City Management Support System Using Supply Demand Forecasting
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Solution Overview
Problem
Existing city management systems do not adequately benefit information providers who actively offer real-time data, as they typically only provide data unilaterally without receiving reciprocal benefits.
Innovation Solution
A method and system that processes service and person-related data to perform supply and demand forecasts, generating feedback data that benefits both service providers and individuals, by organizing data based on location and time, and associating supply forecast data with demand forecast data using location and time as parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time data is actively provided by information providers, then the quality and timeliness of city management information is improved, but the information providers receive no reciprocal benefits and have less merit
Solution Approach 1:
The system generates feedback data for service providers based on the real-time data they contribute. This feedback includes supply forecast data and demand forecast data that are associated with the providers' services, creating a reciprocal benefit mechanism that encourages continued data provision while improving city management information quality
Solution Approach 2:
Service providers receive personalized feedback reports that enable them to independently optimize their service operations. The system automatically processes their contributed data and returns actionable insights, allowing providers to self-adjust their service supply based on predicted demand without requiring manual intervention from city management authorities
2Measurement precision
If service relevant data and person relevant data are collected and processed, then accurate supply and demand forecasts are achieved, but the system complexity increases
Solution Approach 1:
The data processing system is divided into distinct functional modules: a data acquisition unit that collects service relevant data and person relevant data separately, an organization unit that structures the data based on location and time, a forecast generation unit that produces supply and demand forecasts, and a feedback generation unit that creates actionable insights. This segmentation manages complexity by assigning specific functions to separate components
Solution Approach 2:
The system introduces a city management support device as an intermediary between raw data sources and decision-making processes. This intermediary automatically performs data organization, association, and forecast generation, simplifying the overall system architecture by centralizing complex processing functions in a dedicated intermediary component
3Measurement precision
If service relevant data and person relevant data are organized based on location and time, then accurate association between supply and demand forecasts is achieved, but the processing time and computational resources increase
Solution Approach 1:
Service relevant data and person relevant data are pre-organized by location and time attributes as they are collected. This preliminary organization prepares the data in advance for association operations, so when supply and demand forecasts need to be matched, the data is already structured and ready for efficient comparison and association
Solution Approach 2:
The system transforms raw data into standardized formats with consistent location and time parameters. By changing the parameter representation to a uniform structure during data collection, the subsequent association operations become more efficient as the system only needs to match standardized parameters rather than processing heterogeneous data formats
Data Source
AI summary
A city management support method comprises the steps of: obtaining service relevant data to perform a future supply forecast for one or more services; obtaining person relevant data to perform a future demand forecast for the one or more services; associating the supply forecast data with the demand forecast data by using at least one of location data and time data as a parameter; generating feedback data for service provider and feedback data for person based on the associated dataset; and transmitting the feedback data for service provider to a terminal of a service provider and transmitting the feedback data for person to a terminal of a person.