Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

A method and device for positioning and partitioning geographic information based on deep learning

A geographic information and deep learning technology, which is applied in the field of geographic information positioning and partitioning methods and devices, and can solve the problem of low positioning and partitioning accuracy.

Inactive Publication Date: 2020-11-03
WUHAN UNIV
View PDF5 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] In view of this, the present invention provides a method and device for positioning and partitioning based on geographic information of deep learning, to solve or at least partially solve the technical problem of low positioning and partitioning accuracy in the methods of the prior art

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
  • A method and device for positioning and partitioning geographic information based on deep learning
  • A method and device for positioning and partitioning geographic information based on deep learning
  • A method and device for positioning and partitioning geographic information based on deep learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0056] This embodiment provides a positioning partition method based on deep learning geographic information, please refer to figure 1 , the method includes:

[0057] First, step S1 is performed: obtaining the prediction result PerdictedInfs obtained through the preset deep learning model, wherein the prediction result includes multiple records corresponding to the records in the user information UserInfs.

[0058] Specifically, the preset deep learning model can be a known BiLSTM+CRF deep neural network model or an improved model. According to this model, the original user information can be predicted and identified, and the prediction result can be obtained. The records in the prediction result are pieces of information, and these records correspond to the user information one by one.

[0059] Through a lot of practice, the applicant of the present invention found that after obtaining the prediction result (that is, the user's cell information and location information), it ...

Embodiment 2

[0165] This embodiment provides a device for positioning and partitioning based on deep learning geographic information, please refer to image 3 , the device consists of:

[0166] The prediction result acquisition module 301 is used to obtain the prediction result PerdictedInfs obtained through the preset deep learning model, wherein the prediction result includes multiple records corresponding to the records in the user information UserInfs;

[0167] The record judging module 302 is used to judge whether all records in the prediction result have been processed, if not, then read a record inf in the prediction result, wherein the subscript of the record inf is index;

[0168] Geographic information acquisition module 303, configured to acquire geographic information in the record inf, wherein the geographic information includes districts, cities and counties;

[0169] Solve the judgment module 304, be used for judging whether the subscript index of record inf has been solved...

Embodiment 3

[0196] Based on the same inventive concept, the present application also provides a computer-readable storage medium 400, please refer to Figure 4 , on which a computer program 411 is stored, and the method in Embodiment 1 is implemented when the program is executed.

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 provides a geographic information positioning partitioning method and device based on deep learning. According to the method, a Geology API is used as a mapping tool; the problem that split attributes cannot be selected originally is solved; on this basis, a foundation is provided, A pre-pruning method and a PEP pruning method are designed; a decision tree model of the accurate positioning partition is obtained by combining data and Geology API characteristics and improving based on a decision tree C4.5 algorithm; and according to a preset information gain rate solving algorithm,a split attribute is selected from the solved attribute values, a prediction result obtained through a preset deep learning model is further positioned and partitioned through a preset decision treemodel, accurate partition information is obtained, and the technical effect that the positioning partition precision is not high is achieved.

Description

technical field [0001] The present invention relates to the technical field of artificial intelligence, in particular to a method and device for positioning and partitioning geographic information based on deep learning. Background technique [0002] With the increasing improvement of the national economy, especially the vigorous development of the e-commerce industry, whether it is for social security considerations, or for business optimization management, cost reduction and efficiency improvement considerations, the user's geographical location information is accurately and quickly Analytics is getting more and more attention. [0003] However, due to the rapid growth of user data and the ever-changing changes in geographic information, especially the channels for collecting data are diverse and random due to historical reasons and work scenarios and other factors, resulting in user information There are many problems, mainly in the two aspects of "lack of standardizatio...

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): G06F16/29
Inventor 凌广明徐爱萍穆晓峰徐武平
Owner WUHAN UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products