A queuing peak period estimation method based on big data and a machine learning algorithm

A machine learning, peak time technology, applied in data processing applications, instruments, forecasting, etc., can solve the problems of loss of information, large waiting time estimation error, low stability, etc., to improve meal efficiency, reduce time waste, improve The effect of accuracy

Active Publication Date: 2019-06-18
美味不用等(上海)信息科技股份有限公司
View PDF10 Cites 5 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the existing technology simply and roughly divides the whole day with a fixed length of time, and performs simple averaging in the same time period, resulting in: 1. The information of fluctuations in the same time period is lost; 2. It is difficult Accurately locate the turning point in the "relationship curve between waiting time and number-taking time"; 3. Two adjacent time periods are independent of each other, and a certain correlation between adjacent time periods is lost; 4. Ineffective use Characteristics of different months and seasons
Ultimately, the error of waiting time estimation is large and the stability is low

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0010] The technical solution adopted in this specific embodiment is: the method for queuing peak hour estimation based on big data and machine learning algorithm specifically includes the following steps: 1. Effectively clean the data based on the actual order of meals between adjacent numbers (refer to The submitted patent "CCP118040081, a method and device for estimating the waiting time of a meal"); 2. Based on the cleaned historical data, combined with machine learning algorithms, the waiting time, number pick-up time, and number pick-up date (day of the week, Months, whether it is a holiday), etc., to obtain the specific relationship between the waiting time and these parameters, and save it as an estimated model; 3. Based on the estimated model, the estimated specific number-taking period and time for C-end users Date parameters, estimated corresponding queuing waiting time; 4. Scan the time points during the normal business hours of the day to obtain the relationship cu...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a queuing peak period estimation method based on big data and a machine learning algorithm, and relates to the technical field of queuing peak period estimation. The queuing peak period estimation method based on the big data and the machine learning algorithm specifically comprises the following steps: effectively cleaning data based on an actual dining sequence between adjacent numbers; Carrying out model training on the relationship between the waiting time and the number taking time, the number taking date and the like in combination with a machine learning algorithm to obtain a specific relationship between the waiting time and the parameters; Estimating the corresponding queuing waiting time according to the predicted specific number taking time period and date parameters of the C-end user; Scanning time points in all-day normal business time periods to obtain a relation curve between each number taking moment in one day of normal business time periods andthe estimated queuing waiting time. After the technical scheme is adopted, the method has the beneficial effects that the estimation time accuracy is improved, the time waste of a user is reduced, and the dining efficiency of the whole catering industry is improved.

Description

technical field [0001] The invention relates to the technical field of queuing peak period estimation, in particular to a method for queuing peak period estimation based on big data and machine learning algorithms. Background technique [0002] Queuing for dining has become the norm in popular restaurants in major business circles. In order to standardize the order of queuing, the order of taking numbers is generally used for orderly dining. Indicate the number of tables ahead that still need to wait. With the popularization of smart phones and the development of the mobile Internet, the new mode of offline dining based on online reservations has gradually become mainstream. Users can obtain reservations online without going to the restaurant and arrange their own reservations reasonably according to the current number of waiting tables. journey. Based on the patented technology of "CCP118040081: A Method and Device for Estimating Waiting Time for Meals", the required wait...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06K9/62G06Q50/12
Inventor 张乐情徐博识郑国春谢新法
Owner 美味不用等(上海)信息科技股份有限公司
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap