Navigation Server Parking Recommendation Scoring
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Solution Overview
Problem
Current navigation systems lack the ability to efficiently recommend parking lots based on real-time parking difficulty levels and availability, leading to user frustration and increased time spent searching for suitable parking.
Innovation Solution
A method and system that determine a target parking lot by calculating a score for each candidate lot based on parking difficulty level, remaining spaces, walking distance, and driving distance, using historical data and real-time positioning, and returning the best option to the navigation terminal.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional navigation systems only provide basic route guidance, then the system complexity remains low, but the parking lot recommendation accuracy deteriorates
Solution Approach 1:
The patent segments the parking lot recommendation system into multiple independent modules: a data acquisition module that collects parking data from various sources, a data processing module that analyzes and structures the data, a scoring module that calculates parking difficulty levels, and a recommendation module that generates final suggestions. This modular segmentation allows each component to specialize in specific tasks, improving overall recommendation accuracy while managing system complexity through clear separation of concerns.
Solution Approach 2:
The system performs preliminary actions by pre-collecting and pre-processing parking lot data before it is actually needed for recommendation. Historical parking data, real-time occupancy information, and parking difficulty metrics are gathered and analyzed in advance, allowing the system to quickly generate accurate recommendations when users need them without performing complex calculations in real-time.
2Measurement precision
If the system collects and processes multiple parking-related parameters, then the recommendation quality improves, but the information processing time increases
Solution Approach 1:
The system performs preliminary data processing and scoring calculations before recommendation is actually requested. Parking difficulty levels, occupancy rates, and other metrics are pre-calculated and stored, so when a user requests a recommendation, the system only needs to retrieve and combine pre-processed data rather than performing complex real-time analysis.
Solution Approach 2:
The patent transforms multiple complex parking parameters into a simplified scoring system. Instead of presenting users with raw data about occupancy rates, parking duration, fees, and accessibility separately, the system converts these into a unified parking difficulty score and recommendation ranking, making the information more digestible while maintaining comprehensive analysis in the background.
3Measurement precision
If the system uses real-time data from multiple sources, then the parking difficulty assessment accuracy improves, but the data acquisition complexity increases
Solution Approach 1:
The patent introduces intermediary components that act as bridges between multiple data sources and the core recommendation system. Data collection interfaces are standardized and abstracted, allowing the system to gather information from diverse sources such as parking lot sensors, GPS data, user feedback, and external databases through uniform protocols. This intermediary layer shields the core system from the complexity of handling multiple different data formats and sources.
Solution Approach 2:
The system employs universal data collection mechanisms that can handle multiple types of data sources through a single interface. The data acquisition module is designed to work with various input formats and sources (IoT sensors, mobile app data, third-party APIs) using standardized methods, reducing the need for separate specialized collection systems for each data type and simplifying the overall architecture.
Data Source
AI summary
The present disclosure provides a parking lot recommendation method, and a navigation server for determining a score of a candidate parking lot, based on a parking difficulty level of the candidate parking lot in an target area where a destination located, quantity of remaining parking spaces, walking distance from the candidate parking lot to the destination, and driving distance from a present position of a vehicle to the candidate parking lot, and selecting an object parking lot from the candidate parking lots existing in the target area, according to the score of the candidate parking lot, and providing parking lot information of the object parking lot to a navigation terminal.