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Parking guidance decision-making method based on mamdani algorithm

A technology of parking induction and decision-making method, which is applied in the direction of calculation, data processing applications, instruments, etc., and can solve the problems that the parking lot cannot accurately grasp the needs of the served, the system has not found meaning, and the analysis of user needs is unclear.

Active Publication Date: 2019-03-26
XUZHOU COLLEGE OF INDAL TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

A large amount of raw data has not been fully processed, and the system has not discovered the meaning behind it
[0004] (2) Ubiquitous RFID
[0008] To sum up, at this stage, the intelligent guidance service of domestic parking lots has not been perfected, the user's demand analysis is not clear, and the experience is lacking. At this stage, it is mainly troubled by the following problems:
[0009] (1) The parking lot cannot accurately grasp the needs of the served;
[0010] (2) Driving into the car is limited to technology and can only passively receive information;

Method used

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  • Parking guidance decision-making method based on mamdani algorithm
  • Parking guidance decision-making method based on mamdani algorithm
  • Parking guidance decision-making method based on mamdani algorithm

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0139] The user is an office worker in the CBD complex, and he went to work for 26 days last month. When the user drove into the parking lot to recognize the license plate, he said "I often work here", the voice was 58 decibels, and it took 1.953 seconds. Among them, the degreed word corresponding to the keyword Keyword was Often, and Speed=1.953 / 6=0.3255, which was normal Speech rate, sound decibel is 58, greater than 40, take Loud=1. Therefore, the reliability index of natural language input is obtained Calculated,

[0140] Through Fuzzy Editor's 18 fuzzy rule operations (such as Figure 8 ), based on the center of gravity method, the system automatically obtains the target parking space of the car as 1.9. The induction parking decision based on Mamdani shows that the most suitable parking position is the 1.9 position near the elevator of the office building. The mapping surface between Type, Frequency, Reliability and Target is as follows Figure 9 shown.

[0141] ...

Embodiment 2

[0143] The user parked 12 times last month and drove into the CBD complex again today. There is no natural language input when entering the parking lot. Assume that according to the result of the system's last self-learning, its target parking space is 0.03. The system calculates its reliability index Reliability Index=0.67*(0.45+(12-13.5) / 30=0.268. According to the Mamdani model, its target position is 0.015, such as Figure 10 . When it drives out of the parking lot, the system finds that the parking time is 3 hours, then the system self-learning results are as follows: That is to say, the next target parking space will find a new balance between the elevator for shopping and watching movies. 0.31 is between 0 and 1. According to the result of system learning, on the one hand, the system concludes that the user’s recent parking purpose is between shopping (escalator) and watching movies and eating (movie elevator); on the other hand, the user Every time I come to park, ...

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Abstract

The present invention discloses a novel and service body-oriented guidance parking decision-making method, and relates to the field of fuzzy algorithm control. The method comprises: processing non-quantitative data of a natural language or a personal habit by establishing a membership function, blurring, and a fuzzy rule base; and then performing an anti-blurring process, so as to obtain final control result information to further smoothly guide customer parking. A highlight of the decision-making algorithm is a breakthrough of a conventional sitting-and-floundering parking mode. On the one hand, the natural language of a parking person may be actively analyzed; and on the other hand, the personal habit may be analyzed by using a history background data warehouse of a system, so as to finally reasonably arrange limited resources of a parking lot. The decision-making algorithm can cooperate with a self-learning function of the system, so as to intellectualize parking lot management. The algorithm has high practical significance and practical value.

Description

technical field [0001] The invention relates to the field of fuzzy control, in particular to a natural language-oriented decision-making method for inducing parking by utilizing precipitated metadata information. Background technique [0002] Due to the hierarchical and stepped form of domestic economic development, the demand and understanding of smart parking lots are also in different states. In short, the transparency and visibility of parking lot resources in economically underdeveloped areas have not yet been perfected, because the number of cars is not as large as that of first- and second-tier cities, and the demand for parking lots is also different; for large-scale parking lots with high economic development As far as cities are concerned, the information of urban public or private parking lots is transparent, and can be displayed in real time on web pages and even some mobile clients. However, the induced parking service in the parking lot is not satisfactory, ma...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/24G06Q50/26
CPCG06F16/24573G06F16/2468G06Q50/26
Inventor 宋培森
Owner XUZHOU COLLEGE OF INDAL TECH