CN109657701B - Real-time pedestrian flow retention amount detection method for macroscopic region - Google Patents

Abstract

The invention discloses a macroscopic region real-time people flow reserve detection method, which is a macroscopic region flow information detection method based on a unique ID (identity) of a portable mobile terminal, a collected data timestamp and detection equipment position information, and is used for mining and analyzing data collected by the mobile terminal, so that the real-time macroscopic region people flow reserve is efficiently and accurately detected.

Description

Real-time pedestrian flow retention amount detection method for macroscopic region

Technical Field

The invention belongs to the mobile intelligent Internet technology, and particularly relates to a scenic spot and other people flow detection and evaluation algorithm.

Background

The regional flow data is an important information source of the current smart campus, smart scenic spot and smart city, and can provide decision assistance for regional people flow density evaluation, regional people flow change, flow induction and the like.

With the rapid development of economy and science and technology in China, in order to better manage the areas of daily life and travel of people such as campuses, scenic spots and towns, the convenience and the happiness of daily life and travel of people are improved, and the construction of smart campuses, smart scenic spots and smart cities becomes a new trend of future development.

In the construction process of smart campuses, smart cities and the like, dynamic monitoring, estimation, dynamic detection, prediction and guidance of people flow are indispensable important components.

Taking scenic spots as an example, a great deal of digital monitoring is established at present to monitor the flow of people in the scenic spots, for example, the total flow of people in the scenic spots is recorded through an entrance and exit gate; the method comprises the steps that visual screen monitoring is arranged in a key area, and people flow in a current area is identified; and displaying the related information in the scenic spot and the like to the tourists through a digital display screen.

However, although various devices are deployed at present, management requirements cannot be met in terms of people flow detection and prediction, for example, an import-export gate can only record the total reservation in a scenic spot, and cannot record people flow scattered in each scenic spot, and videos and the like can be clearly visualized, even people number can be identified in an image identification mode, full coverage cannot be achieved, global statistics cannot be achieved naturally, and prediction of flow cannot be achieved.

Disclosure of Invention

Aiming at the problems in the prior art, the invention provides a method for detecting the real-time pedestrian flow retention amount in a macroscopic region, and the method realizes the efficient and accurate detection of the real-time pedestrian flow retention amount in the macroscopic region.

In order to solve the technical problem, the invention adopts the following technical scheme: a real-time pedestrian flow conservation quantity detection method for a macro area comprises a plurality of detection sub-networks and a remote background server which are deployed in the range of the macro area according to a physical space, wherein each detection sub-network comprises a plurality of extension sets and a host which is in data connection with the extension sets; the extension set collects broadcast type data packets sent to the surrounding environment by mobile terminal equipment based on a WIFI protocol randomly within the coverage range of the extension set through a wireless passive sensing mode, screens the data packets with ID information of the mobile terminal equipment for retrieval, marks an extension set label and uploads the extension set label to a host, the host stores the collected data in a unified mode, marks a time label on the collected data, uploads the collected data to a remote background server for storage, and analyzes the data according to the following steps:

step 1: data D for a complete time period (T-deltat) are collected by the extension set,

wherein D is _ij Indicating ith sub-network extension data; n represents the number of the sub-networks, and M represents the number of the sub-networks in the jth sub-network;

and 2, step: slicing the data and extracting data D 'of a local area S to be analyzed and a complete time period (T-delta T)' _s ，

In the formula D _is Number collected by extension with number i under s sub-networkAccordingly;

and step 3: carrying out space matching on the deployed sub-network and the corresponding local area S to obtain the number information of the sub-network host and the corresponding road section and an extension deployment condition list of the sub-network;

and 4, step 4: sequencing the data collected by each extension according to the ID number of the mobile terminal equipment, and establishing a data matrix { Tower (i, s), T };

and 5: classifying the established ID data list according to the times c of occurrence in different data matrixes: let ID denote the ID that appears in only one data matrix Tower _c＝1 And extracting and analyzing separately, and classifying ID data appearing in more than two data matrixes Tower into ID _c＞1 ；

Step 6: analyzing ID data only appearing in one data matrix Tower in the time period delta t, if the ID data is not found to repeatedly appear in the data matrix Tower in the time period delta t, traversing adjacent subnets around the local area S to find whether the same ID data appears, and if the same ID data does not appear, classifying the ID data as the ID data _c＝1 Identifying the ID data as an ID if it is present in another subnet _c＞1 ；

And 7: repeating the steps 4-6 until the data processing is finished;

and 8: the data obtained by the processing of the steps 5 and 6 are combined together to form two types of data ID _c＝1 And ID _c＞1 ；

And step 9: at T _k And in the time delta t of the moment, the flow retention in the region is V, V = D { ID }, and the real-time region flow retention is evaluated by adopting the following method:

(1) ID (identity) _c＞1 The middle data is subjected to data normalization processing and then is added into effective data;

(2) Will ID _c＝1 The data is processed by approximate fitting of an F (g) approximate fitting function, and a bias reference system S is set in the fitting process to obtain (S-V) _c＞1 )/V _c＝1 As a feedback judgment condition, when

Completing the fitting to obtain ID _c＝1 Where θ is the allowable error of the system setting;

(3) Last pair of ID _c＞1 And ID _c＝1 The effective data are summed to obtain regional people stream holding data V (T),

wherein x and y are each ID _c＞1 And ID _c＝1 The total number of valid data in the data stream.

Preferably, the bias reference system S is a regional video count or an entrance and exit gate count.

Has the beneficial effects that: the invention provides a macroscopic region flow information detection algorithm based on the unique ID of a portable mobile terminal, a collected data timestamp and position information of detection equipment, which realizes data mining and analysis of the collected data of the mobile terminal and realizes efficient and accurate detection of the real-time macroscopic region people flow conservation quantity.

Drawings

FIG. 1 is a schematic logic flow diagram of a real-time pedestrian flow conservation detection method for a macro area according to the present invention;

fig. 2 is a schematic diagram of a machine learning and self-feedback principle in the macro-area real-time people flow conservation quantity detection method of the invention.

FIG. 3 shows a thermodynamic diagram of human resources conservation at a certain time when the present invention is applied to a certain area in Nanjing.

Detailed Description

The invention will be further elucidated with reference to the following description of an embodiment in conjunction with the accompanying drawing. It is to be understood that these examples are for illustrative purposes only and are not intended to limit the scope of the present invention, which is to be given the full breadth of the appended claims and any and all equivalent modifications thereof which may occur to those skilled in the art upon reading the present specification.

As shown in fig. 1 and 2, a basic flow chart of the real-time regional people stream retention amount detection method of the present invention is provided, and the specific method steps are as follows:

step 1: complete data for a complete time period (T- Δ T) is collected, the complete data being expressed as

Wherein D _ij Indicating ith sub-network extension data;

and 2, step: slicing the data, and extracting and analyzing the complete data of the local area S and the time period (T-delta T)

And 3, step 3: mapping physical space positions, corresponding to the deployed sub-networks, and carrying out space matching on the deployed sub-networks and an actual region S, wherein each sub-network host is provided with corresponding region corresponding number information and an extension deployment condition list;

and 4, step 4: sequencing the acquired data according to the ID number of the acquired data, and establishing a data matrix { Tower (i, S), t };

and 5: classifying the established ID data list according to the times c of appearance in different Tower, extracting ID appearing in only one Tower alone for analysis, and classifying ID data appearing in more than two towers into one kind of ID _c＞1 ；

Step 6: analyzing the ID only appearing in one Tower in the time period delta t, if the ID is not found to repeatedly appear in the Tower in the time period delta t, traversing the sub-networks around the area S, searching whether the same ID number appears, and if the ID does not appear, classifying the data into a class ID number _c＝1 If the data appears in other sub-networks, marking the data, and attributing the data to the data ID in the step 5 _c＞1 A list;

and 7: repeating the steps 4, 5 and 6 until the data processing is finished;

and 8: merging the effective data processed in the steps 5 and 6, and dividing the merged data into two types of IDs _c＞1 And ID _c＝1 ；

And step 9: at time T and within a time period Δ T, the local traffic conservation quantity is V = D { ID }, and the real-time local traffic conservation quantity is evaluated by the following method, as shown in the following network:

from the figure, ID _c＞1 X or ID _c＝1 Y denotes valid ID data, where ID _c＞1 The data in _xare normalized and directly added to the effective holding quantity, ID _c＝1 The data in _yneeds to be approximated by an F (·) approximate fitting function, and then effective holding quantity can be counted, and the offset reference system S represents effective reference units, such as video counting, inlet and outlet gate counting and the like. Accumulating ID data, fitting by approximate fitting function F (-) under the feedback judgment condition of (S-V) _c＞1 )/V _c＝1 The threshold range of the ratio can be adjusted according to the detection precision requirement, and the detection precision can be improved

And (theta is the allowable error of the system setting), indicating that the data fitting work is completed and forming effective data.

Step 10: and (5) ending the flow, and outputting the people flow holding amount data through the following formula.

Fig. 3 shows that the invention is applied to a certain area of Nanjing to obtain a people flow conservation thermodynamic diagram at a certain time, a macroscopic region flow information detection device is deployed in the certain area of Nanjing, and data is analyzed to obtain the people flow conservation thermodynamic diagram.

Claims (2)

1. A real-time pedestrian flow retention amount detection method for a macroscopic region is characterized by comprising the following steps: the system comprises a plurality of detection sub-networks and a remote background server, wherein the detection sub-networks are deployed in a macro area according to physical space, and each detection sub-network comprises a plurality of extension sets and a host computer in data connection with the extension sets; the extension set collects broadcast type data packets sent to the surrounding environment by mobile terminal equipment based on a WIFI protocol randomly within the coverage range of the extension set through a wireless passive sensing mode, screens the data packets with ID information of the mobile terminal equipment for retrieval, marks an extension set label and uploads the extension set label to a host, the host stores the collected data in a unified mode, marks a time label on the collected data, uploads the collected data to a remote background server for storage, and analyzes the data according to the following steps:

step 1: data D for a complete time period (T-deltat) are collected by the extension set,

wherein D is _ij Indicating the ith extension data of the jth subnet; n represents the number of the sub-networks, and M represents the number of the sub-networks in the jth sub-network;

step 2: slicing the data and extracting the data D of the local area S to be analyzed and the complete time period (T-delta T) _s '，

In the formula D _is The data collected by the extension set with the number i under the s sub-network;

and step 3: carrying out space matching on the deployed sub-network and the corresponding local area S to obtain the number information of the sub-network host and the corresponding road section and an extension deployment condition list of the sub-network;

and 4, step 4: sequencing data collected by each extension according to the ID number of the mobile terminal equipment, and establishing a data matrix { Tower (i, s), T } in a time period of (T-delta T);

and 5: classifying the established ID data list according to the times c of occurrence in different data matrixes: let ID denote the ID that appears in only one data matrix Tower _c＝1 And extracting and analyzing separately, and classifying ID data appearing in more than two data matrixes Tower into ID _c＞1 ；

Step 6: analyzing ID data only appearing in one data matrix Tower in the time period delta t, if the ID data is not found to repeatedly appear in the data matrix Tower in the time period delta t, traversing adjacent subnets around the local area S to find whether the same ID data appears, and if the same ID data does not appear, classifying the ID data as the ID data _c＝1 If present in other subnets, the ID data is identified as ID _c＞1 ；

And 7: repeating the steps 4-6 until the data processing is finished;

and 8: the data obtained by the processing of the steps 5 and 6 are combined together to form two types of data ID _c＝1 And ID _c＞1 ；

And step 9: at T _k And in the time delta t of the moment, the flow retention in the region is V, V = D { ID }, and the real-time region flow retention is evaluated by adopting the following method:

(1) ID (identity) _c＞1 The middle data is subjected to data normalization processing and then is added into effective data;

(2) ID (identity) _c＝1 The middle data is approximately fitted by an F (-) approximate fitting function, and a bias reference system S is set in the fitting process to obtain (S-V) _c＞1 )/V _c＝1 As a feedback judgment condition, when

Completing the fitting to obtain ID _c＝1 Where θ is the allowable error of the system setting;

(3) Last pair of ID _c＞1 And ID _c＝1 The effective data are summed to obtain regional people stream holding data V (T),

wherein x and y are each ID _c＞1 And ID _c＝1 The total number of valid data in the data stream.

2. The macro-area real-time people flow conservation quantity detection method according to claim 1, characterized in that: the bias reference system S is a regional video count or an entrance and exit gate count.

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