535 results about "Disease risk" patented technology

The invention discloses a chronic disease early detection method and system based on a multi-modal large model, and relates to the technical field of intelligent medical treatment and artificial intelligence, and the method comprises the steps: obtaining a multi-modal data stream of a target user in a target time window from a pathology database, and generating an original multi-modal data set; performing timestamp unification and numerical value standardization processing on the original multi-modal data set to obtain a time sequence feature sequence; inputting the time sequence feature sequence to the multi-modal large model to obtain an abnormal symptom feature; calculating the similarity between the abnormal symptom features and feature vectors of marked cases in a historical case library, and determining matched cases; a diagnosis result and a development process of the matched case are extracted, a disease risk level and a development trend corresponding to the original multi-modal data set are determined in combination with the medical knowledge graph, and a pathology assessment result is obtained; and generating an early warning signal containing the risk type and the intervention suggestion according to the pathological assessment result. By implementing the application, the accuracy of early detection of chronic diseases can be improved.

The invention relates to the technical field of artificial intelligence and medical diagnosis, in particular to a disease diagnosis prediction method and system based on a graph neural network. The disease diagnosis prediction method based on the graph neural network comprises the five steps of heterogeneous medical knowledge graph construction, adaptive node embedding representation, hierarchical graph attention network modeling, incremental learning dynamic graph updating and multi-dimensional feature input and result output. The invention discloses a disease diagnosis and prediction system based on a graph neural network. The system comprises a multi-source data acquisition module, a heterogeneous graph construction module, a self-adaptive embedding module, a graph network calculation engine, a dynamic updating module, a disease prediction module and a feedback optimization module. According to the method, the multi-modal heterogeneous knowledge graph is constructed to integrate the multi-dimensional data of the patient, and the hierarchical graph attention network and the dynamic incremental learning are combined, so that the accurate prediction of the disease risk and the visual explanation of the pathological association path are realized.

The invention discloses a medical intelligent decision-making method based on Deepseek and a time sequence causal knowledge graph, and the method comprises the following steps: 1, constructing an initial static medical knowledge graph, and generating a dynamic time sequence causal knowledge graph; 2, finely adjusting and training the DeepSeek model to enable the DeepSeek model to adapt to the medical field; and step 3, receiving and analyzing the text uploaded by the patient, performing intelligent triage and disease risk prediction, and realizing accurate matching of patient symptoms and target departments and intelligent prediction and early warning of potential diseases. The method aims at providing accurate triage and disease risk prediction for patients, constructing a scientific and efficient medical intelligent decision-making mechanism and optimizing medical resource allocation.

The invention relates to the technical field of data management, in particular to an information digital management system based on an intensive care medicine department. The system is used for executing the following steps: inputting information data of a patient in the intensive care medicine department by using terminal equipment; missing field value filling processing is carried out on the information data of the patient in the severe medicine department to obtain complete data of the critical patient; performing physiological index time risk analysis on the complete critical patient data to obtain physiological index abnormal risk data; performing intelligent risk early warning according to the abnormal risk data of the physiological indexes, and generating intelligent illness state risk early warning data; performing risk intervention opportunity reasoning on the intelligent disease risk early warning data to obtain risk intervention opportunity decision data; and performing medical resource optimization adjustment according to the risk intervention opportunity decision data to obtain an individualized diagnosis and treatment aid decision strategy. Individual intervention opportunity decision making is realized through intelligent illness risk early warning in the system, and the management efficiency and the resource utilization rate of the intensive care medicine department are remarkably improved.

The invention relates to the technical field of medical data processing, in particular to a risk assessment system for chronic diseases. The system is used for performing physiological index analysis according to a medical physiological monitoring signal to generate physiological index data; performing individual physiological index weight empowerment on the physiological index data based on genomics data to obtain individual physiological index data; performing quantitative analysis on pathological influence factors of chronic disease types according to the electronic health record data and the individual physiological index data, and generating individual chronic disease influence factor quantitative data; performing composite interaction risk factor analysis on the individual chronic disease influence factors based on the quantitative data of the individual chronic disease influence factors to generate individual chronic disease composite interaction risk factor data; and performing individual chronic disease risk assessment based on the individual chronic disease composite interaction risk factor data to generate individual chronic disease risk assessment data. According to the invention, chronic disease health management and accurate evaluation are realized through collaborative evaluation work of chronic disease risks.

The invention relates to the technical field of data analysis, and discloses a precise targeted early warning method and device for meteorological disasters of kiwifruit canker and a medium. The method comprises the following steps: performing standardization processing on historical occurrence time, morbidity degree and distribution range data of kiwifruit canker as well as meteorological observation data and cultivation management data of corresponding time periods to obtain a historical disease database; performing multivariate statistical analysis processing on the three-dimensional meteorological monitoring network data and the historical disease database to obtain a disease risk assessment index; performing feature extraction processing on the disease risk assessment indexes through a deep learning algorithm to obtain disease early warning parameters; performing gridding partition processing on the disease early warning parameters to obtain partition early warning data; and performing dynamic optimization processing on the accuracy, the advance and the coverage rate of the partition early warning data to obtain accurate targeted early warning information of the meteorological disaster of the kiwifruit canker. According to the application, the accuracy and timeliness of the existing kiwi fruit canker meteorological disaster early warning method are improved.

The invention relates to the technical field of Internet of Things and artificial intelligence, in particular to a livestock disease prevention, control, monitoring and early warning system based on Internet of Things, which comprises a data acquisition module, a data preprocessing module, a self-adaptive data analysis module, an early warning generation module and a user interface module, wherein the data acquisition module is used for acquiring physiological data, behavior data and environment data of livestock; the data preprocessing module is used for cleaning and processing the data to eliminate noise and interference; the self-adaptive data analysis module is used for dynamically adjusting monitoring parameters and early warning threshold values and identifying potential disease risks; and the early warning generation module is used for generating early warning information of different levels and sending early warning. According to the invention, by combining the Internet of Things, machine learning and deep learning technologies, real-time monitoring of livestock health, accurate identification of disease risks and dynamic adjustment of monitoring parameters and early warning thresholds are realized, and the accuracy and timeliness of disease prevention and control are significantly improved.

The invention discloses a pig farm disease risk attribution method and system based on a knowledge graph. The method comprises the following steps: constructing a pig farm biosafety knowledge graph according to a pig industry biosafety application and a pig industry biosafety field knowledge base; secondly, inputting all pig farm events of the pig farm in N days, risks of the pig farm and risk probabilities; factors of pig farm diseases are searched through the pig farm biosafety knowledge map, and the disease text attribution of the pig farm is given according to constraints such as the contribution degree of pig farm problems. The pig farms with medium and high risks are rectified and predicted according to attributed risk items, and the pig farms with low risks or the pig farms without diseases after early warning can be summarized as excellent management experience. Through analysis and reasoning of the atlas, more logical analysis can be provided for pig farm risk attribution tasks, the pig farms are helped to deeply understand characteristics and prevention and control requirements of various diseases, and a scientific basis is provided for formulating targeted prevention and control strategies.

The invention discloses a road and bridge disease analysis and early warning method and system based on big data, and relates to the technical field of smart cities. Multiple groups of sensors are deployed, acquired related transverse wind load information data are associated with related vertical traffic load information data, and an external force load influence coefficient Xhz is constructed; after comparison, a preliminary early warning signal is sent out, related surface state data information and related vibration state data information of the current road and bridge are further collected and associated with an external force load influence coefficient Xhz, a comprehensive disease degree index Zbh is calculated through fitting in combination with a disease evaluation model, and the comprehensive disease degree index Zbh is compared with an evaluation threshold value K; according to the method and the system, the multi-dimensional data is acquired to judge whether the road bridge has a disease risk, a disease early warning instruction of a corresponding grade is generated and executed, and accurate identification and real-time early warning of the road bridge disease are realized through dynamic association and intelligent analysis of the multi-dimensional data.

The present invention provides a method and system for intelligent follow-up of chronic atrophic gastritis based on multi-source electronic medical records, which relates to the technical field of intelligent disease follow-up, including preprocessing and feature extraction of structured, semi-structured and unstructured electronic medical record data, mining the inter-layer feature association relationship by a meta-path reasoning module, and generating comprehensive graph features of patients; identifying the causal relationship between features by a Bayesian causal network module, and building a disease risk prediction model in combination with a temporal causal graph module; and optimizing the follow-up plan by a Nash equilibrium algorithm based on risk prediction results and medical resource constraints; the present invention can effectively predict the disease risk of chronic atrophic gastritis, and optimize the follow-up plan in combination with medical resources, thereby improving the follow-up efficiency and facilitating the early diagnosis and treatment of chronic atrophic gastritis.

The invention relates to the technical field of disease risk assessment, in particular to a prediction support system for a high risk level of a sepsis patient. According to the abnormal degree of the physiological data in the abnormal time period of each category and the time interval between the abnormal time period and the adjacent abnormal time period, the importance degree of each category is obtained; selecting a feature category and a target time period based on the importance, and obtaining the severity of the target time period according to the importance difference between all categories in the target time period and the feature category and the number of categories with abnormal physiological data in the target time period; and acquiring a high risk value according to the change trend and the time interval of the severity of the target time period and the adjacent target time period, and adjusting and analyzing all types of physiological data in the time period by using the high risk value so as to predict the high risk of sepsis of the to-be-detected patient. According to the method, the difference of the physiological data in the time period in which the abnormal expression has the difference is dynamically expanded through the high-risk value, so that the accuracy of predicting the high-risk condition of sepsis is improved.

The embodiment of the invention provides a method and system for generating medical suggestions based on multi-modal data fusion, and the method comprises the steps: integrating a medical image, a physical examination report and dynamic physiological parameters of a patient through a multi-source data fusion module, generating a multi-modal data set, and synchronously inputting the multi-modal data set into a hybrid reasoning module and a dynamic knowledge graph engine. And the dynamic knowledge graph engine accurately recall a target diagnosis and treatment guide associated with the current multi-modal data set. The rule reasoning sub-module generates a first diagnosis suggestion containing a diagnosis conclusion, a treatment scheme and an evidence level based on a guide structured rule, and meanwhile, the neural network reasoning sub-module analyzes a multi-modal data set by relying on a triple topological structure and an edge weight; and generating a second diagnosis suggestion comprising the disease risk probability, the differentiated treatment suggestion and the evidence source. And finally, the interactive output module fuses the two suggestions to generate a medical suggestion report covering the diagnosis basis, the evidence level and the treatment scheme, so that the diagnosis and treatment precision of chronic disease management and health risk assessment is remarkably improved.

The invention relates to the technical field of medical informatics, and discloses an intestinal flora associated disease risk prediction system based on multi-data set difference mutual identification, and the system comprises the steps: capturing the dynamic evolution characteristics of a network through constructing a species-genus-phylum three-level microbial interaction network, and employing a time sequence diagram convolution network; and designing a network disturbance experiment, introducing a network recovery force index to quantify the network recovery capability, constructing a network vulnerability map to identify key nodes and analyze a disturbance propagation path, and integrating stable and consistent network toughness characteristics in multiple data sets to establish an early warning mechanism. By means of the method, early and accurate early warning of disease risks is achieved, and a key time window is provided for clinical intervention.

The invention discloses a disease prevention management and monitoring system design method based on an adaptive algorithm. The method comprises the following steps: S1, obtaining health data of a user; s2, performing feature extraction and fusion on the health data by using a hypergraph neural network; s3, constructing a disease risk prediction model based on the generative reasoning network, and introducing an anti-fact generation module to generate a health change hypothesis scene; s4, adaptively adjusting a personalized intervention strategy through a nested game model, and adjusting the intervention intensity in the first layer; s5, in the second layer, generating an optimized intervention combination strategy based on a Nash equilibrium driven multi-target adaptive game; s6, risk weighted scoring is carried out on the health data through a structured clustering algorithm, and a feedback event is generated; and S7, dynamically optimizing a disease prevention management strategy through a chained feedback mechanism, and managing node permission. According to the method, the self-adaptive algorithm is combined with the multi-layer game and the distributed anomaly detection, so that personalized disease prevention management and monitoring are realized.

The invention discloses a forest tree disease monitoring method and monitoring system, and relates to the technical field of forest tree monitoring, and the method comprises the steps: obtaining the regional topographic features of a forest tree monitoring region; local data calling is performed on the unmanned aerial vehicle, and a monitoring flight path is generated; in the flight process of the forest monitoring area, forest data acquisition is carried out, and multi-modal monitoring data is obtained; performing spatial alignment on the multi-modal monitoring data to obtain a plurality of area monitoring information graphs; carrying out disease area identification frame selection to obtain disease risk area distribution; carrying out disease diffusion prediction according to the disease type, and carrying out frame selection to obtain a hierarchical disease diffusion region. According to the method, the technical problems of single monitoring means, inaccurate disease area identification, difficulty in accurately predicting the disease diffusion trend and low monitoring efficiency in the existing forest disease monitoring technology are solved, and the technical effects of accurately monitoring the forest diseases, efficiently identifying the disease areas and accurately predicting the disease types and the diffusion trend are achieved.

The invention discloses a multi-agent large-model disease diagnosis knowledge reasoning system based on data dual drive, and relates to the technical field of artificial intelligence assisted medical diagnosis. The system collects patient symptom follow-up records, laboratory test results, observation diagnosis probabilities and expert diagnosis recommendation results in a multi-source manner; time sequence evolution characteristics are extracted, a time sequence diagnosis sensitivity coefficient is calculated, and early recognition of disease risks is achieved; in combination with anti-fact simulation and statistical reasoning, a causal consistency coefficient is obtained and is used for verifying causal reasonability of observation diagnosis and contrast results; based on agent group consensus analysis, calculating a game consistency coefficient for judging the credibility of a diagnosis conclusion; positioning and multi-level verification are carried out on abnormal reasoning steps and knowledge fragments, so that the reliability and safety of a result are guaranteed; continuous optimization of the diagnosis model is realized through a log analysis and knowledge backflow mechanism; according to the invention, the accuracy, interpretability and safety of disease diagnosis can be obviously improved.

Artificial intelligence enabled disease profiling is described. An electrocardiogram analysis module is configured to derive disease vectors for a plurality of diseases using electrocardiogram training data from both disease-negative and disease-positive individuals. A standardized input is generated, via a data preprocessor of the electrocardiogram analysis module, from an electrocardiogram recorded from an individual. The standardized input is encoded, by a deep learning autoencoder of the electrocardiogram analysis module, into an embedding, the embedding being a lower-dimensional latent space representation of features extracted from the standardized input. At least one disease risk score for the individual is generated, by a statistical modeling algorithm of the electrocardiogram analysis module, for the plurality of diseases based on the embedding and the disease vectors.

The invention relates to the technical field of auxiliary medical diagnosis, and particularly discloses a three-branch diagnosis and evaluation method and device based on multi-modal remote medical data. Multi-modal remote medical data of a patient is collected and input into three diagnosis and evaluation models for analysis, and if a diagnosis result is clear, whether the patient is ill or not is determined, and a corresponding result is directly output; and if the diagnosis result is not clear, further evaluating the disease risk of the patient and outputting an evaluation result. The method not only can effectively diagnose the illness state and the non-illness state of the patient, but also breaks through the limitation that a traditional intelligent diagnosis model cannot accurately evaluate the illness risk when the illness state of the patient is uncertain or incompletely meets the diagnosis standard.

The invention relates to the technical field of medical systems, in particular to a multi-disease fusion prediction method and system, and the method comprises the steps: obtaining a medical sequence code, and outputting a disease vector; obtaining a gating memory model, obtaining a doctor-seeing time interval, and configuring an additional time gate based on the doctor-seeing time interval and the disease vector; updating the gated memory model based on a time gate to obtain a health memory state and a context disease hiding state; aggregating the context disease hiding states to obtain aggregation states, calculating attention scores corresponding to the aggregation states, and outputting patient disease states based on the attention scores and the aggregation states; demographic statistical characteristics are obtained, and integration is carried out in combination with the disease state of the patient to obtain a combined vector; and mapping the combined vector to disease risk probability distribution to obtain a prediction result. The method and the device have the effect of predicting various diseases through the general data.

The invention provides an intelligent management system for chronic diseases and common diseases of old people. The intelligent management system comprises an old people comprehensive evaluation module, a multidisciplinary collaborative diagnosis and treatment module, a multi-medication risk prediction management module, an intelligent chronic disease follow-up diagnosis management module and a health science popularization knowledge base module, the senile comprehensive assessment module inputs the personal chronic disease map of the patient into a risk factor prediction model for prediction, and outputs a comprehensive assessment result; the multidisciplinary cooperative diagnosis and treatment module is used for the first diagnosis / receiving diagnosis doctor to coordinate multidisciplinary doctors to perform cooperative diagnosis and treatment; the multi-medication risk prediction management module is used for giving comprehensive diagnosis, multi-medication management and risk prediction to the patient through collaborative diagnosis and treatment; the chronic disease intelligent follow-up diagnosis management module carries out follow-up visit tracking, health risk prediction and review prompting on the patient; and the health science knowledge base module matches and pushes corresponding health education knowledge. The accuracy and timeliness of senile chronic disease co-disease risk assessment prediction and management are improved, and a good auxiliary effect is achieved on improvement of the life quality of patients.

The invention relates to the technical field of livestock and poultry epidemic disease management, and particularly discloses an intelligent livestock and poultry epidemic disease early warning and partitioned prevention and control management system and method, and the method comprises the steps: laying an integrated intelligent sensor network in a livestock and poultry farm, and collecting environment parameters, feeding management data and animal health data in real time through a low-power-consumption Internet of Things technology; transmitting the collected multi-dimensional data to a cloud platform by utilizing an edge computing technology; based on multi-dimensional data, a deep learning and multi-source data fusion analysis technology is adopted, a dynamic disease risk prediction model is constructed, and generalization ability of an adversarial generative network optimization model in a complex environment is introduced; by arranging the integrated intelligent sensor network, environmental parameters, feeding management data and animal health data in the livestock and poultry farm are comprehensively collected in real time, the early monitoring capability of epidemic diseases is remarkably improved, potential epidemic disease risks are found in time, and precious time is won for subsequent prevention and control work.

The invention discloses a chronic disease risk prediction method and system fusing a knowledge graph and a large language model, and the method comprises the following steps: obtaining a natural language problem related to a chronic disease, and carrying out the semantic analysis; according to the analysis content, hypothetical questions and answers related to chronic diseases are generated through a large language model, and key entities are extracted; mapping the key entities to corresponding nodes in a medical knowledge graph, exploring a semantic path and a causal relationship between the key entities, and constructing an inference chain pointing to potential disease risks from acquired information; introducing a fragment granularity sensing mechanism, performing fine granularity analysis on each fragment in the reasoning chain, and rearranging and optimizing a link sequence; and based on the optimized inference chain, converting the question and answer result into a structured diagnosis result for visual display. According to the method, the whole process from question asking to answer generation of the patient is optimized, the efficiency and accuracy of chronic disease risk prediction are effectively improved, and meanwhile, personalized health management service is provided for the patient.

The invention discloses an analysis and prediction system based on disease data, which belongs to the technical field of disease data analysis and comprises a cross-modal distillation data fusion module, a time domain causal chain analysis module, a multi-level disease risk prediction module, a disease critical state real-time monitoring module, a self-adaptive intervention strategy generation module and a clinical collaboration and feedback optimization module. A time decay factor and a smoothing coefficient are introduced through the time domain causal chain analysis module, the causal strength between variables is updated in real time, nonlinear changes of disease progression are adapted, and individual risk scores are generated through the multi-level disease risk prediction module in combination with the dynamic weight and the time decay factor of the causal atlas. A spatial smoothing function is introduced to suppress the influence of an abnormal value, group risks are quantified through a modal mean value and a standard deviation, individual weights are dynamically adjusted through a feedback mechanism, the long-term stability of prediction is improved, causal strength and feature importance are used for joint modeling, and the problem that a traditional statistical model ignores a potential causal path is avoided.

The invention relates to the technical field of medical and multi-modal large models, and discloses a disease prediction and auxiliary diagnosis system construction method and system based on a multi-modal large model. The method comprises the steps of report format conversion, data cleaning and table image-to-structured text conversion. Constructing a retrieval knowledge base to enhance the retrieval capability; precise cue word design and reasoning optimization are carried out; small sample learning and model fine tuning; the invention discloses a multi-modal large model integration and visualization system. According to the system, the accuracy problem of a traditional disease risk prediction method is effectively solved, and a more reliable auxiliary diagnosis tool is provided.

The invention discloses a disease risk prediction method and system based on big data analysis, and relates to the technical field of disease risk prediction. The method comprises the steps of obtaining multi-source data, and performing data cleaning, feature selection and data standardization processing; marking and labeling the collected data, constructing a disease risk prediction model through supervised learning, and performing real-time prediction and dynamic updating; the comprehensive disease risk value of the patient is calculated according to the multiple indexes, and abnormal early warning and personalized intervention including health suggestion, follow-up visit management and psychological support are carried out. The system comprises a data acquisition module, a screening module, a risk prediction module, an evaluation module, an abnormity early warning module and a control module. According to the system, through multi-source data acquisition and intelligent processing, accurate prediction and comprehensive evaluation of disease risks are realized, prediction accuracy is improved, personalization of health management is enhanced, and timely early warning and disease risk reduction are facilitated. According to the invention, the early discovery rate and prevention effect of diseases can be improved, and the medical cost is reduced.

The invention provides a gene detection and phenotype verification health detection method and system, and the method comprises the following steps: collecting multi-source data of a user, locking genetic variation highly related to disease and individual specificity through gene sequencing, and building an individualized genetic risk scoring system; wherein the multi-source data comprises gene sequencing and proteomics data of a user; performing gene phenotype coupling analysis, integrating multi-dimensional data of genes, proteins or phenotypes of the user, and constructing a health assessment system with high confidence; the intervention decision generation is used for generating a decision to intervene the user; through gene-phenotype coupling analysis, the disease risk prediction specificity of healthy people is improved, compared with a conventional health monitoring technology, risks are found in advance, the health behavior compliance rate of a user is increased, seamless connection with mainstream wearable equipment and an electronic medical record system is supported, and the utilization rate of data is increased.

The invention relates to the technical field of newborn medical big data risk assessment, and discloses a newborn disease risk assessment method based on big data analysis. The method comprises the following steps: acquiring an initial multi-modal health record of a newborn from a multi-heterogeneous medical data source; carrying out fusion analysis on the data, and constructing a risk assessment map driven by an event; starting a continuous risk tracking process, performing hierarchical risk scanning by using the map, and identifying a stable risk cluster, an evolution risk cluster and an isolated risk signal; deducing a hierarchical risk management and control plan based on the spatial distribution and time evolution mode of the risk cluster; at the end of each period, integrating latest clinical treatment feedback and monitoring readings, and carrying out reverse calibration on entity node states and relation edge strength in the atlas; and dynamically recombining a measure execution sequence and resource allocation in the plan according to the calibrated atlas. According to the invention, dynamic and continuous evaluation and self-adaptive intervention planning of the neonatal health risk are realized.

The invention discloses a case data analysis system based on a large model, and the system comprises a data collection module, a preprocessing module, a large model training and optimization module, an analysis module, a visualization and interaction module, a security and privacy protection module, a data preprocessing module with an innovative algorithm, a large model training module with an adaptive learning rate formula, and a disease risk prediction module with an evaluation formula. System functions and performance indexes are clear, equipment and medium requirements are clear, the steps of the method are complete, and high efficiency, accuracy and safety of case data analysis are guaranteed; the case data processing efficiency and accuracy are improved, and powerful support is provided for medical decision making. A large model is accurately analyzed, visual and convenient to check, and a security module guarantees data privacy. The medical quality can be improved, the treatment scheme can be optimized, and the method has important application value.

To solve a problem of supporting individual health management by photographing a face, using artificial intelligence to analyze a health condition, a risk of disease and a mental state, and proposing appropriate advice and an improvement method.SOLUTION: A system includes means for: photographing a face; using artificial intelligence to analyze a health condition, a risk of disease and a mental state; and proposing appropriate advice and an improvement method.SELECTED DRAWING: Figure 1

The invention discloses an animal quarantine method, and aims to solve the problems of high omission ratio, early warning lagging and lack of environment optimization of the traditional quarantine means. According to the method, multi-dimensional biological signals such as the heart rate, the respiratory rate, the body temperature and the acceleration of an animal are collected in real time through an ear tag type wearable device, and the anomaly detection precision is improved through a self-adaptive sampling mechanism; meanwhile, the environment monitoring module collects the temperature, humidity and ammonia gas concentration of the farm, and dynamically regulates and controls the temperature, humidity and ammonia gas concentration in case of abnormality. And the edge computing node adopts a long-short-term memory network to fuse and analyze data, outputs an epidemic disease risk probability, and optimizes the model through self-learning. The embodiment shows that the method can perform early warning and environment optimization in the incubation period, the quarantine time is shortened to 5-10 minutes per head, the omission ratio is lower than 5%, and an efficient and accurate epidemic disease prevention and control solution is provided for the breeding industry.