CN108712317B

CN108712317B - Urban crowd space-time dynamic sensing method and system based on mobile social network

Info

Publication number: CN108712317B
Application number: CN201810264531.XA
Authority: CN
Inventors: 王亚沙; 邱昭鹏; 王江涛; 赵俊峰
Original assignee: Peking University
Current assignee: Peking University
Priority date: 2018-03-28
Filing date: 2018-03-28
Publication date: 2020-12-22
Anticipated expiration: 2038-03-28
Also published as: CN108712317A

Abstract

The invention relates to a city crowd space-time dynamic perception method and system based on a mobile social network. The method comprises the following steps: 1) discretizing the urban area into grids with side length of a certain value, and taking the central position of each grid as a candidate perception position set; 2) selecting a position capable of generating the maximum perception income in the current state as a perception position from the candidate perception position set, and perceiving at the perception position to obtain distance information of the user; 3) the specific location of each user is determined by triangulation using the user's distance information obtained at different perceived locations. The invention selects the probe with the largest perception benefit to perceive each time, can perceive the specific position of the user in the whole city only by less probe quantity, has good expansibility, and can represent the space-time dynamic characteristics of the crowd in the whole city by dynamically acquiring the space-time characteristics of the social network users in a large scale.

Description

Urban crowd space-time dynamic sensing method and system based on mobile social network

Technical Field

The invention relates to a data capture method, belongs to the field of sensor data processing, and particularly relates to a city crowd space-time dynamic sensing method and system based on a mobile social network.

Background

The space-time dynamic perception of urban population is an important problem in the fields of urban computing and the like. Based on the space-time dynamic sensing result of urban crowds, the method can be used for urban road planning, functional area division, traffic jam detection, infectious disease transmission analysis and other problems. However, the space-time dynamic perception of urban population is a challenging problem, and if limited observation points are deployed in a city, the problems of high cost, sparse data and the like are caused. And a mobile social network based on the position becomes a part of life of people, such as applications like WeChat. The social software based on the position has a positioning function, so that the software stores track information about the user, and the space-time dynamic characteristics of the user can well represent the space-time dynamic characteristics of the whole urban population. To protect user privacy, however, the application does not display the exact latitude and longitude location of the user, but only the relative distance between the user and the user. The distance information between users can be obtained by accessing an application interface, but the longitude and latitude positions of the users are calculated according to the distance values, and accordingly the space-time dynamic information of the users can be obtained. In the prior art, the user perception is aimed at, and in order to represent the space-time characteristics of urban crowds, the space-time characteristics of the user are necessarily perceived in a large scale, so that high perception efficiency is required.

At present, the prior art does not have a technology capable of sensing the space-time dynamics of urban crowds in a large scale at low cost. Therefore, it is necessary to develop a dynamic urban crowd space-time perception method based on a mobile social network.

Disclosure of Invention

The invention mainly solves the problem of perception efficiency in the prior art, and provides a city crowd space-time dynamic perception method and system based on a mobile social network. By adopting the method, the space-time dynamic characteristics of the whole city population can be represented by dynamically acquiring the space-time characteristics of the social network users on a large scale.

The technical problem of the invention is mainly solved by the following technical scheme:

a city crowd space-time dynamic perception method based on a mobile social network comprises the following steps:

1) discretizing the urban area into grids with side length of a certain value, and taking the central position of each grid as a candidate perception position set;

2) selecting a position capable of generating the maximum perception income in the current state as a perception position from the candidate perception position set, and perceiving at the perception position to obtain distance information of the user;

3) the specific location of each user is determined by triangulation using the user's distance information obtained at different perceived locations.

The above method is specifically described below.

1) Initialization step

For a specific city, the whole city area is discretized into a grid with the side length of L, and the center position of the grid is used as a candidate perception position set. L can be fixed to a small value, such as 10 meters, only to ensure that all users in the city can be located when all candidate location sets are selected. The historical location data for the users is mapped into corresponding grids and the probability of each user appearing in each grid is calculated. There are studies showing that the number of times a user appears in a certain grid obeys poisson distribution, and assuming that user u appears in grid c k times at t in d-day history data, the probability that the user appears in grid c at least once at t in the day is:

wherein λ_u,cThe number of times user u appears in grid c.

2) Sensing position selection step

The method comprises the following steps of greedy selecting the current optimal position in a candidate sensing position set for sensing, wherein the sensing at one sensing position is called to put one probe. The process is an iterative process, and each round of selection is carried out for perception at a position which can generate the maximum perception benefit under the current state. The definition of the perception profit is related to the perception target, the perception target is to perceive the specific positions of all users in the city, so the perception profit represents the help brought by the fact that one probe can help to locate the positions of all users. If the sensing range of one probe is larger, the more the help of all users can be sensed, the sensed distance information can help more users to perform triangulation, and the more the help is brought. The specific definition of perceived benefits is as follows:

Bonus(p,S)＝Utility(S∪{p})-Utility(S) (2)

bonus (p, S) is the perception gain, i.e. the difference in perception ability Utility, which can be brought by adding probe p, S is the selected probe set, and U is the user set. The perception capability consists of two parts, one part is related to the current perceived state of the user and the other part is related to the perception range. Wherein

Is the total area of the city and area(s) is the union of the sensing ranges of the probes that have been selected. Each probe has a circular sensing range whose size is determined by the distance value of the farthest user that can be sensed. The union of the sensing ranges of all probes in S is the size of area (S). α is a weight value that reconciles two factors, and its value can be determined to be optimal by a search method of a control variable.

Prob (S, u, state) is the probability that S can locate user u to the state. Each user can calculate the specific position of the user by using a triangulation method as long as the user is sensed by three different probes, so that the user has three states, namely: sensed by only one probe, two probes and three probes. The state transition function for a state is defined as:

when the user is not fully positioned, the unit gain can be increased for each more probe. And each round of probe selection is performed before the probe is placed at the sensing position, and the sensing benefit of each candidate probe is estimatedThe probability of each probe changing the user state needs to be estimated. When p is able to perceive user u and u has not been fully located, p changes the state of u. So the Probability Probasic of u can be perceived by estimating p_p(u) to calculate Prob (p, u, state).

When S does not sense u yet, the probability that p can sense u needs to be estimated according to historical data, that is, according to the probability that u appears in each grid in the initialization step. And estimating the perception range of p according to the distribution condition of the users at the previous moment, and further estimating the probability of u appearing in the perception range of p according to the probability of u appearing in the grid. Namely:

where Cp is the perceptual range of p, Probasic (u, c) represents the Probability that u appears in c.

When S senses u once, namely a probe senses u before, the circle formed by the distance between the probe and u is the position where u can appear and is marked as Candidate_uTherefore, the probability that p can sense u is the proportion of the sensing range of p and the intersection of the circles to the circle, that is:

when S senses u twice, the intersection point of two circles formed by the sensing distance of two probe pairs u is the position where u can appear, and if the sensing range of p can cover any intersection point, the Prohealth_p(u) is 1, otherwise 0.

Prob (u, state, S) represents the probability that S can determine the state of u as state, and when p is newly added to S, the probability that S { p } determines the state of u is:

Prob(u,state,S∪{p})＝1-Probability_p(u) (7)

Prob(u,Next(state),S∪{p})＝Probability_p(u) (8)

that is, when p can sense u, the state of u is transferred, otherwise it remains unchanged.

In each iteration process, calculating Bonus (p) of all candidate probes, then selecting an optimal position, requesting an interface by setting the position of the candidate probe p as a parameter of the interface, and storing a distance result returned by the interface. And updating the perceived state of each user according to the perception result of p at the current moment, and updating the perceived city range.

3) Iteration stop condition checking step

And the method is used for judging whether the target of sensing all users in the city is reached or not and stopping iteratively selecting the probe or not. In order to ensure that the goal of perceiving the location of all users in a city can be achieved, it is possible to check whether two conditions are met, namely: whether the sensing range of the selected probe set S covers the whole city or not; whether all perceived users can be triangulated, i.e., all perceived by at least 3 probes. Since the interface returns the distance value of the n users nearest to the probe position, and the probe is assumed to be Dis from the nth user_nThen each probe has perceived distance Dis_nAll users in range of-then Dis of all probes in S_nThe union of ranges covers the entire city, all users in the entire city being perceived by at least one probe. Which is a fixed small value whose size is related to the distance accuracy provided by the mobile application, which may be set to 20 meters, for example, when the distance accuracy is 10 meters. And the satisfaction of the second condition ensures that all users in the entire city are perceived and able to be triangulated to determine a particular location.

4) Step of triangulation

For triangulating the distance values to determine the specific location of each user. And (3) integrating the distance information collected by the probes, wherein each user has at least 3 probes and distance values related to the user, drawing a circle by the position of each probe and the corresponding distance information, and the intersection point or the central position of the intersection area of the 3 circles is the specific position of the user.

A city crowd space-time dynamic perception system based on a mobile social network is characterized by comprising:

the candidate perception position acquisition unit is used for discretizing the urban area into lattices with side length of a certain value, and taking the central position of each lattice as a candidate perception position set;

the sensing position selecting unit is used for selecting a position which can generate the maximum sensing benefit in the current state as a sensing position in the candidate sensing position set, and sensing the sensing position to acquire distance information of the user;

and the positioning unit is used for determining the specific position of each user through triangulation positioning by using the distance information of the user obtained at different perceived positions.

Compared with the prior art, the invention has the following advantages:

1. each time, the probe with the largest perception benefit is selected for perception, so that the specific position of the user in the whole city can be perceived only by a small number of probes, and the application server can be requested less;

2. the method has good expansibility and can be better expanded to other cities.

Drawings

FIG. 1 is a flow chart of the method.

Fig. 2 is a diagram illustrating an example of the operation of the method in a city.

Fig. 3 is an exemplary diagram of triangulation used in the method.

Detailed description of the invention

The first embodiment is as follows:

fig. 1 is a flowchart of the sensing method of the present embodiment. Supposing that the task needs to sense the space-time dynamic characteristics of urban users in a certain city, the method allows users to find an interface of certain mobile social network software, and the users can sense the space-time dynamic characteristics of the social network users by using the method.

The sensing method of the embodiment includes:

and an initialization step of discretizing the urban area into a grid of 100 meters by 100 meters, and taking the center position of the grid as a candidate probe position. And mapping the positions in the user history records into the grids, counting the occurrence times of each user in each grid, and calculating the probability of each user in each grid through the formula (1).

And (3) a sensing position selecting step, wherein the sensing income of each candidate probe is calculated according to the formula (2) in each round, and the probe with the maximum sensing income is selected. As shown in fig. 2, which is an example of a process of selecting and sensing probes in a city, each circle represents a sensing range of one probe, each dot represents a position of one user, a black dot shown as "1" in the figure represents a user that has not been sensed, a dark gray dot shown as "2" represents a user that is sensed by one or two probes, and a light gray dot shown as "3" represents a user that is sensed by at least three probes. In the initial stage of selection, the sensing range factor plays a greater role in sensing benefits, and the sensing range is larger in the area with less user distribution, so that the probes selected in the initial stage are all in the area with sparse user distribution. Subsequent selections also select probes whose sensing ranges do not overlap much with the sensing ranges of the previous probes. Until all users are perceived by at least three probes.

The iteration stop condition checking step, in the last diagram of fig. 2, the union of the sensing ranges of all the probes covers the whole city, and all the users are light gray as indicated by "3", i.e. all the users are sensed by at least 3 probes, and the iteration process stops.

And (3) triangulation, namely a triangulation process of a user, wherein an intersection point of three circles related to the user is obtained, and the intersection point is the specific position of the user.

The experiment of the invention verifies that:

in an experiment, a range of 252 square kilometers in a certain city is selected, the size of a grid is set to be 100 meters by 100 meters, and data sensing is performed at 7 points, 12 points and 21 points for 5 consecutive days, so that the average value of sensing times required by sensing the space-time dynamics of users in the city range in different time periods by the method of the present invention and the existing method (sensing the space-time dynamics of users in the whole city based on the sensing method of a single user) is obtained, as shown in table 1:

TABLE 1 results of experiment one

The result shows that the method of the invention can reduce the sensing times by 24.4 to 26.9 percent compared with the prior art, and the performance is superior to the prior art.

The setting of experiment two is the same as experiment one, and the number of users in the city is increased by a simulation method. This experiment compared the ductility of the two methods at different numbers of users. The results of the experiment are shown in table 2.

TABLE 2 results of experiment two

The result shows that the method can reduce the sensing times by 19.4 to 25 percent compared with the prior art, and the performance is superior to the prior art. And as the number of users increases, the sensing times of the method linearly increase, so that the method is good in extensibility and suitable for cities with different population numbers.

Example two:

the embodiment expands the social network application with different types of returned data through a deformation perception revenue function. This type of social network does not return an exact value of the distance between users, but rather a list of other users within a circular range centered on the user, with a fixed value as the radius. For such social networking applications, the perceived benefits may be linked to the extent to which the user is determined, and the Utility function may be defined as follows:

wherein Area_uThe area of the range determined by the probe set S for user u is initially set to

The definition is as follows:

where disk (p) is the sensing range of probe p, which is a circle centered at the position of probe p and having a fixed value as the radius.

Example three:

the embodiment provides a city crowd space-time dynamic perception system based on a mobile social network, which comprises:

and the positioning unit is used for determining the specific position of each user through triangulation positioning by utilizing the distance information of the users obtained at different perception positions.

The above examples are general procedures for sensing the method of the present invention, and are merely illustrative of the spirit of the present invention. Various modifications or additions may be made to the described embodiments or alternatives may be employed by those skilled in the art without departing from the spirit or ambit of the invention as defined in the appended claims.

Claims

1. A city crowd space-time dynamic perception method based on a mobile social network is characterized by comprising the following steps:

2) selecting a position capable of generating the maximum perception income in the current state as a perception position from the candidate perception position set, and perceiving at the perception position to obtain distance information of the user; the perception benefit is the help of a probe for positioning the positions of all users, and the perception at a perception position is called to put a probe; mapping historical position data of the users to corresponding grids, and calculating the probability of each user appearing in each grid; when the probe set S does not sense the user u, estimating the probability that the probe p can sense u according to the probability that the user u appears in each grid;

2. The method of claim 1, wherein step 1) maps the historical location data of the users into respective grids and calculates the probability of each user appearing in each grid using the following formula:

wherein λ_u,cThe number of times user u appears in grid c.

3. The method of claim 1, wherein, step 2) adopts an iterative process to select a sensing position, selects a position which can generate the maximum sensing benefit under the current state in each iteration process for sensing, and judges whether to stop iteration by checking an iteration stop condition; the iteration stop condition includes: whether the sensing range of the selected probe set covers the whole city; whether the perceived users are able to be triangulated.

4. The method of claim 3, wherein the perceived benefit is defined as:

Bonus(p,S)＝Utility(S∪{p})-Utility(S)

wherein, Bonus (p, S) is the perception benefit brought by adding the probe p, namely the difference of perception capability Utility, and S is the selected probe set;

is the total area of the city, area(s) is the union of the sensing ranges of the probes that have been selected; prob (S, u, state) is the probability that S can locate user u to state, and the user has three states: sensed by only one probe, two probes, and three probes.

5. The method of claim 4, wherein when u is not fully located but is perceived by probe p, u is perceived by estimating p's Probability of being able to perceive u_p(u) to calculate Prob (p, u, state).

6. The method of claim 5, wherein Probasic is performed when u is not yet sensed by probe set S_p(u) estimating according to historical data, namely the probability of u appearing in each grid, and calculating by the formula:

wherein C is_pIs the perceptual range of p, Probasic (u, c) represents the Probability of u appearing in the trellis c;

when S senses u once, i.e. one probe senses u, the circle formed by the distance between the probe and u is the position where u may appear, and is marked as Candidate_u，Probability_p(u) is calculated using the formula:

when S senses u twice, the intersection point of two circles formed by the sensing distance of two probe pairs u is the position where u can appear, and if the sensing range of p can cover any intersection point, the Prohability is realized_p(u) is 1, otherwise 0.

7. The method of claim 4, wherein in each iteration, the perceived returns of all candidate probes are calculated and then the optimal position is selected; setting the position of the candidate probe p as a parameter of an interface, requesting the interface and storing a distance result returned by the interface; and updating the perceived state of each user according to the perception result of p at the current moment, and updating the perceived city range.

8. The method as claimed in claim 1, wherein step 3) draws a circle of each probe position and corresponding distance information using the 3 probe positions and distance values associated therewith of each user, and the intersection point of the 3 circles or the center position of the intersection region is the specific position of the user.

9. The method of claim 1, wherein the perceived benefits are related to the determined range of users to obtain a list of other users within a circular range centered on the user with a fixed value as a radius, defining a Utility function as follows:

The definition is as follows:

10. A city crowd space-time dynamic perception system based on a mobile social network by adopting the method of any one of claims 1 to 9, which is characterized by comprising: