CN112312317A - Public aggregation event identification method and device, computer equipment and storage medium - Google Patents

Public aggregation event identification method and device, computer equipment and storage medium Download PDF

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CN112312317A
CN112312317A CN202011191917.6A CN202011191917A CN112312317A CN 112312317 A CN112312317 A CN 112312317A CN 202011191917 A CN202011191917 A CN 202011191917A CN 112312317 A CN112312317 A CN 112312317A
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residence
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CN112312317B (en
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刘科壮
刘祖军
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Smartsteps Data Technology Co ltd
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Abstract

The embodiment of the invention provides a public gathering event identification method, a device, computer equipment and a storage medium, which are characterized in that user residing data in a plurality of preset position areas are obtained, a people number fluctuation curve of each position area is respectively determined according to the user residing data of each position area, clustering analysis is carried out on the plurality of position areas according to the people number fluctuation curves of the plurality of position areas to determine the area type of each position area, a target curve is processed by continuous wavelet transformation, a target date is identified, and therefore the position area with the area type matched with a preset unstable type is determined as a place where a public gathering event occurs, and the target date is determined as the residing date where the public gathering event occurs. By analyzing the public gathering event by utilizing the operator signaling data, the travel and gathering conditions of the user can be reflected more accurately after the big data is processed, so that the time and the place of the event can be identified more intensively and more accurately.

Description

Public aggregation event identification method and device, computer equipment and storage medium
Technical Field
The invention relates to the technical field of big data processing, in particular to a public gathering event identification method and device, computer equipment and a storage medium.
Background
At present, a great amount of public gathering events, such as event information of events, exhibitions, meetings, concerts and singing meetings, are mostly released from a network platform, however, the data are very dispersed and are not centralized enough, so that statistical analysis is not convenient to perform, and the occurrence of the public gathering events cannot be accurately identified.
Disclosure of Invention
In view of the above, the present invention provides a method, an apparatus, a computer device and a storage medium for identifying a common aggregation event, so as to solve the above problems.
In order to achieve the above purpose, the embodiment of the present invention adopts the following technical solutions:
in a first aspect, an embodiment of the present application provides a method for identifying a public aggregated event, where the method for identifying a public aggregated event includes:
acquiring user residence data in a plurality of preset position areas, wherein the user residence data are associated with residence dates and the position areas, and the user residence data comprise the accumulated residence number of the associated position areas in the associated residence dates;
respectively determining a people number fluctuation curve of each position area according to the user residence data of each position area, wherein the people number fluctuation curve is used for representing the change condition of the accumulated residence people number of the associated position area along with the residence date;
performing cluster analysis on the plurality of position areas according to the people number fluctuation curves of the plurality of position areas to determine the area type of each position area;
processing a target curve by using continuous wavelet transform, and identifying a target date, wherein the target curve is a people number fluctuation curve corresponding to a position area with the area type matched with a preset unstable type in a plurality of people number fluctuation curves;
and determining the position area with the area type matched with the preset unstable type as a place where the common aggregation event occurs, and determining the target date as a resident date where the common aggregation event occurs.
Further, the step of processing the target curve by using continuous wavelet transform and identifying the target date comprises:
symmetrically extending the target curve forwards and backwards by preset units respectively to obtain an extension curve;
determining sampling frequency and scale according to the target curve;
determining a scale sequence according to preset wavelet center frequency, the sampling frequency and the scale;
performing wavelet transformation processing on the extension curve based on a preset mother wavelet curve and the scale sequence to obtain a wavelet transformation signal;
drawing a wavelet coefficient distribution map according to the wavelet transformation signal, wherein the wavelet coefficient distribution map represents a variation curve of the real part value of the wavelet coefficient along with the residence date;
and identifying and obtaining the target date according to the wavelet coefficient distribution diagram.
Further, the step of determining a sampling frequency and a scale according to the target curve comprises:
determining sampling frequency according to the horizontal coordinate granularity of the target curve;
and determining a scale according to the number of abscissas of the target curve.
Further, the wavelet transform signal, the mother wavelet curve, and the scale sequence satisfy:
Figure BDA0002752977780000021
wherein W ψ (α, β) is the wavelet transform signal,
Figure BDA0002752977780000022
is the mother wavelet curve, fbFor a preset frequency bandwidth, fcThe preset wavelet center frequency is defined as alpha, beta and t, the alpha is the scale sequence, the beta is the translation amount and the t is the residence date.
Further, the step of determining a scale sequence according to a preset wavelet center frequency, the sampling frequency and the scale comprises:
determining a frequency sequence according to the sampling frequency and the scale;
and determining the scale sequence according to the frequency sequence, the sampling frequency and the preset wavelet center frequency.
Further, the frequency sequence, the sampling frequency, the preset wavelet center frequency and the scale sequence satisfy:
α×f＝fs×fc
wherein α is the scale sequence, fsFor the sampling frequency, fcF is the frequency sequence including a plurality of wavelets, f is the preset wavelet center frequency, f is the frequency sequencei＝fs×i/(2×totalscal)，fsThe frequency of the ith small wave in the frequency sequence is defined, totalscal is the scale, and i is more than or equal to 1 and less than or equal to totalscal.
Further, the step of acquiring the user residence data in the preset plurality of location areas comprises:
acquiring indoor branch base station information and mobile terminal signaling data, wherein the indoor branch base station information comprises base station identifications of a plurality of indoor branch base stations, the indoor branch base stations correspond to the position areas one by one, and the mobile terminal signaling data comprises a residence date, a residence time and a base station identification of the residence base station;
denoising the signaling data of the mobile terminal according to the residence time to obtain effective residence data;
screening the effective resident data according to the indoor branch base station information to obtain indoor resident data;
and counting the indoor resident data according to the resident date and the base station identification of the resident base station to obtain the user resident data in a plurality of preset position areas.
In a second aspect, an embodiment of the present application further provides a common aggregated event identifying apparatus, where the common aggregated event identifying apparatus includes:
the data acquisition module is used for acquiring user residence data in a plurality of preset position areas, wherein the user residence data are associated with residence dates and the position areas, and the user residence data comprise the accumulated residence number of the associated position areas in the associated residence dates;
the people number determining module is used for respectively determining a people number fluctuation curve of each position area according to the user residence data of each position area, wherein the people number fluctuation curve is used for representing the change condition of the accumulated residence people number of the associated position area along with the residence date;
the type determining module is used for carrying out clustering analysis on the plurality of position areas according to the people number fluctuation curves of the plurality of position areas so as to determine the area type of each position area;
the signal processing module is used for processing a target curve by utilizing continuous wavelet transformation and identifying a target date, wherein the target curve is a people number fluctuation curve corresponding to a position area with the area type matched with a preset unstable type in a plurality of people number fluctuation curves;
and the aggregation information determining module is used for determining the position area with the area type matched with the preset unstable type as the place where the common aggregation event occurs and determining the target date as the residence date where the common aggregation event occurs.
In a third aspect, an embodiment of the present application further provides a computer device, including a processor and a memory, where the memory stores a computer program that can be executed by the processor, and the processor can execute the computer program to implement the steps of the common aggregated event identification method according to any one of the foregoing embodiments.
In a fourth aspect, the present application further provides a storage medium, on which a computer program is stored, where the computer program is executed by a processor to implement the steps of the common aggregated event identification method according to any one of the foregoing embodiments.
According to the public gathering event identification method, the device, the computer equipment and the storage medium, the user residing data in the preset position areas are obtained, the people number fluctuation curve of each position area is respectively determined according to the user residing data of each position area, the position areas are subjected to cluster analysis according to the people number fluctuation curves of the position areas to determine the area type of each position area, then the target curve is processed through continuous wavelet transformation, the target date is identified, and therefore the position area with the area type matched with the preset unstable type is determined as the place where the public gathering event occurs, and the target date is determined as the residing date where the public gathering event occurs. By analyzing the public gathering event by utilizing the operator signaling data, the travel and gathering conditions of the user can be reflected more accurately after the big data is processed, so that the time and the place of the event can be identified more intensively and more accurately.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 shows a block schematic diagram of a computer device provided by an embodiment of the present invention.
Fig. 2 is a flowchart illustrating a common aggregated event identification method according to an embodiment of the present invention.
Fig. 3 shows a detailed flowchart of S201 in fig. 2.
Fig. 4 shows the population fluctuation characteristics of 5 different types of population fluctuation curves provided by the embodiment of the present invention.
Fig. 5 shows a detailed flowchart of S204 in fig. 2.
Fig. 6 shows a detailed flowchart of S2042 in fig. 5.
Fig. 7 shows a detailed flowchart of S2043 in fig. 5.
Fig. 8 shows a distribution diagram of wavelet coefficients provided by an embodiment of the present invention.
Fig. 9 is a functional block diagram of a common aggregated event identifying apparatus according to an embodiment of the present invention.
Icon: 100-a computer device; 110-a memory; 120-a processor; 130-a communication unit; 200-common aggregated event identification means; 210-a data acquisition module; 220-a people number determination module; 230-type determination module; 240-a signal processing module; 250 — aggregate information determination module.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
It is noted that relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Fig. 1 is a block diagram of a computer apparatus 100. The computer device 100 includes a memory 110, a processor 120, and a communication unit 130. The elements of the memory 110, the processor 120 and the communication unit 130 are electrically connected to each other directly or indirectly to realize data transmission or interaction. For example, the components may be electrically connected to each other via one or more communication buses or signal lines.
The memory 110 is used to store programs or data. The Memory 110 may be, but is not limited to, a Random Access Memory 110 (RAM), a Read Only Memory 110 (ROM), a Programmable Read Only Memory 110 (PROM), an Erasable Read Only Memory 110 (EPROM), an electrically Erasable Read Only Memory 110 (EEPROM), and the like.
The processor 120 is used to read/write data or programs stored in the memory 110 and perform corresponding functions.
The communication unit 130 is used for establishing a communication connection between the computer apparatus 100 and other communication terminals through the network, and for transceiving data through the network.
It should be understood that the configuration shown in fig. 1 is merely a schematic diagram of the configuration of the computer device 100, and that the computer device 100 may include more or fewer components than shown in fig. 1, or have a different configuration than shown in fig. 1. The components shown in fig. 1 may be implemented in hardware, software, or a combination thereof.
First embodiment
The embodiment of the application provides a public gathering event identification method, which is applied to the computer device 100 and used for accurately identifying the place and the date of a public gathering event. Please refer to fig. 2, which is a flowchart illustrating a method for identifying a common aggregated event according to an embodiment of the present application. The public aggregated event identification method comprises the following steps:
s201, user resident data in a plurality of preset position areas are acquired.
The user residence data is associated with residence dates and location areas, and the user residence data comprises the accumulated residence number of the associated location areas within the associated residence dates.
For example, the location area taken as ID1 is the user resident data on the date of residence of 10 months and 15 days, and it is known from the user resident data that the cumulative resident number is 150. It can thus be determined that the user residence data characterizes that the location area ID1 has 150 people resident within 10 months and 15 days.
Please refer to fig. 3, which is a detailed flowchart of S201. The S201 includes:
s2011, the cell base station information and the mobile terminal signaling data are acquired.
It will be appreciated that communication between terminals may require reliance on base stations of various large operators. And the base station may be divided into a macro base station and a room division base station. In general, macro base stations are large in size and are generally arranged in the field; the indoor sub-base station is generally small in size and is generally arranged in the inner space of a building, such as a high-rise building, a basement, a tunnel, a culvert, an open-air stadium and the like.
The mobile terminal signaling data may include data generated by interaction between each base station and each mobile terminal. The mobile terminal signaling data includes a date of residence, a duration of residence, and a base station identification of the residing base station. The residence date represents the date of data interaction between the mobile terminal and the base station; the residence time represents the duration of data interaction between the mobile terminal and the base station; the base station identity of the camped base station may determine the base station with which the mobile terminal is performing data interaction.
The indoor base station information includes base station identifiers of a plurality of indoor base stations, installation position information, and the like, and the plurality of indoor base stations correspond to the plurality of position areas one to one. Typically, a base station is installed in a location area corresponding to the base station. Thus, once the indoor base station in which the mobile terminal resides is determined, the location area in which the user holding the mobile terminal is located can be determined.
S2012, denoising the signaling data of the mobile terminal according to the residence time to obtain effective residence data.
In an optional implementation manner, the denoising processing is performed on the signaling data of the mobile terminal, which can be understood as filtering out data whose residence time in the signaling data of the mobile terminal is less than a preset time threshold, so as to obtain effective residence data.
And S2013, screening the effective resident data according to the indoor branch base station information to obtain indoor resident data.
It can be understood that all the data for accessing the indoor branch base station in the valid residence data are the indoor residence data obtained by screening.
And S2014, counting the indoor resident data according to the resident date and the base station identification of the resident base station to obtain the user resident data in a plurality of preset position areas.
It can be understood that the indoor resident data with the same resident date and the base station identification of the resident base station is divided into a group, and then the total number of the indoor resident data in the group is counted, so that the accumulated resident number can be obtained.
In an alternative embodiment, the residence date may be divided into a plurality of residence time periods, and the number of residents in each residence time period is counted. Correspondingly, the user residence data can also comprise a plurality of residence time periods and the residence number in each residence time period. Wherein the cumulative resident number is understood to be the sum of the resident numbers in the plurality of resident time periods.
For example, the plurality of dwell periods may be: [00:00:00, 06:00:00), [06:00:00, 12:00:00), (12: 00:00:00, 18:00:00), (18: 00:00, 24:00:00), and the number of residents respectively corresponding to the 4 time periods and the cumulative number of residents in the 4 time periods can be determined.
It is understood that the certain location area may correspond to a plurality of user resident data, and the resident date associated with each user resident data is different.
S202, respectively determining the people number fluctuation curve of each position area according to the user residence data of each position area.
The people number fluctuation curve is used for representing the change condition of the accumulated resident number of the associated position area along with the resident date. It will be appreciated that after determining the cumulative resident count for each location area over the different residences dates, the cumulative resident count for each location area may be determined as a function of the residences dates.
And S203, performing cluster analysis on the plurality of position areas according to the people number fluctuation curves of the plurality of position areas to determine the area type of each position area.
In an alternative embodiment, the population fluctuation curves of a plurality of position areas can be subjected to cluster analysis by using a K-Means clustering algorithm. First, the optimal K-value of the K-Means cluster of the current sample set, which can be understood as the number of sample types, can be determined using the elbow method. Preferably, k is 5 provided in the embodiments of the present application.
Then, the people number fluctuation curves of all the position areas are subjected to cluster analysis, and the people number fluctuation curves of all the position areas are divided into 5 types. Referring to fig. 4, the population fluctuation characteristics of 5 different types of population fluctuation curves are shown. It can be seen that, the types 0, 1, 2, 3 and 4 all present obvious stable periodic fluctuation characteristics, that is, the number of people changes regularly; while class 0 exhibits an unstable aperiodic surge signature, this type of people volatility curve indicates the presence of a common aggregate event.
And S204, processing the target curve by using continuous wavelet transform, and identifying the target date.
The target curve is a people number fluctuation curve corresponding to a position area with the area type matched with a preset unstable type in the plurality of people number fluctuation curves.
Please refer to fig. 5, which is a detailed flowchart of S204. The S204 includes:
s2041, symmetrically extending the target curve forwards and backwards respectively by preset units to obtain an extension curve.
Specifically, a first end point and a second end point of the target curve, which are respectively located at the leftmost position and the rightmost position, are determined, then a first straight line which is perpendicular to the abscissa axis and is used as a first end point and a second straight line which is perpendicular to the abscissa axis and is used as a symmetry axis, the target curve of the first end point in a right preset unit is axially symmetrical to the left by taking the first straight line as a symmetry axis, and the target curve of the second end point in a left preset unit is axially symmetrical to the right by taking the second straight line as a symmetry axis, so that an extension curve is obtained.
It will be appreciated that by deriving an extended curve, the boundary effects of the target curve can be eliminated or reduced.
And S2042, determining sampling frequency and scale according to the target curve.
Please refer to fig. 6, which is a detailed flowchart of S2042. The S2042 includes:
s20421, determining the sampling frequency according to the horizontal coordinate granularity of the target curve.
Wherein, the horizontal coordinate granularity of the target curve is the thickness degree of the statistical date. For example, if the statistics are in days, then the abscissa granularity is "/day", so that the sampling frequency is 1; the statistics are in units of two days, the abscissa granularity is "/two days", so that the sampling frequency is 2.
And S20422, determining a scale according to the number of abscissas of the target curve.
Wherein the dimension is typically about half the number of abscissas. For example, if the data collected in the embodiment of the present application is within one month, the number of abscissa is about 30 in units of days, and the scale may be 14, 15, 16, etc.
And S2043, determining a scale sequence according to preset wavelet center frequency, sampling frequency and scale.
Please refer to fig. 7, which is a detailed flowchart of S2043. The S2043 includes:
and S20431, determining a frequency sequence according to the sampling frequency and the scale.
And S20432, determining a scale sequence according to the frequency sequence, the sampling frequency and the preset wavelet center frequency.
The frequency sequence, the sampling frequency, the preset wavelet center frequency and the preset scale sequence meet the following requirements:
α×f＝fs×fc
wherein, alpha is a scale sequence, fsTo sample frequency, fcF is a frequency sequence including a plurality of wavelets for a preset wavelet center frequency, f is a frequency sequencei＝fs×i/(2×totalscal)，fsThe frequency of the ith small wave in the frequency sequence is shown, totalscal is a scale, and i is more than or equal to 1 and less than or equal to totalscal.
S2044, performing wavelet transformation processing on the extension curve based on the preset mother wavelet curve and the preset scale sequence to obtain a wavelet transformation signal.
Wherein, the wavelet transform signal, the mother wavelet curve and the scale sequence satisfy:
Figure BDA0002752977780000111
wherein W ψ (α, β) is a wavelet transform signal,
Figure BDA0002752977780000112
is a mother wavelet curve, fbFor a preset frequency bandwidth, fcThe method is characterized in that the preset wavelet center frequency is alpha, the scale sequence is alpha, the translation amount is beta, and the residence date is t.
And S2045, drawing a wavelet coefficient distribution map according to the wavelet transform signal.
Wherein, the wavelet coefficient distribution graph represents the variation curve of the real part value of the wavelet coefficient along with the residence date. In an alternative embodiment, the wavelet coefficient distribution map may be obtained by using software such as Matlab and the like to map the wavelet transform signal. The wavelet coefficient profile may be as described in figure 8, for example.
And S2046, identifying and obtaining the target date according to the wavelet coefficient distribution map.
Specifically, a date when there is a sudden increase in population is taken as a target date. Taking fig. 8 as an example, it can be clearly determined that the real part values of the wavelet coefficients are demographically increased on days 23, 24, and 30, and thus 23, 24, and 30 are taken as target dates.
S205, determining the position area with the area type matched with the preset unstable type as the place where the common aggregation event occurs, and determining the target date as the residence date where the common aggregation event occurs.
In order to perform the corresponding steps in the above embodiments and various possible manners, an implementation manner of the common aggregated event identifying apparatus 200 is given below, and optionally, the common aggregated event identifying apparatus 200 may adopt the device structure of the processor 120 shown in fig. 1. Further, referring to fig. 9, fig. 9 is a functional block diagram of a common aggregated event recognition apparatus 200 according to an embodiment of the present invention. It should be noted that the basic principle and the generated technical effect of the common aggregated event identifying device 200 provided in the present embodiment are the same as those of the above embodiments, and for the sake of brief description, no part of the present embodiment is mentioned, and reference may be made to the corresponding contents in the above embodiments. The common aggregated event identifying apparatus 200 includes: a data acquisition module 210, a people number determination module 220, a type determination module 230, a signal processing module 240, and an aggregated information determination module 250.
The data obtaining module 210 is configured to obtain user residence data in a plurality of preset location areas.
In an optional implementation manner, the data obtaining module 210 is configured to obtain indoor base station information and mobile terminal signaling data, perform denoising processing on the mobile terminal signaling data according to residence time to obtain effective residence data, obtain indoor residence data by screening from the effective residence data according to the indoor base station information, and finally count the indoor residence data according to residence dates and base station identifiers of residence base stations to obtain user residence data in a plurality of preset location areas.
It is understood that in an alternative embodiment, the data obtaining module 210 may be configured to perform S201, S2011, S2012, S2013 and S2014.
The people number determining module 220 is configured to determine a people number fluctuation curve of each location area according to the user residence data of each location area.
It is to be appreciated that in an alternative embodiment, the people number determination module 220 may be configured to perform S202.
The type determining module 230 is configured to perform cluster analysis on the plurality of location areas according to the people number fluctuation curves of the plurality of location areas to determine the area type of each location area.
It is to be appreciated that in an alternative embodiment, the type determining module 230 may be configured to perform S203.
The signal processing module 240 is configured to process the target curve by using continuous wavelet transform to identify a target date.
Specifically, the signal processing module 240 is configured to symmetrically extend the target curve forward and backward by a preset unit to obtain an extension curve, determine a sampling frequency and a scale according to the target curve, determine a scale sequence according to a preset wavelet center frequency, a preset sampling frequency and a preset scale, perform wavelet transform processing on the extension curve based on a preset mother wavelet curve and the scale sequence to obtain a wavelet transform signal, then draw a wavelet coefficient distribution map according to the wavelet transform signal, and finally identify and obtain a target date according to the wavelet coefficient distribution map.
The signal processing module 240 is further configured to determine a sampling frequency according to the abscissa granularity of the target curve and determine a scale according to the abscissa number of the target curve.
The signal processing module 240 is further configured to determine a frequency sequence according to the sampling frequency and the scale, and determine a scale sequence according to the frequency sequence, the sampling frequency, and a preset wavelet center frequency.
It is understood that, in an alternative embodiment, the signal processing module 240 may be configured to execute S204, S2041, S2042, S2043, S2044, S2045, S2046, S20421, S20422, S20431, and S20432.
The aggregation information determination module 250 is configured to determine a location area with an area type matching a preset unstable type as a place where the common aggregation event occurs, and determine a target date as a resident date where the common aggregation event occurs.
It is to be appreciated that in an alternative embodiment, the aggregated information determination module 250 may be configured to perform S205.
Alternatively, the modules may be stored in the memory 110 shown in fig. 1 in the form of software or Firmware (Firmware) or be fixed in an Operating System (OS) of the computer device 100, and may be executed by the processor 120 in fig. 1. Meanwhile, data, codes of programs, and the like required to execute the above-described modules may be stored in the memory 110.
The present embodiment also provides a storage medium, on which a computer program is stored, where the computer program is executed by the processor 120 to implement the steps of the common aggregated event identification method according to any one of the above-mentioned embodiments.
In summary, according to the method, the apparatus, the computer device, and the storage medium for identifying a public aggregation event provided in the embodiments of the present application, the user residence data in the preset multiple location areas are obtained, the people number fluctuation curve of each location area is determined according to the user residence data of each location area, the multiple location areas are subjected to cluster analysis according to the people number fluctuation curves of the multiple location areas to determine the area type of each location area, the target curve is processed by using continuous wavelet transform to identify the target date, so that the location area with the area type matching the preset unstable type is determined as the location where the public aggregation event occurs, and the target date is determined as the residence date where the public aggregation event occurs. By analyzing the public gathering event by utilizing the operator signaling data, the travel and gathering conditions of the user can be reflected more accurately after the big data is processed, so that the time and the place of the event can be identified more intensively and more accurately.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, the functional modules in the embodiments of the present invention may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (10)

1. A common aggregated event identification method, characterized in that the common aggregated event identification method comprises:
acquiring user residence data in a plurality of preset position areas, wherein the user residence data are associated with residence dates and the position areas, and the user residence data comprise the accumulated residence number of the associated position areas in the associated residence dates;
respectively determining a people number fluctuation curve of each position area according to the user residence data of each position area, wherein the people number fluctuation curve is used for representing the change condition of the accumulated residence people number of the associated position area along with the residence date;
performing cluster analysis on the plurality of position areas according to the people number fluctuation curves of the plurality of position areas to determine the area type of each position area;
processing a target curve by using continuous wavelet transform, and identifying a target date, wherein the target curve is a people number fluctuation curve corresponding to a position area with the area type matched with a preset unstable type in a plurality of people number fluctuation curves;
and determining the position area with the area type matched with the preset unstable type as a place where the common aggregation event occurs, and determining the target date as a resident date where the common aggregation event occurs.
2. The common aggregate event identification method of claim 1, wherein said processing a target curve using continuous wavelet transform, said step of identifying said target date comprising:
symmetrically extending the target curve forwards and backwards by preset units respectively to obtain an extension curve;
determining sampling frequency and scale according to the target curve;
determining a scale sequence according to preset wavelet center frequency, the sampling frequency and the scale;
performing wavelet transformation processing on the extension curve based on a preset mother wavelet curve and the scale sequence to obtain a wavelet transformation signal;
drawing a wavelet coefficient distribution map according to the wavelet transformation signal, wherein the wavelet coefficient distribution map represents a variation curve of the real part value of the wavelet coefficient along with the residence date;
and identifying and obtaining the target date according to the wavelet coefficient distribution diagram.
3. A common aggregate event identification method according to claim 2, wherein said step of determining a sampling frequency and scale from said target curve comprises:
determining sampling frequency according to the horizontal coordinate granularity of the target curve;
and determining a scale according to the number of abscissas of the target curve.
4. A common gather event identification method according to claim 2 wherein the wavelet transform signal, the mother wavelet curve, the series of scales satisfy:
Figure FDA0002752977770000021
wherein W ψ (α, β) is the wavelet transform signal,
Figure FDA0002752977770000022
is the mother wavelet curve, fbFor a preset frequency bandwidth, fcThe preset wavelet center frequency is defined as alpha, beta and t, the alpha is the scale sequence, the beta is the translation amount and the t is the residence date.
5. The common gather event identification method of claim 2 wherein said step of determining a sequence of scales from a preset wavelet center frequency, said sampling frequency and said scale comprises:
determining a frequency sequence according to the sampling frequency and the scale;
and determining the scale sequence according to the frequency sequence, the sampling frequency and the preset wavelet center frequency.
6. The common gather event identification method of claim 5 wherein the sequence of frequencies, the sampling frequency, the preset wavelet center frequency and the sequence of scales satisfy:
α×f＝fs×fc
wherein α is the scale sequence, fsFor the sampling frequency, fcF is the frequency sequence including a plurality of wavelets, f is the preset wavelet center frequency, f is the frequency sequencei＝fs×i/(2×totalscal)，fsIs the first in the frequency sequenceThe frequency of i small waves, totalscal is the scale, and i is more than or equal to 1 and less than or equal to totalscal.
7. The common aggregated event identifying method according to any one of claims 1 to 6, wherein the step of acquiring user residence data in a plurality of preset location areas comprises:
acquiring indoor branch base station information and mobile terminal signaling data, wherein the indoor branch base station information comprises base station identifications of a plurality of indoor branch base stations, the indoor branch base stations correspond to the position areas one by one, and the mobile terminal signaling data comprises a residence date, a residence time and a base station identification of the residence base station;
denoising the signaling data of the mobile terminal according to the residence time to obtain effective residence data;
screening the effective resident data according to the indoor branch base station information to obtain indoor resident data;
and counting the indoor resident data according to the resident date and the base station identification of the resident base station to obtain the user resident data in a plurality of preset position areas.
8. A common aggregate event recognition apparatus, characterized in that the common aggregate event recognition apparatus comprises:
the data acquisition module is used for acquiring user residence data in a plurality of preset position areas, wherein the user residence data are associated with residence dates and the position areas, and the user residence data comprise the accumulated residence number of the associated position areas in the associated residence dates;
the people number determining module is used for respectively determining a people number fluctuation curve of each position area according to the user residence data of each position area, wherein the people number fluctuation curve is used for representing the change condition of the accumulated residence people number of the associated position area along with the residence date;
the type determining module is used for carrying out clustering analysis on the plurality of position areas according to the people number fluctuation curves of the plurality of position areas so as to determine the area type of each position area;
the signal processing module is used for processing a target curve by utilizing continuous wavelet transformation and identifying a target date, wherein the target curve is a people number fluctuation curve corresponding to a position area with the area type matched with a preset unstable type in a plurality of people number fluctuation curves;
and the aggregation information determining module is used for determining the position area with the area type matched with the preset unstable type as the place where the common aggregation event occurs and determining the target date as the residence date where the common aggregation event occurs.
9. A computer arrangement, characterized by comprising a processor and a memory, said memory storing a computer program executable by said processor, said processor being adapted to execute said computer program to implement the steps of the common aggregated event identification method of any one of claims 1 to 7.
10. A storage medium having stored thereon a computer program, characterized in that the computer program, when being executed by a processor, carries out the steps of the common aggregated event identification method according to any one of claims 1 to 7.
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