invention relates to the field of biosensors, and discloses a cardiovascular data monitoring method for the cardiovascular medicine department. According to the method, electrocardio, respiration and blood oxygen signals are synchronously collected through a non-invasive terminal, sleep apnea events are analyzed and recognized in a combined mode, the electrocardio signals are deeply analyzed to detect cardiovascular abnormalities, and time sequence characterization of the function state of the autonomic nervous system is generated based on heart rate variability; and further constructing a time sequence causal reasoning model, quantifying the triggering effect of the respiratory event on the cardiovascular event, generating a comprehensive night cardiovascular risk layering index, and triggering graded early warning. According to the invention, non-sensitive, long-time-history and high-precision night cardiovascular risk dynamic assessment and causal mechanism analysis are realized.

The invention discloses Man-PFH-ICG (at) PLGA nanoparticles as well as a preparation method and application thereof, and belongs to the technical field of composite material preparation. According to the invention, PLGA is used as a carrier, the surface of the PLGA is marked with mannose by using polylactic acid-glycolic acid (PLGA) nanoparticles (NPs), and ICG and oxygen-carrying PFH are embedded in the PLGA nanoparticles, so that the obtained Man-PFH-ICG-PLGA nanoparticles have an excellent targeting effect on a mannose receptor overexpressed on the surface of a tumor cell, and obviously promote effective endocytosis of the cell and tumor enrichment in vivo in vitro; the hypoxic environment of tumors is directly relieved; in addition, oxygen can be supplemented endogenously, ROS generated by cells is used for activating TRPA1 channels over-expressed on the surfaces of tumor cells, and therefore cell respiration is inhibited, oxygen consumption is reduced, the tumor hypoxia environment is indirectly relieved, tumor cell growth is effectively inhibited, and a new thought is added for clinical exploration of anti-tumor treatment.

An implantable neuromodulation system can include an electrode configured to be disposed at or near a first branch of a hypoglossal nerve of a patient and configured to provide a first electrostimulation therapy to treat a sleep disorder or breathing disorder of a patient. The system can include a sensor configured to provide a sensor signal that includes information about a patient response to the first electrostimulation therapy, information about a patient respiration, or information about one or more other physiologic status indicators for the patient. The sensor signal information can be used to quantify a progression of a breathing disorder for the patient.

The present application provides a respiration feature extraction method based on body surface significance analysis, including: establishing a body surface voxel model of chest and abdomen respiration motion, which includes acquisition of point cloud information, generation of the voxel model and extraction of respiration motion features; establishing a significance evaluation function by performing significance analysis on different regions of a body surface, and selecting the body surface region with high correlation with tumor motion based on the evaluation function; and performing voxelization on the body surface region, and obtaining effective one-dimensional characterization information of the body surface region using a local linear embedding dimension reduction algorithm. Compared with the prior art, the method avoids an influence of body-surface redundant motion information on precision of the association model, and compared with a PCC method, the method can avoid harm to the human body caused by continuous irradiation of X rays.

The disclosure provides systems and methods for treating obstructive sleep apnea using an acoustic sensor configured to detect acoustic sounds generated by the heart and lungs. Sensory data from the acoustic sensor is used by an implanted stimulation system to determine when to deliver electrical stimulation to a nerve which innervates an upper airway muscle, such as the hypoglossal nerve, to treat sleep apnea.

Various examples describe a livestock monitoring system and method. A sensor signal may comprise a rotational component describing a rotation of the sensor within an animal and a linear component describing a linear movement of the sensor within the animal. The rotational component may be used to identify an animal respiration signal. The animal respiration signal and the linear component may be used to generate a respiration-corrected linear component. An animal heart signal may be detected from the respiration-corrected linear component.

The invention discloses a mosquito control method and system based on carbon dioxide luring and powerful airflow repelling. The method comprises the steps that carbon dioxide airflow simulating human respiration is released in a preset attracting area to attract mosquitoes; monitoring whether mosquitoes enter a preset defense range or not in real time; when mosquitoes enter, the airflow injection unit is controlled to output pulse type strong airflow to flush away the mosquitoes, and meanwhile the concentration of carbon dioxide is diluted to destroy the smell positioning capacity of the mosquitoes. And repeating the steps to continuously form the dynamic protection field. The whole process of the method does not contain any catching and killing step, so that'only repelling but not killing 'is realized. The system supports multiple modes of automation, timing, manual operation and silence, COs can be generated by adopting DAC materials through biological fermentation or chemical slow release, and different cost requirements are met. The technical prejudice that mosquito prevention must be killed is overcome, non-killing mosquito management is achieved for the first time, and the method can be widely applied to courtyards, baby rooms, temples, ecological communities and other places and is environmentally friendly and free of pollution.