Map Data Processing Layering Offline Online Merging
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The update frequency of offline map data is low, leading to poor timeliness and increased traffic costs when users access online services for the latest geographic information, especially in weak network conditions.
Innovation Solution
A method that combines low-frequency offline data with high-frequency online data by layering map data based on update frequency and importance, allowing for real-time updates by merging offline and online data within the map application, using a layering model to distinguish and integrate different data types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If offline map data is used, then traffic cost is saved and convenience is improved, but timeliness deteriorates due to low update frequency
Solution Approach 1:
The patent segments map data into two distinct layers: low-frequency updated data (stored offline) and high-frequency updated data (loaded online). This segmentation allows the system to maintain both offline convenience and online timeliness by selectively loading only the frequently changing data portions when needed, rather than requiring complete offline package updates.
Solution Approach 2:
The patent performs preliminary actions by pre-loading high-frequency updated data into the offline package during offline updates, and pre-identifying which data layers require online supplementation. This preparation enables the system to quickly merge offline and online data when network conditions permit, improving timeliness without requiring frequent complete updates.
2Reliability
If offline package updates are performed frequently, then timeliness is improved, but traffic cost increases and convenience deteriorates
Solution Approach 1:
By dividing map data into low-frequency and high-frequency update layers, the system avoids the need for frequent complete offline package updates. Only the high-frequency layers require online supplementation, dramatically reducing the traffic cost and frequency of required updates while maintaining timeliness.
Solution Approach 2:
The patent applies partial action by loading only the necessary high-frequency updated data layers online rather than complete repackaging. This partial loading approach achieves the timeliness benefit of frequent updates without the excessive traffic cost of updating entire offline packages.
3Reliability
If complete online data loading is performed, then timeliness is improved, but traffic cost increases significantly
Solution Approach 1:
The patent segments the data loading process into two parts: stable low-frequency data loaded offline, and dynamic high-frequency data loaded online. This segmentation enables the system to achieve near-real-time timeliness by loading only the necessary high-frequency portions online, rather than loading complete data sets, thereby significantly reducing traffic costs.
4Ease of operation
If offline data is used, then convenience is improved and traffic is saved, but data freshness deteriorates
Solution Approach 1:
The patent segments map data into permanent offline layers and dynamic online layers. The offline layers provide convenience and stability, while the online layers supply fresh, frequently updated information. This segmentation allows the system to maintain both convenience (through offline storage) and data freshness (through selective online loading of high-frequency data).
Solution Approach 2:
The system performs preliminary actions by pre-identifying and pre-loading high-frequency data layers into the offline package structure, and pre-establishing the merging mechanism. When network conditions allow, the system automatically supplements offline data with fresh online data, maintaining data freshness without requiring user awareness or manual intervention.
Data Source
AI summary
The present disclosure provides a map data processing method, an electronic device and a storage medium, relates to a technical field of data processing, and in particular to the field of map data processing. A specific implementation solution is as follows: receiving first data encapsulated in a form of offline data, the first data being used to characterize low-frequency data in the map data; obtaining second data after initiating a first online request, the second data being used to characterize high-frequency data in the map data; and performing merging processing on the first data and the second data to obtain target data to be displayed in a map. By adopting the present disclosure, the timeliness of map data display may be improved.