Intelligent granary management and control method based on machine learning and terminal
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 machine learning and granary technology, applied in the field of intelligent granary management, can solve problems such as poor conductors and temperature hysteresis, achieve fine prediction and traceability results, and optimize manual scheduling.
Pending Publication Date: 2020-04-17
FUJIAN REIDA PRECISION
View PDF3 Cites 2 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
However, no matter which system is used, the following problems cannot be avoided: grain is a poor conductor, and the ambient temperature collected by the temperature sensor is the temperature around the s
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
[0079] Please refer to figure 1 , Embodiment 1 of the present invention is:
[0080] A machine learning-based intelligent granary control method, comprising steps:
[0081] S1. Obtain the input factors to be predicted of the smart granary, and send the input factors to be predicted to the granary analysis and decision-making model to obtain the personnel scheduling strategy. The granary analysis and decision-making model includes the establishment of a multi-layer feed-forward neural network trained by the error back propagation algorithm The feed-forward model of the personnel scheduling strategy includes sampling personnel, sampling objects, sampling time and sampling path.
[0082] Wherein, the specific steps of obtaining the feedforward model in step S1 are:
[0083] S11, construct and initialize the BP neural network model, the initialized BP neural network model includes an input layer, a hidden layer and an output layer;
[0084]S12. Obtain historical data, divide th...
Embodiment 2
[0107] Please refer to figure 2 , the second embodiment of the present invention is:
[0108] An intelligent granary management and control terminal 1 based on machine learning, including a memory 3, a processor 2, and a computer program stored in the memory 3 and operable on the processor 2. When the processor 2 executes the computer program, the first embodiment described above is realized. A step of.
[0109] To sum up, the present invention provides a machine learning-based intelligent granary management and control method and terminal. By learning historical data to obtain a granary analysis and decision-making model, by dividing the test set and training set, so that when there are enough samples The data distribution of the training set and the test set will not be changed, and the prediction accuracy on the test set can better measure the accuracy of the entire model; for air pollution history with multifactor, uncertainty, randomness, and nonlinearity The data is u...
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 an intelligent granary management and control method based on machine learning and a terminal. The method comprises steps of obtaining a to-be-predicted input factor of the intelligent granary, sending the to-be-predicted input factor to a granary analysis decision model to obtain a personnel scheduling strategy, the granary analysis decision model comprising a feedforwardmodel established by using a multi-layer feedforward neural network trained by an error back propagation algorithm, and the personnel scheduling strategy comprising sampling inspection personnel, sampling inspection objects and sampling inspection time. According to the invention, the historical data is learned to obtain the granary analysis decision model, and the output error of the network is continuously reduced by adopting the feedforward model established by the multi-layer feedforward neural network trained by the error back propagation algorithm, so that a finer prediction and traceability result is presented; the optimal manual scheduling strategy is found on the basis of the model, so that an intuitive, clear and effective means is provided for personnel allocation for grain management of government grain departments and storage enterprises, and optimal manual scheduling is realized.
Description
technical field [0001] The invention relates to the field of intelligent granary management, in particular to a machine learning-based intelligent granary management and control method and terminal. Background technique [0002] Grain is one of the country's three major strategic resources. Maintaining a certain amount, variety and quality of grain reserves is an essential measure to ensure national food security. Grain storage technology has always been valued by the country's war preparation materials. my country currently has a total More than 27,000 stores. [0003] The grain in the grain storage bin is closely related to the ambient air temperature and humidity outside the grain storage bin. Due to the self-heating characteristics of grain, the temperature and humidity of the grain pile will change seasonally with the outside world, which will cause the grain to heat and mildew, resulting in changes in the quality of the grain. And low temperature storage will make the...
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