8results about How to "Effective traceability" patented technology

The invention relates to the technical field of pet medical information, and discloses a pet cloud medical record management method and system, and the method comprises the steps: collecting pet health data through a user terminal, and the pet health data comprises video and text description; uploading the video to a video processing module, automatically editing the video through the video processing module, and extracting key segments; the text description and the key segments are integrated, and a structured pet electronic medical record is generated through a medical record generation module; storing the pet electronic medical record to a cloud storage server, and storing the hash value of the pet electronic medical record to a block chain network; and according to a user instruction, safe sharing and multi-terminal synchronization of the pet electronic medical record are realized through the safety control module. According to the method, the problem that the pet cannot know the pain point of the illness state by himself / herself is solved, the behavior performance of the pet can be effectively recorded and traced, the burden of a pet owner for sorting data is relieved, and meanwhile, a more intuitive and complete diagnosis and treatment basis is provided for veterinarians.

This invention provides a method for monitoring volatile pollutants, belonging to the field of pollutant monitoring. The method includes: dividing a monitoring area into grid cells; acquiring the pollutant concentration, wind speed, and wind direction within each grid cell; determining the pollutant concentration residence time based on the wind speed and wind direction; determining the grid weight concentration based on the pollutant concentration and the concentration residence time; dividing the grid into concentration intervals according to the magnitude of the grid weight concentration, with each interval corresponding to a different identifier; and displaying each grid cell with the identifier corresponding to its respective concentration interval. This method couples the monitored pollutant concentration with wind speed and wind direction to determine the grid weight concentration of each grid cell, divides the grid weight concentration into concentration intervals, and displays each grid cell with the identifier corresponding to its respective concentration interval. Through these identifiers, the dynamic change process of the grid cell concentration can be observed, and effective source tracing can be performed.

The invention provides an intelligent manufacturing data exception identification method and system based on artificial intelligence, and relates to the field of data processing. According to the method, multi-source signals of production, control and detection equipment are collected through an energy manifold sensing architecture, and a global signal flow is constructed; performing event triggering type observation through a space-time perception model to generate a local state sequence; identifying a cross-process coupling relationship through the coupling analysis model to form an interference spectrum set; the virtual manufacturing cognitive body is used for executing mimicry copying and generating an abnormal perception event flow; an abnormal path is backtracked by an energy propagation model to identify an energy leakage set; and finally, generating an energy narrative set containing causes, propagation and attenuation fragments through a semantic narrative model, and realizing intelligent identification and interpretation of manufacturing data anomalies. By implementing the technical scheme provided by the invention, the root cause and the propagation path of the abnormity can be effectively traced, and the intelligent manufacturing data abnormity identification effect is improved.

The application discloses a railway freight car configuration history management method, device, medium and equipment, and the method comprises the following steps: acquiring history basic information, including the attributes and attribute fields of each configuration item. An association matrix is constructed according to the correlation intensity of each attribute field, the penetration probability between each attribute field is determined based on the association matrix, the attribute grouping and the history type identification of the attribute field are determined according to the penetration probability. Each classified attribute field is taken as a node to construct a causal reasoning graph, and the causal strength and the causal path between each classified attribute field are determined according to the causal reasoning graph. The classified attribute fields are established into a hierarchical data structure according to a preset configuration level, the causal strength and the causal path between the classified attribute fields are marked in the hierarchical data structure, and the attribute configuration result of each configuration item is obtained. The application is helpful for the effective management and tracing of the attribute information of the railway freight car configuration item.

This invention presents a multi-scale subway disaster risk level prediction method based on multi-source data fusion. It obtains data collected during the construction, operation, and maintenance of subway projects, stores it in a database, and assigns it specific timestamps and spatial coordinates. After preprocessing, the data is divided into three categories: text, images, and sensor data, each containing multiple modalities. A multi-modal data analysis and fusion unit is constructed, including a multi-head cross-modal attention module and three parallel feature extraction branches, generating the final multi-modal fusion feature vector. Using the operating conditions as input and the physical variables as inputs, and the predicted physical quantities and risk level judgment results as outputs, a subway project risk level prediction model based on a physical information neural network is constructed. This method effectively integrates multi-source data related to subway projects, contributing to improved accuracy and practicality in disaster risk level prediction.

This invention discloses a laser welding method for ultra-narrow steel-shell batteries, comprising S1. incoming material preparation, S2. positioning and transfer, S3. laser welding parameter setting, S4. laser welding, S5. post-weld inspection, S6. sorting and traceability, and S7. fixture return. This method solves the technical problems of low welding precision, unstable weld quality, poor compatibility, and difficult traceability in ultra-narrow steel-shell battery casings. The method deeply integrates a high-precision magnetic levitation fixture return system, CCD vision-based precise positioning, differentiated laser welding modes, and a full-process MES traceability system. Specifically adapted to the structural characteristics of ultra-narrow steel shells, it achieves efficient and high-precision welding of the steel shell and casing through precise control of welding parameters and process coordination. This invention ensures that weld dimensional accuracy, penetration depth, and sealing meet requirements, enables full-process information traceability in welding, improves production efficiency and product qualification rate, and is suitable for automated mass production of ultra-narrow steel-shell batteries.

The application discloses an intelligent perception-based Internet of Things traceability hierarchical architecture, and proposes a 'four horizontal and one vertical' architecture; the 'four horizontal' are a perception layer, a transmission layer, a traceability layer and an application layer from bottom to top; the perception layer is responsible for acquiring data required by traceability; the transmission layer is responsible for uploading the perceived traceability data to a trusted traceability platform; the traceability layer is responsible for storing traceability data and providing trusted traceability verification; and the application layer realizes traceability applications such as logistics tracking and quality control based on the trusted traceability result; the 'one vertical' runs through the perception, transmission and traceability layers and provides reliable guarantee mechanisms for the layers. Through analyzing existing Internet of Things, block chain and traceability systems and combining the characteristics of intelligent perception and traceability of the Internet of Things, the architecture realizes accurate and controllable intelligent perception, economical and reliable networking transmission and efficient and trusted traceability process.

This invention discloses a method for marking tobacco material information, including controlling the tobacco material to be conveyed on the discharge bottom conveyor at a certain stacking height I; controlling the next batch of tobacco material to be conveyed on the discharge bottom conveyor at another stacking height II; and so on, until all batches of tobacco material are discharged; the stacking height I, stacking height II, etc., change according to a certain pattern as the batch quantity of tobacco material is updated; by knowing the current stacking height of the tobacco material on the discharge bottom conveyor and the pattern of stacking height change, the batch data of this tobacco material can be obtained. This method aims to improve the efficiency of tobacco material tracking, management and traceability characterized by assembly line operations. By adopting dynamic stacking marking technology, this method can monitor and update tobacco material information in real time, providing more accurate and reliable data for production and supply chain management.