CN110858955B - Crowd classification method and crowd classification device - Google Patents
Crowd classification method and crowd classification device Download PDFInfo
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- CN110858955B CN110858955B CN201810974677.3A CN201810974677A CN110858955B CN 110858955 B CN110858955 B CN 110858955B CN 201810974677 A CN201810974677 A CN 201810974677A CN 110858955 B CN110858955 B CN 110858955B
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/029—Location-based management or tracking services
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Abstract
The application provides a crowd classification method and a crowd classification device, wherein the method comprises the following steps: obtaining the use information of each individual to be classified in the crowd to be classified on a plurality of preset communication base stations; acquiring action track information of each individual to be classified according to the use information of each individual to be classified on a plurality of preset communication base stations; and dividing the crowd to be classified into a plurality of classes based on the action track information of each individual to be classified. The population characteristics are mined based on the wider coverage and more comprehensive movement track information, and a mining result with higher accuracy can be obtained.
Description
Technical Field
The application relates to the technical field of data analysis, in particular to a crowd classification method and a crowd classification device.
Background
The characteristics of personal movement are the premise of mining the characteristics of groups, and the mode of personal travel is the important problem of mining and realizing huge social value and industrial value brought by urban data and researching and solving urban information resources. When mining the group characteristics based on the individual movement characteristics, the group is first classified based on the individual movement characteristics, and in order to obtain an accurate mining result, information on the action trajectory of an individual who moves within a certain area is obtained. When acquiring the action track information of an individual, the action track information is acquired as comprehensively as possible for each individual while covering as much as possible of the individual who moves in the area.
In the related art, the information on the movement trajectory thereof is generally determined by acquiring information on the use of the vehicle by an individual. However, when an individual is traveling, the individual may use public transportation, such as buses, subways, etc., may use private transportation, such as private cars, or may not use transportation, such as walking. For individuals who travel by using a private vehicle and who do not travel by using the vehicle, it is difficult to acquire the information of the action track thereof; and for people who mainly use public transport to travel, the people do not always use the public transport to travel. Therefore, the acquisition method of the action track information has the problems of poor accuracy and comprehensiveness in acquisition, and the accuracy of the mining result is low.
Disclosure of Invention
In view of the above, an object of the embodiments of the present invention is to provide a crowd classification method and a crowd classification device, which can determine individual action trajectory information based on usage of a communication base station by an individual, classify a crowd based on the action trajectory information, mine crowd characteristics based on the classification result, and improve accuracy of the mining result.
In a first aspect, an embodiment of the present application provides a crowd classification method, including:
obtaining the use information of each individual to be classified in the crowd to be classified on a plurality of preset communication base stations;
acquiring action track information of each individual to be classified according to the use information of each individual to be classified on a plurality of preset communication base stations;
and dividing the crowd to be classified into a plurality of classes based on the action track information of each individual to be classified.
Optionally, before the obtaining the usage information of each individual to be classified in the group of people to be classified on the plurality of preset communication base stations, the method further includes:
determining a target area range;
determining a plurality of communication base stations with geographic positions belonging to the target area range as preset communication base stations;
and determining the individuals of which the number of the used communication base stations reaches a preset number threshold as the individuals to be classified.
Optionally, the usage information includes: connection establishment time;
the obtaining of the use information of each individual to be classified in the crowd to be classified on the plurality of preset communication base stations specifically includes:
acquiring connection establishment time of each individual to be classified and each preset communication base station within a preset historical time period aiming at each individual to be classified;
the obtaining of the action track information of each individual to be classified according to the use information of each individual to be classified on a plurality of preset communication base stations specifically comprises:
and aiming at each individual to be classified, generating action track information of the individual to be classified according to the sequence of connection establishment time between the individual to be classified and each preset communication base station and the position information of each preset communication base station.
Optionally, the dividing, based on the action trajectory information of each individual to be classified, the crowd to be classified into a plurality of classifications specifically includes:
and clustering each individual to be classified in the crowd to be classified based on the action track information of each individual to be classified to obtain a plurality of classifications.
Optionally, after dividing the crowd to be classified into a plurality of classifications based on the action trajectory information of each individual to be classified, the method further includes:
for each classification, classifying a plurality of individuals to be classified included in the classification based on the use frequency information of the individuals to be classified included in the classification to each preset communication base station, and obtaining at least one sub-classification corresponding to the classification;
the information of the number of times of use includes: connection establishment frequency or connection establishment number.
Optionally, the classifying the multiple individuals to be classified included in the classification based on the information of the number of times of use of the multiple individuals to be classified included in the classification on the multiple preset communication base stations specifically includes:
acquiring connection establishment time between each individual to be classified and each preset communication base station aiming at each individual to be classified;
counting the use frequency information of the connection establishment time between the individual to be classified and the preset communication base station falling into a preset historical time period aiming at each preset communication base station;
and clustering a plurality of individuals to be classified included in the classification based on the use frequency information of each individual to be classified and each preset communication base station included in the classification to obtain a plurality of sub-classifications.
Optionally, the clustering, based on the similarity between each individual to be classified included in the classification and the usage frequency information of each preset communication base station, the multiple individuals to be classified included in the classification specifically includes:
establishing a frequency characteristic vector corresponding to each individual to be classified included in the classification based on the use frequency information of each individual to be classified included in the classification and each preset communication base station;
and clustering all the individuals to be classified in the classification by using a preset clustering algorithm and based on the times characteristic vector corresponding to each individual to be classified in the classification.
Optionally, before establishing the number of times feature vectors corresponding to each individual to be classified included in the classification based on the number of times of establishing connection between each individual to be classified included in the classification and each preset communication base station, the method further includes:
performing interference elimination processing on the connection establishment times of each individual to be classified and each preset communication base station included in the classification;
establishing a frequency characteristic vector corresponding to each individual to be classified included in the classification based on the connection establishment frequency of each individual to be classified included in the classification and each preset communication base station, specifically comprising:
and establishing a frequency characteristic vector corresponding to each individual to be classified included in the classification based on the result of the interference removal processing.
Optionally, the performing interference elimination processing on the connection establishment times of each individual to be classified included in the classification and each preset communication base station specifically includes:
carrying out logarithm treatment on the connection establishment times of each individual to be classified and each preset communication base station included in the classification;
after the connection establishment times are logarithmic, the connection establishment times of the preset communication base station smaller than the preset threshold value return to zero.
In a second aspect, an embodiment of the present application further provides a crowd classification device, including:
the device comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring the use time information of each individual to be classified in the crowd to be classified on a plurality of preset communication base stations;
the action track calculation unit is used for acquiring action track information of each individual to be classified according to the use time information of each individual to be classified on a plurality of preset communication base stations;
and the classification unit is used for dividing the crowd to be classified into a plurality of classifications based on the action track information of each individual to be classified.
Optionally, the method further comprises: the determining unit is used for determining the range of a target area before acquiring the use information of each individual to be classified in the crowd to be classified on a plurality of preset communication base stations;
determining a plurality of communication base stations with geographic positions belonging to the target area range as preset communication base stations;
and determining the individuals of which the number of the used communication base stations reaches a preset number threshold as the individuals to be classified.
Optionally, the usage information includes: connection establishment time;
the obtaining unit is specifically configured to obtain the usage information of each individual to be classified in the crowd to be classified on the plurality of preset communication base stations by adopting the following modes:
acquiring connection establishment time of each individual to be classified and each preset communication base station within a preset historical time period aiming at each individual to be classified;
the action track calculation unit is specifically configured to acquire action track information of each to-be-classified individual according to the use information of each to-be-classified individual on a plurality of preset communication base stations by adopting the following manner:
and aiming at each individual to be classified, generating action track information of the individual to be classified according to the sequence of connection establishment time between the individual to be classified and each preset communication base station and the position information of each preset communication base station.
Optionally, the classifying unit is specifically configured to divide the crowd to be classified into a plurality of classifications based on the action trajectory information of each individual to be classified by:
and clustering each individual to be classified in the crowd to be classified based on the action track information of each individual to be classified to obtain a plurality of classifications.
Optionally, the classifying unit is further configured to, after dividing the crowd to be classified into multiple classifications based on the action trajectory information of each individual to be classified, classify, for each classification, the multiple individuals to be classified included in the classification based on the information of the number of times of use of each preset communication base station by each individual to be classified included in the classification, and obtain at least one sub-classification corresponding to the classification;
the information of the number of times of use includes: connection establishment frequency or number of connection establishment times
Optionally, the classifying unit is specifically configured to classify the multiple individuals to be classified included in the classification based on the information of the number of times of use of the multiple individuals to be classified included in the classification on the multiple preset communication base stations by adopting the following manner:
acquiring connection establishment time between each individual to be classified and each preset communication base station aiming at each individual to be classified;
counting the use frequency information of the connection establishment time between the individual to be classified and the preset communication base station falling into a preset historical time period aiming at each preset communication base station;
and clustering a plurality of individuals to be classified included in the classification based on the use frequency information of each individual to be classified and each preset communication base station included in the classification to obtain a plurality of sub-classifications.
Optionally, the classifying unit is specifically configured to cluster the multiple individuals to be classified included in the classification based on similarity between the individual to be classified included in the classification and the usage frequency information of each preset communication base station, by adopting the following manner:
establishing a frequency characteristic vector corresponding to each individual to be classified included in the classification based on the use frequency information of each individual to be classified included in the classification and each preset communication base station;
and clustering all the individuals to be classified in the classification by using a preset clustering algorithm and based on the times characteristic vector corresponding to each individual to be classified in the classification.
Optionally, the classifying unit is further configured to perform interference elimination on connection establishment times of each to-be-classified individual and each preset communication base station included in the classification before establishing a time feature vector corresponding to each to-be-classified individual included in the classification;
specifically, the method is used for establishing a frequency feature vector corresponding to each individual to be classified included in the classification based on the connection establishment frequency of each individual to be classified included in the classification and each preset communication base station.
Optionally, the classifying unit is specifically configured to perform interference elimination processing on connection establishment times of each to-be-classified individual included in the classification and each preset communication base station by adopting the following manner:
carrying out logarithm treatment on the connection establishment times of each individual to be classified and each preset communication base station included in the classification;
after the connection establishment times are logarithmic, the connection establishment times of the preset communication base station smaller than the preset threshold value return to zero.
In a third aspect, an embodiment of the present application further provides an electronic device, including: a processor, a memory and a bus, the memory storing machine-readable instructions executable by the processor, the processor and the memory communicating via the bus when the electronic device is running, the machine-readable instructions being executable by the processor to perform the steps of any one of the possible implementations of the first aspect.
In a fourth aspect, this application further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and the computer program is executed by a processor to perform the steps in any one of the possible implementation manners of the first aspect.
According to the method and the device for determining the movement track information of the individuals to be classified, the movement track information of the individuals to be classified is determined by obtaining the use information of each individual to be classified to a plurality of preset communication base stations in the crowd with the classification, and the use area of the mobile terminal is wider, and the communication base stations can continuously obtain the use information of users, so that the movement track information of the individuals to be classified can be determined according to the use condition of the individuals to be classified to the communication base stations and the geographic position of each communication base station. The population characteristics are mined based on the wider coverage and more comprehensive movement track information, and a mining result with higher accuracy can be obtained.
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required 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 application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
FIG. 1 is a flow chart illustrating a method for crowd classification provided by an embodiment of the present application;
FIG. 2 is a flow chart illustrating another method for crowd classification provided by an embodiment of the present application;
fig. 3 is a flowchart illustrating a specific method for classifying a plurality of individuals to be classified included in a classification in the crowd classification method provided in the embodiment of the present application;
fig. 4 is a flowchart illustrating another specific method for classifying a plurality of individuals to be classified included in the classification in the crowd classification method provided in the embodiment of the present application;
fig. 5 is a schematic structural diagram of a crowd classification device provided in an embodiment of the present application;
fig. 6 shows a schematic structural diagram of a computer device provided in an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application.
In order to achieve the above object, the present application provides a crowd classification method and a crowd classification device, which can determine individual action trajectory information based on usage of a communication base station by an individual, classify a crowd based on the action trajectory information, mine crowd characteristics based on the classification result, and improve accuracy of the mining result.
In order to facilitate understanding of the embodiment, a crowd classification method disclosed in the embodiment of the present application is first described in detail, the method can classify the crowd to be classified based on the action track information of the individual to be classified, and the classification result can be used in various fields such as road planning, traffic analysis, traffic management, and the like.
Referring to fig. 1, a crowd classification method provided in an embodiment of the present application includes:
s101: and acquiring the use information of each individual to be classified in the crowd to be classified on a plurality of preset communication base stations.
In the specific implementation, before mining the population characteristics, the population targeted by the characteristic mining is determined, that is, the population to be classified is determined. In the embodiment of the present application, because the crowd to be classified is classified according to the action trajectory information of each individual to be classified in the crowd to be classified, and each individual to be classified mainly moves in a specific area range, the embodiment of the present application first determines a target area range, and then determines people whose main movement areas fall in the area range as the individuals to be classified.
The target area range may be specifically set according to actual needs, and optionally, the area range may be determined by administrative units of a region. For example a certain city, a certain administrative district of a certain city, a certain town. In addition, a certain point can be used as an origin, and an area range, the distance from which to the origin is smaller than a preset distance threshold value, can be determined as a target area range.
After the target area range is determined, a plurality of communication base stations with geographic positions belonging to the target area range can be determined as preset communication base stations, so that the action track information of the individual to be classified in the target area range is obtained.
After the target area range and the preset communication base station are determined, the crowd to be classified is determined in the target area range.
The group to be classified should theoretically include all persons who move within the target area. However, in practice, some people do not use the mobile terminal, and thus, for example, parents are required to take care of children or old people who do not have mobility. Therefore, in the present application, the crowd to be classified may include all persons who use the mobile terminal within the target area. And taking each person using the mobile terminal as an individual to be classified.
In addition, the movement track of a person is accidental to a certain extent on a regular basis. For example, people who work in city a, travel to city B for a certain period of time; or, the person whose main activity area is in the first district of C city needs to go to the second district of C city to handle some business occasionally. For such users who accidentally use the communication base stations in the target area range, since the users themselves do not move in the target area range for a long time, and the action track information formed by the accidental actions may cause adverse interference to the mining result when mining the group characteristics, in another embodiment of the present application, in order to eliminate the interference, the individuals whose number of the preset communication base stations reaches the preset number threshold are determined as the individuals to be classified, or the individuals whose total number of times of using the preset communication base stations reaches the preset number threshold are determined as the individuals to be classified.
The communication base stations are arranged in the range of a target area, and signals transmitted by each communication base station can cover a certain area; that is, the mobile terminal can be connected to a communication base station only when the mobile terminal is located in an area covered by signals of the communication base station. And with the change of the position of the mobile terminal, when the mobile terminal moves from the area covered by the signal of one communication base station to the area covered by the signal of the other communication base station, the mobile terminal can automatically switch the communication base station connected with the mobile terminal. According to the use information of the mobile terminal and each preset communication base station, the action track information of the individual to be classified can be determined.
Specifically, in the embodiment of the present application, the usage information includes: connection setup time.
Here, the connection establishment time refers to a time when the mobile terminal used by the individual to be classified establishes a connection with each of the preset communication base stations.
The communication base station is part of a mobile communication system. A home location register is also included in the mobile communication system. The home location register is the data center of the mobile communication system, it stores all the information of the location information, service data, account management, etc. of the mobile terminal signed by the home location register, and can provide the inquiry and modification of the location information of the mobile terminal in real time, and implement various service operations including location update, call processing, authentication and supplementary service, etc. to complete the mobile management of the user in the mobile communication network. The location home register can record information such as connection establishment time, identification of the intelligent terminal, identification of the communication base station establishing connection with the mobile terminal and the like when the mobile terminal establishes connection with a certain communication base station.
When the use information of each individual to be classified to a plurality of preset communication base stations in the crowd to be classified is obtained, the connection establishment time of the individual to be classified and each preset communication base station in a preset historical time period is obtained for each individual to be classified.
During the obtaining, the identification of the intelligent terminal used by the individual to be classified and the identification of the preset communication base station can be used as matching keywords, the connection establishment time of the intelligent terminal and all the preset communication base stations is read from the home location register, and the read connection establishment time is screened according to the read connection establishment time and the preset historical time period, so that the connection establishment time of the individual to be classified to each preset communication base station in the preset historical time period is determined.
S102: and acquiring the action track information of each individual to be classified according to the use information of each individual to be classified on a plurality of preset communication base stations.
In specific implementation, after the use information of the individual to be classified on the plurality of preset communication base stations is obtained, the action track information of the individual to be classified can be determined according to the use sequence of the individual to be classified on the plurality of preset communication base stations and the geographic position of each preset communication base station.
Specifically, the embodiment of the application obtains the action track information of each individual to be classified by adopting the following modes:
and aiming at each individual to be classified, generating action track information of the individual to be classified according to the sequence of connection establishment time between the individual to be classified and each preset communication base station and the position information of each preset communication base station.
Here, a sequence in which the individuals to be classified use the preset communication base stations may be formed according to the sequence of the connection establishment time between the individuals to be classified and each preset communication base station, each node in the sequence corresponds to one preset communication base station, and the preset communication base stations corresponding to different time nodes may be the same or different. And then determining action track information of the individual to be classified along the time line according to the geographic positions of all the involved communication base stations.
It should be noted here that the individual to be classified may use all the preset communication base stations or only a part of the preset communication base stations within a preset time period.
The generated action track information comprises geographical position information and time information. The geographical location information may be represented as coordinates in a geographical coordinate system. In this way, the action track of the user to be classified is represented in a three-dimensional space including the geographic position and the time through the geographic coordinate system and the time.
S103: and dividing the crowd to be classified into a plurality of classes based on the action track information of each individual to be classified.
In the specific implementation, the higher the similarity between the action track information of different individuals to be classified is, the more similar the characteristics of the two individuals to be classified are.
The embodiment of the application provides a specific method for dividing the crowd to be classified into a plurality of classifications based on the action track information of each individual to be classified, which comprises the following steps:
and clustering each individual to be classified in the crowd to be classified based on the action track information of each individual to be classified to obtain a plurality of classifications.
When the method is concretely realized, a preset clustering algorithm is adopted, and each individual to be classified in a crowd to be classified is clustered based on the action track information of each individual to be classified, in order to obtain an accurate classification result as much as possible, the action track information of any individual to be classified which is not classified currently can be used as a classification center, and the similarity between the action track information of other individuals to be classified and the classification center is calculated in sequence; then, the individuals to be classified with the similarity between the classification centers larger than a preset similarity threshold are classified into the same classification with the classification center, and the individuals to be classified are used as the classified individuals to be classified. Through multiple iterations of the above process, multiple classifications are obtained.
In addition, other clustering methods can be adopted to cluster the individuals to be classified, such as any one of a k-means clustering algorithm, a hierarchical clustering algorithm, a Self-Organizing mapping neural network (SOM) clustering algorithm and a fuzzy mean clustering algorithm, so as to form a plurality of classifications. Each classification includes a plurality of individuals to be classified.
According to the method and the device for determining the movement track information of the individuals to be classified, the movement track information of the individuals to be classified is determined by obtaining the use information of each individual to be classified to a plurality of preset communication base stations in the crowd with the classification, and the use area of the mobile terminal is wider, and the communication base stations can continuously obtain the use information of users, so that the movement track information of the individuals to be classified can be determined according to the use condition of the individuals to be classified to the communication base stations and the geographic position of each communication base station. The population characteristics are mined based on the wider coverage and more comprehensive movement track information, and a mining result with higher accuracy can be obtained.
Referring to fig. 2, in another embodiment of the present application, a method for classifying a crowd includes:
s201: and acquiring the use information of each individual to be classified in the crowd to be classified on a plurality of preset communication base stations.
The S201 is similar to the above S101, and is not described herein again.
S202: and acquiring the action track information of each individual to be classified according to the use information of each individual to be classified on a plurality of preset communication base stations.
The S202 is similar to the S102, and is not described herein again.
S203: and dividing the crowd to be classified into a plurality of classes based on the action track information of each individual to be classified.
The step S203 is similar to the step S103, and is not described herein again.
S204: for each classification, classifying a plurality of individuals to be classified included in the classification based on the use frequency information of the individuals to be classified included in the classification to each preset communication base station, and obtaining at least one sub-classification corresponding to the classification;
the information of the number of times of use includes: connection establishment frequency or connection establishment number.
In the specific implementation, after the individuals to be classified are classified based on the action track information of the individuals to be classified, the individuals to be classified in the formed multiple classifications are actually the individuals to be classified with similar behavior characteristics.
For example, after the individuals to be classified are classified, four classifications are formed, and the individuals to be classified in the four classifications correspond to static residents, dynamic residents, commuters and visitors in sequence. For commuters, the action tracks of the commuters are turned back and forth between two places most of the time, and for static residents, the action tracks of the commuters are often limited in a small area and distributed irregularly. The classification result is rough, and although the population features can be mined based on the classification result, many specific features of the individual to be classified are ignored. These specific characteristics are likely to be some of those possessed by some smaller populations.
Therefore, in order to mine the features, the embodiment of the application further classifies the plurality of to-be-classified individuals included in each classification again based on the use frequency information of each to-be-classified individual included in each classification to each preset communication base station, so that the classification granularity is reduced, and the specific features of the to-be-classified individuals included in each sub-classification can be mined better and more accurately by the subsequent population feature mining process.
Referring to fig. 3, an embodiment of the present application further provides a specific method for classifying a plurality of to-be-classified individuals included in the classification based on information on the number of times of use of each to-be-classified individual included in the classification on a plurality of preset communication base stations, including:
s301: acquiring connection establishment time between each individual to be classified and each preset communication base station aiming at each individual to be classified;
s302: counting the use frequency information of the connection establishment time between the individual to be classified and the preset communication base station falling into a preset historical time period aiming at each preset communication base station;
s303: and clustering a plurality of individuals to be classified included in the classification based on the use frequency information of each individual to be classified and each preset communication base station included in the classification to obtain a plurality of sub-classifications.
In a specific implementation, the connection establishment time between the individual to be classified and each preset communication base station is obtained to be similar to the connection establishment time in S101, and therefore, the details are not repeated herein.
After the connection establishment time before the individual to be classified and each communication base station is obtained, the use frequency information, such as the connection establishment frequency or the connection establishment frequency, of the connection establishment time between the individual to be classified and the preset communication base station falling into the preset historical event section can be counted for each preset communication base station.
Specifically, referring to fig. 4, an embodiment of the present application further provides a specific process for clustering a plurality of individuals to be classified included in the classification based on similarity between the individuals to be classified included in the classification and the usage frequency information of each preset communication base station, including:
s401: and establishing a frequency characteristic vector corresponding to each individual to be classified included in the classification based on the use frequency information of each individual to be classified included in the classification and each preset communication base station.
Here, it is assumed that there are 20 preset communication base stations, and the connection establishment times of the 20 preset communication base stations by the individual to be classified are respectively: 130. 78, 79, 230, 0, 2, 66, 59, 61, 0, 3, 0, 233, 0, 5, 2, 7, 21, 0, the corresponding degree feature vector of the individual to be classified is [130, 78, 79, 230, 0, 2, 66, 59, 61, 0, 3, 0, 233, 0, 5, 2, 7, 21, 0 ].
S402: and clustering all the individuals to be classified in the classification by using a preset clustering algorithm and based on the times characteristic vector corresponding to each individual to be classified in the classification.
Here, when clustering all the individuals to be classified included in the classification based on the degree feature vectors corresponding to the individuals to be classified included in the classification, the similarity between the degree feature vectors may be used as a clustering basis. For example, any one of the euclidean distance, the manhattan distance, the chebyshev distance, the minkowski distance, the normalized euclidean distance, the mahalanobis distance, the cosine of the included angle, the hamming distance, the jaccard distance, the correlation distance, and the information entropy between the two order eigenvectors is determined.
In addition, the similarity can also be measured by the distribution of the use cases of different preset communication base stations. For example, in the above example, the connection establishment times of a certain pair of 20 preset communication base stations of the individual b to be classified are respectively: 70. 37, 35, 320, 0, 45, 40, 43, 1, 0, 2, 349, 0, 1, 0, 3, 7. When the distance between the first and second order feature vectors is calculated, it can be seen that the distance between the first and second order feature vectors is actually larger, that is, the similarity is smaller, but the use conditions of the first and second order feature vectors to 20 preset communication base stations are similar, that is, the distribution of the use orders is similar, so that the first and second order feature vectors can be divided into the same sub-classification.
In addition, it should be noted here that there is a certain contingency because the action track of each individual to be classified is consistent with a certain rule. The action track formed by the accidental action can influence the clustering of the individuals to be classified based on the action track, and the accuracy of classification is reduced. Therefore, the action tracks formed by the accidental actions are excluded, and only action tracks with similar characteristics capable of more characterizing the user are reserved.
Specifically, when the to-be-classified individual is in an accidental behavior, the to-be-classified individual uses other unused preset communication base stations except for the preset communication base station frequently used by the to-be-classified individual when the to-be-classified individual is in the accidental behavior, so that in the embodiment of the present application, based on the connection establishment times of each to-be-classified individual and each preset communication base station included in the classification, before the number feature vector corresponding to each to-be-classified individual included in the classification is established, the connection establishment times of each to-be-classified individual and each preset communication base station included in the classification are subjected to interference elimination, that is, an accidental track of the to-be-classified individual caused by the accidental behavior is eliminated. And then establishing a frequency characteristic vector corresponding to each individual to be classified included in the classification based on the result of the interference removal processing.
Specifically, the interference removing processing may be performed on the connection establishment times of each individual to be classified included in the classification and each preset communication base station by using any one of the following two manners:
one is as follows: carrying out logarithm treatment on the connection establishment times of each individual to be classified and each preset communication base station included in the classification;
after the connection establishment times are logarithmic, the connection establishment times of the preset communication base station smaller than the preset threshold value return to zero.
When the number of times of connection establishment is logarithmized, the smaller the number of times of connection establishment is, the smaller the result of the logarithmization is. The smaller the number of connection establishment times, the greater the chance of the action track of the user. Therefore, in the embodiment of the present application, after the connection establishment times are logarithmized, the connection times of the preset communication base station smaller than the preset threshold value are reset to zero.
For example, in the above example, it is assumed that there are 20 preset communication base stations, and the connection establishment times of the 20 preset communication base stations by the individual to be classified are respectively: 130. 78, 79, 230, 0, 2, 66, 59, 61, 0, 3, 0, 233, 0, 5, 2, 7, 21, 0, after the respective connection establishment times are logarithmized and the connection establishment times of the preset communication base station smaller than the preset threshold value are zeroed, the obtained times feature vector corresponding to the individual ja to be classified is [130, 78, 79, 230, 0, 66, 59, 61, 0, 233, 0, 21, 0 ].
Secondly, the connection establishment times of each individual to be classified and each preset communication base station included in the classification list can be directly compared with a preset time threshold.
And if the connection establishment times are smaller than the preset times threshold, returning the connection establishment times of the preset communication base station corresponding to the connection establishment times to zero.
Based on the same inventive concept, the embodiment of the present application further provides a crowd classification device corresponding to the crowd classification method, and as the principle of solving the problem of the device in the embodiment of the present application is similar to the crowd classification method in the embodiment of the present application, the implementation of the device can be referred to the implementation of the method, and repeated details are not repeated.
Referring to fig. 5, the crowd classification apparatus provided in the embodiment of the present application includes:
the acquiring unit 51 is configured to acquire use time information of each individual to be classified in the crowd to be classified on a plurality of preset communication base stations;
the action track calculation unit 52 is configured to obtain action track information of each to-be-classified individual according to the use time information of each to-be-classified individual on multiple preset communication base stations;
and the classifying unit 53 is configured to divide the crowd to be classified into a plurality of classes based on the action trajectory information of each individual to be classified.
According to the method and the device for determining the movement track information of the individuals to be classified, the movement track information of the individuals to be classified is determined by obtaining the use information of each individual to be classified to a plurality of preset communication base stations in the crowd with the classification, and the use area of the mobile terminal is wider, and the communication base stations can continuously obtain the use information of users, so that the movement track information of the individuals to be classified can be determined according to the use condition of the individuals to be classified to the communication base stations and the geographic position of each communication base station. The population characteristics are mined based on the wider coverage and more comprehensive movement track information, and a mining result with higher accuracy can be obtained.
Optionally, the method further comprises: the determining unit 54 is configured to determine a target area range before obtaining usage information of each individual to be classified in the crowd to be classified on the plurality of preset communication base stations;
determining a plurality of communication base stations with geographic positions belonging to the target area range as preset communication base stations;
and determining the individuals of which the number of the used communication base stations reaches a preset number threshold as the individuals to be classified.
Optionally, the usage information includes: connection establishment time;
the obtaining unit 51 is specifically configured to obtain the usage information of each individual to be classified in the group of people to be classified on a plurality of preset communication base stations by taking the following manners:
acquiring connection establishment time of each individual to be classified and each preset communication base station within a preset historical time period aiming at each individual to be classified;
the action track calculation unit 52 is specifically configured to obtain action track information of each to-be-classified individual according to the usage information of each to-be-classified individual on a plurality of preset communication base stations by adopting the following manner:
and aiming at each individual to be classified, generating action track information of the individual to be classified according to the sequence of connection establishment time between the individual to be classified and each preset communication base station and the position information of each preset communication base station.
Optionally, the classifying unit 53 is specifically configured to divide the crowd to be classified into a plurality of classifications based on the action trajectory information of each individual to be classified by:
and clustering each individual to be classified in the crowd to be classified based on the action track information of each individual to be classified to obtain a plurality of classifications.
Optionally, the classifying unit 53 is further configured to, after dividing the crowd to be classified into multiple classifications based on the action trajectory information of each individual to be classified, classify, for each classification, the multiple individuals to be classified included in the classification based on the information of the number of times of use of each preset communication base station by each individual to be classified included in the classification, and obtain at least one sub-classification corresponding to the classification;
the information of the number of times of use includes: connection establishment frequency or number of connection establishment times
Optionally, the classifying unit 53 is specifically configured to classify the multiple individuals to be classified included in the classification based on the information of the number of times of use of the multiple individuals to be classified included in the classification on the multiple preset communication base stations by adopting the following manner:
acquiring connection establishment time between each individual to be classified and each preset communication base station aiming at each individual to be classified;
counting the use frequency information of the connection establishment time between the individual to be classified and the preset communication base station falling into a preset historical time period aiming at each preset communication base station;
and clustering a plurality of individuals to be classified included in the classification based on the use frequency information of each individual to be classified and each preset communication base station included in the classification to obtain a plurality of sub-classifications.
Optionally, the classifying unit 53 is specifically configured to cluster the multiple individuals to be classified included in the classification based on the similarity between the individual to be classified included in the classification and the usage frequency information of each preset communication base station by adopting the following manner:
establishing a frequency characteristic vector corresponding to each individual to be classified included in the classification based on the use frequency information of each individual to be classified included in the classification and each preset communication base station;
and clustering all the individuals to be classified in the classification by using a preset clustering algorithm and based on the times characteristic vector corresponding to each individual to be classified in the classification.
Optionally, the classifying unit 53 is further configured to perform interference elimination on the connection establishment times of each to-be-classified individual and each preset communication base station included in the classification before establishing a time feature vector corresponding to each to-be-classified individual included in the classification;
specifically, the method is used for establishing a frequency feature vector corresponding to each individual to be classified included in the classification based on the connection establishment frequency of each individual to be classified included in the classification and each preset communication base station.
Optionally, the classifying unit 53 is specifically configured to perform interference elimination processing on connection establishment times of each to-be-classified individual included in the classification and each preset communication base station by adopting the following manner:
carrying out logarithm treatment on the connection establishment times of each individual to be classified and each preset communication base station included in the classification;
after the connection establishment times are logarithmic, the connection establishment times of the preset communication base station smaller than the preset threshold value return to zero.
Corresponding to the crowd classification method in fig. 1, an embodiment of the present application further provides a computer device, as shown in fig. 6, the device includes a memory 1000, a processor 2000 and a computer program stored on the memory 1000 and executable on the processor 2000, wherein the processor 2000 implements the steps of the crowd classification method when executing the computer program.
Specifically, the memory 1000 and the processor 2000 can be general memories and processors, which are not specifically limited herein, and when the processor 2000 runs a computer program stored in the memory 1000, the crowd classification method can be executed, so as to solve the problem that the accuracy and comprehensiveness of the acquisition method of the action trajectory information are poor, which results in low accuracy of the mining result, and further, the action trajectory information of an individual can be determined based on the usage of the communication base station by the individual, the crowd can be classified based on the action trajectory information, the crowd characteristics can be mined based on the classification result, and the effect of improving the accuracy of the mining result can be achieved.
Corresponding to the crowd classification method in fig. 1, an embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored, and the computer program is executed by a processor to perform the steps of the crowd classification method.
Specifically, the storage medium can be a general-purpose storage medium, such as a mobile disk, a hard disk, or the like, and when a computer program on the storage medium is executed, the guest group classification method can be executed, so that the problem that the accuracy and comprehensiveness of the acquisition method of the action trajectory information are poor and the accuracy of the mining result is low is solved, the action trajectory information of an individual can be determined based on the use condition of the individual on the communication base station, the group can be classified based on the action trajectory information, the characteristics of the group can be mined based on the classification result, and the effect of improving the accuracy of the mining result can be achieved.
The computer program product of the crowd classification method and the classification apparatus provided in the embodiments of the present application includes a computer-readable storage medium storing a program code, where instructions included in the program code may be used to execute the method described in the foregoing method embodiments, and specific implementations may refer to the method embodiments and are not described herein again.
It is clear to those skilled in the art that, for convenience and brevity of description, the above-described apparatus and the specific working process of the apparatus may refer to the corresponding process in the foregoing method embodiments, and are not described herein again. In the several embodiments provided in the present application, it should be understood that the disclosed apparatus, device and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another apparatus, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a non-volatile computer-readable storage medium executable by a processor. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including 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 application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present application, and are used for illustrating the technical solutions of the present application, but not limiting the same, and the scope of the present application is not limited thereto, and although the present application is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope disclosed in the present application; such modifications, changes or substitutions do not depart from the spirit and scope of the exemplary embodiments of the present application, and are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (6)
1. A method of crowd classification, comprising:
obtaining the use information of each individual to be classified in the crowd to be classified on a plurality of preset communication base stations;
acquiring action track information of each individual to be classified according to the use information of each individual to be classified on a plurality of preset communication base stations;
dividing the crowd to be classified into a plurality of classes based on the action track information of each individual to be classified;
the dividing the crowd to be classified into a plurality of classifications based on the action track information of each individual to be classified specifically comprises:
clustering each individual to be classified in the crowd to be classified based on the action track information of each individual to be classified to obtain a plurality of classifications;
after dividing the crowd to be classified into a plurality of classifications based on the action track information of each individual to be classified, the method further comprises the following steps:
for each classification, classifying a plurality of individuals to be classified included in the classification based on the use frequency information of the individuals to be classified included in the classification to each preset communication base station, and obtaining at least one sub-classification corresponding to the classification;
the information of the number of times of use includes: connection establishment frequency or connection establishment frequency;
the classifying the multiple individuals to be classified included in the classification based on the information of the number of times of using the multiple individuals to be classified included in the classification to the multiple preset communication base stations specifically includes:
acquiring connection establishment time between each individual to be classified and each preset communication base station aiming at each individual to be classified;
counting the use frequency information of the connection establishment time between the individual to be classified and the preset communication base station falling into a preset historical time period aiming at each preset communication base station;
clustering a plurality of individuals to be classified included in the classification based on the use frequency information of each individual to be classified and each preset communication base station included in the classification to obtain a plurality of sub-classifications;
the clustering, based on the similarity between the usage frequency information of each individual to be classified included in the classification and each preset communication base station, of the plurality of individuals to be classified included in the classification specifically includes:
establishing a frequency characteristic vector corresponding to each individual to be classified included in the classification based on the use frequency information of each individual to be classified included in the classification and each preset communication base station;
and clustering all the individuals to be classified in the classification by using a preset clustering algorithm and based on the times characteristic vector corresponding to each individual to be classified in the classification.
2. The method according to claim 1, wherein before obtaining the usage information of each individual to be classified in the population to be classified on a plurality of preset communication base stations, the method further comprises:
determining a target area range;
determining a plurality of communication base stations with geographic positions belonging to the target area range as preset communication base stations;
and determining the individuals of which the number of the used communication base stations reaches a preset number threshold as the individuals to be classified.
3. The method of claim 1, wherein the usage information comprises: connection establishment time;
the obtaining of the use information of each individual to be classified in the crowd to be classified on the plurality of preset communication base stations specifically includes:
acquiring connection establishment time of each individual to be classified and each preset communication base station within a preset historical time period aiming at each individual to be classified;
the obtaining of the action track information of each individual to be classified according to the use information of each individual to be classified on a plurality of preset communication base stations specifically comprises:
and aiming at each individual to be classified, generating action track information of the individual to be classified according to the sequence of connection establishment time between the individual to be classified and each preset communication base station and the position information of each preset communication base station.
4. The method according to claim 1, wherein before establishing the number of times of feature vectors corresponding to each individual to be classified included in the classification based on the number of times of establishing connection between each individual to be classified included in the classification and each preset communication base station, the method further comprises:
performing interference elimination processing on the connection establishment times of each individual to be classified and each preset communication base station included in the classification;
establishing a frequency characteristic vector corresponding to each individual to be classified included in the classification based on the connection establishment frequency of each individual to be classified included in the classification and each preset communication base station, specifically comprising:
and establishing a frequency characteristic vector corresponding to each individual to be classified included in the classification based on the result of the interference removal processing.
5. The method according to claim 4, wherein the performing interference elimination processing on the connection establishment times of each individual to be classified and each preset communication base station included in the classification specifically includes:
carrying out logarithm treatment on the connection establishment times of each individual to be classified and each preset communication base station included in the classification;
after the connection establishment times are logarithmic, the connection establishment times of the preset communication base station smaller than the preset threshold value return to zero.
6. A crowd classification device, the device comprising:
the device comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring the use time information of each individual to be classified in the crowd to be classified on a plurality of preset communication base stations;
the action track calculation unit is used for acquiring action track information of each individual to be classified according to the use time information of each individual to be classified on a plurality of preset communication base stations;
the classification unit is used for dividing the crowd to be classified into a plurality of classifications based on the action track information of each individual to be classified;
the classification unit is specifically configured to, when the classification unit is configured to classify the crowd to be classified into a plurality of classifications based on the action trajectory information of each individual to be classified in the following manner:
clustering each individual to be classified in the crowd to be classified based on the action track information of each individual to be classified to obtain a plurality of classifications;
the classification unit is further configured to classify the crowd to be classified into multiple classifications based on the action trajectory information of each individual to be classified, and then classify, for each classification, the multiple individuals to be classified included in the classification based on the information on the number of times of use of each preset communication base station by each individual to be classified included in the classification, so as to obtain at least one sub-classification corresponding to the classification;
the information of the number of times of use includes: connection establishment frequency or connection establishment frequency;
the classification unit is specifically configured to classify the multiple individuals to be classified included in the classification based on the information of the number of times of use of the multiple individuals to be classified included in the classification on the multiple preset communication base stations by adopting the following manner:
acquiring connection establishment time between each individual to be classified and each preset communication base station aiming at each individual to be classified;
counting the use frequency information of the connection establishment time between the individual to be classified and the preset communication base station falling into a preset historical time period aiming at each preset communication base station;
clustering a plurality of individuals to be classified included in the classification based on the use frequency information of each individual to be classified and each preset communication base station included in the classification to obtain a plurality of sub-classifications;
the classification unit is specifically configured to cluster the multiple individuals to be classified included in the classification based on the similarity between the usage frequency information of each individual to be classified included in the classification and each preset communication base station, by adopting the following manner:
establishing a frequency characteristic vector corresponding to each individual to be classified included in the classification based on the use frequency information of each individual to be classified included in the classification and each preset communication base station;
and clustering all the individuals to be classified in the classification by using a preset clustering algorithm and based on the times characteristic vector corresponding to each individual to be classified in the classification.
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