9 results about "Disease transmission" patented technology

This application discloses a cattle and sheep slaughtering overhead conveyor line with blood collection function, belonging to the field of slaughtering equipment technology. It mainly includes: a base plate, a conveying assembly mounted on the base plate, suitable for suspending cattle and sheep and conveying them along a preset trajectory to each processing station, and a longitudinal adjustment assembly mounted on the base plate, the longitudinal adjustment assembly including an assembly box mounted on the base plate. In the cattle and sheep slaughtering overhead conveyor line with blood collection function of this application, the longitudinal adjustment assembly drives the adapter plate and the collection assembly to move longitudinally through the cooperation of a first one-way screw and a rectangular slider. The collection assembly achieves height adjustment of the collection box through the action of a second one-way screw and a non-circular slider, thereby allowing the collection box to be flexibly adjusted in position and aligned with the blood dripping area, improving blood collection efficiency, effectively preventing blood spillage and environmental pollution, and reducing cleaning costs and the risk of disease transmission.

This application discloses a method and apparatus for early warning of disease infection risk, relating to the field of educational technology. The method includes: acquiring audio and video data of a target classroom; performing voiceprint recognition on target audio in the audio and video data to identify students at risk of disease infection; locating the sound source of the target audio based on student seating information in the target classroom to obtain first seating information; and performing a consistency check between the first seating information and second seating information corresponding to the at-risk students; if the consistency condition is met, identifying infection-risk actions of the at-risk students based on the target video corresponding to the target audio; and, in response to identifying infection-risk actions, determining disease infection information and issuing a disease transmission warning in the target classroom. This application improves the accuracy of disease transmission monitoring through seating verification and action recognition, and enhances the effectiveness of classroom disease infection risk early warning by determining disease infection information and issuing warnings.

The present application relates to the technical field of livestock safety management, and particularly relates to a digital management system and method for biological safety access of a breeding pig farm. The system is cooperatively operated by a management platform, a mobile terminal, a video monitoring system and an Internet of Things device, controls the whole process of personnel, vehicles, materials, food and feed entering and leaving the breeding pig farm, and builds an integrated closed-loop management mechanism of "plan, execution, supervision, rectification and data". Through plan reporting and approval, the orderly arrangement of access activities is realized. Through job reporting and standard execution, the key operation process is standardized. Through video monitoring and parameter collection, the whole process is supervised. Through the negative list and rectification mechanism, a closed loop of problem processing is formed. Through data collection and report analysis, the access management information is visualized and decision support is realized. The method can effectively improve the biological safety management level, reduce the risk of disease transmission and improve the efficiency of breeding pig breeding.

This patent presents an integrated system for monitoring coastal ecosystems, focusing on mangroves and corals in Gulf region-specific environments. It utilizes a Multi-Faceted Neural Network (MFNN) to process diverse data sources, offering insights into mangrove health, carbon sequestration potential, coral health, disease spread prediction, and site selection for mangrove restoration. The system employs various training and optimization techniques to ensure precision and adaptability, including backpropagation, regularization, fine-tuning with regional datasets, and data augmentation. It introduces mathematical formulas for evaluating mangrove canopies and coral ecosystems, addressing disease prediction and optimal planting site selection for mangrove restoration. The system's interconnection with carbon storage research enhances its scientific foundation for assessing overall carbon storage in mangrove ecosystems. This system and its associated methods provide a powerful toolset for coastal ecosystem monitoring, preservation, and restoration, promoting sustainable management practices.

This invention discloses a two-layer dynamic model of a composite transportation-medical network based on Markov chains, effectively overcoming the shortcomings of traditional complex network failure models in static network construction and the dynamic coupling and integration of disease transmission and infrastructure failure. This model integrates the functional relationships between transportation and medical infrastructure, introduces the background of infectious disease transmission, combines the SEIHR model and Markov chains to describe disease transmission dynamics, and utilizes a load-capacity model to capture the dynamic process of cascading failures, thus forming a two-layer dynamic model. This invention can not only accurately simulate network response and resource allocation driven by individual mobility and medical treatment decisions, but also effectively reveal the triggering mechanism and propagation path of cascading failures, providing scientific support for urban infrastructure planning optimization, public health emergency resource scheduling, and risk early warning. The applicability and analytical accuracy of the model are improved in infectious disease outbreak scenarios.

This utility model discloses a pigsty disinfection device, including a movable frame. A partition is fixedly connected inside the movable frame, and a side-opening water tank is placed between the partition and the movable frame. A small self-priming pump is placed inside the movable frame, and a water frame is fixedly connected inside the movable frame. An ultrasonic atomizing plate is placed inside the water frame. This utility model utilizes three rotatable L-shaped tubes to flexibly adjust the water mist spray angle. Furthermore, the movable frame can move freely via handles and casters, allowing the water mist to cover previously inaccessible areas such as corners and high places in the pigsty. This effectively solves the problem of incomplete disinfection due to missed or repeated spraying in existing technologies, greatly reducing the possibility of disease transmission. The L-shaped baffle can be quickly pulled open via a handle to open the fan outlet, creating airflow to spray the water mist further. The overall operation is simple and labor-saving, ensuring the continuity of the disinfection process and improving operational efficiency.

This invention discloses a method and system for assessing the risk of maize pest and disease invasion based on big data, relating to the field of agricultural pest and disease control. The method includes: preprocessing multi-source data to generate a preprocessed structured dataset; constructing a complex network for pest and disease transmission based on wind direction data and vector migration patterns, using grid cells in the preprocessed structured dataset as nodes; learning the network topology using a graph neural network model and introducing a dynamic edge weight adjustment mechanism to respond to real-time wind direction changes, outputting pest and disease transmission paths and a transmission probability matrix; and extracting cross-grid transmission dependency features based on the pest and disease transmission paths and the transmission probability matrix to generate a transmission risk correlation map. This invention achieves a full-process maize pest and disease risk assessment from multi-source data fusion, dynamic transmission modeling, collaborative decision optimization to closed-loop iteration, improving early warning accuracy and the efficiency of control resource utilization.

This invention relates to the field of conveying equipment and discloses an automated viscera separation device for simultaneous quarantine during pig slaughter. The device includes a conveying mechanism, a truss, and multiple separation mechanisms. Each separation mechanism includes a tray, a support frame, a rotating frame, a rotating component, and multiple adsorption assemblies. Each adsorption assembly includes a suction head, a first flexible hose, a reversing valve, and a hanging frame. An air chamber is formed within the rotating frame. The reversing valve is a push-button type, with its two inner ports connected to the inner ports of the first flexible hose and the suction head, respectively, and its outer port connected to the outside. The air chamber is connected to an external negative pressure fan. A diversion mechanism is provided on the bottom side of the output end of the conveying mechanism, and a synchronous pushing mechanism is provided on the bottom side of the conveying mechanism. This invention solves the problem that quarantine operators' manual contact with the surface of viscera significantly increases the risk of disease transmission and spread during the slaughtering process.

This invention provides a multi-scale spatial disease prediction method, apparatus, and electronic device, comprising: acquiring the macroscopic and microscopic graph structures of each region within a region to be predicted; invoking a pre-trained disease prediction model, wherein the disease prediction model includes at least a pooling module and a prediction module, the pooling module being used to obtain microscopic feature vectors within the region corresponding to the microscopic graph structure; the prediction module being used to obtain disease prediction results for the region to be predicted within a future preset time period based on the macroscopic graph structure and the microscopic feature vectors within the region; inputting the macroscopic and microscopic graph structures into the pre-trained disease prediction model to obtain the disease prediction results for the region to be predicted within the future preset time period output by the disease prediction model, wherein the disease prediction results include at least the number of infected individuals, the disease transmission rate, and the disease recovery rate. This approach achieves both the capture of macroscopic trends and consideration of individual heterogeneity, thereby improving prediction accuracy.