CN109657700B - Macroscopic region communicating channel heat degree detection method - Google Patents
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Abstract
The invention discloses a macroscopic region real-time flow detection method, which is based on a macroscopic region flow information detection algorithm of a portable mobile terminal unique ID, a collected data timestamp and detection equipment position information, realizes data mining and analysis of collected data of a mobile terminal, can be applied to accurate and efficient detection of macroscopic region real-time flow and provides guidance for a macroscopic region people flow control strategy.
Description
Technical Field
The invention belongs to the mobile intelligent Internet technology, and particularly relates to a scenic spot and other people stream detection and evaluation method.
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, convenience and happiness of daily life and travel of people are improved for better managing daily life and travel areas of people such as campuses, scenic spots and towns, 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 a scenic spot as an example, a large number of digital monitors are established at present to monitor the flow of people in the scenic spot, for example, the total flow of people in the scenic spot 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 the detection and prediction of people flow, for example, an import and export gate can only record the total reserved quantity in scenic spots, but cannot record the people flow scattered in each scenic spot, and videos and the like can be clearly visualized, even the people number is identified by an image identification mode, but cannot be fully covered, so that global statistics cannot be achieved naturally, and the estimation of the flow cannot be made.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a method for detecting the heat of a communication channel in a macroscopic region.
In order to solve the technical problem, the invention adopts the following technical scheme: a heat detection method for a communication channel in a macroscopic region comprises a plurality of detection sub-networks and a far-end background server which are deployed in the macroscopic region 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: collecting complete time periods (T) by extension sets respectively 1 - Δ T) and (T) 2 Δ t) complete data of the region A and the connecting channel R (g)
And
wherein, T 1 ＞T 2 And (T) 1 -T 2 ) S/v, s being the shortest distance in all connecting channels, v being the theoretical maximum moving speed, D 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-machines in the jth sub-network;
step 2: slicing the data and extracting the partial region S to be analyzed and the complete time period (T) k Data D of- Δ t) s '，
In the formula D is The data collected by the extension set with the number i under the s sub-network is obtained, M is the total number of the extension sets in the s sub-network, and k takes the value of 1 or 2;
and 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: sorting 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 k }；
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 a time period delta t, if the ID data are 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 appear, and if the same ID data do not appear, classifying the ID data into ID data c＝1 If present in other subnets, the ID will be usedData is marked with 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 During the time period of Δ t at the time, the inflow heat σ of the connection channel of the local region is calculated and detected by the following method in (g) And outflow heat σ out (g)：
(1) First, the ID is added c＞1 The middle data is subjected to data normalization processing and then is added into effective data;
(2) Then, the ID is added c＝1 The data is fitted to the function by approximation
Approximate fitting process, setting bias reference system in fitting process
To be provided with
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) Then, for ID c＞1 And ID c＝1 The region traffic retention amount data V is obtained by the following formula Tk ：
Wherein x and y are each ID c＞1 And ID c＝1 Total number of medium valid data;
(4) Continuing, T is calculated by equation (2) 1 -T 2 Time scaleOutflow W of persons in the area out And inflow W in ：
In the formula, delta 1 And delta 2 Are respectively T 1 And T 2 A very small amount of (c);
(5) Then, T is calculated by the formula (3) 1 -T 2 In the time range, the area interacts with each channel to realize flow, and the flow comprises the following steps: outflow of channels to zones
And inflow of area to channel
In the formula, epsilon in And ε out To regulate factors and ensure
(6) Finally, the inflow factor σ of the region into the channel is set in (g) And σ in (1)+σ in (2)+…+σ in () g =1; inflow factor σ of a channel into a region out (g) And σ out (1)+σ out (2)+…+σ out (g) =1, then:
get the corresponding channelHeat of road inflow σ in (g) And heat of flow σ out (g)。
Preferably, the biased reference frame
Counting regional videos or counting the inlet and outlet gates.
Preferably, in step 9, δ 1 And delta 2 The value of (a) is determined by the following method: let y = F (·), n be the number of times y fits within the system error range, then,
in the formula, y n Data obtained by F (g) approximate fitting function approximate fitting processing at Tn moment; y is mean Is y 1 ,y 2 ,…,y n Average value of (a).
Has the beneficial effects that: the invention provides a macroscopic region flow information detection method based on the unique ID of a portable mobile terminal, a collected data timestamp and detection equipment position information, which realizes data mining and analysis of the collected data of the mobile terminal and realizes efficient and accurate detection of the heat of a connecting channel of a macroscopic region in real time.
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 human flow conservation quantity detection method.
Detailed Description
The invention will be further elucidated by means of specific embodiments in the following figures. 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, the above diagram shows a background server including a plurality of detection sub-networks and remote terminals, which are deployed in a macro area according to a physical space, where the detection sub-networks include a plurality of extensions and a host in data connection with the extensions; the extension sets acquire broadcast type data packets sent to the surrounding environment by mobile terminal equipment within the coverage range of the extension sets randomly based on a WIFI protocol through a wireless passive sensing mode, screens the data packets with ID information of the mobile terminal equipment for retrieval, marks extension set labels and uploads the extension set labels to the host, the host stores the collected data in a unified mode, marks time labels 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: collecting a complete time period (T) 1 - Δ T) and (T) 2 Δ T), including the region a and all connecting channels R (g) (g =1, 2.), (T) 1 ＞T 2 And (T) 1 -T 2 < s/v), s is the shortest distance in all channels, v is the theoretical maximum moving speed) the complete data are expressed as:
wherein D ij Indicating ith sub-network extension data;
and 2, step: slicing the data, and extracting and analyzing a local area S and a time period (T) k - Δ t) complete data
And 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: sorting the collected data according to the ID numbers of the collected data, and establishing a data matrix { Tower (i, S), T k }；
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 T k At the moment, within the time period of delta t, the conservation quantity of regional people flow is
The real-time regional people stream retention is evaluated by the following method, as shown in the following network:
from the figure, ID c＞1_x And go up ID c＞1_y Indicating valid ID data, wherein ID c＞1_x Directly count the effective reserve amount, ID after the data in (1) are normalized c＞1_y The data in (1) needs to pass
After the approximate fitting function is approximated, the effective reserve can be included, and the offset reference system S represents an effective reference unit, such as a video counter and an inlet-outlet gate meterNumber, etc. Accumulating the ID data and fitting a function by approximation
Fitting is carried out, and the feedback judgment condition is
The range of the ratio threshold value can be adjusted according to the detection precision requirement, and the ratio threshold value can be adjusted according to the detection precision requirement
And (theta is the allowable error of the system setting), indicating that the data fitting work is completed and forming effective data.
Step 10: calculating T 1 -T 2 In the time frame, the zone flow: zone outflow W out And area inflow W in ：
First, let y = F (g), n is the number of times y is within the system error range, then,
step 11: calculating T 1 -T 2 In the time range, the area interacts with each channel to obtain the flow: channel → regional outflow
And region → channel inflow
ε in 、ε out To adjust factors, ensure
Step 12: setting region → channel inflow Heat factor σ in (g)：σ in (1)+σ in (2)+…+σ in (g) =1; channel → region outflow Heat factor σ out (g)：σ out (1)+σ out (2)+…+σ out (g) =1, then
Step 13: obtaining the inflow heat degree sigma of the corresponding channel in (g) And heat of flow σ out (g)。
Step 14: and ending the flow.
Claims (3)
1. A method for detecting the heat of a macroscopic region communication channel 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 sets acquire broadcast type data packets sent to the surrounding environment by mobile terminal equipment within the coverage range of the extension sets randomly based on a WIFI protocol through a wireless passive sensing mode, screens the data packets with ID information of the mobile terminal equipment for retrieval, marks extension set labels and uploads the extension set labels to the host, the host stores the collected data in a unified mode, marks time labels 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: collecting complete time periods (T) by extension sets respectively 1 - Δ T) and (T) 2 Δ t) complete data of the region A and the connecting channel R (g)
And
wherein, T 1 ＞T 2 And (T) 1 -T 2 ) S/v, s being the shortest distance in all connecting channels, v being the theoretical maximum moving speed, D 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 local area S to be analyzed and the complete time period (T) k Data D of- Δ t) s '，
In the formula D is The data collected by the extension set with the number i under the s sub-network is obtained, M is the total number of the extension sets in the s sub-network, and k takes the value of 1 or 2;
and 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 data collected by each extension according to the ID number of the mobile terminal equipment, and establishing (T) k Δ T) time period { Tower (i, s), T k }；
And 5: classifying the established ID data list according to the times c of occurrence in different data matrixes: recording the IDs appearing in only one data matrix Tower as IDs c＝1 And extracting and analyzing separately, and attributing ID data appearing in more than two data matrixes Tower as ID c＞1 ；
And 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 During the time period Δ t of the time, the inflow heat degree σ of the connection channel of the local region is calculated and detected by the following method in (g) And heat of flow σ out (g)：
(1) First, the ID is added c＞1 The middle data is subjected to data normalization processing and then is added into effective data;
(2) Then, the ID is added c＝1 The middle data is approximately fitted and processed by an approximate fitting function F (g), and a bias reference system is set in the fitting process
To be provided with
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) Then, for ID c＞1 And ID c＝1 The effective data of (2) is obtained by the following formula
Wherein x and y are each ID c＞1 And ID c＝1 The total number of the middle valid data;
(4) Continuing, T is calculated by equation (2) 1 -T 2 The outflow of people W in the local area in the time range out And inflow W in ：
In the formula, delta 1 And delta 2 Are respectively T 1 And T 2 A very small amount of (c);
(5) Then, T is calculated by the formula (3) 1 -T 2 In the time range, the area interacts the flow with each channel, and the method comprises the following steps: outflow of channels to zones
And inflow of area to channel
In the formula, epsilon in And epsilon out To regulate factors and ensure
(6) Finally, the inflow factor σ of the region into the channel is set in (g) And is and
inflow factor σ of a channel into a region out (g) And is and
then:
obtaining the inflow heat degree sigma of the corresponding channel in (g) And heat of flow σ out (g)。
2. The macroscopic region connected channel heat detection method of claim 1, wherein: said biased reference frame
Counting regional videos or counting inlet and outlet gates.
3. The macroscopic region connected channel heat detection method of claim 1, wherein: in step 9, δ 1 And delta 2 The value of (a) is determined by the following method: let y = F (g) first, n being the number of times y fits within the system error range, then,
in the formula, y n Data obtained by approximate fitting processing of an F (g) approximate fitting function at the moment Tn; y is mean Is y 1 ,y 2 ,…,y n Average value of (a).
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