2181results about "Epidemiological alert systems" patented technology

A system for remotely monitoring and managing health statuses of a plurality of users includes software instructions storable on a memory device usable by a computing device, the software instructions causing a hardware processor of the computing device to receive a plurality of sets of health-related information from a plurality of mobile computing devices of a plurality of users. The health-related information includes physiological information derived from wearable devices of the users indicative of an onset of symptoms associated with an infection, contact tracing data, and diagnosis data. The hardware processor determines exposure levels based on at least the contact tracing data, and determines user-specific risk states based on physiological information, diagnosis data, and exposure levels.

The invention provides an infectious disease early warning and monitoring method and system based on big data and deep learning, and relates to the technical field of infectious disease early warning, and the method comprises the steps: obtaining infectious disease historical data, crowd movement track data, medical treatment data and environment monitoring data as a training data set; establishing a space-time propagation link diagram and carrying out dynamic segmentation coding to generate a propagation characteristic sequence; training a deep learning model based on the propagation feature sequence to predict an infectious disease propagation inflection point and a high-risk area propagation probability; and when the propagation probability exceeds a preset threshold value, generating early warning information and pushing the early warning information to a monitoring terminal. According to the invention, accurate prediction and timely early warning of the transmission trend of infectious diseases can be realized, and the epidemic prevention and control efficiency is improved.

The invention provides a hospital infection prevention and control related data online management method and system, and relates to the technical field of data processing, and the method comprises the steps: carrying out the dynamic graph calculation of an infection transmission path through a multi-modal deep learning model, and recognizing a high-risk department, equipment and personnel interaction mode, so as to obtain a real-time calculation result; generating a dynamic risk threshold based on the real-time calculation result, and generating a risk event vector; mapping the risk event vector into an executable supervision task list, and calculating a final task allocation path through a reinforcement learning algorithm; based on the final task distribution path, constructing a department-level risk portrait and a hospital-level risk topological graph; expected loss values of different prevention and control strategies are calculated through Monte Carlo simulation, and a department specific prevention and control scheme is generated; and when a multi-drug-resistant bacterium propagation risk is detected, automatically associating historical data of related departments, and calculating a final isolation region division scheme. The false alarm rate can be reduced.

The invention discloses an infectious disease multi-point triggering intelligent early warning method based on multi-source data fusion, and relates to the technical field of public health, and the method comprises the following steps: 1, collecting and integrating multi-source data, building data interfaces with all levels of medical institutions and grassroots public health units, collecting diagnosis and treatment data and abnormal health condition information, and storing the diagnosis and treatment data and abnormal health condition information; accessing online platform data, screening and removing invalid data, and constructing a regional diagnosis and treatment database to provide a comprehensive data basis for subsequent analysis; according to the invention, by integrating multi-source data, the accuracy and real-time performance of the infectious disease early warning system are significantly improved; according to the method, symptom description is preprocessed and vectorized by using a natural language processing technology, a similar mode between symptoms and examination data is deeply mined in combination with standardized examination data, so that the characteristics of infectious diseases are accurately extracted, a potential characteristic set of the infectious diseases is identified through clustering analysis and an association rule mining algorithm, and a characteristic knowledge base is constructed.

The invention discloses an animal epidemic disease cross-border transmission risk analysis method based on big data, and particularly relates to the technical field of data analysis. Through a multi-modal data fusion algorithm of deep learning, multi-source heterogeneous data is integrated in real time to form a unified data set, a cross-border propagation path model based on an infectious disease propagation model and an epidemiological theory is constructed, a propagation path of epidemic diseases is accurately simulated in combination with spatio-temporal information and propagation characteristics, and on the basis, the propagation path of the epidemic diseases is accurately simulated. A machine learning algorithm is utilized to analyze the fusion degree of cross-border propagation data between different regions, and based on a multi-source data fusion evaluation result, a high-risk region and a propagation speed are predicted in real time, so that potential cross-border propagation risks are evaluated, and targeted prevention and control intervention measures are proposed; according to the method, the problems of data isomerism, timeliness difference and fusion processing in the prior art are effectively solved, the accuracy, the real-time performance and the scientificity of epidemic disease cross-border transmission risk analysis are improved, and accurate support is provided for epidemic prevention and control decisions.

A system having a processor and memory coupled to the processor is provided. The memory stores a central testing database having a plurality of test configuration profiles and a central patient database. A first communications interface coupled to the processor receives a request from a test device for at least one of the plurality of test configuration profiles and sends the requested test configuration profile to the test device. The first communications interface also receives a test result collection instance from the test device. The processor is configured to store the test result collection instance in the central patient database.

The invention relates to the technical field of health management, and provides a health management system and method.According to the health management system, in the prevention and control of infectious diseases, real-time data drives model and strategy updating, for example, high-risk area disinfection and personnel protection are rapidly enhanced and propagation is restrained when an epidemic situation suddenly occurs; with ease of an epidemic situation and precise reduction of prevention and control intensity, through continuous data monitoring and analysis, the spreading change trend of infectious diseases in a hospital can be caught sensitively, prevention and control measures can be enhanced rapidly, high-frequency disinfection is achieved, personnel protection is upgraded, and resources are allocated emergently in the rapid spreading stage of the early stage of the epidemic situation, so that epidemic situation spreading is effectively restrained; and as the epidemic situation enters a stable or slowed-down stage, management and control can be accurately and moderately relaxed, resources are reasonably allocated, excessive investment is avoided, the dynamic adjustment mechanism always ensures that the management strategy is closely matched with the actual epidemic situation, and the flexibility and accuracy of a hospital for coping with infectious diseases are greatly improved.

The invention discloses a livestock epidemic disease early warning method and system based on big data analysis, and the method comprises the steps: generating a preliminary epidemic situation diffusion path through employing the farm distribution and traffic network data of a geographic information system in combination with a space-time propagation model, and carrying out the dynamic adjustment according to the real-time updated data; predicting a high-risk area by adopting a multi-source data fusion early warning model; the strain mutation risk is judged by analyzing the relevance between pathogen genomes and environmental parameters; deploying a deep learning model at an edge computing node, analyzing animal husbandry behaviors and voiceprint data in real time, and evaluating the animal husbandry health state; and integrating mutation risk analysis and animal husbandry health assessment results to generate a comprehensive epidemic situation early warning report. Through multi-dimensional data analysis and edge intelligent calculation, accurate early warning of animal epidemic situations is realized, and a scientific basis is provided for epidemic situation prevention and control.

The invention provides a three-dimensional simulation and infection scene decision optimization system and method based on digital twinning, and relates to the technical field of intelligent medical treatment and digital twinning crossing technologies. The three-dimensional simulation and infection scene decision optimization system and method based on digital twinborn comprises the following modules: a data acquisition module, a path optimization module, a risk analysis module, a disinfection scheduling module and a collaborative prevention and control module, by arranging an air microorganism sampler, ultra-wideband positioning equipment and a temperature and humidity sensor, pathogen concentration, personnel trajectory and environmental parameters are collected in real time. Through a multi-source sensor fusion algorithm and a space-time attention mechanism, weight fusion and noise filtering are carried out on pathogen concentration, personnel tracks and environmental parameters in a hospital environment, the problems of information isolation and noise interference in traditional data collection are solved, and the space-time relevance of multi-modal data is remarkably enhanced.

The invention relates to the technical field of epidemic situation transmission prediction, in particular to an infectious disease transmission prediction method and system based on a dynamic space-time diagram neural network, and the method comprises the following steps: extracting space-time features from historical case data through a dynamic space-time diagram learning module, and generating an adjacent matrix representing an infectious disease transmission network; predicting a future epidemic situation development trend based on the adjacent matrix through an infectious disease dynamic modeling module; wherein the dynamic space-time diagram learning module comprises a time sequence convolutional network, a multi-head self-attention mechanism and a long short-term memory network; the infectious disease dynamic modeling module comprises a time sequence convolutional network, a graph neural network and a gating circulation unit.According to the infectious disease transmission prediction method and system based on the dynamic space-time graph neural network, by introducing virtual nodes and a dynamic graph structure, the response ability to external factors is improved, and the prediction accuracy is improved; data missing and external influence factors can be effectively processed, and a real propagation mode can be embodied from the generated propagation network.

The invention discloses an oral cancer multi-task diagnosis system based on a Raman spectrum, and belongs to the technical field of biomedical detection. Comprising an acquisition module and an oral cancer multi-task diagnosis module, and the acquisition module is used for acquiring Raman spectrum data; the oral cancer multi-task diagnosis module is used for processing the Raman spectrum data through a trained oral cancer multi-task diagnosis model so as to generate and output a tumor staging result, a lymph node staging result and a histological staging result; the oral cancer multi-task diagnosis model comprises a bidirectional Mama branch and a residual attention fusion branch, local feature information in the Raman spectrum data is extracted by using the residual attention fusion branch, and multi-scale features are generated by fusing global feature information extracted by the bidirectional Mama branch. Feature information in Raman spectrum data can be effectively extracted, the accuracy of oral cancer diagnosis is improved, and multi-task diagnosis of oral cancer is realized; the problems that multi-task common diagnosis cannot be realized and the diagnosis accuracy needs to be improved in the prior art are solved.

The invention relates to the technical field of public health safety, in particular to an infectious disease transmission path prediction method and device and a medium, and the method comprises the following steps: extracting an initial tetrad of an event from infectious disease outbreak data, mapping the initial tetrad into a numerical value, and obtaining a numerical value tetrad; dividing the numerical tetrad according to time to obtain a time sequence knowledge graph; performing initialization embedding representation on entities and relationships in the time sequence knowledge graph; constructing a relational graph attention network, and learning structure information in the time sequence knowledge graph to obtain entity embedding and relation embedding; calling a gating circulation unit, and extracting time sequence information of the time sequence knowledge graph; and combining the structure information with the time sequence information by adopting a decoder, generating a prediction result, and realizing infectious disease transmission path prediction based on the prediction result. According to the method, the transmission path of the infectious diseases is predicted by adopting the time sequence knowledge graph, and compared with a traditional public health prevention method, the method has higher dynamic nature and multi-dimensional correlation analysis capability.

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 an adverse drug reaction event recognition method and system based on multivariate knowledge mixed retrieval enhancement, and the method comprises the steps: extracting drug entities from a to-be-recognized clinical disease course record, segmenting the disease course record, and obtaining a drug entity set and a sentence set; for each extracted drug entity, retrieving drug concept knowledge having a hyponymy relationship with the drug entity and drug adverse reaction knowledge having an adverse reaction relationship with the drug entity in a pre-constructed multivariate knowledge base; for each sentence obtained through segmentation, searching suspected adverse drug reaction events meeting a first similarity requirement and drug field text knowledge meeting a second similarity requirement in a pre-constructed multivariate knowledge base; and finally, calling a large language model, taking all the retrieved knowledge as reference knowledge, and identifying the adverse drug reaction event from the to-be-identified clinical disease course record. According to the invention, the accuracy and reliability of adverse drug reaction event identification can be improved.

The invention relates to a respiratory infectious disease transmission risk assessment method based on a multi-person dynamic scene. The method sequentially comprises the steps of building models of a risk assessment object and a research area, setting analysis parameters according to infection source characteristics, setting crowd movement parameters, carrying out numerical simulation aiming at diffusion of infectious substances, calculating a personnel exposure level and carrying out infectious disease infection risk comprehensive assessment. Building models of the risk assessment object and the research area, to be specific, building a calculation area of a multi-person dynamic scene where the respiratory infectious disease occurs, and performing calculation grid division on the area to build a calculation model; analysis parameters are set according to characteristics of infection sources, release characteristics of the infection substances from different areas of the respiratory system of a patient are considered, different infection source types and exhalation characteristics are set according to different scenes, then the process of exhalation of the infection substances and aerodynamic characteristics of the process are simulated, and a result with universality is constructed to serve as an input module.

The invention discloses a medical insurance cost prediction and optimization method based on big data analysis, and belongs to the technical field of medical data processing. The method comprises the following steps: constructing a multi-source data acquisition module, and integrating data of an HIS system, a medical insurance platform and wearable equipment by using an FHIR interface and a block chain; a BERT-BiLSTM-CRF model is adopted to fuse multi-modal data, a dynamic model group containing Transform anomaly detection and LSTM-ARIMA time sequence prediction is established, and the prediction error rate is reduced to 12% (reduced by 28% compared with that of a traditional method); a multi-objective optimization system is designed, medical quality and cost control are balanced based on an improved NSGA-II algorithm, the cost of a single disease is reduced by 18%-25%, and the quality standard reaching rate exceeds 95%; a hybrid cloud and homomorphic encryption module is deployed, and the data leakage risk is reduced by 90%; a policy sandbox system is constructed, medical insurance policy simulation deduction is supported, and the response time is shortened to 72 hours. According to the invention, the problems of data islands, low prediction precision and privacy risks are solved, and the whole-process intelligent management and control of medical insurance fees is realized.

The invention relates to the technical field of livestock medicine management and control, in particular to a medicine delivery system based on livestock veterinary epidemic prevention, and the system comprises a data collection and fluctuation rate analysis module which is used for collecting livestock farm environment data and animal health data, aggregating the environment data and the health data layer by layer in a recursive layering manner, and obtaining a multi-level livestock farm data set, calculating the change range of the animal health data in the multi-level data set, determining the range of volatility, and generating abnormal fluctuation mark data. According to the method and the system, the environmental data and the animal health parameters of the livestock farm are subjected to recursive hierarchical aggregation, so that the fluctuation condition of the data is comprehensively monitored. By marking the high fluctuation item with the abnormal fluctuation range exceeding the safety threshold, the potential epidemic disease risk can be quickly identified. By referring to influence factors of high fluctuation items on epidemic disease occurrence and dynamically adjusting risk values, the system can reflect potential influences of changes of environment and health conditions on epidemic disease transmission in real time.

The invention relates to the technical field of network public opinion evolution, in particular to an emergency network public opinion evolution prediction and early warning method based on an SEIR model, and the method comprises the following steps: S1, individual state division: dividing an individual into a plurality of states; s2, an individual state transition process: finally entering a shift-out state R; s3, establishing a network public opinion information diffusion model: calculating a network public opinion diffusion range and a network public opinion diffusion speed; s4, a network public opinion information diffusion process in the complex social network: simulating a public opinion diffusion process; s5, building an emergency network public opinion early warning model based on SPN: calculating an early warning probability; s6, early warning index risk rating: dividing early warning risk grades; s7, setting an early warning threshold value and a rating rule: starting early warnings of different levels according to the rating rule; according to the invention, the development situation of the network public opinion can be described more accurately.

The invention discloses a children respiratory tract infection diagnosis method and system based on multi-modal data fusion, and relates to the technical field of respiratory tract infection diagnosis. Distributing the priority of each modal data according to the change trend of the physiological data monitored in real time; generating a comprehensive diagnosis characteristic index of the child health state; through time sequence analysis, the fused feature indexes are monitored in real time, when an abnormal mode is detected, early warning information is automatically generated, and diagnosis result suggestions are sent through a medical staff or parent interface; the comprehensive diagnosis characteristic indexes are optimized according to comparison between a diagnosis result and an actual situation; and storing each diagnosis data and early warning record in a children exclusive health database for long-term trend analysis and future diagnosis reference. According to the invention, the real-time performance and accuracy of children respiratory tract infection diagnosis are improved, the disease monitoring and health management capabilities are enhanced, and a more scientific and comprehensive health protection means is provided for children.

The embodiment of the invention discloses a new infectious disease monitoring and early warning method and device based on multi-point triggering. The method comprises the following steps: acquiring clinical data of a patient in HIS, LIS, PACS, operation and electronic medical records from a medical institution; setting a screening strategy of new infectious diseases; identifying suspected cases according to the clinical data of the patient; identifying abnormal diagnosis and treatment behaviors of the patient from clinical data of the patient based on big data analysis; determining a suspected case level according to the suspected case and the abnormal diagnosis and treatment behavior; obtaining relevant survey data of suspected cases; judging whether the suspected cases have aggregated cases or not according to the related survey data of the suspected cases; and if the suspected cases have aggregated cases, determining an infectious disease transmission route, and performing case management. By implementing the method provided by the embodiment of the invention, real-time monitoring and early warning of infectious disease epidemic situations and other public health events can be realized, and the processing efficiency and accuracy of new infectious diseases are improved.

The invention discloses a livestock feed proportioning method based on livestock growth, and relates to the technical field of feed proportioning, and the livestock feed proportioning method comprises the following steps: collecting the body temperature and motion track data and environmental temperature and humidity data of livestock individuals, monitoring the concentration of amino acids in digestive tracts, preprocessing the collected data, and generating a standardized biological data set; analyzing the compensation amount of vitamins and trace elements according to the metabolic fingerprint spectrum, and constructing a multi-objective optimization function in combination with an individual growth curve; solving the multi-objective optimization function through a quantum annealing algorithm, and matching local raw material inventory data of block chain evidence storage to generate an optimization strategy; and executing the optimization strategy and detecting the mixing uniformity, and when the mixing uniformity index exceeds a preset mixing uniformity threshold, starting the compensation device to adjust the optimization strategy. According to the invention, through a cooperation mechanism of dynamic nutritional requirement identification and multi-constraint optimization, the precise regulation and control capability of livestock breeding is significantly improved.

The invention relates to the technical field of virtual simulation, and particularly discloses a sudden major respiratory infectious disease on-site epidemiological investigation virtual simulation experiment system which comprises a virtual scene management module, a data management module, a disease transmission simulation module, an epidemiological analysis module, a virtual on-site interaction module, a learning support module and an analysis and evaluation module. The virtual scene management module is used for creating and managing a simulation experiment environment and simulating an epidemic background and a development process of sudden major respiratory infectious diseases; the data management module is used for simulating the process of collecting case information, environment information and social contact data when students perform on-site epidemiological investigation; according to the system, big data analysis, the GIS technology and an AI intelligent analysis tool are integrated, a disease transmission path and a development trend are simulated, and virtual simulation not only helps students to master how to support a decision through scientific data, but also can improve the emergency response capability and the comprehensive analysis capability of the students in a complex epidemic situation.

Provided is a process, including: obtaining, with a computer system, a next geolocation to be visited by a user or at which the user has arrived; obtaining, with the computer system, a geolocation-pathogen-risk score of the next geolocation; determining, with the computer system, a topic of message based on the geolocation-pathogen-risk score; determining, with the computer system, by a natural-language-text generation model, natural language text within the topic of the message; and causing, with the computer system, the natural language text of the message to be presented to the user.

The invention relates to the technical field of medical or health data information, in particular to an intelligent health old-age care safety monitoring system, which comprises an old-age information acquisition module for acquiring face and basic information; the body health monitoring module predicts health parameters through LSTM, and if the health parameters exceed a threshold value or change abnormally, one is added to the alarm frequency; the psychological health monitoring module uses a StyleGAN to generate an enhanced data set and performs emotion classification through a convolutional neural network, and if the emotion deviates from a baseline, one is added to the alarm frequency; the tumble posture monitoring module judges tumble through posture estimation, and if tumble is detected, one is added to the alarm frequency; the position anomaly monitoring module identifies abnormal movement or residence, and if an anomaly is detected, the number of alarms is increased by one. And guardians and medical staff can obtain alarm information and monitoring reports in time according to the alarm times, so that the efficiency of monitoring the health and safety of the elderly is improved.

The invention discloses a large model hospital infection judgment method and system based on an intelligent agent, and relates to the technical field of risk assessment, and the method comprises the steps: obtaining a target physiological feature of a target patient; the target physiological feature at least comprises one of a disease course description, an examination result and an inspection result of the target patient; retrieving a hospital infection knowledge map according to the target physiological features to obtain a corresponding target sub-map; wherein the hospital infection knowledge graph is constructed according to a hospital infection diagnosis standard document; generating a relation vector set according to the target sub-atlas to obtain a target relation vector set; and inputting the target physiological features and the target relationship vector set into a pre-trained hospital infection prediction model to obtain a target infection outcome, thereby reducing the data set screening difficulty of the hospital infection prediction model, and improving the prediction accuracy of hospital infection prediction.

The invention provides a hospital infection propagation path tracing method based on multi-source data fusion, and belongs to the technical field of infection propagation paths in hospitals, and the method comprises the steps: building a data set through collecting hospital multi-source infection monitoring data, constructing a multi-layer network propagation model comprising physical contact, spatial proximity and time co-occurrence, and carrying out the tracing of the multi-source infection propagation path. Community detection is carried out by using a random block model, infection propagation path identification is converted into graph coloring problem solving, an individual behavior heterogeneity model is established, and SEIR model propagation parameters are corrected by fusing individual characteristic parameters such as the immune state of a patient and the protection level of medical staff; a propagation situation awareness model based on a hybrid expert mechanism is adopted to optimize propagation parameter estimation, and a propagation parameter weight coefficient is obtained through a game optimization mechanism and Nash equilibrium solution for iterative optimization. And finally, outputting a high-confidence infection propagation path sequence and a propagation source positioning result by adopting a propagation path backtracking algorithm in combination with spatial-temporal correlation analysis.

The invention relates to the technical field of model construction, in particular to a construction method of a disease feature recognition and evaluation model based on the Internet of Things. The method comprises the following steps that multi-modal physiological data of a patient are collected in real time through Internet of Things medical equipment, data preprocessing is conducted on the multi-modal physiological data of the patient, standard patient physiological data are generated, and the standard patient physiological data are stored in a distributed database; extracting gene sequencing data based on the biological sample library; performing global health risk factor perception on the standard patient physiological data to generate user level health risk perception data; and carrying out feature fusion on the user level health hazard perception data and the gene sequencing data to construct a disease feature matrix containing a space-time dimension. Through multi-modal data fusion, spatio-temporal evolution modeling, deep learning architecture and personalized risk assessment, the precision and timeliness of disease feature recognition assessment model construction are improved.

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 infectious disease prevention and control, in particular to an infectious disease epidemic situation space-time aggregation analysis method and system, and the method comprises the steps: constructing a case growth trajectory propagation model and an epidemic situation propagation and diffusion model through a data warehouse; predicting an epidemic situation diffusion path and a time-space coverage change graph by using the case growth trajectory propagation model and the epidemic situation propagation and diffusion model; and fusing the predicted epidemic situation diffusion path and the time-space coverage change map with the spatial data of the key public place, and calculating to obtain an epidemic situation influence index of the key public place. By constructing the case growth trajectory propagation model and the epidemic situation propagation and diffusion model, the diffusion path and the space-time coverage change of the epidemic situation can be predicted more accurately, a more scientific basis is provided for prevention and control work, and according to the predicted epidemic situation diffusion condition and the key public occasion epidemic situation influence index, the prediction of the epidemic situation is realized. Medical resources, manpower, materials and the like can be configured more reasonably, and the prevention and control efficiency is improved.

The invention relates to the technical field of crop disease and insect pest monitoring, and particularly provides a crop disease and insect pest intelligent monitoring system based on image recognition and machine learning, and the system comprises a reference model generation subsystem which obtains a continuous state flow in a crop growth period through a multispectral imaging array; performing micro-variation field comparison on the real-time image of the continuous state flow and the ideal form reference vector, and outputting a biological stress difference chart; the mutual exclusion association engine subsystem purifies a pathological response map, inputs the pathological response map into a pathological characteristic dissociation network, and outputs an environmental immune type disease mark set; and the disease source trajectory deduction subsystem diffuses a power chain through organisms on the basis of the initial infection focus coordinates in combination with a real-time wind direction vector field and a plant density topological graph. According to the method, the problem of high false positive rate in a complex farmland scene is solved through a dual denoising mechanism of growth rhythm decoupling and environmental interference stripping; and finally, monitoring dimension jump from single-point identification to group prevention and control is realized.