144 results about "Communicable disease" patented technology

The invention discloses a sow vaccine injection method based on deep learning and machine vision. The sow vaccine injection method comprises the following steps that 1, an AGV chassis is controlled to arrive at a work station according to an instruction; 2, visually recognizing the posture of the sow to be injected, and if the recognition result is that the sow to be injected is in a non-standing posture, driving up the sow to be injected with manual assistance; if the recognition result is a standing posture, visually guiding the mechanical arm to move to a photographing position; 3, the RGBD camera shoots in real time to obtain the injection position and distance of the sow to be injected, and the mechanical arm moves to position the injection clamp to the pre-injection position; 4, injection is completed, and an injection result is recorded; the invention further discloses a sow vaccine injection system based on deep learning and machine vision. Manual operation is replaced by mechanical operation, automation of the vaccine injection process is achieved, direct contact between personnel and sows is reduced, and the risk of infectious disease transmission is reduced.

The invention discloses an interpretable medical big data dynamic security protection method. The method relates to the technical field of security technology digital processing, and comprises the following steps: collecting and processing infectious disease patient data; performing classification and encryption processing; monitoring the encrypted data in real time; obtaining an infectious disease patient identity information data encryption security assessment index, an infectious disease patient movement path key management security assessment index and an infectious disease patient electronic medical record data leakage risk assessment index; and comprehensively analyzing, evaluating and adjusting. According to the method, medical big data dynamic safety protection performance evaluation analysis is performed on the encrypted data of the infectious disease patient, and the medical big data dynamic safety is adjusted according to the comprehensive evaluation result, so that the interpretable medical big data dynamic safety protection dynamic capability is improved; the problem that in the prior art, the explainable medical big data dynamic security protection dynamic capability is insufficient is solved.

The invention provides a network information tracing method and device based on a graph neural network, and belongs to the technical field of information data processing, and the method comprises the steps: obtaining original infectious disease data; performing first linear transformation on the original infectious disease data, performing node embedding according to a first linear transformation result and the adjacent matrix, and performing time dynamic embedding according to a node embedding result to obtain embedded data; inputting the embedded data into the residual image convolutional network to obtain features of the embedded data; and inputting the characteristics of the embedded data into a bidirectional gating loop unit to obtain a first result, and taking the first result as a network information traceability result. According to the method, only the network structure of the original infectious disease data and the infection data at some moments are needed, the node state at each moment does not need to be traced, and the traceability accuracy is improved.

The invention discloses a crowd evacuation simulation method and system based on panic emotion propagation and hazard source influence, and the method comprises the steps: setting a simulation scene, and modifying the infection rate of infectious disease dynamics into a state transition function affected by an emotion threshold value; after simulation is started, calculating a state transfer function for each individual in the crowd, and establishing a panic emotion propagation model; setting danger repulsive force exclusive to a danger source, establishing a social force model based on the panic emotion propagation model and the danger repulsive force, and simulating crowd evacuation based on the social force model. According to the method, the infection rate of infectious disease dynamics is changed into a dynamic parameter influenced by an emotion threshold value from a static parameter, the dynamic parameter is used as a state transition function of individuals, the function is used as a bridge, a social force model and panic emotion propagation are fused together, individual differences in crowd evacuation are deeply reflected, and the social force model and the panic emotion propagation are integrated. Therefore, simulation of crowd evacuation is realized.

The invention discloses a hospital infectious disease recognition and early warning method and system based on big data and artificial intelligence, and relates to the technical field of infectious disease recognition and early warning, and the method comprises a data collection module which is used for collecting multi-source data, and the multi-source data comprises information data of a hospital, environment sensor data and migrant bird migration track data; the data processing module distributes data source weights for multi-source data, and the intelligent early warning module is used for generating a self-adaptive early warning threshold value according to standardized data and historical data and triggering graded early warning according to the generated self-adaptive early warning threshold value; a migrant bird migration track and environmental sensor data are fused, for example, a migrant bird migration abnormal track and environmental virus survival rate parameters are linked, a hospital information system and non-medical data are in butt joint through a standardized interface, and a dynamic time warping algorithm is adopted to calibrate time axis offset of multi-source data, so that the migrant bird migration accuracy is improved. The problem that sensor data and medical record time sequences are inconsistent is solved.

The invention provides a data analysis processing method and system for a hospital HIS system, and belongs to the technical field of energy storage devices, and the method specifically comprises the steps: determining a coincidence time period according to the coincidence condition of a quality inspection analysis recommendation time period of medical records of each hospital department and a time period of a doctor of the hospital department with infectious disease diagnosis and treatment, and when the quality inspection analysis recommendation time periods of the hospital departments belong to the overlapping time periods, determining the quality inspection processing time periods of the medical records of the hospital departments based on the distribution data and the registration data of the hospital departments in different quality inspection analysis recommendation time periods in the overlapping time periods, thereby improving the quality inspection processing efficiency.

The invention provides a malicious instruction cross-space propagation risk analysis method. The method comprises the following steps: after establishing an active power distribution network information physical system coupling network structure, researching the process of a malicious instruction in a coupling network and establishing a cross-space propagation risk quantitative evaluation model by utilizing a prior probability theory, a posterior probability theory and a minimum load loss rate theory; according to the method, the relation between information nodes and physical nodes after invasion and the spreading process of risk propagation are considered, finally, three likelihood functions of risk propagation after malicious instruction invasion are approximated by using the Bayesian theory, and a quantitative value is calculated, so that an index is provided for safe and stable operation of the active power distribution network, and the risk propagation efficiency is improved. And the directivity and the interdependence of the link between the physical layer and the information layer are considered, and the risk propagation mechanism between the information layer and the physical layer is represented by using an infectious disease-based infection mode, so that the comprehensiveness and the accuracy of malicious instruction cross-space propagation risk analysis are improved.

The invention discloses a non-contact vital sign monitoring system and method based on a mechanical programmable metasurface, and belongs to the technical field of intelligent medical monitoring. According to the system, 360-degree continuous phase regulation and control are achieved through the mechanical programmable metasurface, the three-dimensional dynamic beam focusing technology and the improved variational mode decomposition algorithm are combined, limb movement noise is effectively restrained, and high-precision multi-person non-contact respiration monitoring is achieved. The system adopts a modular detachable metasurface architecture, supports the fast reconstruction of wavefront coding, and can reduce the average error of respiratory rate monitoring of a human body in a double-arm swinging / walking state to 0.5 times / minute without continuous power supply. Experiments show that when the system is used for synchronously monitoring breathing signals of the motion state of two persons in a complex indoor environment, the signal-to-noise is improved by 14 dB or above compared with that of a traditional scheme, and the manufacturing cost is reduced by 77.5%. The method is suitable for the fields of intelligent old-age care, infectious disease isolation monitoring, sleep breathing disorder screening and the like, and the clinical requirements of medical privacy protection and long-term health monitoring are met.

The invention relates to the technical field of public health information, in particular to an infectious disease transmission path generation and early warning method and system based on big data, and the method comprises the steps: collecting and cleaning multi-modal data, and obtaining a pulse event flow through neuromorphic coding; and constructing a variable order hyperedge graph in a second-level window, extracting loop life to adjust diffusion of the cellular automaton, and iteratively generating an infection prediction field. And then splicing the hyperedge graph, the prediction field and the topological fingerprint into a conditional tensor, and inputting the conditional tensor into a conditional score diffusion network to generate a future propagation event sequence. And constructing a tensor network according to the sequence, mapping the tensor network into Isin Hamiltonian, quickly selecting an intervention node through an optical delay ring reservoir and gradient descent, and issuing a ventilation or purification instruction. And after a virus load difference value before and after intervention is normalized, a tensor network, a diffusion network and a cellular automaton are synchronously updated to form a self-adaptive closed loop, so that a peak time error can be shortened, and the recall rate of a high-risk region is improved.

Exemplary embodiments of the present disclosure are directed towards artificial intelligence-based system for automated preventative health screening and data-driven health risk analysis. The system integrates Generative AI with the CDC's Syndemic Model to enable anonymous, stigma-free health screening and care linkage. The system leverages oral health as neutral entry point to assess interconnected risks across sexual, mental, and behavioral health domains. Using Large Language Models trained on validated sources and AI-based image analysis of oral and skin abnormalities, system computes “Chance screening Scores” to classify risks for communicable (e.g., HIV, STIs, Mpox) and non-communicable diseases (e.g., diabetes, hypertension). Accessible through mobile apps / QR codes without requiring login, system generates Unique IDs for test kits, telehealth services, and rewards. Features include actionable recommendations, gamified Stigma Meter, geographic insights, language translation, and data control options. This innovative solution fosters informed decision-making, reduces stigma, and empowers users to manage their health confidentially and effectively.

The invention discloses an SBSIR model construction method and an IMGPE algorithm for identifying network node influence in the field of Internet social network modeling analysis. The method is improved on the basis of a traditional SIR. Different from a traditional infectious disease model, the SBSIR model not only considers a direct interaction relationship between users, but also introduces interest similarity as a spreading mechanism of information spreading, so that the SBSIR model is more in line with an actual spreading rule of an interest social network compared with the traditional infectious disease model. The IMGPE algorithm reconstructs the network based on interaction and interest features among users in the social network, and identifies nodes with influence in the interested social network by measuring local features and global features of the network and position features among the users.

A medical information processing apparatus according to an aspect example includes a data receiving unit and a data processor. The data receiving unit is configured to receive ocular image data and ocular blood flow data acquired from a patient. The data processor is configured to process at least the ocular image data and the ocular blood flow data received by the data receiving unit to detect a conditional change in a circulatory system associated with an infectious disease.

A molecular sensing device and molecular sensing method that are based on quantum plexcitonic states on strongly coupled nanophotonic systems are disclosed. The molecular sensing device includes a metasurface, a layer of a semiconducting and / or dielectric material disposed onto a layer of electrically conducting material, a metal nanohole array disposed onto the metasurface, a biological recognition element disposed onto a surface of the metal nanohole array, and where the surface of the metal nanohole array is configured to receive a bioanalyte and a plurality of quantum emitters. The molecular sensing device can be a mixture of quantum emitters, comprising gold, having more than one aspect ratio. The molecular sensing device may include a spectroscopic instrument, including a portable detector. The one or more biological recognition elements are configured to recognize a communicable disease in response to the presence of a specific biological recognition element.

A device (Fig. 2, 2) comprising a sonication chamber (Fig. 11, 48), an ultrasonic transducer (Fig. 11, 49) configured to generate ultrasonic waves to lyse cells in a sample fluid within the chamber, a

The invention relates to the technical field of protective covers, and discloses a multipurpose protective cover for infectious disease nursing, which comprises a mask body, an oxygen supply tube, an atomization tube and a collection device detachably connected with one end, far away from the mask body, of the catheter, a mounting cylinder is fixedly connected to the mask body, and a loudspeaker is mounted in the mounting cylinder. The outer wall of the loudspeaker is provided with a limiting assembly used for fixing the loudspeaker in the mounting cylinder, and a plurality of through holes are formed in the position, located in the mounting cylinder, of the mask body. According to the multipurpose protective cover for infectious disease nursing, when the protective cover is worn, the bandage is fixed to the fixing block through the pressing assembly, so that the protective mask can be suitable for patients with different head circumferences, through the loudspeaker detachably connected to the mask body, communication between the patients and medical staff can be facilitated, and when the mask body cannot be used continuously, the protective cover is convenient to use. The loudspeaker can be detached and installed on a new mask body, so that the loudspeaker can be recycled.

The present invention discloses a method and system for tracing and warning the source of infectious diseases based on privacy protection and anti-attack, which belongs to the field of information protection and attack detection. The method collects a user's contact flow data time series; determines a normal contact flow data time series based on the mutation of the contact flow data time series at a time switching node, injects noise into the normal contact flow data time series to form a pseudo-labeled flow time series, and trains a comparison model. The normal contact flow data time series is determined based on the trained comparison model; constructs a traceability graph based on the normal contact flow data time series, and adopts an iterative aggregation method to aggregate the multi-hop connections between the nodes in the traceability graph to obtain a contact knowledge graph of direct contact and indirect contact. According to the contact knowledge graph, all users' contact information is obtained as training data, and a prediction model is trained based on the training data. The trained model outputs the user's infection probability.

The invention relates to the technical field of fly infectious disease prevention and control, in particular to an intelligent fly monitoring device, comprising: a box body comprising a plurality of wall parts defining a containing cavity, the plurality of wall parts comprising a front wall, a rear wall and a bottom wall which are oppositely arranged, and the bottom wall being provided with a hole; the trapping module comprises a fly sticking plate and a bait disc, the fly sticking plate is arranged on the front wall in the containing cavity, the bait disc is located at the preset distance position of the lower side of the bottom wall, and a preset angle is formed between the connecting line between the center of the bait disc and the edge of the hole and the horizontal plane where the bait disc is located; the electric control box is arranged on the rear wall in the containing cavity, the image collecting module is configured to collect fly monitoring images on the fly sticking plate, and the intelligent control module is configured to collect and preprocess the fly monitoring images and conduct recognition, number statistics and risk early warning on fly types in the fly monitoring images. According to the method, the problem of fly missing detection in a complex environment can be effectively solved, the insect pest early warning timeliness is greatly improved, and the monitoring accuracy is high.

The invention provides a vaccine anti-propagation protection effect evaluation method and device and electronic equipment. The method comprises the following steps: determining the probability # imgabs1 # that a # imgabs0 # primary case causes 0 filial generation case number due to the influence of enhanced control measures; determining the negative binomial probability distribution of the offspring case number # imgabs3 # generated by the # imgabs2 # primary case; determining a zero-expansion negative binomial distribution probability quality function based on the negative binomial probability distribution of the probability # imgabs5 # of the 0 offspring case number generated by the # imgabs4 # primary case due to the influence of the enhanced control measures and the negative binomial probability distribution of the offspring case number # imgabs7 # generated by the # imgabs6 # primary case; based on the zero-expansion negative binomial distribution probability quality function, determining the vaccine anti-propagation effectiveness; therefore, the anti-spreading capability of the vaccine can be estimated, indispensable scientific basis and technical support are provided for infectious disease prevention and treatment work, and public health and social safety are better guaranteed.

The utility model relates to medical instrument technical field especially is involved in a kind of anti-stab needle, including needle body and the protective shell of outer sleeve in the outside of needle body, the needle body includes needle tip, needle seat and needle tube connected in turn, the side of needle seat is also equipped with needle wing, the inside of protective shell is equipped with the passageway for needle body reciprocating sliding, and the side wall of protective shell is also equipped with the notch for needle wing to penetrate, the inner wall of passageway is equipped with first limit piece and second limit piece, when needle tip extends to the outside of passageway first limit piece is limited with needle seat cooperation, when needle tip is retracted to the inside of passageway second limit piece is limited with needle seat cooperation, the utility model protects needle head by protective shell, eliminates needle tip of needle head stab injury, reduces the probability of infection infectious disease by needle tip stab injury, with safe and reliable advantage.

An air flow delivery system for delivering conditioned air to an enclosure housing infectious disease patients, includes: (a) an air intake pipe having a first bidirectional valve therein, wherein when the bidirectional valve is in an open state, ambient air enters the enclosure; (b) an air handler, the air handler coupled to the air intake pipe; and (c) an air supply duct installed within at least one wall of the enclosure, wherein the air supply duct delivers conditioned air from the air handler to one or more air supply vents positioned at or near a bottom surface of the enclosure. The enclosure is selected from a group consisting of a pavilion, a building, a room, a nursing home, an airplane, a vehicle, a train, a ship, a critical care unit in a hospital, and a free-standing, mobile enclosure.

The present application relates to the technical field of public health safety, in particular to an infectious disease tracing method and system based on a space-time graph neural network, which integrates multi-source heterogeneous contact data, constructs a topologically enhanced space-time graph, converts the data into a space-time point cloud, constructs a multi-scale simplex complex, extracts persistent topological features, integrates the features into a graph representation, trains a multi-scale space-time graph neural network, uses multi-level time encoding and a double attention mechanism, designs a topological feature guided message passing, and finally performs topologically guided backpropagation tracing, reversely traces persistent homologous information flow and spectral graph convolution, generates multiple propagation source point hypotheses, and outputs the probability distribution of potential infection sources and the propagation path.

The invention belongs to an event analysis method, and provides a public opinion event propagation analysis method, a public opinion event propagation analysis system and a computer program product in order to solve the technical problem that a mathematical model is not fully used for exploring causes and a propagation mechanism in a network space in current research about moods of netizens. In different stages of the public opinion event, different models are adopted to analyze the propagation condition of the public opinion event, the progress of the public opinion event is divided according to whether bifurcation occurs, the network public opinion event is described in a mode of combining a chaos dynamics model and an infectious disease model, and the public opinion propagation process is analyzed in each stage. Therefore, reasons and essential reasons for changing network public opinion events into network violence events can be known fundamentally, and a strategy for controlling the trend of public opinions can be formulated based on the reasons. According to the method, the comprehensive model is improved, the public opinion process in which the network storm possibly occurs can be completely described, and in practical application, disposal suggestions can be given in combination with data analysis.

The invention discloses a school infectious disease identification and early warning method based on big data and an AI technology, and belongs to the technical field of intelligent prediction, and the method comprises the steps: obtaining case data, carrying out the preprocessing of the case data, and obtaining a preprocessing data set; constructing a random forest model, and training the random forest model through the preprocessed data set to obtain a feature recognition model; constructing an autoregression model, training the autoregression model through the time sequence data, and generating an autoregression prediction model; and performing early warning and prevention and control on sensing disease transmission based on results generated by the feature recognition model and the autoregression prediction model. According to the invention, the trained optimal random forest model is used to identify the cause of new data. Secondly, on the basis of the above model, an autoregression prediction model is constructed.

1. Name of the design product: Display screen panel with a graphical user interface for managing research data on respiratory infectious disease infection information. 2. Use of the design product: To display the content of the graphical user interface, and this display screen is applicable to mobile phones. 3. Design key points of the design product: Lies in the content of the graphical user interface. 4. Picture or photo that best shows the design key points: Front view. 5. The display screen belongs to the usual design, and other views are omitted. 6. Use of the graphical user interface: For managing respiratory infectious disease infection information data. 7. Description of the change states of the graphical user interface: The front view is the initial interface. After filling in and clicking "Log in", it changes to the interface change state diagram 1; clicking "Follow-up Information" in the interface change state diagram 1 changes to the interface change state diagram 2; clicking "All Users" in the interface change state diagram 2 changes to the interface change state diagram 3; clicking "View Screening Results" in the interface change state diagram 3 changes to the interface change state diagram 4; clicking the "Edit" on the right side of "Baseline Research" in the interface change state diagram 4 changes to the interface change state diagram 5.

The application discloses a respiratory infectious disease population flow network construction and feature mining method and device based on multi-source heterogeneous traffic big data. The method collects multi-source traffic travel data such as railways, civil aviation, highways and mobile signaling and pre-processes, constructs a time-space correlation database, and generates multiple standard data tables through cleaning, desensitization, time-space alignment standardization processing. Based on the standardized data, the network topology parameters are defined, the multi-traffic mode OD flow matrix is generated, and the multiple verification and calibration are completed, the civil aviation, railway and highway hierarchical networks are respectively built, the network edge weight is fused through node alignment and interlayer coupling, the super-adjacency matrix is constructed, the multi-dimensional topological features of the multi-layer network are mined, and finally the exclusive feature data set suitable for respiratory infectious disease cross-regional transmission prediction is generated. The scheme can efficiently fuse standardized multi-source heterogeneous data, accurately construct the population flow network, and effectively improve the accuracy and timeliness of the infectious disease transmission risk assessment.

The application discloses a kind of source network load system attack path tracking and security protection method, first, source network load system network attack monitoring collection technology is proposed, and power generation side, energy storage system, load side, power grid operation, environmental factors and network security data are collected.Then, whether the instruction or the information collected is subjected to network attack by verifying whether the instruction and the uploaded power consumption data are reasonable;Secondly, according to the monitored source network load system network attack behavior, based on the mechanism of infectious disease model, the path of the source network load system subjected to network attack is given by reverse tracking the uploaded data subjected to attack.Finally, based on the key network node in the source network load system attack path selected by the sea squirt colony algorithm, the security protection measures are configured, considering the deployment cost and the expected effect, and finally outputting the protection scheme to guide the source network load system to deploy defense measures at the key nodes of the attack path and improve the security of the power grid.

The application discloses a magnetic capsule gastroscopy vehicle and a magnetic body thereof. The magnetic capsule gastroscopy vehicle comprises a vehicle body, the vehicle body comprises a vehicle compartment, the inside of the vehicle compartment is divided into a front cavity and a rear cavity arranged in front and back through an isolation door, the front cavity is a medical staff control area for medical staff activities, and the rear cavity is a patient disinfection and detection area for patient activities; the isolation door is a sliding glass isolation door; a front door for entering the front cavity is arranged at the front end of the vehicle compartment, a rear door for entering the rear cavity is arranged at the rear end of the vehicle compartment, and a general rear door for sealing the rear end of the rear cavity is further arranged at the rear side of the vehicle compartment; a check bed is detachably arranged in the rear cavity, and a magnetic body movement mechanism is arranged beside the check bed; a console is arranged in the front cavity, and the console is connected with the magnetic body movement mechanism in control. The application is convenient for flexible and mobile gastrointestinal disease screening, realizes contactless examination, expands the working range and the beneficiary population, and can provide medical aid in special environments such as infectious diseases and wars.

The invention provides a strategic mineral resource overseas supply chain risk space-time monitoring and early warning method and device based on a complex network and an improved SEIR infectious disease model, and is applied to the technical field of data processing. According to the method, on the basis of strategic mineral resource overseas supply chain risk monitoring and early warning, multiple types of data of a supply chain are integrated, a key path is identified based on an algorithm, and network topology information is generated; modeling five types of risk causal relationships by adopting a Bayesian network to obtain risk conduction information; then preprocessing the unstructured text, designing an NLP model, and generating risk identification information; setting evaluation rules according to multi-level requirements, and generating evaluation information; optimizing and improving SEIR model parameters, and generating risk propagation prediction information; defining Agent behavior rules, and generating evolution simulation information; and finally, integrating multi-source data, generating full-process intelligent early warning response information including monitoring, early warning and plan matching, and realizing risk dynamic monitoring and efficient response.

The application relates to a tracing method for latent period asymptomatic transmission based on an SAIR model, which comprises the following steps: S1, determining a single initial transmission source node in an interpersonal relationship complex network, and transmitting according to the source node according to an SAIR model until a moment t0; S2, randomly selecting a certain number of nodes as observation nodes, and knowing the state of the observation nodes at the moment; S3, screening a candidate source node set in the complex network by using a reverse propagation algorithm according to the state of the partial observation nodes, and the hop distance and the effective distance, and the number of nodes is determined by a sampling method; S4, constructing a dynamic message transmission equation according to the SAIR model, and calculating the probability of the candidate source nodes being in different states at a moment Delta t by using the partial observation nodes; and S5, calculating the likelihood probability of the nodes in the candidate source node set becoming sources by using an average field theory, and the source is the node with the maximum probability. According to the application, the spread of infectious diseases of patients with latent period and asymptomatic can be accurately reflected, and the problem of tracing the source of infection can be solved.

The present invention discloses a train control defense method and system based on an infectious disease model. The method includes: S1. Establish an APT attack propagation model to describe the node state transition and propagation process of the attack in the train control system; S2. Based on the APT attack propagation model, obtain the performance of the information domain network of the train control system and the train operation performance in the physical domain; S3. Collect the physical state data of train operation and the communication state data of the information domain to construct a multi-dimensional feature vector; S4. Use the random forest algorithm and the long short-term memory network to dynamically detect the train operation data and identify the false data injection attack; S5. After the APT attack occurs, optimize the train operation path and speed control strategy, and reduce the impact of the attack on the system by adjusting the mobile authorization information flow path. Through the dynamic interaction between the information domain and the physical domain, the present invention makes up for the neglect of the impact on the physical domain in the traditional defense method.