4results about How to "Eliminate silos" patented technology

This invention discloses a trusted carbon footprint storage and intelligent traceability system for gold recycling based on blockchain and AI technologies, belonging to the intersection of green finance technology and environmental protection technology. The system adopts a four-layer architecture: the data acquisition layer binds batch IDs through GS1 encoding and integrates multi-source data through IoT sensors, a mobile app, and edge preprocessing; the AI ​​processing layer accurately calculates the carbon footprint based on a dynamic LCA model, optimizes process parameters through deep reinforcement learning, and simulates emission reduction potential; the blockchain storage layer uses a consortium blockchain (PBFT consensus) + IPFS storage, deploying smart contracts for storage and verification to automatically generate CDCEER carbon credits; the application layer provides traceability queries, ESG dashboards, regulatory audits, and green finance interfaces. This invention solves the problems of data fragmentation, inefficient calculation, and difficulty in value conversion, achieving trusted management and value conversion of the entire carbon footprint process, helping enterprises reduce costs and increase efficiency, comply with emission reduction regulations, and promote the green upgrading of the recycled precious metals industry.

This invention proposes an intelligent remote emergency rescue management system capable of rapidly matching resources, relating to the field of remote emergency medical technology. The alarm receiving module triggers the case analysis module to generate priorities, the resource scheduling module allocates instructions to the drone control module, and after the drone completes deployment, the information matching module generates navigation information. Data from the person scanning the code is fed back to the system via the data association unit, forming a complete closed loop of resource scheduling-deployment-feedback. Through real-time data interaction between modules, the traditional emergency resource matching time is shortened from 10-20 minutes to within 2 minutes, while simultaneously improving the accuracy and utilization rate of drone AED deployment, achieving a qualitative leap in emergency response efficiency.

The application provides a kind of production safety accident scene's survey information collection system, belong to information collection technical field, including: scene preliminary survey module and survey query module;Scene preliminary survey module is used to receive preliminary survey instruction, according to the preliminary survey instruction display scene preliminary survey page when receiving preliminary survey instruction;Also be used for receiving the basic information and scene survey data input by user according to the scene preliminary survey page, and obtain the survey data and plan sketch related to current accident;Also be used for generating survey record according to the basic information, the scene survey data, the survey data and the plan sketch;Improve the sharing and reuse of data, eliminate the phenomenon of enterprise information island, need to introduce the whole process of survey production management into information management, can improve the real-time nature of the survey information, information coverage, objectivity, automation degree.

The application belongs to the technical field of multi-source data processing and collaborative computing, and aims at the problems of multi-source heterogeneous data in complex dynamic scenes, such as time and space asynchronization, non-uniform characteristics, high processing delay, and weak concurrent support capability, and proposes a real-time processing and collaborative computing method for multi-source heterogeneous data with unified time and space reference. The method completes device coordinate calibration and timestamp calibration with Beidou time and space reference, realizes millisecond-level time and space synchronization; through a lightweight dynamic feature coding network and a self-adaptive weight distribution mechanism, unified feature extraction and target matching of multi-modal data are completed; an edge-cloud collaborative architecture is constructed, combined with hierarchical feature compression and asynchronous federated learning, supporting concurrent access of ten thousand devices and reducing end-to-end delay; integrating a digital twin engine and an operational optimization algorithm, scene deduction, resource scheduling and accurate instruction issuing are realized, which can be widely applied to global precise management and control in complex dynamic scenes such as ports and parks.