CN112541852B - Urban people stream monitoring method and device, electronic equipment and storage medium - Google Patents
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Abstract
The embodiment of the invention discloses a method, a device, electronic equipment and a storage medium for monitoring urban people stream, wherein the method comprises the following steps: acquiring original people stream data and urban grid data, wherein the original people stream data comprises a plurality of original track points, and the time intervals among the plurality of original track points are unequal; preprocessing the original people stream data based on the urban grid data to obtain standardized grid data, wherein the standardized grid data comprises a plurality of standard track points, and the time intervals among the standard track points are equal; and generating a people stream monitoring view according to the standardized grid data. The embodiment of the invention reduces the influence of uncertainty factors such as GPS signals, network signals, equipment power supply and the like on the generation of the traffic monitoring view, so that traffic monitoring is more flexible, professional characteristic extraction is not needed manually, dependence of urban traffic monitoring on professional technicians is reduced, and the universality of urban personnel flow condition monitoring is improved.
Description
Technical Field
The embodiment of the invention relates to the technical field of smart cities, in particular to a city people stream monitoring method, a device, electronic equipment and a storage medium.
Background
In the smart city field, real-time monitoring and predicting the people's flowing situation in cities is very important for many city computing applications (such as public resource dynamic scheduling, high congestion area early warning, etc.), two kinds of information are needed by government traffic departments and location-based service providing enterprises in decision making and optimizing services: current time demographics and flow conditions and predicted conditions of population flow over a period of time.
At present, monitoring of urban personnel flow conditions requires professionals to extract key information from collected original people flow big data, and the key information is processed into a form which is convenient for general personnel to understand. The processing mode depends on the processing of professional technicians, and the dependence on the technicians is too high, so that the monitoring cost of the urban personnel flow condition is high.
Disclosure of Invention
In view of the above, the embodiment of the invention provides a method, a device, electronic equipment and a storage medium for monitoring urban people stream, so as to improve the universality of urban people flow condition monitoring and reduce the dependence of urban people flow monitoring on professional technicians.
In a first aspect, an embodiment of the present invention provides a method for monitoring urban people stream, including:
acquiring original people stream data and urban grid data, wherein the original people stream data comprises a plurality of original track points, and the time intervals among the plurality of original track points are unequal;
preprocessing the original people stream data based on the urban grid data to obtain standardized grid data, wherein the standardized grid data comprises a plurality of standard track points, and the time intervals among the standard track points are equal;
and generating a people stream monitoring view according to the standardized grid data.
Further, preprocessing the original people stream data based on the city grid data to obtain standardized grid data includes:
calibrating the original people stream data to obtain calibration track data, wherein the calibration track data comprises a plurality of calibration track points, and the time intervals among the plurality of calibration track points are equal;
and matching the calibration track data with the urban grid data to obtain standardized grid data.
Further, generating a people stream monitoring view from the standardized grid data includes:
determining people stream characteristic parameters according to the standardized grid data, wherein the people stream characteristic parameters comprise people stream density, people stream and people stream transfer;
and generating a people stream monitoring view according to the people stream characteristic parameters.
Further, generating the people stream monitoring view according to the people stream characteristic parameter includes:
based on a deep learning model, acquiring a people stream prediction parameter according to the people stream characteristic parameter;
and generating a people stream monitoring view according to the people stream prediction parameters.
Further, based on the deep learning model, obtaining the people stream prediction parameter according to the people stream feature parameter includes:
inputting the people stream density into a ConvLSTM network model to obtain a people stream prediction density;
and inputting the people flow into a ConvLSTM network model to obtain people flow prediction.
Further, based on the deep learning model, obtaining the people stream prediction parameter according to the people stream feature parameter includes:
generating fusion characteristics based on the partial transfer condition of the people stream transfer quantity analysis;
capturing space-time dependence based on the fusion characteristics to obtain hidden layer information;
predicting to obtain a multi-order people stream transfer prediction matrix based on the hidden layer information;
and determining the people stream prediction transfer amount based on the multi-order people stream transfer prediction matrix.
In a second aspect, an embodiment of the present invention provides an urban people stream monitoring apparatus, including:
the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring original people stream data and city grid data, the original people stream data comprises a plurality of original track points, and the time intervals among the plurality of original track points are unequal;
the data preprocessing module is used for preprocessing the original people stream data based on the urban grid data to obtain standardized grid data, wherein the standardized grid data comprises a plurality of standard track points, and the time intervals among the standard track points are equal;
and the monitoring view generation module is used for generating a people stream monitoring view according to the standardized grid data.
Further, the data preprocessing module is specifically configured to:
calibrating the original people stream data to obtain calibration track data, wherein the calibration track data comprises a plurality of calibration track points, and the time intervals among the plurality of calibration track points are equal;
and matching the calibration track data with the urban grid data to obtain standardized grid data.
In a third aspect, an embodiment of the present invention provides an electronic device, including:
one or more processors;
storage means for storing one or more programs,
when the one or more programs are executed by the one or more processors, the one or more processors implement the urban people stream monitoring method provided by any embodiment of the present invention.
In a fourth aspect, an embodiment of the present invention provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the urban people stream monitoring method provided by any embodiment of the present invention.
According to the urban mass flow monitoring method provided by the embodiment of the invention, through preprocessing the original track points, the influence of uncertainty factors such as GPS signals, network signals and equipment power supply on the generation of mass flow monitoring views is reduced, so that the mass flow monitoring views are more accurate; by matching city grid data, the generation of a people stream monitoring view of a specific city area is realized, and the people stream monitoring has more flexibility. In addition, the monitoring personnel can obtain the corresponding people flow monitoring view only by accessing the original people flow data without manually extracting the special characteristics, so that the dependence of urban people flow monitoring on special technicians is reduced, and the universality of urban personnel flow condition monitoring is improved.
Drawings
Fig. 1 is a schematic flow chart of a city traffic monitoring method according to a first embodiment of the present invention;
fig. 2 is a schematic flow chart of a city traffic monitoring method according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of an urban people stream monitoring device according to a third embodiment of the present invention.
Fig. 4 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting thereof. It should be further noted that, for convenience of description, only some, but not all of the structures related to the present invention are shown in the drawings.
Before discussing exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart depicts steps as a sequential process, many of the steps may be implemented in parallel, concurrently, or with other steps. Furthermore, the order of the steps may be rearranged. The process may be terminated when its operations are completed, but may have additional steps not included in the figures. The processes may correspond to methods, functions, procedures, subroutines, and the like.
Furthermore, the terms "first," "second," and the like, may be used herein to describe various directions, acts, steps, or elements, etc., but these directions, acts, steps, or elements are not limited by these terms. These terms are only used to distinguish one direction, action, step or element from another direction, action, step or element. The terms "first," "second," and the like, are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. In the description of the present invention, "plurality", "batch" means at least two, for example, two, three, etc., unless specifically defined otherwise.
Example 1
Fig. 1 is a schematic flow chart of a city traffic monitoring method according to an embodiment of the present invention, which is applicable to monitoring of urban personnel flow conditions. As shown in fig. 1, the urban people stream monitoring method provided by the embodiment of the invention includes:
s110, acquiring original people stream data and city grid data, wherein the original people stream data comprises a plurality of original track points, and the time intervals among the plurality of original track points are unequal.
Specifically, the original people stream data is a plurality of original track points describing action tracks of people in cities, and the formats of the original track points are (id, time, lat, lon), specifically, user id corresponding to the track points, time stamp (time) generated by the track points and longitude and latitude positions (lat, lon) of the track points. If the distinction is made according to the user id, the original stream data includes original track data of a plurality of users. The original people stream data can be imported through a third party platform, such as a management office, a location-based service platform and the like, and the acquisition mode can be RESTAPI, websocket, text files and the like.
Because of the influence of uncertainty factors such as GPS signals, network signals, device power supplies and the like, the time interval between every two original track points of a user in the original people stream data is not fixed, namely the time intervals among a plurality of original track points are not equal.
Optionally, the original people stream data may further include weather data, emergency data, and other data, which are important factors affecting the flow of people, and have a certain importance for people stream prediction. The weather data and the emergency data are in the form of (timestamp, lat, lon, content), i.e. include the timestamp of the data generation (timestamp), the latitude and longitude position (lat, lon) and the specific weather or event content (content).
The city grid data refers to data formed by dividing one city into a plurality of city areas. One city can be divided into equal-size grids with the granularity of H in the length direction and the granularity of W in the width direction based on longitude and latitude, so that one city is provided with N city grids, N=H×W, and each city grid g m (m∈[1，N]) Representing a metropolitan area, i.e., urban grid data.
S120, preprocessing the original people stream data based on the urban grid data to obtain standardized grid data, wherein the standardized grid data comprises a plurality of standard track points, and the time intervals among the standard track points are equal.
Specifically, the original people stream data is generated based on the real-time position information of the user, and the original people stream data is preprocessed based on the urban grid data, namely, the real-time position information in the original people stream data is converted into the urban grid information, and the track of the user is represented by the urban grid in the obtained standardized grid data. When calibration is carried out, the time intervals among the original track points are required to be preprocessed, so that the time intervals among a plurality of standard track points of the preprocessed standardized grid data are equal, and uncertain influence factors in the original people stream data are reduced.
S130, generating a people stream monitoring view according to the standardized grid data.
Specifically, a traffic monitoring view is generated according to the standardized grid data, and the traffic monitoring view can display traffic conditions in each city grid, namely, traffic conditions in each city area. In this embodiment, the people flow monitoring view may be displayed through a visual interface, through which a monitoring person may adjust the people flow monitoring view. For example, if people flow in a specific area in a city needs to be monitored, people flow monitoring views only need to be generated by selecting a city grid corresponding to the specific area in a visual interface.
According to the urban mass flow monitoring method provided by the embodiment of the invention, through preprocessing the original track points, the influence of uncertainty factors such as GPS signals, network signals and equipment power supply on the generation of mass flow monitoring views is reduced, so that the mass flow monitoring views are more accurate; by matching city grid data, the generation of a people stream monitoring view of a specific city area is realized, and the people stream monitoring has more flexibility. In addition, the monitoring personnel can obtain the corresponding people flow monitoring view only by accessing the original people flow data without manually extracting the special characteristics, so that the dependence of urban people flow monitoring on special technicians is reduced, and the universality of urban personnel flow condition monitoring is improved.
Example two
Fig. 2 is a schematic flow chart of a city people stream monitoring method according to a second embodiment of the present invention, which is a further refinement of the foregoing embodiment. As shown in fig. 2, the urban people stream monitoring method provided by the second embodiment of the invention includes:
s210, acquiring original people stream data and city grid data, wherein the original people stream data comprises a plurality of original track points, and the time intervals among the plurality of original track points are unequal.
Specifically, the format of the original track point is (id, timestamp, lat, lon), and the format of the original track data of one user is (timestamp, lat, lon) and is abbreviated as (t, l), where t represents a timestamp (timestamp), and l represents a longitude and latitude position (lat, lon). Urban grid data denoted g m (m∈[1，N])。
S220, calibrating the original people stream data to obtain calibration track data, wherein the calibration track data comprises a plurality of calibration track points, and the time intervals among the plurality of calibration track points are equal.
Specifically, the calibration is performed on the original people stream data, so as to calibrate the time interval between every two original track points in the original people stream data, so that the time intervals between every two original track points are equal, that is, the time intervals between a plurality of calibration track points of the calibration track data obtained by calibration are equal. In this embodiment, calibration is performed by a nearest neighbor interpolation method, a linear interpolation method, a line network matching method, or the like. In calibration, the original people stream data is usually distinguished according to users, and then the original track data of each user is calibrated.
After calibration, the number of original trace points may change, and if the time interval after calibration is Δt, the calibration trace Γ of one user i 1 Can be expressed as:
where i represents the user id and k represents the total number of calibration trace points for that user.
Furthermore, before the original people stream data is calibrated, the method further comprises the step of cleaning the original people stream data so as to clean abnormal values and null values in the original people stream data, and error correction is carried out, so that the processed original people stream data is more accurate.
Furthermore, the method for calibrating the original people stream data further comprises the step of supplementing the blank value of the original people stream data, namely supplementing the blank value of the original track data of each user. The data volume of the original people stream data which is actually obtained is limited, and the data volume which does not necessarily meet the data volume requirement of the actual need to be processed is required to be supplemented at the moment, so that the calibrated data is more complete. For example, the original track data of one user actually acquired is data in the time period of 8:00-18:00, and the data in the time period of 0:00-24:00 needs to be processed actually, so that the data in the time periods of 0:00-8:00 and 18:00-24:00 of the user are supplemented. In general, the replenishment of the null value is usually based on the original track data closest to the null value time, for example, the original track data of 0:00-8:00 is replenished with the original track data of the user 8:00.
And S230, matching the calibration track data with the urban grid data to obtain standardized grid data.
Specifically, according to the longitude and latitude positions of the calibration track points in the calibration track data, determining the urban grids where the calibration track points are located, and replacing the longitude and latitude positions of the calibration track points with the urban grids to obtain standardized grid data. Calibration track f of a user i Can be expressed as:
s240, according to the standardized grid data, determining people stream characteristic parameters, wherein the people stream characteristic parameters comprise people stream density, people stream and people stream transfer quantity.
Specifically, urban people stream conditions are generally represented by people stream characteristic parameters, namely people stream density, people stream and people stream transfer quantity.
In this embodiment, the people stream density refers to the number of user tracks, that is, the number of users, at a certain moment in an area. Then city grid g at time t m Is the people stream density d t，m Can be expressed as:
d t，m ＝|{i||Γ i ·g t ＝g m }|
the people stream densities of all urban grids at the moment t form a urban people stream density matrix at the moment t, the size of the urban people stream density matrix is W multiplied by H, and elements in the matrix represent the corresponding urban grid g at the moment t m Number of users in the network.
In this embodiment, the traffic refers to the inflow and outflow of people between two times in one area. Urban grid g from time t-1 to time t m Population inflow of (C)And outflow of human mouth->Can be expressed as:
inflow of populationAnd outflow of human mouth->All are matrices with the size W multiplied by H, so that the people flow of all urban grids at the moment t forms the urban people flow moment at the moment tThe matrix has a size W x H x 2, i.e. the urban mass flow matrix has two channels, one representing the inflow of population +.>Another channel indicates the outflow of human mouth>
In this embodiment, the traffic transfer amount refers to the number of users from one area to another between two times. The traffic transfer amount is represented by a traffic transfer matrix, where Ω is a matrix of size n×n representing the traffic transfer amount between two urban grids, and elements in the matrix located in the ith row and jth column represent the traffic transfer amount from the urban grid g within the time interval Δt i To city grid g j Is the number of users. The people stream transfer matrix from the time t to the time (t+delta t) is defined as a first order transfer matrix omega t，Δt The method comprises the steps of carrying out a first treatment on the surface of the The people stream transfer matrix from the time t to the time (t+2Deltat) is defined as a second order transfer matrix omega t，zΔt And so on.
Furthermore, according to the people stream transfer quantity, people stream transfer process tensors can be obtained. tensor of people stream transfer process at t momentThe system consists of a 1-lambda order people stream transfer matrix, namely: />
S250, generating a people stream monitoring view according to the people stream characteristic parameters.
Specifically, a people stream density monitoring view is generated according to the people stream density, a people stream monitoring view is generated according to the people stream, and a people stream transfer monitoring view is generated according to the people stream transfer amount.
The station is exemplified by a subway station, an electric train station or a bus station and surrounding areas thereof, the urban grid where the subway station, the electric train station or the bus station is located can be positioned at the station, and the station can also be an irregular area selected by manual frames of monitoring personnel. And selecting a certain station to monitor the traffic flow, and displaying a traffic flow density monitoring view, a traffic flow monitoring view and a traffic flow transfer monitoring view of the station by a visual interface, wherein the traffic flow transfer monitoring view comprises a plurality of traffic flow transfer matrixes from 1 to lambda, such as 2-order, 4-order and 6-order traffic flow transfer matrixes.
Optionally, in an alternative embodiment, after determining the people stream feature parameter, the method further includes: based on a deep learning model, acquiring a people stream prediction parameter according to the people stream characteristic parameter; and generating a people stream monitoring view according to the people stream prediction parameters. The people stream feature parameters are predicted through the deep learning model to obtain people stream prediction parameters of a period of time in the future, and then a people stream monitoring view is generated based on the people stream prediction parameters, so that the prediction of urban people stream is realized.
Specifically, the people flow density and the people flow are predicted through the deep learning model, so as to obtain the people flow predicted density and the people flow predicted quantity, which specifically comprise the following steps: inputting the people stream density into a ConvLSTM network model to obtain a people stream prediction density; and inputting the people flow into a ConvLSTM network model to obtain people flow prediction. In this embodiment, the input data of the ConvLSTM network model includes the current calculated people flow density or people flow, the historical people flow density or historical people flow calculated at the historical moment stored in the database, and metadata, where the metadata refers to data such as weather data and emergency data. And constructing a nearest neighbor+cycle+trend people stream feature and a metadata feature according to the people stream density or the people stream, inputting a pyramid ConvLSTM network to obtain hidden layer expression of the people stream data, extracting importance of the input data by using an attention module, and outputting a final prediction result to obtain the people stream prediction density or the people stream prediction quantity. The nearest neighbor people stream feature refers to a feature extracted based on people stream density or people stream of a preset number of times nearest to a prediction moment, the periodic people stream feature refers to a feature extracted based on people stream density or people stream of a preset number of days, and the trend people stream feature refers to a feature extracted based on people stream density or people stream of a preset number of weeks.
By way of example, a prediction of people flow density at 18:00 of 10/30/2020 is required. The nearest neighbor people stream feature is a feature extracted based on 5 times of people stream density nearest 18:00 a distance of 10 months 30 days 2020, which 5 times of people stream density includes: people stream density at these times 17:50, 17:40, 17:30, 17:20, and 17:10 on 10 months 30 of 2020 (assuming that people stream density calculations are performed every 10 minutes). Periodic people stream features are features extracted based on a 3 day people stream density, which 3 day people stream density includes: the people stream density at 18:00 on the year 2020, 10, 29, 28, 18:00 and 18:00 on the year 2020. Trend people stream features are features extracted based on a 2 week people stream density, including a people stream density at 18:00 on 10 months 23 in 2020 and a people stream density at 18:00 on 10 months 16 in 2020 for 2 weeks.
And finally, generating a traffic prediction density monitoring view based on the traffic prediction density, and generating a traffic prediction quantity monitoring view based on the traffic prediction quantity.
The people stream transfer amount is predicted by the deep learning model to obtain the people stream prediction transfer amount, and the people stream transfer amount can be realized by adopting an urban people stream transfer prediction model based on a graph convolution network and a space-time cyclic neural network, and the method specifically comprises the steps S261-S264 (not shown in the figure).
S261, generating fusion characteristics based on the partial transfer condition analysis of the people stream transfer quantity.
Specifically, the fusion characteristics are obtained by the fusion of metadata, grid embedding of a people stream transfer process tensor and the people stream transfer process tensor. Metadata is the time at which the result of the stream transfer needs to be predicted and other additional information that may affect the predicted result, such as weather data, emergency data, etc. The mesh embedding of the tensor of the people stream transfer process is obtained by a local transfer quantity-based graph neural network. The local transfer amount indicates the transfer condition of the people stream from one city grid to the adjacent city grid in all city grids. To explore the correlation of people stream transfers between urban grids, we need to learn each grid g from people stream transfer amounts and metadata i Is a significant vector representation of (c). This can be done by learning its transfer matrix Ω at each people stream separately t ∈R N×N Is a net of (2)Lattice embedding.
S262, capturing space-time dependence based on the fusion characteristics to obtain hidden layer information.
Specifically, hidden layer information is a comprehensive representation of the transfer situation and the space-time situation of the people stream between lattices. And capturing the space-time dependence by utilizing space-time convolution on the basis of the fusion characteristics, and further obtaining hidden layer information comprehensively representing the people stream transfer condition and the space-time dependence. And capturing space-time dependence through the ConvLSTM network, and inputting the fusion characteristics into the ConvLSTM network to obtain hidden layer information.
S263, predicting and obtaining a multi-order people stream transfer prediction matrix based on the hidden layer information.
Specifically, the people stream transfer prediction matrix is a prediction result of the observed value of the people stream transfer matrix. Setting a plurality of ConvLSTM networks for predicting the people stream transfer matrix, copying the hidden layer information for a plurality of times, respectively inputting the hidden layer information into the ConvLSTM networks to obtain a multi-order people stream transfer prediction matrix, and inputting each hidden layer information into one ConvLSTM network to obtain a people stream transfer prediction matrix with a corresponding order.
S264, determining the people stream prediction transfer quantity based on the multi-order people stream transfer prediction matrix.
Specifically, the multi-order people stream transfer prediction matrixes are spliced together according to the time sequence to obtain a predicted people stream transfer process tensor, in fact, the time sequence corresponds to the order of the multi-order people stream transfer prediction matrixes, the more the time is, the larger the order is, namely the multi-order people stream transfer prediction matrixes are spliced according to the order from small to large, and the people stream prediction transfer quantity is obtained.
Furthermore, in this embodiment, all the data obtained in the above steps may be stored in a database, where the data stored in the database before the current time constitutes historical data, such as the historical people flow density, the historical people flow transfer, and so on. When monitoring personnel need to monitor urban people flow conditions at a certain historical moment, historical data can be acquired from a database to generate people flow monitoring views, namely, historical people flow density monitoring views, historical people flow monitoring views and historical people flow transfer monitoring views.
According to the urban mass flow monitoring method provided by the embodiment of the invention, through preprocessing the original track points, the influence of uncertainty factors such as GPS signals, network signals and equipment power supply on the generation of mass flow monitoring views is reduced, so that the mass flow monitoring views are more accurate; by matching city grid data, the generation of a people stream monitoring view of a specific city area is realized, and the people stream monitoring has more flexibility; the prediction of the people stream characteristic parameters is realized through the deep learning model, and an effective reference can be provided for urban planning. In addition, the monitoring personnel can obtain the corresponding people flow monitoring view only by accessing the original people flow data without manually extracting the special characteristics, so that the dependence of urban people flow monitoring on special technicians is reduced, and the universality of urban personnel flow condition monitoring is improved.
Example III
Fig. 3 is a schematic structural diagram of an urban mass flow monitoring device according to a third embodiment of the present invention, where the present embodiment is applicable to monitoring urban mass flow conditions. The urban people stream monitoring device provided by the embodiment can realize the urban people stream monitoring method provided by any embodiment of the invention, has the corresponding functional structure and beneficial effects of the realization method, and the details which are not described in detail in the embodiment can be referred to the description of any method embodiment of the invention.
As shown in fig. 3, the urban people stream monitoring device provided in the third embodiment of the present invention includes: a data acquisition module 310, a data preprocessing module 320, and a monitor view generation module 330, wherein:
the data acquisition module 310 is configured to acquire original people stream data and city grid data, where the original people stream data includes a plurality of original track points, and time intervals between the plurality of original track points are unequal;
the data preprocessing module 320 is configured to preprocess the raw people stream data based on the urban grid data, so as to obtain standardized grid data, where the standardized grid data includes a plurality of standard track points, and time intervals between the plurality of standard track points are equal;
the monitoring view generation module 330 is configured to generate a people stream monitoring view according to the standardized grid data.
Further, the data preprocessing module 320 includes:
the data calibration unit is used for calibrating the original people stream data to obtain calibration track data, wherein the calibration track data comprises a plurality of calibration track points, and the time intervals among the plurality of calibration track points are equal;
and the grid matching unit is used for matching the calibration track data with the urban grid data to obtain standardized grid data.
Further, the monitor view generation module 330 includes:
the characteristic parameter determining unit is used for determining a people stream characteristic parameter according to the standardized grid data, wherein the people stream characteristic parameter comprises people stream density, people stream and people stream transfer quantity;
and the monitoring view generating unit is used for generating a people stream monitoring view according to the people stream characteristic parameters.
Further, the monitor view generating unit includes:
the prediction subunit is used for acquiring a people stream prediction parameter according to the people stream characteristic parameter based on a deep learning model;
and the monitoring view generation subunit is used for generating a people stream monitoring view according to the people stream prediction parameters.
Further, the prediction subunit is specifically configured to:
inputting the people stream density into a ConvLSTM network model to obtain a people stream prediction density;
and inputting the people flow into a ConvLSTM network model to obtain people flow prediction.
Further, the predictor unit is further configured to:
generating fusion characteristics based on the partial transfer condition of the people stream transfer quantity analysis;
capturing space-time dependence based on the fusion characteristics to obtain hidden layer information;
predicting to obtain a multi-order people stream transfer prediction matrix based on the hidden layer information;
and determining the people stream prediction transfer amount based on the multi-order people stream transfer prediction matrix.
According to the urban mass flow monitoring device provided by the embodiment of the invention, through preprocessing the original track points, the influence of uncertainty factors such as GPS signals, network signals and equipment power supply on the generation of mass flow monitoring views is reduced, so that the mass flow monitoring views are more accurate; by matching city grid data, the generation of a people stream monitoring view of a specific city area is realized, and the people stream monitoring has more flexibility. In addition, the monitoring personnel can obtain the corresponding people flow monitoring view only by accessing the original people flow data without manually extracting the special characteristics, so that the dependence of urban people flow monitoring on special technicians is reduced, and the universality of urban personnel flow condition monitoring is improved.
Example IV
Fig. 4 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present invention. Fig. 4 illustrates a block diagram of an exemplary electronic device 412 suitable for use in implementing embodiments of the invention. The electronic device 412 shown in fig. 4 is only an example and should not be construed as limiting the functionality and scope of use of embodiments of the invention.
As shown in fig. 4, the electronic device 412 is in the form of a general-purpose electronic device. Components of electronic device 412 may include, but are not limited to: one or more processors 416, a storage 428, and a bus 418 that connects the various system components (including the storage 428 and the processors 416).
Bus 418 represents one or more of several types of bus structures, including a memory device bus or memory device controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include industry standard architecture (Industry Subversive Alliance, ISA) bus, micro channel architecture (Micro Channel Architecture, MAC) bus, enhanced ISA bus, video electronics standards association (Video Electronics Standards Association, VESA) local bus, and peripheral component interconnect (Peripheral Component Interconnect, PCI) bus.
Electronic device 412 typically includes a variety of computer system readable media. Such media can be any available media that is accessible by electronic device 412 and includes both volatile and nonvolatile media, removable and non-removable media.
The storage 428 may include computer system readable media in the form of volatile memory, such as random access memory (Random Access Memory, RAM) 430 and/or cache memory 432. The electronic device 412 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 434 may be used to read from or write to non-removable, nonvolatile magnetic media (not shown in FIG. 4, commonly referred to as a "hard disk drive"). Although not shown in fig. 4, a magnetic disk drive for reading from and writing to a removable nonvolatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive for reading from or writing to a removable nonvolatile optical disk such as a Read Only Memory (CD-ROM), digital versatile disk (Digital Video Disc-Read Only Memory, DVD-ROM), or other optical media, may be provided. In such cases, each drive may be coupled to bus 418 via one or more data medium interfaces. Storage 428 may include at least one program product having a set (e.g., at least one) of program modules configured to carry out the functions of embodiments of the invention.
A program/utility 440 having a set (at least one) of program modules 442 may be stored, for example, in the storage 428, such program modules 442 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment. Program modules 442 generally perform the functions and/or methodologies in the described embodiments of the invention.
The electronic device 412 may also communicate with one or more external devices 414 (e.g., keyboard, pointing terminal, display 424, etc.), with one or more terminals that enable a user to interact with the electronic device 412, and/or with any terminal (e.g., network card, modem, etc.) that enables the electronic device 412 to communicate with one or more other computing terminals. Such communication may occur through an input/output (I/O) interface 422. Also, the electronic device 412 may communicate with one or more networks (e.g., a local area network (Local Area Network, LAN), a wide area network (Wide Area Network, WAN) and/or a public network, such as the internet) via the network adapter 420. As shown in fig. 4, network adapter 420 communicates with other modules of electronic device 412 over bus 418. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with electronic device 412, including, but not limited to: microcode, end drives, redundant processors, external disk drive arrays, disk array (Redundant Arrays of Independent Disks, RAID) systems, tape drives, data backup storage systems, and the like.
Processor 416, by running a program stored in storage device 428, performs various functional applications and data processing, such as implementing the urban people stream monitoring method provided by any embodiment of the present invention, may include:
acquiring original people stream data and urban grid data, wherein the original people stream data comprises a plurality of original track points, and the time intervals among the plurality of original track points are unequal;
preprocessing the original people stream data based on the urban grid data to obtain standardized grid data, wherein the standardized grid data comprises a plurality of standard track points, and the time intervals among the standard track points are equal;
and generating a people stream monitoring view according to the standardized grid data.
Example five
The fifth embodiment of the present invention further provides a computer readable storage medium having a computer program stored thereon, where the program when executed by a processor implements the urban people stream monitoring method according to any embodiment of the present invention, the method may include:
acquiring original people stream data and urban grid data, wherein the original people stream data comprises a plurality of original track points, and the time intervals among the plurality of original track points are unequal;
preprocessing the original people stream data based on the urban grid data to obtain standardized grid data, wherein the standardized grid data comprises a plurality of standard track points, and the time intervals among the standard track points are equal;
and generating a people stream monitoring view according to the standardized grid data.
The computer storage media of embodiments of the invention may take the form of any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or terminal. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer, for example, through the internet using an internet service provider.
Note that the above is only a preferred embodiment of the present invention and the technical principle applied. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, while the invention has been described in connection with the above embodiments, the invention is not limited to the embodiments, but may be embodied in many other equivalent forms without departing from the spirit or scope of the invention, which is set forth in the following claims.
Claims (8)
1. A method for monitoring urban mass flow of people, comprising:
acquiring original people stream data and urban grid data, wherein the original people stream data comprises a plurality of original track points, and the time intervals among the plurality of original track points are unequal;
preprocessing the original people stream data based on the urban grid data to obtain standardized grid data, wherein the standardized grid data comprises a plurality of standard track points, and the time intervals among the standard track points are equal;
generating a people stream monitoring view according to the standardized grid data;
the preprocessing the original people stream data based on the urban grid data to obtain standardized grid data comprises the following steps:
cleaning the original people stream data to remove abnormal values and null values in the original people stream data, and correcting errors;
distinguishing the cleaned original people stream data according to users, and calibrating the original track data of each user to obtain calibration track data, wherein the calibration track data comprises a plurality of calibration track points, and the time intervals among the plurality of calibration track points are equal;
and determining the urban grids where the calibration track points are positioned according to the longitude and latitude positions of the calibration track points in the calibration track data, and replacing the longitude and latitude positions of the calibration track points with the urban grids to obtain standardized grid data.
2. The method of claim 1, wherein generating a people stream monitoring view from the standardized grid data comprises:
determining people stream characteristic parameters according to the standardized grid data, wherein the people stream characteristic parameters comprise people stream density, people stream and people stream transfer;
and generating a people stream monitoring view according to the people stream characteristic parameters.
3. The method of claim 2, wherein generating a people stream monitor view from the people stream feature parameters comprises:
based on a deep learning model, acquiring a people stream prediction parameter according to the people stream characteristic parameter;
and generating a people stream monitoring view according to the people stream prediction parameters.
4. The method of claim 3, wherein obtaining the people stream prediction parameters from the people stream feature parameters based on a deep learning model comprises:
inputting the people stream density into a ConvLSTM network model to obtain a people stream prediction density;
and inputting the people flow into a ConvLSTM network model to obtain people flow prediction.
5. The method of claim 3, wherein obtaining the people stream prediction parameters from the people stream feature parameters based on a deep learning model comprises:
generating fusion characteristics based on the partial transfer condition of the people stream transfer quantity analysis;
capturing space-time dependence based on the fusion characteristics to obtain hidden layer information;
predicting to obtain a multi-order people stream transfer prediction matrix based on the hidden layer information;
and determining the people stream prediction transfer amount based on the multi-order people stream transfer prediction matrix.
6. An urban people stream monitoring device, comprising:
the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring original people stream data and city grid data, the original people stream data comprises a plurality of original track points, and the time intervals among the plurality of original track points are unequal;
the data preprocessing module is used for preprocessing the original people stream data based on the urban grid data to obtain standardized grid data, wherein the standardized grid data comprises a plurality of standard track points, and the time intervals among the standard track points are equal;
the monitoring view generation module is used for generating a people stream monitoring view according to the standardized grid data;
the data preprocessing module is specifically used for:
cleaning the original people stream data to remove abnormal values and null values in the original people stream data, and correcting errors;
distinguishing the cleaned original people stream data according to users, and calibrating the original track data of each user to obtain calibration track data, wherein the calibration track data comprises a plurality of calibration track points, and the time intervals among the plurality of calibration track points are equal;
and determining the urban grids where the calibration track points are positioned according to the longitude and latitude positions of the calibration track points in the calibration track data, and replacing the longitude and latitude positions of the calibration track points with the urban grids to obtain standardized grid data.
7. An electronic device, comprising:
one or more processors;
storage means for storing one or more programs,
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the urban people stream monitoring method of any of claims 1-5.
8. A computer readable storage medium having stored thereon a computer program, which when executed by a processor implements the urban people stream monitoring method according to any of claims 1-5.
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| PCT/CN2021/115551 WO2022134649A1 (en) | 2020-12-24 | 2021-08-31 | Urban people flow monitoring method and apparatus, electronic device, and storage medium |
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| CN112541852B (en) * | 2020-12-24 | 2024-04-12 | 南方科技大学 | Urban people stream monitoring method and device, electronic equipment and storage medium |
| CN112633608B (en) * | 2021-01-06 | 2022-04-12 | 南方科技大学 | People flow transfer prediction method, device, equipment and storage medium |
| CN112927270A (en) * | 2021-03-30 | 2021-06-08 | 中国建设银行股份有限公司 | Track generation method and device, electronic equipment and storage medium |
| CN113127594B (en) * | 2021-06-17 | 2021-09-03 | 脉策(上海)智能科技有限公司 | Method, computing device and storage medium for determining grouping data of geographic area |
| CN113409018B (en) * | 2021-06-25 | 2024-03-05 | 北京红山信息科技研究院有限公司 | People stream density determining method, device, equipment and storage medium |
| CN115408452B (en) * | 2022-11-02 | 2023-04-07 | 中南大学 | Urban facility association pattern mining method and related equipment |
| CN118035941B (en) * | 2024-03-20 | 2024-08-02 | 今创集团股份有限公司 | Method and system for monitoring traffic safety of high-speed rail station |
| CN117975178B (en) * | 2024-04-02 | 2024-05-28 | 北京市计量检测科学研究院 | Taxi track data analysis method based on big data analysis |
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