Smart Agriculture Multi-Source Data Parallel Processing and Mining System

CN122086629APending Publication Date: 2026-05-26LAIWU VOCATIONAL & TECHNICAL COLLEGE
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
LAIWU VOCATIONAL & TECHNICAL COLLEGE
Filing Date
2026-04-22
Publication Date
2026-05-26

AI Technical Summary

Technical Problem

Existing smart agriculture multi-source data parallel processing and mining systems suffer from errors caused by data time deviations, which reduce the accuracy and reliability of multi-source data fusion. They also have poor sensitivity and accuracy in monitoring agricultural environmental anomalies, high network bandwidth consumption, heavy cloud processing pressure, low computing resource utilization efficiency, and are unable to achieve efficient allocation and low-latency execution. Furthermore, they lack the ability to identify crop growth status and environmental changes.

Method used

A smart agriculture multi-source data parallel processing and mining system is adopted, including a data acquisition and capture module, a sparsity detection module, a parallel processing module, a partitioned scheduling module, a management and mapping module, an extraction and perception module, a constraint compilation module, an inference and detection module, an analysis and diagnosis module, a compression and transmission module, an adversarial simulation module, a learning and optimization module, a planning and execution module, and an alarm interaction module. Through unified time synchronization, resampling, baseline modeling, change event detection, spatiotemporal partitioned scheduling, feature extraction, logical constraints, contradiction detection, adversarial simulation, and agricultural expert rules, agricultural intervention plans and alarm information are generated.

Benefits of technology

It effectively reduces errors caused by data time deviation, improves the accuracy and reliability of multi-source data fusion, enhances the sensitivity and accuracy of agricultural environmental anomaly monitoring, reduces network bandwidth consumption and cloud processing pressure, improves system operating efficiency, achieves efficient allocation of computing resources and low-latency task execution, and enhances the ability to identify crop growth status and environmental changes.

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Abstract

This invention discloses a smart agriculture multi-source data parallel processing and mining system, belonging to the field of agricultural Internet of Things data processing. It includes a data acquisition and capture module, a sparsity detection module, a parallel processing module, a sharding and scheduling module, a management and mapping module, an extraction and sensing module, a constraint compilation module, an inference and detection module, an analysis and diagnosis module, a compression and transmission module, an adversarial simulation module, a learning and optimization module, a planning and execution module, and an alarm interaction module. This invention effectively reduces errors caused by data time deviations, improves the accuracy and reliability of multi-source data fusion, significantly enhances the sensitivity and accuracy of agricultural environmental anomaly monitoring, significantly reduces network bandwidth usage and cloud processing pressure, improves overall system operating efficiency, and enhances computing resource utilization efficiency. It achieves efficient allocation of computing resources and low-latency task execution, and can fully utilize information from different data sources to improve the ability to identify crop growth status and environmental changes.
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