A big data-based ship sand volume measurement data management method

By combining an independent preprocessing channel, a distributed message queue, and an in-memory computing engine, and utilizing hash operations and a three-dimensional spatiotemporal grid for data routing and analysis, the problem of high-concurrency data access in multi-ship parallel operation scenarios is solved, enabling accurate and efficient execution of real-time monitoring and anomaly detection, and improving data management efficiency and operational agility.

CN122363899APending Publication Date: 2026-07-10QINGYUAN SURVEYING & MAPPING INST CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
QINGYUAN SURVEYING & MAPPING INST CO LTD
Filing Date
2026-04-10
Publication Date
2026-07-10

AI Technical Summary

Technical Problem

Existing technologies are insufficient to meet the high-concurrency access and real-time analysis requirements of ship sand volume measurement big data in multi-ship parallel operation scenarios. This results in data processing delays and insufficient response capabilities. Furthermore, the lack of a flexible resource scheduling mechanism makes it difficult to dynamically adapt to data peak fluctuations during peak operation periods, thus limiting the mining of data value and operational agility.

Method used

By combining independent preprocessing channel distribution, distributed message queue load balancing, in-memory computing engine and sliding time window, data routing and analysis are performed using hash operations and three-dimensional job spatiotemporal grids, and elastic scaling scheduling is performed using time series prediction algorithms, thereby achieving the orderliness and real-time monitoring of data flow, marking abnormal data and tracing its source.

Benefits of technology

It enables high-concurrency data access and real-time analysis in multi-vessel parallel operation scenarios, ensuring accurate and efficient execution of sand flow rate monitoring and anomaly detection, dynamically adapting to data peak fluctuations, and improving the agility of operational decision-making and the predictive maintenance capabilities of equipment.

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Abstract

This invention discloses a data management method for ship sand volume measurement based on big data, belonging to the field of data management technology. Specifically, it includes: first, receiving raw sand volume measurement data from multiple ships; parsing and extracting the ship's unique identification code and high-precision timestamp; then distributing the data to an independent preprocessing channel; using hash operations combined with the load status of a distributed message queue to route the data to a low-load partition, generating an ordered data stream; using a sliding time window of an in-memory computing engine to calculate the instantaneous sand flow rate and cumulative workload, assembling a monitoring vector containing hydrodynamic characteristics; mapping the data to a three-dimensional spatiotemporal grid and comparing it with a historical benchmark model to mark abnormal data frames; generating elastic scaling instructions for computing nodes based on abnormal frequency and data backlog depth through time-series prediction; adjusting cluster resources and then performing source analysis on the abnormal data, outputting a management report containing the causes of the abnormalities.
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