A People Flow Prediction Method Based on Thinking Evolutionary Algorithm

A technology of thinking evolution algorithm and forecasting method, which is applied in the field of traffic flow forecasting, can solve the problem of low forecasting accuracy, achieve accurate scientific basis, and improve the effect of forecasting level

Active Publication Date: 2020-11-24
JIANGSU ELECTRIC POWER CO +4
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AI Technical Summary

Problems solved by technology

Although the traditional neural network can be used to numerically predict the flow of people, its prediction accuracy is not high and still needs to be improved. Therefore, it is necessary to design a more accurate modeling method for predicting the flow of people

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  • A People Flow Prediction Method Based on Thinking Evolutionary Algorithm
  • A People Flow Prediction Method Based on Thinking Evolutionary Algorithm

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Embodiment Construction

[0020] Below in conjunction with accompanying drawing, technical scheme of the present invention is described in further detail:

[0021] Those skilled in the art can understand that, unless otherwise defined, all terms (including technical terms and scientific terms) used herein have the same meaning as commonly understood by those of ordinary skill in the art to which this invention belongs. It should also be understood that terms such as those defined in commonly used dictionaries should be understood to have a meaning consistent with the meaning in the context of the prior art, and will not be interpreted in an idealized or overly formal sense unless defined as herein Explanation.

[0022] The invention provides a method for forecasting and modeling human flow based on the thinking evolution algorithm. The model is based on the BP neural network, and the thinking evolution algorithm is introduced to optimize the structure of the BP neural network, thereby improving the pre...

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Abstract

The invention discloses a mind evolutionary algorithm based people flow prediction method. On the basis of a neural network prediction model, power supply business hall people flow influence factors are considered comprehensively, input data of the prediction model include corresponding time points, current weather and workday or rest day, and output data include a people flow prediction value atan expected moment. A traditional neural network structure is optimized through a mind evolutionary algorithm, implicit strata weight and threshold optimization is realized through convergence and dissimilation operation, prediction errors caused by random generation of implicit strata weights and thresholds are reduced, and accordingly a high-precision prediction model is established, the power supply business hall people flow prediction level is raised, and accurate scientific bases are provided for power supply business halls to make schemes for raising own service level.

Description

technical field [0001] The invention relates to a method for predicting human flow, which belongs to the technical field of intelligent prediction. Background technique [0002] As the business of power companies is gradually integrated into a deeper market economy environment, improving the power marketing business with high-quality services has become a necessary task for current power companies to carry out daily operation and management. As a window unit directly facing power users, the service level of the power supply business hall directly affects the impression of power customers on the enterprise. [0003] The flow of people in the power supply business hall is the core reference basis for the staffing and equipment of the power supply business hall, and it is a key influencing factor for the service quality of the power supply business hall itself. Although the traditional neural network can be used to numerically predict the flow of people, its prediction accurac...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q10/04G06Q50/06G06N3/00G06N3/08
CPCG06N3/006G06N3/084G06Q10/04G06Q50/06
Inventor 庄岭李维夏飞黄伟李达赵新建俞俊王召籍天明缪静文杨春松
Owner JIANGSU ELECTRIC POWER CO
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