CN114238793A

CN114238793A - Track point data mining method and device, electronic equipment and medium

Info

Publication number: CN114238793A
Application number: CN202111564976.8A
Authority: CN
Inventors: 慎东辉
Original assignee: Apollo Zhixing Information Technology Nanjing Co ltd; Apollo Intelligent Connectivity Beijing Technology Co Ltd
Current assignee: Apollo Zhixing Information Technology Nanjing Co ltd; Apollo Intelligent Connectivity Beijing Technology Co Ltd
Priority date: 2021-12-20
Filing date: 2021-12-20
Publication date: 2022-03-25

Abstract

The invention provides a method and a device for track point data mining, electronic equipment and a medium, and relates to the technical field of computers, in particular to the technical field of intelligent traffic, data mining and machine learning. The implementation scheme is as follows: acquiring a track point set of a user; acquiring track point characteristic information of a user based on the track point set; preprocessing the track point characteristic information of the user to obtain a track point characteristic matrix; and classifying the plurality of track points based on the track point feature matrix to obtain the address types corresponding to the users.

Description

Track point data mining method and device, electronic equipment and medium

Technical Field

The present disclosure relates to the field of computer technologies, and in particular, to the field of intelligent transportation, data mining, and machine learning technologies, and in particular, to a method and an apparatus for track point data mining, an electronic device, a computer-readable storage medium, and a computer program product.

Background

In the intelligent transportation, the address type of the user has important significance, and the travel demand of the user can be further analyzed through the data, so that the service can be better provided for the user.

The approaches described in this section are not necessarily approaches that have been previously conceived or pursued. Unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section. Similarly, unless otherwise indicated, the problems mentioned in this section should not be considered as having been acknowledged in any prior art.

Disclosure of Invention

The disclosure provides a trace point data mining method and device, electronic equipment, a computer readable storage medium and a computer program product.

According to one aspect of the disclosure, a method for track point data mining is provided, which includes: acquiring a track point set of a user, wherein the track point set of the user comprises a plurality of track points of the user; based on the track point set, obtaining track point characteristic information of the user, wherein the track point characteristic information comprises at least the following characteristic information of each track point in the plurality of track points: clustering labels and place names, wherein the clustering labels are obtained by clustering analysis on the plurality of track points; preprocessing the track point characteristic information of the user to obtain a track point characteristic matrix, wherein the track point characteristic matrix comprises a plurality of characteristic vectors corresponding to the plurality of track points respectively; and classifying the plurality of track points based on the track point feature matrix so as to obtain the address types corresponding to the users.

According to another aspect of the present disclosure, there is provided a training method of a trajectory recognition model, including: obtaining a sample data set, wherein each sample data in the sample data set comprises a track point set of a sample user and an address type label corresponding to the sample user, and the track point set comprises a plurality of track points of the sample user; initializing a plurality of parameters of the trajectory recognition model; and for each sample data, performing the following operations: based on the track point set, obtaining track point characteristic information, wherein the track point characteristic information comprises at least the following characteristic information of each track point in the plurality of track points: clustering labels and place names, wherein the clustering labels are obtained by clustering analysis on the plurality of track points; preprocessing the trace point characteristic information to obtain a trace point characteristic matrix, wherein the trace point characteristic matrix comprises a plurality of characteristic vectors respectively corresponding to the plurality of trace points; inputting the track point feature matrix into the track recognition model to obtain an output value, wherein the output value comprises a predicted address type recognition result of the sample user; and adjusting a plurality of parameters of the track recognition model based on the address type recognition result of the sample user and the address type label corresponding to the sample user.

According to another aspect of the present disclosure, there is provided a trace point data mining device, including: the first acquisition unit is configured to acquire a track point set of a user, wherein the track point set of the user comprises a plurality of track points of the user; a second obtaining unit configured to obtain track point feature information of the user based on the track point set, wherein the track point feature information includes at least the following feature information of each of the plurality of track points: clustering labels and place names, wherein the clustering labels are obtained by clustering analysis on the plurality of track points; the preprocessing unit is configured to preprocess the trace point feature information of the user to obtain a trace point feature matrix, wherein the trace point feature matrix comprises a plurality of feature vectors respectively corresponding to the plurality of trace points; and the classification unit is configured to classify the plurality of track points based on the track point feature matrix so as to obtain the address types corresponding to the users.

According to another aspect of the present disclosure, there is provided a training apparatus for a trajectory recognition model, including: the third obtaining unit is configured to obtain a sample data set, wherein each sample data in the sample data set comprises a track point set of a sample user and an address type label corresponding to the sample user, and the track point set comprises a plurality of track points of the sample user; an initialization unit configured to initialize a plurality of parameters of the trajectory recognition model; and an execution unit configured to execute, for each sample data, operations of the following sub-units, wherein the execution unit includes: an obtaining subunit configured to obtain track point feature information based on the track point set, where the track point feature information includes at least the following feature information of each of the plurality of track points: clustering labels and place names, wherein the clustering labels are obtained by clustering analysis on the plurality of track points; the preprocessing subunit is configured to preprocess the trace point feature information to obtain a trace point feature matrix, where the trace point feature matrix includes a plurality of feature vectors respectively corresponding to the plurality of trace points; the input subunit is configured to input the track point feature matrix to the track recognition model to obtain an output value, wherein the output value comprises a predicted address type recognition result of the sample user; and an adjusting subunit, configured to adjust a plurality of parameters of the trajectory recognition model based on the address type recognition result of the sample user and the corresponding address type label of the sample user.

According to another aspect of the present disclosure, there is provided an electronic device including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the above-described trajectory point data mining method or the trajectory recognition model training method.

According to another aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the above-described trajectory point data mining method or the training method of the trajectory recognition model.

According to another aspect of the present disclosure, a computer program product is provided, comprising a computer program, wherein the computer program, when being executed by a processor, implements the above-mentioned trajectory point data mining method or the trajectory recognition model training method.

According to one or more embodiments of the disclosure, various information in each track point can be fully utilized, and the identification accuracy of the address type corresponding to the user is improved.

It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the embodiments and, together with the description, serve to explain the exemplary implementations of the embodiments. The illustrated embodiments are for purposes of illustration only and do not limit the scope of the claims. Throughout the drawings, identical reference numbers designate similar, but not necessarily identical, elements.

FIG. 1 illustrates a schematic diagram of an exemplary system in which various methods described herein may be implemented, according to an embodiment of the present disclosure;

FIG. 2 illustrates a flow diagram of a method of trajectory point data mining in accordance with an embodiment of the present disclosure;

FIG. 3 shows a flow diagram of a method of training a trajectory recognition model according to an embodiment of the present disclosure;

FIG. 4 shows a block diagram of a track point data mining apparatus according to an embodiment of the present disclosure;

FIG. 5 shows a block diagram of a training apparatus for a trajectory recognition model according to an embodiment of the present disclosure;

FIG. 6 illustrates a block diagram of an exemplary electronic device that can be used to implement embodiments of the present disclosure.

Detailed Description

Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

In the present disclosure, unless otherwise specified, the use of the terms "first", "second", and the like to describe various elements is not intended to limit the positional relationship, the temporal relationship, or the importance relationship of the elements, and such terms are used only to distinguish one element from another. In some examples, a first element and a second element may refer to the same instance of the element, and in some cases, based on the context, they may also refer to different instances.

The terminology used in the description of the various described examples in this disclosure is for the purpose of describing particular examples only and is not intended to be limiting. Unless the context clearly indicates otherwise, if the number of elements is not specifically limited, the elements may be one or more. Furthermore, the term "and/or" as used in this disclosure is intended to encompass any and all possible combinations of the listed items.

Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

Fig. 1 illustrates a schematic diagram of an exemplary system 100 in which various methods and apparatus described herein may be implemented in accordance with embodiments of the present disclosure. Referring to fig. 1, the system 100 includes one or

more client devices

101, 102, 103, 104, 105, and 106, a server 120, and one or more communication networks 110 coupling the one or more client devices to the server 120.

Client devices

101, 102, 103, 104, 105, and 106 may be configured to execute one or more applications.

In embodiments of the present disclosure, server 120 may run one or more services or software applications that enable a trace point data mining method or a training method of a trace recognition model to be performed.

In some embodiments, the server 120 may also provide other services or software applications that may include non-virtual environments and virtual environments. In certain embodiments, these services may be provided as web-based services or cloud services, for example, provided to users of

client devices

101, 102, 103, 104, 105, and/or 106 under a software as a service (SaaS) model.

In the configuration shown in fig. 1, server 120 may include one or more components that implement the functions performed by server 120. These components may include software components, hardware components, or a combination thereof, which may be executed by one or more processors. A user operating a

client device

101, 102, 103, 104, 105, and/or 106 may, in turn, utilize one or more client applications to interact with the server 120 to take advantage of the services provided by these components. It should be understood that a variety of different system configurations are possible, which may differ from system 100. Accordingly, fig. 1 is one example of a system for implementing the various methods described herein and is not intended to be limiting.

The user may upload their track point related information using

client devices

101, 102, 103, 104, 105, and/or 106. The client device may provide an interface that enables a user of the client device to interact with the client device. The client device may also output information to the user via the interface. Although fig. 1 depicts only six client devices, those skilled in the art will appreciate that any number of client devices may be supported by the present disclosure.

Client devices

101, 102, 103, 104, 105, and/or 106 may include various types of computer devices, such as portable handheld devices, general purpose computers (such as personal computers and laptops), workstation computers, wearable devices, smart screen devices, self-service terminal devices, service robots, gaming systems, thin clients, various messaging devices, sensors or other sensing devices, and so forth. These computer devices may run various types and versions of software applications and operating systems, such as MICROSOFT Windows, APPLE iOS, UNIX-like operating systems, Linux, or Linux-like operating systems (e.g., GOOGLE Chrome OS); or include various Mobile operating systems such as MICROSOFT Windows Mobile OS, iOS, Windows Phone, Android. Portable handheld devices may include cellular telephones, smart phones, tablets, Personal Digital Assistants (PDAs), and the like. Wearable devices may include head-mounted displays (such as smart glasses) and other devices. The gaming system may include a variety of handheld gaming devices, internet-enabled gaming devices, and the like. The client device is capable of executing a variety of different applications, such as various Internet-related applications, communication applications (e.g., email applications), Short Message Service (SMS) applications, and may use a variety of communication protocols.

Network 110 may be any type of network known to those skilled in the art that may support data communications using any of a variety of available protocols, including but not limited to TCP/IP, SNA, IPX, etc. By way of example only, one or more networks 110 may be a Local Area Network (LAN), an ethernet-based network, a token ring, a Wide Area Network (WAN), the internet, a virtual network, a Virtual Private Network (VPN), an intranet, an extranet, a Public Switched Telephone Network (PSTN), an infrared network, a wireless network (e.g., bluetooth, WIFI), and/or any combination of these and/or other networks.

The server 120 may include one or more general purpose computers, special purpose server computers (e.g., PC (personal computer) servers, UNIX servers, mid-end servers), blade servers, mainframe computers, server clusters, or any other suitable arrangement and/or combination. The server 120 may include one or more virtual machines running a virtual operating system, or other computing architecture involving virtualization (e.g., one or more flexible pools of logical storage that may be virtualized to maintain virtual storage for the server). In various embodiments, the server 120 may run one or more services or software applications that provide the functionality described below.

The computing units in server 120 may run one or more operating systems including any of the operating systems described above, as well as any commercially available server operating systems. The server 120 may also run any of a variety of additional server applications and/or middle tier applications, including HTTP servers, FTP servers, CGI servers, JAVA servers, database servers, and the like.

In some implementations, the server 120 may include one or more applications to analyze and consolidate data feeds and/or event updates received from users of the

client devices

101, 102, 103, 104, 105, and 106. Server 120 may also include one or more applications to display data feeds and/or real-time events via one or more display devices of

client devices

101, 102, 103, 104, 105, and 106.

In some embodiments, the server 120 may be a server of a distributed system, or a server incorporating a blockchain. The server 120 may also be a cloud server, or a smart cloud computing server or a smart cloud host with artificial intelligence technology. The cloud Server is a host product in a cloud computing service system, and is used for solving the defects of high management difficulty and weak service expansibility in the traditional physical host and Virtual Private Server (VPS) service.

The system 100 may also include one or more databases 130. In some embodiments, these databases may be used to store data and other information. For example, one or more of the databases 130 may be used to store information such as audio files and video files. The database 130 may reside in various locations. For example, the database used by the server 120 may be local to the server 120, or may be remote from the server 120 and may communicate with the server 120 via a network-based or dedicated connection. The database 130 may be of different types. In certain embodiments, the database used by the server 120 may be, for example, a relational database. One or more of these databases may store, update, and retrieve data to and from the database in response to the command.

In some embodiments, one or more of the databases 130 may also be used by applications to store application data. The databases used by the application may be different types of databases, such as key-value stores, object stores, or regular stores supported by a file system.

The system 100 of fig. 1 may be configured and operated in various ways to enable application of the various methods and apparatus described in accordance with the present disclosure.

Currently, the identification of the address type of the user is generally based on the following means: the method comprises the steps of firstly obtaining a user address through a user track Point, then obtaining POI (Point of Interest) information or WIFI (Wireless Fidelity) information of the user address, and conducting classification prediction based on one of the POI information or the WIFI information so as to identify the address type of a user. The method cannot fully utilize the information in each track point of the user, so that the accuracy cannot be further improved.

According to an embodiment of the present disclosure, as shown in fig. 2, there is provided a trace point data mining method, including: step S201, obtaining a track point set of a user, wherein the track point set of the user comprises a plurality of track points of the user; step S202, based on the track point set, obtaining track point characteristic information of the user, wherein the track point characteristic information comprises at least the following characteristic information of each track point in the plurality of track points: clustering labels and place names, wherein the clustering labels are obtained by clustering analysis on the plurality of track points; step S203, preprocessing the track point characteristic information of the user to obtain a track point characteristic matrix, wherein the track point characteristic matrix comprises a plurality of characteristic vectors corresponding to the plurality of track points respectively; and step S204, classifying the plurality of track points based on the track point feature matrix so as to obtain the address types corresponding to the users.

Therefore, according to the track point data mining method disclosed by the invention, various information in each track point can be fully utilized, and the identification accuracy of the address type corresponding to the user is improved.

The address types corresponding to the users may include urban, suburban, entertainment, office, residential, industrial, commercial, and the like. In one example, the address types of the user may include both office and residential types.

After a track point set of a user is obtained, a plurality of track points in the set need to be clustered and analyzed first, so as to obtain a plurality of cluster clusters of the user corresponding to a plurality of addresses of the user and a cluster confidence of each cluster. Each cluster is numbered (e.g., 1, 2, 3 … …), and the number is added as a cluster label to the feature information of the trace point in the cluster. For track points which are identified by clustering and are scattered outside each clustering cluster, the corresponding clustering labels can be numbered as 0, and the 0 is also added into the characteristic information of the track points.

And then acquiring characteristic information of other track points based on the track points, wherein the place name and the interest point type are acquired in a reverse geocoding retrieval mode, and the signal name of the wireless local area network and the service set identification of the wireless local area network are peripheral wireless local area network information which is recorded in the track points and acquired by mobile equipment of a user when the user is positioned at the track points.

The information can not be directly input into the classification model for analysis, and the information needs to be correspondingly preprocessed firstly to construct a track point characteristic matrix as the input of the classification model. Each row in the track point feature matrix represents a track point feature vector corresponding to a track point, and the track point feature vector comprises a plurality of feature values corresponding to a plurality of kinds of feature information after preprocessing.

According to some embodiments, preprocessing the track point feature information of the user to obtain a track point feature matrix includes: acquiring a plurality of preset place name sets, wherein each preset place name set in the preset place name sets corresponds to one type of interest point, and each preset place name set in the preset place name sets comprises a plurality of preset place names of corresponding types of interest points; and on the basis of the location name of the track point and the preset location names in the preset location name sets, respectively acquiring the similarity between the location name of the track point and each preset location name set in the preset location name sets so as to acquire a plurality of characteristic values corresponding to the location name in the characteristic vector corresponding to the track point.

In one example, the preset location name sets may respectively correspond to types of residence, office, industrial, commercial, entertainment, and the like, and the preset location name set of the residence type may include names of a plurality of residential cells. When address names of certain track points are preprocessed, the similarity between the location names of the track points and each preset location name set in the three preset location name sets needs to be acquired respectively. Taking one preset location name set as an example, text similarity calculation can be performed by respectively calculating the address name and all preset address names in the preset location name set, and an average value of the text similarities is obtained as the similarity between the location name and the preset location name set, and is used as a feature value in the feature vector of the track point. The text similarity calculation can be obtained by performing cosine similarity calculation on word vectors corresponding to the two place names.

In one example, the plurality of preset location name sets may also correspond to residential, office, business, and other four types, respectively.

In one example, the plurality of preset location name sets may also correspond to residential, office, and other three types, respectively.

It is understood that the number and the category of the preset location name sets can be set by those skilled in the art according to the actual situation, and are not limited herein.

According to some embodiments, the trace point feature information further includes a wireless local area network signal name of each of the plurality of trace points, and wherein the pre-processing the trace point feature information of the user to obtain a trace point feature matrix includes: acquiring a plurality of preset signal name sets, wherein each preset signal name set in the preset signal name sets corresponds to a point of interest type, and each preset signal name set in the preset signal name sets comprises a plurality of first signal names of a plurality of places corresponding to the point of interest type; and on the basis of the wireless local area network signal name of the track point and the first signal names in each preset signal name set in the preset signal name sets, the similarity of the wireless local area network signal name of the track point and each preset signal name set in the preset signal name sets is respectively acquired so as to acquire a plurality of characteristic values corresponding to the wireless local area network signal name in the characteristic vector corresponding to the track point.

In one example, the preset signal name sets may respectively correspond to types of residence, office, industrial, commercial, entertainment, and the like, and the office-type preset signal name set is taken as an example, and the set may include signal names of wireless local area networks applied by a plurality of companies. When the wireless local area network signal name of a certain track point is preprocessed, the similarity between the wireless local area network signal name of the track point and each preset signal name set in the three preset signal name sets needs to be acquired respectively. Taking one preset signal name set as an example, text similarity calculation can be performed by calculating the wireless local area network signal name and all preset wireless local area network signal names in the preset signal name set respectively, and an average value of the text similarities is obtained as the similarity between the wireless local area network signal name and the preset signal name set and is used as a feature value in the feature vector of the trace point. The text similarity calculation can be obtained by performing cosine similarity calculation on word vectors corresponding to the two wireless local area network signal names.

In one example, the plurality of preset signal name sets may also correspond to residential, office, commercial, and other four types, respectively.

In one example, the plurality of preset signal name sets may also correspond to residential, office, and other three types, respectively.

It is understood that the number and kinds of the preset signal name sets can be set by those skilled in the art according to the actual situation, and are not limited herein.

According to some embodiments, the trace point feature information further includes a service set identifier of a wireless local area network of each of the plurality of trace points, and wherein the preprocessing the trace point feature information of the user to obtain a trace point feature matrix includes: for each of the plurality of trace points, performing the following operations: based on the service set identification of the wireless local area network of the track point, acquiring the location name of the equipment of the wireless local area network corresponding to the track point; based on the location name and the preset location names in each preset location name set in the preset location name sets, respectively obtaining the similarity between the location name and each preset location name set in the preset location name sets so as to obtain a plurality of characteristic values corresponding to the service set identifier of the wireless local area network in the characteristic vector corresponding to the track point.

When preprocessing a Service Set Identifier (SSID) of a wireless local area network of a track point, first, a location name of a device corresponding to the SSID of the wireless local area network may be obtained based on an SSID database. Then, the similarity between the location name of the device and each preset location name set in the preset location name sets is respectively obtained. Taking one preset location name set as an example, text similarity calculation can be performed by respectively calculating the address name and all preset address names in the preset location name set, and an average value of the text similarities is obtained as the similarity between the location name of the device and the preset location name set, and is used as a feature value in the feature vector of the track point. The text similarity calculation can be obtained by performing cosine similarity calculation on word vectors corresponding to the two place names.

According to some embodiments, the trace point characteristic information further includes one or more of the following characteristic information for each of the plurality of trace points: the system comprises time information, coordinate information, speed information, positioning mode information, interest point types and clustering confidence degrees, wherein the positioning mode information comprises one of positioning based on a GPS (global positioning system), positioning based on a wireless local area network and positioning based on a base station, and the clustering confidence degrees are obtained by clustering and analyzing the plurality of track points.

The characteristic information is also required to be preprocessed separately. In one example, preprocessing the time information of a certain track point may include, first, converting the time information into an identifier of whether the track point is a weekday (for example, the weekday identifier is 1, and the non-weekday identifier is 2), and using the identifier as a feature value in a feature vector of the track point; then, the hours of the time information are normalized, namely, the time of 0-24 hours is normalized to an interval of 0-1, and the normalized hours are used as another characteristic value in the characteristic vector of the track point.

In one example, the coordinate information and the speed information of a certain track point may be normalized correspondingly, and each piece of normalized feature information is used as a feature value in a feature vector of the track point.

In one example, the positioning mode type, the clustering label, and the interest point type of a certain track point may be respectively encoded, then normalization is performed correspondingly through encoding of each feature of the track point, and each normalized feature information is used as a feature value in a feature vector of the track point.

In one example, the feature information of the track point may further include a clustering confidence and a track confidence, where the clustering confidence is obtained in a clustering analysis of the track point, and the track confidence is obtained when the track point is collected, and is used to characterize the confidence of the track. The two pieces of information are data within the range of 0-1, so that the two pieces of information can be directly used as characteristic values in the characteristic vector of the track point.

Through the data preprocessing mode, the trace point characteristic information can be converted into the trace point characteristic matrix, and meanwhile, corresponding normalization processing is respectively carried out on the characteristic information of different value ranges, so that the condition that smaller numerical values are ignored when prediction analysis is carried out can be avoided.

And then inputting the track point feature matrix into a trained recognition model to obtain the address type recognition result of the user. The recognition model may be a random forest model, and is not limited herein.

According to some embodiments, as shown in fig. 3, there is provided a training method of a trajectory recognition model, including: step S301, obtaining a sample data set, wherein each sample data in the sample data set comprises a track point set of a sample user and an address type label corresponding to the sample user, and the track point set comprises a plurality of track points of the sample user; step S302, initializing a plurality of parameters of the track recognition model; and for each sample data, performing the following operations: step S303, acquiring track point characteristic information based on the track point set, wherein the track point characteristic information comprises at least the following characteristic information of each track point in the plurality of track points: clustering labels and place names, wherein the clustering labels are obtained by clustering analysis on the plurality of track points; step S304, preprocessing the track point characteristic information to obtain a track point characteristic matrix, wherein the track point characteristic matrix comprises a plurality of characteristic vectors corresponding to the plurality of track points respectively; step S305, inputting the track point feature matrix into the track recognition model to obtain an output value, wherein the output value comprises a predicted address type recognition result of the sample user; and step S306, adjusting a plurality of parameters of the track recognition model based on the address type recognition result of the sample user and the address type label corresponding to the sample user.

Therefore, the trained track recognition model can make full use of various information in each track point, and the recognition accuracy of the address type of the user is improved

According to some embodiments, the trajectory recognition model may be a random forest model. The random forest model can judge the importance of the features with different dimensions, and a final classification result is obtained based on the importance judgment.

The sample data used for training the model can label the trace point set of the address type information for the user, and before applying the data to train the model, the data also needs to be preprocessed correspondingly, so that a trace point feature matrix used for training is obtained.

According to some embodiments, preprocessing the trace point feature information to obtain a trace point feature matrix includes: acquiring a plurality of preset place name sets, wherein each preset place name set in the preset place name sets corresponds to one type of interest point, and each preset place name set in the preset place name sets comprises a plurality of preset place names of corresponding types of interest points; and on the basis of the location name of the track point and the preset location names in the preset location name sets, respectively acquiring the similarity between the location name of the track point and each preset location name set in the preset location name sets so as to acquire a plurality of characteristic values corresponding to the location name in the characteristic vector corresponding to the track point.

According to some embodiments, the trace point feature information further includes a wireless local area network signal name of each of the plurality of trace points, and wherein the preprocessing the trace point feature information to obtain a trace point feature matrix includes: acquiring a plurality of preset signal name sets, wherein each preset signal name set in the preset signal name sets corresponds to a point of interest type, and each preset signal name set in the preset signal name sets comprises a plurality of first signal names of a plurality of places corresponding to the point of interest type; and on the basis of the wireless local area network signal name of the track point and the first signal names in each preset signal name set in the preset signal name sets, the similarity of the wireless local area network signal name of the track point and each preset signal name set in the preset signal name sets is respectively acquired so as to acquire a plurality of characteristic values corresponding to the wireless local area network signal name in the characteristic vector corresponding to the track point.

According to some embodiments, the trace point feature information further includes a service set identifier of a wireless local area network of each of the plurality of trace points, and wherein the preprocessing the trace point feature information to obtain a trace point feature matrix includes: for each of the plurality of trace points, performing the following operations: based on the service set identification of the wireless local area network of the track point, acquiring the location name of the equipment of the wireless local area network corresponding to the track point; based on the location name and the preset location names in each preset location name set in the preset location name sets, respectively obtaining the similarity between the location name and each preset location name set in the preset location name sets so as to obtain a plurality of characteristic values corresponding to the service set identifier of the wireless local area network in the characteristic vector corresponding to the track point.

The track point feature matrix of the sample user is input into the track recognition model, and parameters of the model can be adjusted based on the output value and the address type label marked by the sample user, so that the model is trained.

According to some embodiments, as shown in fig. 4, there is provided a trace point data mining apparatus 400, including: a first obtaining unit 410 configured to obtain a track point set of a user, where the track point set of the user includes a plurality of track points of the user; a second obtaining unit 420, configured to obtain track point feature information of the user based on the track point set, where the track point feature information includes at least the following feature information of each of the plurality of track points: clustering labels and place names, wherein the clustering labels are obtained by clustering analysis on the plurality of track points; a preprocessing unit 430 configured to preprocess the trace point feature information of the user to obtain a trace point feature matrix, where the trace point feature matrix includes a plurality of feature vectors respectively corresponding to the plurality of trace points; and the classifying unit 440 is configured to classify the plurality of track points based on the track point feature matrix to obtain the address types corresponding to the users.

The operations of the units 410 to 440 of the trace point data mining device 400 are similar to the operations of the steps S201 to S204 of the trace point data mining method, and are not described herein again.

According to some embodiments, as shown in fig. 5, there is provided a training apparatus 500 for a trajectory recognition model, including: a third obtaining unit 510, configured to obtain a sample data set, where each sample data in the sample data set includes a track point set of a sample user and an address type tag corresponding to the sample user, where the track point set includes multiple track points of the sample user; an initialization unit 520 configured to initialize a plurality of parameters of the trajectory recognition model; and an execution unit 530 configured to perform, for each sample data, operations of the following sub-units, wherein the execution unit 530 includes: an obtaining subunit 531 configured to obtain track point feature information based on the track point set, where the track point feature information includes at least the following feature information of each track point in the plurality of track points: clustering labels and place names, wherein the clustering labels are obtained by clustering analysis on the plurality of track points; a preprocessing subunit 532 configured to preprocess the trace point feature information to obtain a trace point feature matrix, where the trace point feature matrix includes a plurality of feature vectors respectively corresponding to the plurality of trace points; an input subunit 533, configured to input the trajectory point feature matrix to the trajectory recognition model to obtain an output value, where the output value includes a predicted address type recognition result of the sample user; and an adjusting subunit 534 configured to adjust a plurality of parameters of the trajectory recognition model based on the address type recognition result of the sample user and the address type tag corresponding to the sample user.

The operations of the units 510 to 530 and the sub-units 531 to 534 of the training apparatus 500 for a trajectory recognition model are similar to the operations of the steps S301 to S306 of the training method for a trajectory recognition model, and are not described herein again.

In the technical scheme of the disclosure, the collection, storage, use, processing, transmission, provision, disclosure and other processing of the personal information of the related user are all in accordance with the regulations of related laws and regulations and do not violate the good customs of the public order.

According to an embodiment of the present disclosure, there is also provided an electronic device, a readable storage medium, and a computer program product.

Referring to fig. 6, a block diagram of a structure of an electronic device 600, which may be a server or a client of the present disclosure, which is an example of a hardware device that may be applied to aspects of the present disclosure, will now be described. Electronic device is intended to represent various forms of digital electronic computer devices, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.

As shown in fig. 6, the electronic device 600 includes a computing unit 601, which can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM)602 or a computer program loaded from a storage unit 608 into a Random Access Memory (RAM) 603. In the RAM603, various programs and data necessary for the operation of the electronic apparatus 600 can also be stored. The calculation unit 601, the ROM 602, and the RAM603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.

Various components in the electronic device 600 are connected to the I/O interface 605, including: an input unit 606, an output unit 607, a storage unit 608, and a communication unit 609. The input unit 606 may be any type of device capable of inputting information to the electronic device 600, and the input unit 606 may receive input numeric or character information and generate key signal inputs related to user settings and/or function controls of the electronic device, and may include, but is not limited to, a mouse, a keyboard, a touch screen, a track pad, a track ball, a joystick, a microphone, and/or a remote control. Output unit 607 may be any type of device capable of presenting information and may include, but is not limited to, a display, speakers, a video/audio output terminal, a vibrator, and/or a printer. The storage unit 608 may include, but is not limited to, a magnetic disk, an optical disk. The communication unit 609 allows the electronic device 600 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunications networks, and may include, but is not limited to, a modem, a network card, an infrared communication device, a wireless communication transceiver, and/or a chipset, such as bluetooth^TMDevices, 802.11 devices, WiFi devices, WiMax devices, cellular communication devices, and/or the like.

The computing unit 601 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and so forth. The calculation unit 601 performs the respective methods and processes described above, such as the track point data mining method. For example, in some embodiments, the trace point data mining method may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 608. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 600 via the ROM 602 and/or the communication unit 609. When the computer program is loaded into RAM603 and executed by the computing unit 601, one or more steps of the trace point data mining method described above may be performed. Alternatively, in other embodiments, the computing unit 601 may be configured to perform the trace point data mining method in any other suitable manner (e.g., by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.

Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.

The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server with a combined blockchain.

It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be performed in parallel, sequentially or in different orders, and are not limited herein as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved.

Although embodiments or examples of the present disclosure have been described with reference to the accompanying drawings, it is to be understood that the above-described methods, systems and apparatus are merely exemplary embodiments or examples and that the scope of the present invention is not limited by these embodiments or examples, but only by the claims as issued and their equivalents. Various elements in the embodiments or examples may be omitted or may be replaced with equivalents thereof. Further, the steps may be performed in an order different from that described in the present disclosure. Further, various elements in the embodiments or examples may be combined in various ways. It is important that as technology evolves, many of the elements described herein may be replaced with equivalent elements that appear after the present disclosure.

Claims

1. A track point data mining method comprises the following steps:

acquiring a track point set of a user, wherein the track point set of the user comprises a plurality of track points of the user;

based on the track point set, obtaining track point characteristic information of the user, wherein the track point characteristic information comprises at least the following characteristic information of each track point in the plurality of track points: clustering labels and place names, wherein the clustering labels are obtained by clustering analysis on the plurality of track points;

preprocessing the track point characteristic information of the user to obtain a track point characteristic matrix, wherein the track point characteristic matrix comprises a plurality of characteristic vectors corresponding to the plurality of track points respectively; and

and classifying the plurality of track points based on the track point feature matrix so as to obtain the address types corresponding to the users.

2. The method of claim 1, wherein the preprocessing the track point feature information of the user to obtain a track point feature matrix comprises:

acquiring a plurality of preset place name sets, wherein each preset place name set in the preset place name sets corresponds to one type of interest point, and each preset place name set in the preset place name sets comprises a plurality of preset place names of corresponding types of interest points; and

for each track point in the plurality of track points, based on the location name of the track point and the preset location names in each preset location name set in the preset location name sets, respectively obtaining the similarity between the location name of the track point and each preset location name set in the preset location name sets so as to obtain a plurality of characteristic values corresponding to the location name in the characteristic vector corresponding to the track point.

3. The method according to claim 1 or 2, wherein the track point feature information further includes a wireless local area network signal name of each of the plurality of track points, and wherein the pre-processing the track point feature information of the user to obtain a track point feature matrix comprises:

acquiring a plurality of preset signal name sets, wherein each preset signal name set in the preset signal name sets corresponds to a point of interest type, and each preset signal name set in the preset signal name sets comprises a plurality of first signal names of a plurality of places corresponding to the point of interest type; and

for each track point in the plurality of track points, based on the wireless local area network signal name of the track point and the plurality of first signal names in each preset signal name set in the plurality of preset signal name sets, the similarity of the wireless local area network signal name of the track point and each preset signal name set in the plurality of preset signal name sets is respectively obtained so as to obtain a plurality of characteristic values corresponding to the wireless local area network signal name in the characteristic vector corresponding to the track point.

4. The method according to any one of claims 1 to 3, wherein the track point feature information further includes a service set identifier of a wireless local area network of each of the plurality of track points, and wherein the preprocessing the track point feature information of the user to obtain a track point feature matrix includes:

for each of the plurality of trace points, performing the following operations:

based on the service set identification of the wireless local area network of the track point, acquiring the location name of the equipment of the wireless local area network corresponding to the track point;

based on the location name and the preset location names in each preset location name set in the preset location name sets, respectively obtaining the similarity between the location name and each preset location name set in the preset location name sets so as to obtain a plurality of characteristic values corresponding to the service set identifier of the wireless local area network in the characteristic vector corresponding to the track point.

5. The method of any of claims 1-4, wherein the trace point characteristic information further includes one or more of the following characteristic information for each of the plurality of trace points: the system comprises time information, coordinate information, speed information, positioning mode information, interest point types and clustering confidence degrees, wherein the positioning mode information comprises one of positioning based on a GPS (global positioning system), positioning based on a wireless local area network and positioning based on a base station, and the clustering confidence degrees are obtained by clustering and analyzing the plurality of track points.

6. A training method of a track recognition model comprises the following steps:

obtaining a sample data set, wherein each sample data in the sample data set comprises a track point set of a sample user and an address type label corresponding to the sample user, and the track point set comprises a plurality of track points of the sample user;

initializing a plurality of parameters of the trajectory recognition model; and

for each sample data, the following operations are performed:

based on the track point set, obtaining track point characteristic information, wherein the track point characteristic information comprises at least the following characteristic information of each track point in the plurality of track points: clustering labels and place names, wherein the clustering labels are obtained by clustering analysis on the plurality of track points;

preprocessing the trace point characteristic information to obtain a trace point characteristic matrix, wherein the trace point characteristic matrix comprises a plurality of characteristic vectors respectively corresponding to the plurality of trace points;

inputting the track point feature matrix into the track recognition model to obtain an output value, wherein the output value comprises a predicted address type recognition result of the sample user; and

and adjusting a plurality of parameters of the track recognition model based on the address type recognition result of the sample user and the address type label corresponding to the sample user.

7. The method of claim 6, wherein the preprocessing the trace point feature information to obtain a trace point feature matrix comprises:

8. The method of claim 6 or 7, wherein the trace point feature information further includes a wireless local area network signal name for each of the plurality of trace points, and wherein the pre-processing the trace point feature information to obtain a trace point feature matrix comprises:

9. The method according to any one of claims 6 to 8, wherein the trace point feature information further includes a service set identifier of a wireless local area network of each of the plurality of trace points, and wherein the preprocessing the trace point feature information to obtain a trace point feature matrix includes:

for each of the plurality of trace points, performing the following operations:

10. The method of any of claims 6-9, wherein the trace point characteristic information further includes one or more of the following characteristic information for each of the plurality of trace points: the system comprises time information, coordinate information, speed information, positioning mode information, interest point types and clustering confidence degrees, wherein the positioning mode information comprises one of positioning based on a GPS (global positioning system), positioning based on a wireless local area network and positioning based on a base station, and the clustering confidence degrees are obtained by clustering and analyzing the plurality of track points.

11. A method as claimed in any one of claims 6 to 10, wherein the trajectory recognition model is a random forest model.

12. A tracing point data mining device, comprising:

the first acquisition unit is configured to acquire a track point set of a user, wherein the track point set of the user comprises a plurality of track points of the user;

a second obtaining unit configured to obtain track point feature information of the user based on the track point set, wherein the track point feature information includes at least the following feature information of each of the plurality of track points: clustering labels and place names, wherein the clustering labels are obtained by clustering analysis on the plurality of track points;

the preprocessing unit is configured to preprocess the trace point feature information of the user to obtain a trace point feature matrix, wherein the trace point feature matrix comprises a plurality of feature vectors respectively corresponding to the plurality of trace points; and

and the classification unit is configured to classify the plurality of track points based on the track point feature matrix so as to obtain the address types corresponding to the users.

13. A training apparatus for a trajectory recognition model, comprising:

the third obtaining unit is configured to obtain a sample data set, wherein each sample data in the sample data set comprises a track point set of a sample user and an address type label corresponding to the sample user, and the track point set comprises a plurality of track points of the sample user;

an initialization unit configured to initialize a plurality of parameters of the trajectory recognition model; and

an execution unit configured to execute, for each sample data, operations of the following sub-units, wherein the execution unit includes:

an obtaining subunit configured to obtain track point feature information based on the track point set, where the track point feature information includes at least the following feature information of each of the plurality of track points: clustering labels and place names, wherein the clustering labels are obtained by clustering analysis on the plurality of track points;

the preprocessing subunit is configured to preprocess the trace point feature information to obtain a trace point feature matrix, where the trace point feature matrix includes a plurality of feature vectors respectively corresponding to the plurality of trace points;

the input subunit is configured to input the track point feature matrix to the track recognition model to obtain an output value, wherein the output value comprises a predicted address type recognition result of the sample user; and

and the adjusting subunit is configured to adjust a plurality of parameters of the track recognition model based on the address type recognition result of the sample user and the address type label corresponding to the sample user.

14. An electronic device, comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein

The memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-5 or 6-11.

15. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any of claims 1-5 or 6-11.

16. A computer program product comprising a computer program, wherein the computer program realizes the method of any one of claims 1-5 or 6-11 when executed by a processor.