Speech-Based Power Demand Queries for Fast Emergency User Response
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Solution Overview
Problem
Conventional methods for power demand side management, such as manual calling and short message notifications, are inefficient and difficult to scale, especially during emergency situations, leading to challenges in timely communication and information sharing.
Innovation Solution
A power demand side speech interaction method and system that converts user input into text format, performs industry term-based statistical analysis, and automatically searches a database to provide users with accurate and timely information about their electricity consumption, while reducing manual labor and enabling parallel processing of multiple user demands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual calling and short message notification are used for power demand side management, then communication can be established with users, but the efficiency is low and it is difficult to handle large-scale emergency situations
Solution Approach 1:
The system enables users to automatically query their electricity consumption information through speech interaction without requiring manual service from power company staff. Users can independently obtain their consumption data, policy information, and alerts, eliminating the need for manual calling and significantly improving communication efficiency while reducing notification time.
Solution Approach 2:
The patent replaces manual mechanical operations (staff manually calling users) with an automated speech-based information processing system. The system uses speech-to-text conversion, text statistical analysis, and automated database querying to deliver information, substituting human labor with automated technological processes that can handle large-scale communications simultaneously.
2Ease of operation
If manual checking of power supply conditions is performed, then users can obtain their electricity consumption information, but users need to queue up and the process is time-consuming
Solution Approach 1:
Users can independently query their electricity consumption information through speech interaction without needing to visit service centers or wait in queues. The system automatically processes speech input, converts it to text, analyzes the demand, and retrieves relevant information from the database, enabling completely self-service operation that eliminates waiting time.
Solution Approach 2:
The system performs preliminary processing of user speech input by converting speech to text and analyzing the text to identify user demands before querying the database. This preliminary action prepares the system to quickly retrieve and deliver relevant information, reducing overall query time and improving user convenience.
3Measurement precision
If speech-to-text conversion and text statistical analysis are performed, then accurate industry terms and user demands can be identified, but the processing complexity increases
Solution Approach 1:
The speech processing system divides the complex task of understanding user speech into separate sequential stages: speech-to-text conversion, text statistical analysis for industry term identification, and demand identification. This segmentation allows each stage to be optimized independently, maintaining high accuracy in demand identification while managing processing complexity through modular architecture.
Data Source
AI summary
Disclosed are a power demand side speech interaction method and system. The method includes: obtaining original demand information, the original demand information including user's basic information, user demand information, and a user demand time; converting the original demand information into first information in text format; performing text statistical analysis based on an industry term on the first information in text format, to obtain second information; searching for corresponding user's actual information from a database according to the second information; outputting the user's actual information; searching for a corresponding forecasting model from the database, according to the second information and the user's basic information; calculating, according to a policy limit value of latest policy information in the database, a time for which the model corresponding to the user's basic information reaches the policy limit value; and transmitting an early warning message.