阅读说明：本技术 一种基于移动通信大数据的人群流动监控方法及系统 (Crowd flow monitoring method and system based on mobile communication big data ) 是由 兰海翔 杨启帆 张涛 雷厚宇 陆苇 黄纪萍 于 2020-11-30 设计创作，主要内容包括：本发明涉及一种基于移动通信大数据的人群流动监控方法及系统,其方法包括以下步骤,获取预设区域人群中每个个体的历史移动通信大数据和实时移动通信数据；分别对每个个体的历史移动通信大数据进行分析,得到每个个体的历史移动轨迹；基于每个个体的历史移动轨迹,分别计算出每个个体的未来移动方向初始概率集；根据每个个体的实时移动通信数据和未来移动方向初始概率集,得到每个个体的未来移动方向；基于所有个体的未来移动方向,对人群流动进行综合性监控。本发明可以为城市交通规划提供决策依据,减轻交通压力。(The invention relates to a crowd flow monitoring method and a system based on mobile communication big data, wherein the method comprises the following steps of obtaining historical mobile communication big data and real-time mobile communication data of each individual in a crowd in a preset area; analyzing the historical mobile communication big data of each individual to obtain the historical mobile track of each individual; respectively calculating an initial probability set of the future moving direction of each individual based on the historical moving track of each individual; obtaining the future moving direction of each individual according to the real-time mobile communication data and the future moving direction initial probability set of each individual; crowd flow is monitored comprehensively based on the future movement directions of all individuals. The invention can provide decision basis for urban traffic planning and reduce traffic pressure.)

1. A crowd flowing monitoring method based on mobile communication big data is characterized in that: comprises the following steps of (a) carrying out,

s1, acquiring historical mobile communication big data and real-time mobile communication data of each individual in the crowd in the preset area;

s2, analyzing the historical mobile communication big data of each individual to obtain the historical mobile track of each individual;

s3, respectively calculating the future moving direction initial probability set of each individual based on the historical moving track of each individual;

s4, obtaining the future moving direction of each individual according to the real-time mobile communication data and the future moving direction initial probability set of each individual;

s5, comprehensively monitoring the crowd flow based on the future moving directions of all the individuals.

2. The crowd flow monitoring method based on mobile communication big data as claimed in claim 1, wherein: in S1, the real-time mobile communication data is mobile communication data of the current day, and the historical mobile communication big data is mobile communication big data of a week before the current day.

3. The crowd flow monitoring method based on mobile communication big data according to claim 1 or 2, characterized in that: specifically, in S3, habitual analysis and periodic analysis are performed on the historical movement trajectory of each individual, and an initial probability set of the future movement direction of each individual is calculated.

4. The crowd flow monitoring method based on mobile communication big data according to claim 1 or 2, characterized in that: specifically, the S4 is to input the future movement direction initial probability set and the real-time mobile communication data of each individual into the neural network prediction model based on deep learning to be processed, so as to obtain the future movement direction of each individual.

5. The crowd flow monitoring method based on mobile communication big data according to claim 1 or 2, characterized in that: the step S5 is specifically to count the future moving directions of all individuals, and comprehensively monitor the crowd flow.

6. The utility model provides a crowd monitoring system that flows based on mobile communication big data which characterized in that: comprises the following modules which are used for realizing the functions of the system,

the mobile communication data acquisition module is used for acquiring historical mobile communication big data and real-time mobile communication data of each individual in a preset area crowd;

the historical mobile communication big data module is used for analyzing the historical mobile communication big data of each individual to obtain the historical movement track of each individual;

the future moving direction initial probability calculation module is used for calculating a future moving direction initial probability set of each individual based on the historical moving track of each individual;

the future moving direction prediction module is used for obtaining the future moving direction of each individual according to the real-time mobile communication data and the future moving direction initial probability set of each individual;

and the comprehensive monitoring module is used for comprehensively monitoring the crowd flow based on the future moving directions of all the individuals.

7. The crowd flow monitoring system based on big data of mobile communication according to claim 6, characterized in that: in the mobile communication data acquisition module, the real-time mobile communication data is specifically the mobile communication data of the current day, and the historical mobile communication big data is specifically the mobile communication big data in the previous week of the current day.

8. The crowd flow monitoring system based on mobile communication big data according to claim 6 or 7, characterized in that: the future moving direction initial probability calculation module is specifically configured to perform habitual and periodic analysis on the historical moving trajectory of each individual, and calculate a future moving direction initial probability set of each individual respectively.

9. The crowd flow monitoring system based on mobile communication big data according to claim 6 or 7, characterized in that: the future moving direction prediction module is specifically used for inputting the future moving direction initial probability set and the real-time mobile communication data of each individual into a neural network prediction model based on deep learning for processing to obtain the future moving direction of each individual.

10. The crowd flow monitoring system based on mobile communication big data according to claim 6 or 7, characterized in that: the comprehensive monitoring module is specifically used for counting the future moving directions of all individuals and comprehensively monitoring the crowd flow.

Technical Field

The invention relates to the field of mobile communication, in particular to a crowd flow monitoring method and system based on mobile communication big data.

Background

The population is a general term for people living in a certain social system and a certain region and having a certain quantity and quality. Population information is important national situation information and has important significance for administrative management, population research, understanding market supply and demand, making social and economic development plans and the like. In particular, for the development of a city, the traffic is an urgent problem to be solved, and the uncertainty of the urban population flow can bring great pressure to the traffic.

Disclosure of Invention

The invention aims to solve the technical problem of providing a crowd flow monitoring method and system based on mobile communication big data, which can predict the flow direction of crowds, thereby providing decision basis for urban traffic planning and reducing traffic pressure.

The technical scheme for solving the technical problems is as follows: a crowd flow monitoring method based on mobile communication big data comprises the following steps,

s1, acquiring historical mobile communication big data and real-time mobile communication data of each individual in the crowd in the preset area;

s2, analyzing the historical mobile communication big data of each individual to obtain the historical mobile track of each individual;

s3, respectively calculating the future moving direction initial probability set of each individual based on the historical moving track of each individual;

s4, obtaining the future moving direction of each individual according to the real-time mobile communication data and the future moving direction initial probability set of each individual;

s5, comprehensively monitoring the crowd flow based on the future moving directions of all the individuals.

On the basis of the technical scheme, the invention can be further improved as follows.

Further, in S1, the real-time mobile communication data is mobile communication data of the current day, and the historical mobile communication big data is mobile communication big data of a week before the current day.

Further, in step S3, specifically, the historical movement trajectory of each individual is subjected to habitual and periodic analyses, and an initial probability set of future movement directions of each individual is calculated.

Further, in step S4, the future movement direction initial probability set and the real-time mobile communication data of each individual are input into a neural network prediction model based on deep learning to be processed, so as to obtain the future movement direction of each individual.

Further, the step S5 is specifically to count the future moving directions of all individuals, and comprehensively monitor the crowd movement.

Based on the crowd flow monitoring method based on the mobile communication big data, the invention also provides a crowd flow monitoring system based on the mobile communication big data.

A crowd flow monitoring system based on mobile communication big data comprises the following modules,

the mobile communication data acquisition module is used for acquiring historical mobile communication big data and real-time mobile communication data of each individual in a preset area crowd;

the historical mobile communication big data module is used for analyzing the historical mobile communication big data of each individual to obtain the historical movement track of each individual;

the future moving direction initial probability calculation module is used for calculating a future moving direction initial probability set of each individual based on the historical moving track of each individual;

the future moving direction prediction module is used for obtaining the future moving direction of each individual according to the real-time mobile communication data and the future moving direction initial probability set of each individual;

and the comprehensive monitoring module is used for comprehensively monitoring the crowd flow based on the future moving directions of all the individuals.

On the basis of the technical scheme, the invention can be further improved as follows.

Further, in the mobile communication data acquisition module, the real-time mobile communication data is specifically mobile communication data of the current day, and the historical mobile communication big data is specifically mobile communication big data of a week before the current day.

Further, the future moving direction initial probability calculation module is specifically configured to perform habitual and periodic analysis on the historical moving trajectory of each individual, and calculate a future moving direction initial probability set of each individual respectively.

Further, the future moving direction prediction module is specifically configured to input the future moving direction initial probability set and the real-time mobile communication data of each individual into a neural network prediction model based on deep learning to be processed, so as to obtain the future moving direction of each individual.

Further, the comprehensive monitoring module is specifically configured to count future moving directions of all individuals and comprehensively monitor crowd movement.

The invention has the beneficial effects that: a crowd flow monitoring method and system based on mobile communication big data obtains a historical moving track through the historical mobile communication big data, calculates a future moving direction initial probability set through the historical moving track, predicts a future moving direction through real-time mobile communication data and the future moving direction initial probability set, and finally comprehensively monitors crowd flow based on the future moving directions of all individuals; the method can provide decision basis for urban traffic planning and reduce traffic pressure.

Drawings

Fig. 1 is a flow chart of a crowd flow monitoring method based on mobile communication big data according to the invention;

fig. 2 is a block diagram of a crowd flow monitoring system based on mobile communication big data according to the present invention.

Detailed Description

The principles and features of this invention are described below in conjunction with the following drawings, which are set forth by way of illustration only and are not intended to limit the scope of the invention.

As shown in fig. 1, a crowd flow monitoring method based on mobile communication big data comprises the following steps,

s1, acquiring historical mobile communication big data and real-time mobile communication data of each individual in the crowd in the preset area;

s2, analyzing the historical mobile communication big data of each individual to obtain the historical mobile track of each individual;

s3, respectively calculating the future moving direction initial probability set of each individual based on the historical moving track of each individual;

s4, obtaining the future moving direction of each individual according to the real-time mobile communication data and the future moving direction initial probability set of each individual;

s5, comprehensively monitoring the crowd flow based on the future moving directions of all the individuals.

In this embodiment, the following preferred embodiments are also provided:

preferably, in S1, the real-time mobile communication data is mobile communication data of the current day, and the historical mobile communication big data is mobile communication big data of a week before the current day.

Preferably, in S3, the historical movement trajectory of each individual is habitually and periodically analyzed, and an initial probability set of future movement directions of each individual is calculated.

Preferably, in S4, the future moving direction initial probability set and the real-time mobile communication data of each individual are input into a neural network prediction model based on deep learning to be processed, so as to obtain the future moving direction of each individual.

Preferably, the step S5 is specifically to count the future moving directions of all individuals, so as to comprehensively monitor the crowd flow.

Based on the crowd flow monitoring method based on the mobile communication big data, the invention also provides a crowd flow monitoring system based on the mobile communication big data.

As shown in fig. 2, a crowd flow monitoring system based on mobile communication big data comprises the following modules,

the mobile communication data acquisition module is used for acquiring historical mobile communication big data and real-time mobile communication data of each individual in a preset area crowd;

the historical mobile communication big data module is used for analyzing the historical mobile communication big data of each individual to obtain the historical movement track of each individual;

the future moving direction initial probability calculation module is used for calculating a future moving direction initial probability set of each individual based on the historical moving track of each individual;

the future moving direction prediction module is used for obtaining the future moving direction of each individual according to the real-time mobile communication data and the future moving direction initial probability set of each individual;

and the comprehensive monitoring module is used for comprehensively monitoring the crowd flow based on the future moving directions of all the individuals.

In this embodiment, the following preferred embodiments are also provided:

preferably, in the mobile communication data obtaining module, the real-time mobile communication data is mobile communication data of the current day, and the historical mobile communication big data is mobile communication big data of a week before the current day.

Preferably, the future moving direction initial probability calculation module is specifically configured to perform habitual and periodic analysis on the historical moving trajectory of each individual, and calculate the future moving direction initial probability set of each individual respectively.

Preferably, the future moving direction prediction module is specifically configured to input the future moving direction initial probability set and the real-time mobile communication data of each individual into a neural network prediction model based on deep learning to be processed, so as to obtain the future moving direction of each individual.

Preferably, the comprehensive monitoring module is specifically configured to count future moving directions of all individuals and comprehensively monitor the crowd movement.

A crowd flow monitoring method and system based on mobile communication big data obtains a historical moving track through the historical mobile communication big data, calculates a future moving direction initial probability set through the historical moving track, predicts a future moving direction through real-time mobile communication data and the future moving direction initial probability set, and finally comprehensively monitors crowd flow based on the future moving directions of all individuals; the method can provide decision basis for urban traffic planning and reduce traffic pressure. For example, when it is predicted that the flow of people to a certain direction is large, traffic investment can be increased in the direction, for example, several buses are increased, the departure time interval is shortened, and the like, so that a dynamic bus decision is realized.

The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.