CN114238792A

CN114238792A - Method and device for track point data mining, electronic equipment and medium

Info

Publication number: CN114238792A
Application number: CN202111564803.6A
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 present disclosure provides a method and an apparatus for track point data mining, an electronic device and a medium, and relates to the technical field of computers, in particular to the technical field of intelligent transportation, data mining and machine learning. The implementation scheme is as follows: acquiring a track point set of a user; and for any two of the plurality of trace points: acquiring the space distance between the two track points based on the coordinate information corresponding to the two track points; based on two place names corresponding to the two track points, acquiring text similarity between the two place names to acquire a first weight; and acquiring a fusion distance between the two track points based on the spatial distance and the first weight, and determining at least one permanent station of the user.

Description

Method and device for track point data mining, 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, an apparatus, an electronic device, a computer-readable storage medium, and a computer program product for trajectory point data mining.

Background

In the wisdom traffic, have comparatively important meaning to the data mining of user track point, data mining through the track point can further analysis user's trip demand, for better the providing service of 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 present disclosure provides a method, an apparatus, an electronic device, a computer-readable storage medium, and a computer program product for trajectory point data mining.

According to an aspect of the present disclosure, there is provided a method for trajectory point data mining, comprising: 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, and each track point in the plurality of track points comprises coordinate information and a place name; and for any two of the plurality of trace points: acquiring the space distance between the two track points based on the coordinate information corresponding to the two track points; based on two place names corresponding to the two track points, acquiring text similarity between the two place names to acquire a first weight; and acquiring a fusion distance between the two track points based on the space distance and the first weight, and determining at least one permanent station of the user.

According to another aspect of the present disclosure, there is provided an apparatus for trajectory point data mining, comprising: an obtaining unit configured to obtain a track point set of a user, wherein the track point set of the user includes a plurality of track points of the user, and wherein each of the plurality of track points includes coordinate information and a place name; and an execution unit configured to execute the following sub-units of operations for any two of the plurality of track points, wherein the execution unit includes: a first acquisition subunit configured to acquire a spatial distance between the two track points based on coordinate information corresponding to the two track points; a second obtaining subunit, configured to obtain a text similarity between two location names based on the two location names corresponding to the two track points to obtain a first weight; and a third acquiring subunit, configured to acquire a fusion distance between the two track points based on the spatial distance and the first weight, for determining at least one permanent location of the 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 cause the at least one processor to perform the method for trajectory point data mining described above.

According to another aspect of the present disclosure, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing a computer to perform the above-described method for trajectory point data mining.

According to another aspect of the present disclosure, a computer program product is provided, comprising a computer program, wherein the computer program, when executed by a processor, implements the above-described method for trajectory point data mining.

According to one or more embodiments of the present disclosure, a distance between two track points in which more track point information is fused can be obtained.

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 for trajectory point data mining in accordance with an embodiment of the present disclosure;

FIG. 3 shows a block diagram of an apparatus for trajectory point data mining, according to an embodiment of the present disclosure;

FIG. 4 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 the execution of methods for trace point data mining.

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 points and 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.

In track point data mining, for example, when identifying a user's resident or occupational sites, the distance between two track points is often applied. In the prior art, the obtained distance between two track points is only the distance in a physical space, and the expression of the distance cannot fully utilize other dimension information carried by the track points, so that the distance is applied to track point data mining, and the precision of the data mining cannot be further improved.

According to an embodiment of the present disclosure, as shown in fig. 2, there is provided a method for trajectory point data mining, 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, and each track point of the plurality of track points comprises coordinate information and a place name; step S202, for any two track points in the plurality of track points: acquiring the space distance between the two track points based on the coordinate information corresponding to the two track points; step S203, acquiring text similarity between two place names based on the two place names corresponding to the two track points to acquire a first weight; and step S204, acquiring a fusion distance between the two track points based on the space distance and the first weight, and determining at least one permanent station of the user.

Therefore, according to the method disclosed by the invention, the distance between two track points with more track point information fused can be obtained.

The track Point of the user may include rich information, for example, coordinate information, a location name, a Point of Interest (POI) type, and the like, and in order to fuse the information and express the distance, the information needs to be processed to a certain extent.

In one example, the processing of the location name may include: and calculating the text similarity of two corresponding place names of the two track points, normalizing the obtained text similarity to the interval of [0.5, 1.5], and taking the normalized text similarity as a first weight P1. And calculating the fusion distance between the two track points after the location name information is fused by the following formula:

DisNew＝(1/2)×Dis×(1+P1)

wherein, DisNew represents the fusion distance between two track points, and Dis represents the space distance between two track points.

The text similarity calculation can be obtained by performing cosine similarity calculation on word vectors corresponding to the two place names. It is understood that other text similarity calculation methods may be used by those skilled in the art, and are not limited herein.

According to some embodiments, each of the plurality of trace points further comprises point of interest type information, and wherein for any two of the plurality of trace points, the method further comprises: based on two interest point type information corresponding to the two track points, acquiring text similarity between the two interest point type information to acquire a second weight; and updating the fusion distance between the two track points based on the second weight.

In one example, the processing for the point of interest type may include: and calculating the text similarity of the two interest point types corresponding to the two track points, normalizing the obtained text similarity to the interval of [0.5, 1.5], and taking the normalized text similarity as a second weight P2. The text similarity calculation can be obtained by performing cosine similarity calculation on word vectors corresponding to the two interest point types. It is understood that other text similarity calculation methods may be used by those skilled in the art, and are not limited herein.

In one example, the processing for the point of interest type may also include: when the types of two interest points corresponding to the two track points are the same, assigning the value of P2 as 0.5; if the two interest point types corresponding to the two track points are different, the value of P2 is assigned to 1.5.

In one example, the fusion distance between two track points after fusing the location name and the interest point type information can be calculated by the following formula:

DisNew＝(1/3)×Dis×(1+P1+P2)

According to some embodiments, each of the plurality of trace points further comprises a wireless local area network signal name, and wherein for any two of the plurality of trace points, the method further comprises: acquiring text similarity between two wireless local area network signal names based on the two wireless local area network signal names corresponding to the two track points to acquire a third weight; and updating the fusion distance between the two track points based on the third weight.

The wireless local area network signal name is derived from the peripheral wireless local area network information recorded in the track point and acquired by the mobile device of the user when the user is at the track point. The processing of the wireless local area network signal name may include: and calculating the text similarity of two wireless local area network signal names corresponding to the two track points, normalizing the obtained text similarity to the interval of [0.5, 1.5], and taking the normalized text similarity as a third weight P3. 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. It is understood that other text similarity calculation methods may be used by those skilled in the art, and are not limited herein.

The fusion distance between the two track points after the location name, the interest point type information and the wireless local area network signal name are fused can be calculated by the following formula:

DisNew＝(1/4)×Dis×(1+P1+P2+P3)

According to some embodiments, each of the plurality of trace points further comprises a service set identification of a wireless local area network, and wherein for any two of the plurality of trace points, the method further comprises: respectively acquiring the names of the places where two devices corresponding to the two wireless local area networks are located based on the service set identifications of the two wireless local area networks corresponding to the two track points; acquiring text similarity between the names of the two devices to acquire a fourth weight; and updating the fusion distance between the two track points based on the fourth weight.

The Service Set Identifier (SSID) of the wlan is derived from the surrounding wlan information recorded in the track point and acquired by the mobile device of the user when the user is located at the track point. The processing of the service set identification for the wireless local area network may include: firstly, the names of the locations of two wireless local area network devices corresponding to service set identifiers of two wireless local area networks are obtained based on an SSID database; then, the text similarity of the names of the two corresponding devices is calculated, and the obtained text similarity is normalized to the interval of [0.5, 1.5], and the normalized text similarity is taken as a fourth weight P4. The text similarity calculation can be obtained by performing cosine similarity calculation on word vectors corresponding to the names of the two devices. It is understood that other text similarity calculation methods may be used by those skilled in the art, and are not limited herein.

The fusion distance between the two track points after the name of the fusion place, the type information of the interest point, the name of the wireless local area network signal and the service set identification of the wireless local area network can be calculated by the following formula:

DisNew＝(1/5)×Dis×(1+P1+P2+P3+P4)

According to some embodiments, each of the plurality of trace points further comprises time information, and wherein for any two of the plurality of trace points, the method further comprises: acquiring a time difference value between two pieces of time information based on the two pieces of time information corresponding to the two track points; acquiring a fifth weight based on the time difference; and updating the fusion distance between the two track points based on the fifth weight.

In one example, the processing of the time information may include: and calculating the time difference between two corresponding times of the two track points, and meanwhile, counting the time difference between every two other track points in the track point set and obtaining the maximum time difference. The time difference between the two trace points is normalized based on the maximum time difference, and normalized to the interval of [0.5, 1.5], and the normalized time difference is taken as a fifth weight P5.

The fusion distance between the two track points after the location name, the interest point type information, the wireless local area network signal name, the service set identifier of the wireless local area network and the time information are fused can be calculated by the following formula:

DisNew＝(1/6)×Dis×(1+P1+P2+P3+P4+P5)

From this, through having fused above-mentioned track point information, when two track points are comparatively close at the information that contains, for example two track points' interest point type is the same or two track point time differences are less, the distance of fusing between two track points that obtain can be less than the spatial distance between two track points, promptly, with the similar information characterization between these two track points in the distance, makes its information that contains richer.

In one example, the fusion distance between two track points obtained by the above method can be applied to the identification of the user stationary point. For example, in the process of performing clustering analysis on the track points of the user through the DBSCAN clustering algorithm to obtain the user stationary points, the method includes the step of judging whether the track point is a neighborhood track point of the center track point according to the distance between the center track point and some other track point. The distance is represented by the fusion distance of the scheme, so that a more accurate user stationary point identification result can be obtained.

According to some embodiments, as shown in fig. 3, there is also provided an apparatus 300 for trajectory point data mining, comprising: an obtaining unit 310 configured to obtain a track point set of a user, wherein the track point set of the user includes a plurality of track points of the user, and wherein each of the plurality of track points includes coordinate information and a place name; and an execution unit 320 configured to execute the following sub-units for any two track points of the plurality of track points, wherein the execution unit 320 includes: a first acquisition subunit 321 configured to acquire a spatial distance between the two trace points based on coordinate information corresponding to the two trace points; a second obtaining subunit 322, configured to obtain, based on two location names corresponding to the two track points, a text similarity between the two location names to obtain a first weight; and a third obtaining subunit 323 configured to obtain, based on the spatial distance and the first weight, a fused distance between the two track points, for determining at least one permanent location of the user.

The operations performed by the units 310 to 320 and the sub-units 321 to 323 of the apparatus 300 for track point data mining are similar to the operations performed by the steps S201 to S204 of the method for track point data mining, and are not described herein again.

According to some embodiments, each of the plurality of trace points further includes point of interest type information, and wherein the execution unit may further include: a fourth obtaining subunit, configured to obtain, based on two interest point type information corresponding to the two track points, a text similarity between the two interest point type information to obtain a second weight; and a first updating subunit configured to update the blending distance between the two track points based on the second weight.

According to some embodiments, each of the plurality of trace points further comprises a wireless local area network signal name, and wherein the execution unit further comprises: a fifth obtaining subunit, configured to obtain, based on two wireless local area network signal names corresponding to the two track points, a text similarity between the two wireless local area network signal names to obtain a third weight; and a second updating subunit configured to update the blending distance between the two track points based on the third weight.

According to some embodiments, each of the plurality of trace points further comprises a service set identification of a wireless local area network, and wherein the execution unit may further comprise: the sixth obtaining subunit is configured to obtain, based on the service set identifiers of the two wireless local area networks corresponding to the two track points, names of locations of two devices corresponding to the two wireless local area networks respectively; a seventh obtaining subunit, configured to obtain text similarity between names of locations of the two devices, so as to obtain a fourth weight; and a third updating subunit configured to update the blending distance between the two track points based on the fourth weight.

According to some embodiments, each of the plurality of trace points further comprises time information, and wherein the execution unit further comprises: the eighth acquiring subunit is configured to acquire a time difference value between two pieces of time information based on the two pieces of time information corresponding to the two track points; a ninth obtaining subunit configured to obtain a fifth weight based on the time difference value; and a fourth updating subunit configured to update the blending distance between the two track points based on the fifth weight.

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. 4, a block diagram of a structure of an electronic device 400, 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. 4, the electronic device 400 includes a computing unit 401 that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM)402 or a computer program loaded from a storage unit 408 into a Random Access Memory (RAM) 403. In the RAM403, various programs and data required for the operation of the electronic device 400 can also be stored. The computing unit 401, ROM 402, and RAM403 are connected to each other via a bus 404. An input/output (I/O) interface 405 is also connected to bus 404.

A number of components in the electronic device 400 are connected to the I/O interface 405, including: an input unit 406, an output unit 407, a storage unit 408, and a communication unit 409. The input unit 406 may be any type of device capable of inputting information to the electronic device 400, and the input unit 406 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 controller. Output unit 407 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. Storage unit 408 may include, but is not limited to, magnetic or optical disks. The communication unit 409 allows the electronic device 400 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication 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.

Computing unit 401 may be a variety of general and/or special purpose processing components with processing and computing capabilities. Some examples of the computing unit 401 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 401 executes the respective methods and processes described above, such as the method for trajectory point data mining. For example, in some embodiments, the method for trajectory point data mining may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 408. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 400 via the ROM 402 and/or the communication unit 409. When the computer program is loaded into RAM403 and executed by computing unit 401, one or more steps of the method for trajectory point data mining described above may be performed. Alternatively, in other embodiments, the computing unit 401 may be configured to perform the method for trace point data mining by any other suitable means (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 method for trajectory point data mining, comprising:

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, and each track point in the plurality of track points comprises coordinate information and a place name; and

for any two of the plurality of trace points:

acquiring the space distance between the two track points based on the coordinate information corresponding to the two track points;

based on two place names corresponding to the two track points, acquiring text similarity between the two place names to acquire a first weight; and

and acquiring a fusion distance between the two track points based on the space distance and the first weight, and determining at least one permanent station of the user.

2. The method of claim 1, wherein each of the plurality of trace points further includes point of interest type information, and wherein for any two of the plurality of trace points, the method further comprises:

based on two interest point type information corresponding to the two track points, acquiring text similarity between the two interest point type information to acquire a second weight; and

and updating the fusion distance between the two track points based on the second weight.

3. The method of claim 1 or 2, wherein each of the plurality of trace points further comprises a wireless local area network signal name, and wherein for any two of the plurality of trace points, the method further comprises:

acquiring text similarity between two wireless local area network signal names based on the two wireless local area network signal names corresponding to the two track points to acquire a third weight; and

and updating the fusion distance between the two track points based on the third weight.

4. The method of any of claims 1-3, wherein each of the plurality of track points further comprises a service set identification of a wireless local area network, and wherein for any two of the plurality of track points, the method further comprises:

respectively acquiring the names of the places where two devices corresponding to the two wireless local area networks are located based on the service set identifications of the two wireless local area networks corresponding to the two track points;

acquiring text similarity between the names of the two devices to acquire a fourth weight; and

and updating the fusion distance between the two track points based on the fourth weight.

5. The method of any of claims 1-4, wherein each of the plurality of trace points further includes time information, and wherein for any two of the plurality of trace points, the method further comprises:

acquiring a time difference value between two pieces of time information based on the two pieces of time information corresponding to the two track points;

acquiring a fifth weight based on the time difference; and

and updating the fusion distance between the two track points based on the fifth weight.

6. An apparatus for trace point data mining, comprising:

an obtaining unit configured to obtain a track point set of a user, wherein the track point set of the user includes a plurality of track points of the user, and wherein each of the plurality of track points includes coordinate information and a place name; and

an execution unit configured to execute the following sub-units of operations for any two of the plurality of trace points, wherein the execution unit includes:

a first acquisition subunit configured to acquire a spatial distance between the two track points based on coordinate information corresponding to the two track points;

a second obtaining subunit, configured to obtain a text similarity between two location names based on the two location names corresponding to the two track points to obtain a first weight; and

and the third acquisition subunit is configured to acquire a fusion distance between the two track points based on the spatial distance and the first weight, so as to determine at least one permanent station of the user.

7. The apparatus of claim 6, wherein each of the plurality of trace points further comprises point of interest type information, and wherein the execution unit further comprises:

a fourth obtaining subunit, configured to obtain, based on two interest point type information corresponding to the two track points, a text similarity between the two interest point type information to obtain a second weight; and

a first updating subunit configured to update the blending distance between the two track points based on the second weight.

8. The apparatus of claim 6 or 7, wherein each of the plurality of trace points further comprises a wireless local area network signal name, and wherein the execution unit further comprises:

a fifth obtaining subunit, configured to obtain, based on two wireless local area network signal names corresponding to the two track points, a text similarity between the two wireless local area network signal names to obtain a third weight; and

a second updating subunit configured to update the blending distance between the two track points based on the third weight.

9. The apparatus of any of claims 6-8, wherein each of the plurality of trace points further comprises a service set identification of a wireless local area network, and wherein the execution unit further comprises:

the sixth obtaining subunit is configured to obtain, based on the service set identifiers of the two wireless local area networks corresponding to the two track points, names of locations of two devices corresponding to the two wireless local area networks respectively;

a seventh obtaining subunit, configured to obtain text similarity between names of locations of the two devices, so as to obtain a fourth weight; and

a third updating subunit configured to update the blending distance between the two track points based on the fourth weight.

10. The apparatus of any of claims 6-9, wherein each of the plurality of trace points further comprises time information, and wherein the execution unit further comprises:

the eighth acquiring subunit is configured to acquire a time difference value between two pieces of time information based on the two pieces of time information corresponding to the two track points;

a ninth obtaining subunit configured to obtain a fifth weight based on the time difference value; and

a fourth updating subunit configured to update the blending distance between the two track points based on the fifth weight.

11. 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.

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

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