Cloud edge collaboration based surveying and mapping data distributed collection and statistics method

By constructing a spatiotemporal-caliber-resource joint meta-model and causal chain constraint task modeling, the problems of insufficient consistency and autonomy in traditional surveying and mapping data processing are solved, achieving efficient and real-time data acquisition and processing, and improving the system's adaptability and end-to-end consistency.

CN122309080APending Publication Date: 2026-06-30YANCHENG JINGWEI SURVEYING & MAPPING TECH CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
YANCHENG JINGWEI SURVEYING & MAPPING TECH CO LTD
Filing Date
2026-04-03
Publication Date
2026-06-30

AI Technical Summary

Technical Problem

Traditional surveying and mapping data processing methods suffer from problems such as high data transmission and processing pressure, difficulty in ensuring real-time performance, disconnect between statistical standards and the data acquisition process, imperfect cloud-edge collaboration mechanisms, insufficient autonomy of edge nodes, weak end-to-end statistical consistency control, and insufficient system self-adaptability.

Method used

We construct a spatiotemporal-caliber-resource joint meta-model anchored to statistical value. Through task modeling and distributed game scheduling constrained by causal chains, we perform distributed collection and lightweight preprocessing collaboration guided by causal chains. We also construct a dynamically topology-driven distributed hierarchical statistics and full-link causal consistency control to achieve full-process adaptive closed-loop optimization.

Benefits of technology

It achieves high efficiency, real-time performance, and consistency in data acquisition and processing, ensures the autonomy of edge nodes and statistical consistency across the entire link, and enhances the system's adaptability and closed-loop optimization effect.

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Abstract

This invention discloses a distributed data acquisition and statistics method for surveying and mapping based on cloud-edge collaboration, specifically relating to the field of data processing technology. The method includes: constructing a spatiotemporal-caliber-resource joint meta-model for statistical value anchoring, forming a binding relationship between grid, indicators, and nodes; constructing task modeling and distributed game scheduling with causal chain constraints to generate optimal allocation schemes and autonomous boundary rules; executing distributed acquisition and lightweight preprocessing collaboration guided by causal chain pre-processing to achieve linked execution of acquisition and preprocessing and edge autonomy; constructing distributed hierarchical statistics driven by dynamic topology and end-to-end causal consistency control to complete incremental fusion and consistency alignment in disconnected scenarios; and executing full-process adaptive closed-loop iterative optimization based on multi-dimensional spatiotemporal joint prediction to generate optimization strategies and feed them back into the underlying model. This invention improves the accuracy and efficiency of surveying and mapping data acquisition and statistics through cloud-edge collaboration and causal consistency constraints, achieving end-to-end adaptive closed-loop optimization.
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