Unlock instant, AI-driven research and patent intelligence for your innovation.

Intelligent decision-making system and method based on deep learning

A technology of intelligent decision-making and deep learning, applied in neural learning methods, data processing applications, instruments, etc.

Active Publication Date: 2022-03-11
TERMINUSBEIJING TECH CO LTD
View PDF7 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] However, since the smart brain is a new thing in urban management after all, no effective smart management model has been established in various specific application fields. There is a close relationship between the number of parking lots in the surrounding area, and the division of traffic management resources within the entire city and the correction of the operation mode of urban monitoring equipment can be achieved through the linkage needs of the two. However, how to express the above-mentioned close relationship with a model, And how to implement the division of traffic management resources within the entire city and the correction of the operation mode of urban monitoring equipment based on the established model is still a blank area for the application of smart brains

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

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Intelligent decision-making system and method based on deep learning
  • Intelligent decision-making system and method based on deep learning
  • Intelligent decision-making system and method based on deep learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0057] figure 2 It is a schematic structural diagram of an intelligent decision-making system based on deep learning according to Embodiment 1 of the present invention.

[0058] Such as figure 2 As shown, the intelligent decision-making system based on deep learning includes the following components:

[0059] The network building module is used to build a deep feedforward neural network, the deep feedforward neural network includes an input layer, an output layer and a plurality of hidden layers, and the input layer has a plurality of input data of a preset input quantity, The plurality of input data is a plurality of parking numbers corresponding to the nearest set number of parking lots around a fixed road section in the city at 12 o'clock in the morning. The output layer has two output data, and the first output data is all The total number of vehicles passing through the fixed road section of the city during the rush hour of the next day relative to 12 o'clock in the m...

Embodiment 2

[0070] image 3 It is a schematic structural diagram of an intelligent decision-making system based on deep learning according to Embodiment 2 of the present invention.

[0071] Such as image 3 As shown, compared with the foregoing embodiments, the intelligent decision-making system based on deep learning also includes:

[0072] The quantity extraction module is used to determine the quantity of traffic management personnel dispatched to the fixed road section of the city before the peak hours of work on the day based on the total number of predicted traffic received;

[0073] Wherein, determining the number of traffic management personnel dispatched to the fixed road section of the city before the rush hour of the day based on the total number of predicted traffic received includes: the more the total number of predicted traffic received, the more determined the number of traffic managers dispatched to the city before the rush hour of the day. The greater the number of traff...

Embodiment 3

[0076] Figure 4 It is a schematic structural diagram of an intelligent decision-making system based on deep learning according to Embodiment 3 of the present invention.

[0077] Such as Figure 4 As shown, compared with the foregoing embodiments, the intelligent decision-making system based on deep learning also includes:

[0078] A content storage module, connected to the sequential training module and the parameter analysis module, for receiving and storing the trained deep feedforward neural network, and receiving and storing the predicted total number of passes and the predicted total number of violations ;

[0079] Wherein, the content storage module may use multiple storage addresses that are physically isolated for storage operations in different locations of the trained deep feedforward neural network, the predicted total number of passes, and the predicted total number of violations.

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 present invention relates to an intelligent decision-making system based on deep learning. The system includes: a parameter analysis module, which is used to input the multiple parking numbers corresponding to the nearest set number of multiple parking lots around a fixed road section in the city collected in the early morning of the day To execute the trained deep feed-forward neural network, and obtain the total number of predicted passages and the total number of predicted violations; the frame rate mapping module is used to determine the vicinity of the signal lights of the fixed road section of the city during the rush hour of the day based on the received total number of predicted violations The capture frame rate of the visual capture terminal. The invention also relates to an intelligent decision-making method based on deep learning. Through the present invention, it is possible to establish an intelligent mapping model between the total number of vehicles passing by each fixed road section in the city, the total number of illegal vehicles, and the number of parking lots in the surrounding parking lots within the preset range, thereby completing the mapping between parking lot data and road section vehicle data. connections and interactions between them.

Description

technical field [0001] The present invention relates to the field of intelligent brains, in particular to an intelligent decision-making system and method based on deep learning. Background technique [0002] The goal of building a smart city is to give the common people a sense of gain and experience. The smart brain (also known as the city smart operation center) can actually help city managers improve the level of urban operation and management, build a civilized and environmentally friendly city, and improve the level of government services. [0003] The smart brain is the distribution center of business and data in the smart city, and it is the perception center, interconnection center, management center and decision-making center of the smart city. All kinds of business and data are gathered in the operation center, and through decision-making analysis, they spread to surrounding businesses in the form of instructions to realize comprehensive management and joint comma...

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 Patents(China)
IPC IPC(8): G06N3/04G06N3/08G06Q10/06G06Q50/26G06Q50/30
CPCG06N3/08G06Q10/06375G06Q50/26G06N3/044G06N3/045G06Q50/40
Inventor 李启娟
Owner TERMINUSBEIJING TECH CO LTD