Unlock instant, AI-driven research and patent intelligence for your innovation.
Method and system for realizing water level prediction based on GRU network
What is Al technical title?
Al technical title is built by PatSnap Al team. It summarizes the technical point description of the patent document.
A network implementation, water level technology, applied in prediction, neural learning method, biological neural network model, etc., can solve problems such as poor adaptability
Inactive Publication Date: 2020-04-10
ZHEJIANG PONSHINE INFORMATION TECH CO LTD
View PDF5 Cites 6 Cited by
Summary
Abstract
Description
Claims
Application Information
AI Technical Summary
This helps you quickly interpret patents by identifying the three key elements:
Problems solved by technology
Method used
Benefits of technology
Problems solved by technology
For example, the Xin'anjiang model, etc., these models have a certain scope of application, and can only be mastered by some professionals, so the adaptability is not strong
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
Click on the blue label to locate the original text in one second.
Reading with bidirectional positioning of images and text.
Smart Image
Examples
Experimental program
Comparison scheme
Effect test
Embodiment 1
[0041] This embodiment provides a system for realizing water level prediction based on the GRU network, such as figure 1 As shown, comprising: a building block 11, a training module 12, a verification module 13;
[0042] The building block 11 is used to build a GRU-based GRU network model;
[0043] Described training module 12 is used for collecting water level and rainfall information, and the feature vector input corresponding to water level and rainfall information in the GRU network realizes the training of GRU network model;
[0044] The verification module 13 is configured to input test data into the trained GRU network model, and predict the height of the water level through the trained GRU network model.
[0045] In building block 11, build a GRU-based GRU network model.
[0046] In this embodiment, the GRU module of TensorFlow is used as a framework to form a GRU network model.
[0047] Among them, TensorFlow is a symbolic mathematical system based on dataflow prog...
Embodiment 2
[0077] This embodiment provides a method for realizing water level prediction based on the GRU network, including steps:
[0078] S1. Constructing a GRU network model based on GRU;
[0079] S2. Collect water level and rainfall information, and input the feature vector corresponding to water level and rainfall information into the GRU network to realize the training of the GRU network model;
[0080] S3. Input the test data into the trained GRU network model, and predict the height of the water level through the trained GRU network model.
[0081] Further, the step S2 includes:
[0082] S21. Collect water level and rainfall information, and perform characteristic statistics on the collected water level and rainfall information;
[0083] S22. Converting the statistical features of the water level and rainfall information into feature vectors;
[0084] S23. Using the feature vector as the input vector of the GRU network, the GRU network outputs the height of the predicted wate...
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
Login to View More
Abstract
The invention discloses a system for realizing water level prediction based on a GRU network. The system comprises a construction module, a training module and a verification module, the constructionmodule is used for constructing a GRU network model based on the GRU; the training module is used for collecting water level and rainfall information and inputting feature vectors corresponding to thewater level and rainfall information into the GRU network to achieve training of the GRU network model; and the verification module is used for inputting test data into the trained GRU network modeland predicting the height of the water level through the trained GRU network model. The height of the water level can be accurately predicted, early warning is achieved in advance, countermeasures aretaken in advance, the water level of the water area is made to be at the normal height, and life safety and property safety of people are guaranteed.
Description
technical field [0001] The invention relates to the technical field of water level prediction, in particular to a method and system for realizing water level prediction based on a GRU network. Background technique [0002] With the rapid development of my country's economy and the continuous advancement of science and technology, especially the rise of the "Internet", many jobs have become simplified and informationized. In recent years, the flood season in our country has been getting longer and longer, which has greatly hindered the safety of our residents and economic development. Every time the rainy season enters, our country's flood control work will be daily severe. Therefore, how to improve the informatization of our country's flood control work , Rapid and accurate early warning has become the most critical and urgent matter for current flood control, and effective analysis methods should be used to predict and early warning in time. [0003] There are currently ma...
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
Application Date:The date an application was filed.
Publication Date:The date a patent or application was officially published.
First Publication Date:The earliest publication date of a patent with the same application number.
Issue Date:Publication date of the patent grant document.
PCT Entry Date:The Entry date of PCT National Phase.
Estimated Expiry Date:The statutory expiry date of a patent right according to the Patent Law, and it is the longest term of protection that the patent right can achieve without the termination of the patent right due to other reasons(Term extension factor has been taken into account ).
Invalid Date:Actual expiry date is based on effective date or publication date of legal transaction data of invalid patent.
Login to View More 
Login to View More