CN113518328A

CN113518328A - Identification method, positioning method, device and equipment of network access equipment

Info

Publication number: CN113518328A
Application number: CN202010275413.6A
Authority: CN
Inventors: 方兴; 李江涛; 刘宇
Original assignee: Alibaba Group Holding Ltd
Current assignee: Alibaba Group Holding Ltd
Priority date: 2020-04-09
Filing date: 2020-04-09
Publication date: 2021-10-19

Abstract

The embodiment of the invention provides an identification method, a positioning method, a device and equipment of network access equipment. The method comprises the following steps: acquiring a positioning log and an acquisition log of the terminal equipment, wherein the positioning log comprises network positioning data; the acquisition log comprises satellite positioning data; acquiring subway line data of the terminal equipment and a subway station corresponding to the subway line data based on the positioning log; and determining the network access equipment deployed in the subway station according to the subway line data, the positioning log and the acquisition log. According to the technical scheme provided by the embodiment, the positioning log and the acquisition log are acquired, and the subway line data of the terminal equipment and the subway station corresponding to the subway line data are acquired based on the positioning log; according to the subway line data, the positioning log and the acquisition log, the network access equipment deployed in the subway station is determined, so that the network access equipment is effectively identified, and the positioning operation based on the network access equipment is facilitated.

Description

Identification method, positioning method, device and equipment of network access equipment

Technical Field

The present invention relates to the field of mobile communications technologies, and in particular, to an identification method, a positioning method, an apparatus, and a device for network access equipment.

Background

With the rapid development of science and technology, subways are used as a form of public transportation, and the construction mileage and coverage area range is more and more. However, because the subway is usually located underground, when a user is on the subway or enters a subway station, a terminal device used by the user cannot receive a satellite positioning signal (such as a GPS or a beidou signal) and cannot adopt a satellite positioning mode, and only network positioning can be adopted, and the accuracy of the network positioning depends on the positioning fingerprint characteristics of network access devices such as wifi and a base station uploaded by the terminal device, so that how to identify the network access devices in a subway scene is required to be solved, and the device can ensure the positioning accuracy of the terminal device in the subway scene.

Disclosure of Invention

In view of this, embodiments of the present invention provide an identification method, a positioning method, an apparatus, and a device for network access equipment, which can effectively identify the network access equipment, thereby facilitating positioning operation based on the network access equipment, and also reducing positioning cost and maintenance cost.

In a first aspect, an embodiment of the present invention provides an identification method for a network access device, including:

acquiring a positioning log and an acquisition log of the terminal equipment, wherein the positioning log comprises network positioning data; the acquisition log comprises satellite positioning data;

acquiring subway line data of the terminal equipment and a subway station corresponding to the subway line data based on the positioning log;

and determining the network access equipment deployed in the subway station according to the subway line data, the positioning log and the acquisition log.

In a second aspect, an embodiment of the present invention provides an apparatus for identifying a network access device, including:

the acquisition module is used for acquiring a positioning log and an acquisition log of the terminal equipment, wherein the positioning log comprises network positioning data; the acquisition log comprises satellite positioning data;

the processing module is used for acquiring subway line data of the terminal equipment and subway stations corresponding to the subway line data based on the positioning logs;

and the determining module is used for determining the network access equipment deployed in the subway station according to the subway line data, the positioning log and the acquisition log.

In a third aspect, an embodiment of the present invention provides an electronic device, including a processor and a memory, where the memory is used to store one or more computer instructions, and when executed by the processor, the one or more computer instructions implement the method for identifying a network access device in the first aspect. The electronic device may also include a communication interface for communicating with other devices or a communication network.

In a fourth aspect, an embodiment of the present invention provides a computer storage medium for storing a computer program, where the computer program is used to make a computer implement the method for identifying a network access device in the first aspect when executed.

In a fifth aspect, an embodiment of the present invention provides a positioning method, where a network access device deployed in a subway station is determined based on the identification method of the network access device in the first aspect, so as to perform positioning of a subway scene.

In a sixth aspect, an embodiment of the present invention provides a positioning apparatus, where the positioning apparatus includes: a memory, a processor; wherein the content of the first and second substances,

the memory is configured to store one or more computer instructions, wherein the one or more computer instructions, when executed by the processor, implement the positioning method according to the fifth aspect.

In a seventh aspect, an embodiment of the present invention provides a computer storage medium, which is used to store and store a computer program, and when the computer program is executed, the positioning method in the fifth aspect is implemented.

Acquiring subway line data and a subway station corresponding to the subway line data based on the positioning log by acquiring the positioning log and the acquisition log; according to the subway line data, the positioning log and the acquisition log, the network access equipment deployed in the subway station is determined, and effective identification of the network access equipment is achieved, so that positioning operation based on the network access equipment is facilitated, accuracy and reliability of processing of the object positioning request taking the subway are effectively guaranteed, and further the practicability of the method is improved.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.

Fig. 1 is a first flowchart of an identification method of a network access device according to an embodiment of the present invention;

fig. 2 is a scene flowchart of an identification method of a network access device according to an embodiment of the present invention;

fig. 3 is a second flowchart of an identification method of a network access device according to an embodiment of the present invention;

fig. 4 is a flowchart of a third method for identifying a network access device according to an embodiment of the present invention;

fig. 5 is a flowchart of acquiring a subway station corresponding to the subway line data according to the embodiment of the present invention;

fig. 6 is a flowchart for determining an expected running time corresponding to the subway line data according to an embodiment of the present invention;

fig. 7 is a fourth flowchart of an identification method of a network access device according to an embodiment of the present invention;

fig. 8 is a fifth flowchart of an identification method of a network access device according to an embodiment of the present invention;

fig. 9 is a sixth flowchart of an identification method of a network access device according to an embodiment of the present invention;

fig. 10 is a flowchart of an identification method of a network access device according to an embodiment of the present invention;

fig. 11 is a schematic structural diagram of an identification apparatus of a network access device according to an embodiment of the present invention;

fig. 12 is a schematic structural diagram of an electronic device corresponding to the identification apparatus of the network access device provided in the embodiment shown in fig. 11;

fig. 13 is a schematic structural diagram of a positioning apparatus according to an embodiment of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

The terminology used in the embodiments of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the examples of the present invention and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, and "a" and "an" generally include at least two, but do not exclude at least one, unless the context clearly dictates otherwise.

It should be understood that the term "and/or" as used herein is merely one type of association that describes an associated object, meaning that three relationships may exist, e.g., a and/or B may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship.

The words "if," "if," as used herein may be interpreted as "at … …" or "at … …" or "in response to a determination" or "in response to a recognition," depending on the context. Similarly, the phrases "if determined" or "if identified (a stated condition or event)" may be interpreted as "when determined" or "in response to a determination" or "when identified (a stated condition or event)" or "in response to an identification (a stated condition or event)", depending on the context.

It is also noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a good or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such good or system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a commodity or system that includes the element.

Definition of terms

Positioning a log: the method comprises the steps that a network positioning log generated when an object is positioned by using a map, namely the positioning log comprises network positioning data, and the network positioning refers to the positioning of mobile equipment through scanned peripheral WiFi and network access equipment signals when the mobile equipment does not have GPS positioning.

Collecting logs: when there is a Global Positioning System (GPS) signal, and an object is located using a map, all current information can be saved, that is, the acquisition log includes satellite Positioning data.

Mobile WiFi: WiFi devices whose physical location changes frequently, for example: mobile phone hotspots, 4G mobile routers, WiFi hotspots on buses/subways/high-speed rails, etc.

The network access equipment: the network access equipment is different from the ground network access equipment in structure and has smaller coverage range.

Hidden markov model: hidden Markov models are time-sequential probabilistic models that describe the process of randomly generating a sequence of non-observable states (hidden states) from a hidden Markov chain, and then generating an observation from each state to produce a random sequence of observations (explicit states).

Positioning and acquiring ratio: and wifi or the ratio of the number of the positioning objects to the number of the acquisition objects in a certain time of the network access equipment.

DBSCAN (sensitivity-Based Spatial Clustering of Applications with Noise): is a relatively representative density-based clustering algorithm. Unlike the partitioning and hierarchical clustering method, which defines clusters as the largest set of density-connected points, it is possible to partition areas with sufficiently high density into clusters and find clusters of arbitrary shape in a spatial database of noise.

In order to facilitate understanding of the technical solution of the present embodiment, the following description relates to the prior art:

the subway scene is usually located underground, and when an object is positioned on the subway, a GPS signal cannot be easily received, and at the moment, network positioning can only be adopted. The network positioning is to give the position information of the object by off-line training wifi and network access device data and adopting the methods of off-line data and on-line positioning algorithm to realize the positioning operation. However, for subways, although there is a public wifi in some cities, the wifi is often a mobile wifi or a cloned wifi, and it is difficult to have a positioning capability, or in such an implementation, a router needs to be set at a suitable position in a subway scene, and positioning is performed according to scanned wifi signal characteristics. This approach has the advantage of accurate positioning, but has the disadvantage of requiring consideration of various factors, such as: location selection, power, frequency band, etc., may also require agreements with several map service providers, with higher post-maintenance costs. Therefore, when positioning operation is performed on the subway, only network access equipment can be used for positioning.

In addition, the sequence of steps in each method embodiment described below is only an example and is not strictly limited.

Fig. 1 is a first flowchart of an identification method of a network access device according to an embodiment of the present invention; fig. 2 is a scene flowchart of an identification method of a network access device according to an embodiment of the present invention; referring to fig. 1-2, the embodiment provides an identification method of a network access device, where an execution subject of the identification method of the network access device is an identification apparatus, and it is understood that the identification apparatus may be implemented as software, or a combination of software and hardware. Specifically, the method may include:

s101: acquiring a positioning log and an acquisition log of the terminal equipment, wherein the positioning log comprises network positioning data; the acquisition log comprises satellite positioning data.

The identification device of the network access equipment can be in communication connection with the terminal equipment, a positioning log and a collecting log are stored in the terminal equipment, the positioning log comprises data positioned through a network, and the collecting log comprises data positioned through a GPS. At this time, the identification device may obtain the positioning log and the collection log through the terminal device, specifically, the specific implementation manner of obtaining the positioning log and the collection log is not limited in this embodiment, and a person skilled in the art may set the positioning log and the collection log according to a specific application scenario and an application requirement, for example: the identification device may send a log acquisition request to the terminal device, and after the terminal device acquires the log acquisition request, the identification device may send a positioning log and a collection log to the identification device. Or, the terminal device may send the positioning log and the collection log to the identification apparatus according to a preset collection period, where the collection period may be 1 day, 2 days, 3 days, 7 days, or the like.

Of course, those skilled in the art may also use other manners to obtain the positioning log and the collection log, as long as the accuracy and reliability of obtaining the positioning log and the collection log can be ensured, which is not described herein again.

S102: and acquiring subway line data of the terminal equipment and a subway station corresponding to the subway line data based on the positioning log.

After the positioning log is obtained, the positioning log can be analyzed and processed, so that one or more subway line data scanned in the positioning log can be extracted, and it should be noted that the obtained subway line data can be track information corresponding to subway stations, that is, the subway stations corresponding to the subway line data are obtained through the positioning log.

In other examples, when the subway line data does not correspond to the track information of the subway station, in order to obtain the subway station corresponding to the subway line data, a person skilled in the art may set the track information according to a specific application scenario and a design requirement, for example: the subway station corresponding to the subway line data can be obtained by accessing a preset database, wherein the preset database stores a plurality of subway line data and the corresponding relation between the subway line data and the subway station.

Of course, those skilled in the art may also use other methods to obtain the subway station corresponding to the subway line data, as long as it is ensured that the subway station corresponding to the subway line data is accurately and stably obtained, which is not described herein again.

S103: and determining the network access equipment deployed in the subway station according to the subway line data, the positioning log and the acquisition log.

After acquiring the subway line data, the positioning log and the acquisition log, analyzing and processing the subway line data, the positioning log and the acquisition log, so as to determine the network access equipment deployed in the subway station, wherein it can be understood that the number of the acquired network access equipment can be one or more. Specifically, determining the network access device deployed in the subway station according to the subway line data, the positioning log and the acquisition log may include:

s1031: and determining all the network access devices in the positioning log and the acquisition log, and the positioning object quantity and the acquisition object quantity corresponding to each network access device.

After the positioning log and the collection log are obtained, the positioning log and the collection log can be analyzed and processed, so that all network access devices included in the positioning log and the collection log and a positioning object quantity and a collection object quantity corresponding to each network access device can be determined. Specifically, determining the positioning object quantity and the collecting object quantity corresponding to each network access device may include:

s10311: and determining the quantity of the positioning objects corresponding to each network access device based on the positioning log.

S10312: and determining the collection object quantity corresponding to each network access device based on the collection log.

For each network access device, a positioning object quantity and a collection object quantity are corresponding, wherein the positioning object quantity refers to the number of objects for performing positioning operation on the network access device, and the collection object quantity refers to the number of times for performing positioning operation on the network access device and the objects. Specifically, the positioning log may include a positioning object amount corresponding to each network access device, and the acquisition log may include an acquisition object amount corresponding to each network access device, it can be understood that, for the same network access device, the positioning object amount is less than or equal to the acquisition object amount.

For example, the location log includes the location data of the network access device as follows: the positioning information of the object A at the time of T1, the positioning information of the object B at the time of T2, the positioning information of the object A at the time of T3, the positioning information of the object C at the time of T3 and the like, at this time, for the network access equipment, the positioning object amount is the number formed by the object A, the object B and the object C, that is, the positioning object amount is 3. The acquisition log includes the positioning data of the network access device as follows: the method comprises the steps that positioning information of a target A at the time of T1, positioning information of a target B at the time of T2, positioning information of a target A at the time of T3, positioning information of a target C at the time of T3 and the like, wherein for the network access equipment, the acquisition target amount is number information consisting of the positioning information of the time of T1, the positioning information of the time of T2, the positioning information of the target A at the time of T3 and the positioning information of the target C at the time of T3, namely the acquisition target amount is 4.

It should be noted that the execution sequence of the above steps in this embodiment is not limited to the execution sequence represented by the above step number, that is, step S10312 may be executed before step S10311, or may be executed at the same time, and those skilled in the art may perform any setting according to the specific application requirements and design requirements.

In the embodiment, the positioning object quantity corresponding to each network access device is determined through the positioning log, and the acquisition object quantity corresponding to each network access device is determined through the acquisition log, so that the accuracy and reliability of acquiring the positioning object quantity and the acquisition object quantity are effectively ensured, and the accuracy and reliability of identifying the network access devices are further improved.

S1032: and analyzing and processing the current position, the subway station and the positioning object quantity and the acquisition object quantity corresponding to each network access device by using a first machine learning model, determining first access devices which are arranged in the subway station and are included in all the network access devices, and training the first machine learning model to be used for identifying the subway device information included in all the network access devices.

The first machine learning model (also referred to as "classification model") is trained in advance, and is trained to identify subway equipment information included in all network access devices, and specifically, when the first machine learning model is subjected to learning training, the following information can be obtained: the subway line data, the subway stations corresponding to the subway line data, the positioning object quantity and the acquisition object quantity corresponding to each network access device, and the mapping relationship between the information and the types of the network access devices may specifically be obtained by performing learning training with the subway line data corresponding to the network access devices, the subway stations corresponding to the subway line data, and the positioning object quantity and the acquisition object quantity corresponding to each network access device as positive samples, and with the subway line data corresponding to non-network access devices, the subway stations corresponding to the subway line data, and the positioning object quantity and the acquisition object quantity corresponding to each network access device as negative samples, so as to obtain the first machine learning model.

Then, after acquiring the subway line data, the subway station corresponding to the subway line data, and the positioning object quantity and the acquisition object quantity corresponding to each network access device, the first machine learning model may be used to analyze and process the subway line data, the subway station corresponding to the subway line data, and the positioning object quantity and the acquisition object quantity corresponding to each network access device, so as to determine the first access devices included in all the network access devices, where the first access devices have a characteristic that the positioning object quantity is greater than the acquisition object quantity.

In the identification method of the network access device provided by this embodiment, the positioning log and the acquisition log are acquired, and the subway line data of the terminal device and the subway station corresponding to the subway line data are acquired based on the positioning log; according to the subway line data, the positioning log and the acquisition log, the network access equipment deployed in the subway station is determined, and effective identification of the network access equipment is achieved, so that positioning operation based on the network access equipment is facilitated, accuracy and reliability of processing of the object positioning request taking the subway are effectively guaranteed, and further the practicability of the method is improved.

Fig. 3 is a second flowchart of an identification method of a network access device according to an embodiment of the present invention; on the basis of the foregoing embodiment, with continued reference to fig. 3, after acquiring subway line data based on a positioning log, the method in this embodiment may further include:

s301: and identifying the data quality of the subway line data.

S302: and when the data quality does not meet the preset requirement, filtering the subway line data.

In order to accurately perform identification operation of the network access device based on the subway line data, after the subway line data is acquired based on the positioning log, the subway line data can be analyzed and processed to identify the data quality of the subway line data, wherein the data quality of the subway line data is related to the continuity of time and the continuity of track. Specifically, scanning processing may be performed on the subway line data by using a preset scanning device, so as to obtain a first continuous characteristic of the subway line data in time and a second continuous characteristic of the subway line data, and if the first continuous characteristic is greater than or equal to a first preset threshold value and the second continuous characteristic is greater than or equal to a second preset threshold value, it is determined that the data quality of the subway line data meets a preset requirement; if the first continuous characteristic is smaller than the first preset threshold value and/or the second continuous characteristic is smaller than the second preset threshold value, it is determined that the data quality of the subway line data does not meet the preset requirement, and then the subway line data can be filtered, that is, the subway line data with the data quality not meeting the preset requirement is removed, and the subway line data with the data quality meeting the preset requirement is reserved, so that the accuracy and reliability of network access equipment identification based on the subway line data can be improved.

For example, the existing subway line data is as follows: the subway line data A, the subway line data B, the subway line data C and the subway line data D are analyzed and identified, wherein the subway line data A is disconnected within the time from T1 to T2; the subway line data B is continuous in time and the track is also continuous; the subway line data C is continuous in time, but there are disconnected intervals on the track, for example: subway line data C is at station C1 at time T1, at station C5 at time T2, and lacks information of station C2, station C3, and station C4 between station C1 and station C5, at which time the track of subway line data C is not continuous; the subway line data D is continuous in time and the track is also continuous.

The following conclusions can be drawn through the above analytical identification: the data quality of the subway line data A and the subway line data C does not meet the preset requirement, and the data quality of the subway line data B and the subway line data D meets the preset requirement, so that the subway line data B and the subway line data D can be reserved, the subway line data A and the subway line data C are filtered, and the accuracy and reliability of network access equipment identification based on the subway line data are improved.

Fig. 4 is a flowchart of a third method for identifying a network access device according to an embodiment of the present invention; on the basis of the foregoing embodiment, with continued reference to fig. 4, after acquiring subway line data based on a positioning log, the method in this embodiment may further include:

s401: and acquiring standard station information corresponding to the subway line data.

S402: and detecting whether the subway stations are omitted in the subway line data or not based on the standard station information and the subway stations.

S403: when the subway station is omitted in the subway line data, the subway line data is divided into a plurality of new subway line data based on the omitted subway station.

In order to accurately perform the identification operation of the network access device based on the subway line data, after the subway line data is acquired based on the positioning log, the subway line data can be analyzed and processed to identify whether the subway station is missed by the subway line data. Specifically, the standard station information (the preconfigured standard station information) corresponding to the subway line data can be acquired first, and then the subway stations through which the subway line data passes are analyzed and compared with the standard station information, so that whether the subway stations are omitted in the subway line data can be identified. When subway stations are omitted in subway line data, in order to ensure the accuracy of identifying the network access equipment based on the subway line data, the subway line data can be divided into a plurality of new subway line data based on the omitted subway stations, and then the network access equipment can be identified based on the new subway line data.

For example, the existing subway line data a, the subway station through which the subway line data a passes, is as follows: a1, a2, A3, a5, a6, a7, A8; the standard station information corresponding to the subway line data is as follows: a1, a2, A3, a4, a5, a6, a7, A8; as can be seen by comparison, the subway station a4 is missed from the subway line data a, and at this time, the subway line data a can be divided into two new subway line data a 'and subway line data a ″ based on the missed subway station a4 in the subway line data a, where the subway station where the subway line data a' passes through includes a1, a2, and A3; the subway stations through which the subway line data a ″ passes include a5, a6, a7, A8; the method and the system have the advantages that the omitted subway stations are used as disconnection points, the subway line data are divided into a plurality of new subway line data, then the network access equipment can be identified based on the new subway line data, and therefore the accuracy and the reliability of identifying the network access equipment can be effectively guaranteed.

Fig. 5 is a flowchart of acquiring a subway station corresponding to subway line data according to an embodiment of the present invention; on the basis of the foregoing embodiment, as shown in fig. 5 with continued reference to the drawing, in this embodiment, a specific implementation manner for acquiring the subway station corresponding to the subway line data is not limited, and a person skilled in the art may set the subway station according to specific application requirements and design requirements, and preferably, the acquiring the subway station corresponding to the subway line data in this embodiment may include:

s501: and acquiring original station information which is not matched with subway line data.

In order to acquire the subway station corresponding to the subway line data, original station information which is not matched with the subway line data can be acquired first, specifically, the original station information can be stored in a preset database, and the original station information is acquired by accessing the preset database; alternatively, the original site information may be collected manually.

S502: determining initial state probability information, observation probability information and state transition probability information between stations in the subway line data, wherein the initial state probability information corresponds to the first station in the subway line data, and the observation probability information comprises transition probabilities corresponding to the stations in the subway line data.

For the subway line data, because the input-output relationship of each position point exists in the subway line data, the initial state probability information, the observation probability information and the state transition probability information between the station and the station in the subway line data corresponding to the subway line data can be determined based on the input-output relationship of each position point in the subway line data, wherein the initial state probability information and the observation probability information conform to gaussian distribution. Specifically, determining the initial state probability information corresponding to the subway line data may include:

s5021: and acquiring a first station included in the subway line data.

S5022: and determining the station longitude and latitude of the first station and the track longitude and latitude corresponding to the subway line data.

S5023: and determining initial state probability information corresponding to subway line data according to the station longitude and latitude and the track longitude and latitude.

After acquiring the subway line data, setting an operation direction for the subway line data so as to determine a first station included in the subway line data, wherein the first station can be an initial station in certain subway line data; specifically, the station longitude and latitude of the first station and the track longitude and latitude corresponding to the subway line data can be obtained, and then the station longitude and latitude and the track longitude and latitude are analyzed and processed, so that initial state probability information corresponding to the subway line data can be determined, wherein the initial state probability information comprises a transition probability corresponding to the first station in the subway line data.

In addition, determining state transition probability information between stations in the subway line data may include:

s5024: and acquiring the actual running time of the terminal equipment based on the subway line data.

S5025: an expected run time corresponding to the subway line data is determined.

S5026: and determining state transition probability information corresponding to the subway line data according to the actual running time and the expected running time.

After acquiring the subway line data of the terminal equipment, acquiring the actual running time of the terminal equipment on the subway line data based on the subway line data; an expected running time corresponding to the subway line data can then be determined, which is understood to be a preconfigured theoretical running time corresponding to the subway line data, which may be the same as or different from the actual running time. After the actual running time and the expected running time are obtained, the actual running time and the expected running time can be analyzed to determine state transition probability information corresponding to the subway line data; specifically, determining the state transition probability information corresponding to the subway line data according to the actual running time and the expected running time may include:

s50261: when the actual operation time is the same as the expected operation time, the state transition probability information corresponding to the subway line data is determined to be 1. Alternatively, the first and second electrodes may be,

s50262: when the actual operation time is less than the expected operation time, determining that the state transition probability information corresponding to the subway line data satisfies a first Gaussian distribution. Alternatively, the first and second electrodes may be,

s50263: and when the actual running time is greater than the expected running time, determining that the state transition probability information corresponding to the subway line data meets a second Gaussian distribution, wherein the attenuation characteristic of the second Gaussian distribution is slower than that of the first Gaussian distribution.

Specifically, after the actual running time and the expected running time are obtained, the actual running time and the expected running time can be analyzed and compared, and when the actual running time is the same as the expected running time, the expectation of the running time of the object taking the subway line data is more accurate, so that the state transition probability information corresponding to the subway line data can be determined to be 1. When the actual running time is less than the expected running time, the expected error of the running time of the object taking the subway line data is shown, and then the state transition probability information corresponding to the subway line data can be determined to meet the first Gaussian distribution. When the actual running time is greater than the expected running time, it is indicated that there may be other things to prolong the running time when the object takes the subway line data, and then it may be determined that the state transition probability information corresponding to the subway line data satisfies the second gaussian distribution, and it should be noted that the attenuation characteristic of the second gaussian distribution is slower than that of the first gaussian distribution.

It should be noted that the execution sequence of the above steps in this embodiment is not limited to the execution sequence represented by the above step number, that is, the steps S5024 to S5026 may be executed before the steps S5021 to S5023, or may be executed simultaneously, and those skilled in the art may perform any setting according to the specific application requirement and design requirement.

In the embodiment, the state transition probability information corresponding to the subway line data can be accurately determined through the actual running time and the expected running time, so that the subway station corresponding to the subway line data can be conveniently determined based on the state transition probability information, and the accurate reliability of the method is effectively ensured.

S503: and analyzing and processing the original station information, the initial state probability information, the observation probability information and the state transition probability information by using a Viterbi algorithm, and determining the subway station corresponding to the subway line data.

The viterbi algorithm is a dynamic programming algorithm for finding the sequence of-viterbi path-hidden states that is most likely to produce the sequence of observed events. After the original station information, the initial state probability information, the observation probability information and the state transition probability information are obtained, the original station information, the initial state probability information, the observation probability information and the state transition probability information can be analyzed and processed by utilizing a Viterbi algorithm, so that the subway station corresponding to the subway line data can be accurately and effectively determined.

Of course, those skilled in the art may also determine the subway station corresponding to the subway line data by using other manners or other algorithms, as long as the accurate reliability of determining the subway station corresponding to the subway line data can be ensured, which is not described herein again.

Fig. 6 is a flowchart for determining an expected running time corresponding to subway line data according to an embodiment of the present invention; based on the foregoing embodiment, with reference to fig. 6 continuously, in this embodiment, a specific implementation manner for determining the expected running time corresponding to the subway line data is not limited, and a person skilled in the art may set the expected running time according to specific application requirements and design requirements, and preferably, the determining the expected running time corresponding to the subway line data in this embodiment may include:

s601: and determining the distance between the stations and the subway running speed between the stations and the subway line data.

S602: and determining the expected running time corresponding to the subway line data according to the distance between the stations and the subway running speed.

After acquiring the subway line data, the inter-station distance and the subway running speed between the stations on the subway line data can be determined, it can be understood that the inter-station distance and the subway running speed between the stations on the subway line data can be configured in advance, and in specific application, the inter-station distance and the subway running speed between the stations on the subway line data can be stored in a preset database, and the inter-station distance and the subway running speed between the stations on the subway line data can be acquired by accessing the preset database. After the inter-site distance and the subway running speed are acquired, the inter-site distance and the subway running speed can be analyzed to determine expected running time corresponding to subway line data. Specifically, determining the expected running time of the object on the subway line data according to the inter-station distance and the subway running speed may include: and determining the ratio of the distance between the stations to the subway running speed as the expected running time corresponding to the subway line data.

It is to be understood that a person skilled in the art may also use other manners to obtain the expected running time corresponding to the subway line data, as long as the accurate reliability of determining the expected running time corresponding to the subway line data can be ensured, and details are not described herein again.

Fig. 7 is a fourth flowchart of an identification method of a network access device according to an embodiment of the present invention; on the basis of the foregoing embodiment, with continued reference to fig. 7, after determining the expected running time corresponding to the subway line data, the method in this embodiment may further include:

s701: and detecting whether the subway line data changes.

S702: and when the subway line data is changed, increasing the expected running time by preset transfer time.

The subway line data can comprise a plurality of different subway line data, when the object takes the subway, the object can be transferred among different subway line data, and at the moment, under the application scene that the object transfers the subway line data, the expected running time can be correspondingly adjusted. Therefore, in order to ensure accurate reliability of obtaining the expected running time, after the expected running time of the object on the subway line data is determined, whether the subway line data taken by the object changes or not can be detected, specifically, whether the subway line data corresponding to the terminal device at time t1 and the subway line data corresponding to time t2 are the same subway line data or not can be detected, and when the subway line data corresponding to the terminal device at time t1 and the subway line data corresponding to time t2 are different subway line data or not, the subway line data taken by the object changes can be determined. When the subway line data taken by the object changes, the expected running time is increased by a preset transfer time, for example: when the object is transferred to different subway line data, the expected running time can be increased by 180 s; the expected running time may be increased by 120s when the object is transferred in the opposite direction of the same subway line data.

In the embodiment, whether the subway line data taken by the object changes or not is detected, and when the subway line data taken by the object changes, the expected running time is increased by the preset transfer time, so that the expected running time can be accurately obtained in the application scene of the object in the subway line data transfer or subway line data replacement taking direction, and the accuracy and reliability of the method are further improved.

Fig. 8 is a fifth flowchart of an identification method of a network access device according to an embodiment of the present invention; on the basis of any one of the above embodiments, referring to fig. 8, after determining all the network access devices included in the positioning log and the collection log and the positioning object quantity and the collection object quantity corresponding to each network access device, the method in this embodiment may further include:

s801: neighboring devices included in all network access devices and location information of the neighboring devices are identified.

In order to improve the accuracy and reliability of identifying the network access devices, after all the network access devices included in the positioning log and the acquisition log are acquired, the neighboring devices included in all the network access devices and the location information of the neighboring devices may be identified. Specifically, an implementation manner for identifying neighboring devices included in all network access devices includes:

s8011: a first network location request is obtained.

S8012: and scanning based on the first network positioning request, and determining all the scanned network access devices as first adjacent devices.

When the object has a positioning requirement, a first network positioning request can be sent to the identification device through the terminal equipment, and the first network positioning request is used for positioning the current position of the object. Specifically, after the identification device obtains the first network positioning request, scanning may be performed based on the first network positioning request, and all the scanned network access devices may be determined as first neighboring devices.

For example: after obtaining the first network location request, scanning may be performed based on the first network location request, and assuming that the network access device a, the network access device b, the network access device c, and the network access device d are scanned, at this time, all the scanned network access devices may be determined as first neighboring devices.

In addition, another implementation of identifying neighboring devices included in all network access devices includes:

s8013: and acquiring a second network positioning request, wherein the request time of the first network positioning request is different from that of the second network positioning request.

S8014: and scanning based on the second network positioning request, and determining all the scanned second access equipment and all the scanned network access equipment as second adjacent equipment.

For example: after acquiring the first network location request at time T1, a scan may be performed based on the first network location request, for example: scanning a network access device a, a network access device b, a network access device c and a network access device d; after acquiring the second network location request at time T2, a scan may be performed based on the second network location request, for example: second access device a, second access device b, second access device c, and second access device d are scanned, and then all the scanned network access devices and all the scanned second access devices may be determined as second neighboring devices.

s8015: acquiring a coincidence device between the network access device and the second access device.

S8016: and determining a third network access device of the network access device without the overlapping device and a fourth network access device of the second access device without the overlapping device.

S8017: determining the third network access device and the fourth network access device as third neighboring devices.

For example: after acquiring the first network location request at time T1, scanning may be performed based on the first network location request to acquire all network access devices, assuming that all network access devices include: the network access device comprises a network access device A, a network access device B, a network access device C and a network access device D; after acquiring the second network location request at time T2, scanning may be performed based on the second network location request to obtain all the second access devices, assuming that all the second access devices include: the network access equipment C, the network access equipment D, the network access equipment E and the network access equipment F are analyzed and identified, the coincidence equipment between the network access equipment and the second access equipment is determined to comprise the network access equipment C and the network access equipment D, then the third network access equipment without the coincidence equipment in the network access equipment is determined to comprise the network access equipment A and the network access equipment B, the fourth network access equipment without the coincidence equipment in the second access equipment is determined to comprise the network access equipment E and the network access equipment F, and then the third network access equipment and the fourth network access equipment can be determined to be third adjacent equipment.

In some examples, the priority of the first neighboring device is greater than the priority of the second neighboring device, and the priority of the second neighboring device is greater than the priority of the third neighboring device.

When determining the neighboring devices of all the network access devices, one network access device may be of the above one or more neighboring device types, that is, one network access device may be the first neighboring device, or may be both the first neighboring device and the second neighboring device. In order to ensure the accuracy and reliability of determining the neighboring device type of the network access device, the neighboring device type of the network access device may be determined according to the priority corresponding to the neighboring device type. For example, the network access device a may be a first neighboring device or a second neighboring device, and at this time, since the priority of the first neighboring device is higher than that of the second neighboring device, it is determined that the network access device a is the first neighboring device. The network access device a may be a second neighboring device, or may be a third neighboring device, and at this time, since the priority of the second neighboring device is greater than that of the third neighboring device, it is determined that the network access device a is the second neighboring device.

It is to be understood that the type of the proximity device is not limited to the above examples, and those skilled in the art may set other types of proximity devices according to specific application requirements and design requirements, which are not described herein again.

After identifying the neighboring devices included in all the network access devices, all the neighboring devices may be analyzed and identified, so that the location information of the neighboring devices may be acquired. Specifically, the identifying the location information of the neighboring device may include:

s8018: and acquiring the network access equipment type and weight information of each adjacent equipment.

S8019: and clustering the adjacent equipment and the weight information corresponding to the adjacent equipment by using a clustering algorithm to obtain the position information of the adjacent equipment.

Wherein the network access device type of the neighboring device may include at least one of: a first proximity device, a second proximity device, and a third proximity device; and different weight information is preconfigured for each network access device type, the weight of the first neighboring device is greater than the weight of the second neighboring device, and the weight of the second neighboring device is greater than the weight of the third neighboring device. After determining the neighboring devices included in all the network access devices, the network access device type of each neighboring device can be determined, then the weight information corresponding to the neighboring device can be determined according to the network access device type, and the clustering algorithm is utilized to perform clustering processing on the neighboring devices and the weight information corresponding to the neighboring devices, so that the position information of the neighboring devices can be obtained.

In this embodiment, by acquiring the type and weight information of the network access device of each neighboring device, the clustering algorithm is used to perform clustering processing on the neighboring devices and the weight information corresponding to the neighboring devices, thereby effectively achieving accurate acquisition of the location information of the neighboring devices.

S802: and analyzing and processing the adjacent equipment, the position information of the adjacent equipment, the positioning object quantity and the acquisition object quantity by using a second machine learning model, and determining second access equipment corresponding to the positioning log and the acquisition log, wherein the second access equipment is arranged in the subway station, and the second machine learning model is trained to be used for identifying the subway equipment in all the network access equipment.

A second machine learning model (also referred to as a "classification model") is trained in advance, and is trained to identify subway devices included in all network access devices, and specifically, when the second machine learning model is subjected to learning training, the following information may be obtained: specifically, the location information of the neighboring device and the neighboring device corresponding to the network access device, the location object quantity and the collection object quantity corresponding to each network access device may be used as a positive sample, and the location information of the neighboring device and the location object quantity and the collection object quantity corresponding to the non-network access device may be used as a negative sample to perform learning training, so as to obtain the second machine learning model.

Then, after the position information, the positioning object quantity and the acquisition object quantity of the neighboring device and the neighboring device are obtained, the position information, the positioning object quantity and the acquisition object quantity of the neighboring device and the neighboring device may be analyzed and processed by using a second machine learning model, so that second access devices included in all network access devices may be determined, and the second access devices have a characteristic that the positioning object quantity is greater than the acquisition object quantity.

S803: and determining target network access equipment corresponding to the current position according to the network access equipment and the second access equipment.

After the network access device and the second access device are acquired, the network access device and the second access device may be analyzed to determine a target network access device corresponding to the current location. Specifically, determining the target network access device corresponding to the current location according to the network access device and the second access device may include:

s8031: and when the range of the network access equipment is larger than that of the second access equipment, determining the network access equipment as target network access equipment corresponding to the current position.

Alternatively, the first and second electrodes may be,

s8032: and when the range of the network access equipment is different from the range of the network access equipment of the second access equipment, determining the sum of the ranges of the network access equipment formed by the network access equipment and the second access equipment as the target network access equipment corresponding to the current position. Alternatively, the first and second electrodes may be,

s8033: and when the range of the network access equipment is smaller than that of the second access equipment, determining the second access equipment as target network access equipment corresponding to the current position.

Specifically, after the network access device and the second access device are obtained, the range of the network access device and the range of the network access device of the second access device may be analyzed and compared, and when the range of the network access device is greater than the range of the network access device of the second access device, the network access device is determined as the target network access device corresponding to the current location. For example, if the network access device range of the network access device includes the network access device a, the network access device B, the network access device C, the network access device D, and the network access device E, and the network access device range of the second access device includes the network access device B, the network access device C, and the network access device D, the network access device range of the network access device is greater than the network access device range of the second access device, and at this time, the network access device may be determined as the target network access device.

And when the range of the network access equipment is different from or partially different from that of the second access equipment, determining the sum of the ranges of the network access equipment formed by the network access equipment and the second access equipment as the target network access equipment corresponding to the current position. For example, it is assumed that the network access device range of the network access device includes a network access device a, a network access device B, a network access device C, a network access device D, and a network access device E, and the network access device range of the second access device includes a network access device F, a network access device G, and a network access device H, or the network access device range of the second access device includes a network access device B, a network access device C, a network access device F, a network access device G, and a network access device H, at this time, the network access device range of the network access device is different from or partially different from the network access device range of the second access device, and then the sum of the network access device ranges formed by the network access device and the second access device may include: the method comprises the following steps that network access equipment A, network access equipment B, network access equipment C, network access equipment D, network access equipment E, network access equipment F, network access equipment G and network access equipment H are used, and then the sum of the ranges of the network access equipment is determined to be target network access equipment.

And when the range of the network access equipment is smaller than that of the second access equipment, determining the second access equipment as target network access equipment corresponding to the current position. For example, if the network access device range of the network access device includes the network access device a, the network access device B, and the network access device C, and the network access device range of the second access device includes the network access device a, the network access device B, the network access device C, and the network access device D, the network access device range of the network access device is smaller than the network access device range of the second access device, and at this time, the second access device may be determined as the target network access device.

In this embodiment, by identifying neighboring devices and location information of the neighboring devices included in all network access devices, analyzing and processing the location information, the location object amount, and the acquisition object amount of the neighboring devices and the location information of the neighboring devices by using a second machine learning model, determining second access devices corresponding to a location log and an acquisition log included in all network access devices, and then determining a target network access device corresponding to a current location according to the network access devices and the second access devices, accuracy and reliability of identifying the target network access device are effectively improved.

Fig. 9 is a sixth flowchart of an identification method of a network access device according to an embodiment of the present invention; on the basis of any of the foregoing embodiments, with continuing reference to fig. 9, after determining the network access device corresponding to the current location, the method in this embodiment may further include:

s901: the number of network access devices is identified.

S902: and when the number of the network access devices is larger than a preset threshold value, determining that the terminal device is positioned in the subway station.

Specifically, after the network access devices are acquired, the number of the network access devices can be acquired, then the number of the network access devices can be analyzed and compared with a preset threshold value, and when the number of the network access devices is larger than the preset threshold value, it can be determined that the terminal device is located in a subway station, that is, an object corresponding to the terminal device is taking a subway, so that a scene that the object takes the subway can be stably and effectively identified.

In some examples, after the network access device is acquired, a positioning operation may be performed on a current location where the object is located based on the first access device.

In the embodiment, by identifying the number of the network access devices, when the number of the network access devices is greater than the preset threshold value, the object is determined to be taking the subway, so that the identification of the application scene whether the object takes the subway is effectively realized, and the practicability of the method is effectively improved.

In specific application, referring to fig. 10, the present application embodiment provides a method for identifying a network access device, which can identify the network access device, so as to identify a subway scene based on the network access device, and perform a positioning operation based on the identified network access device. Specifically, the method may include two parts, where the first part is active identification and location training of the network access device, and includes: the position of the network access equipment is associated with the characteristics such as iteration, the positioning acquisition ratio of the network access equipment, the positioning object quantity, the acquisition object quantity and the like, and the network access equipment is identified; the second part is the network access equipment position mining based on subway line data, which comprises the following steps: the method comprises the steps of extracting subway line data, matching the positions of network access equipment and subway stations and identifying the network access equipment. And finally, merging the identification results of the network access equipment obtained by the two parts to determine the final result of the network access equipment.

Network access equipment active identification and position training

1.1, acquiring the characteristics of the network access equipment such as the positioning acquisition ratio, the number of positioning objects, the number of acquisition objects and the like.

Acquiring a positioning log and an acquisition log from terminal equipment, wherein the positioning log comprises data for positioning through a network; the acquisition log comprises data positioned by a GPS. Specifically, the positioning log and the collection log can be obtained through the terminal device according to a preset collection period (for example, 14 days, 15 days, 20 days, or the like); analyzing and processing the positioning log and the acquisition log to obtain all the network access devices scanned by the positioning log and the acquisition log, wherein the network access devices can comprise overground network access devices and overground network access devices; then, the number of positioning objects and the number of acquisition objects corresponding to all network access devices may be obtained, specifically, the number of positioning objects may be obtained based on the positioning log, and the number of acquisition objects may be obtained based on the acquisition log. And then, determining a positioning acquisition ratio based on the number of the positioning objects and the number of the acquisition objects, and after the number of the positioning objects, the number of the acquisition objects and the positioning acquisition ratio are obtained, analyzing and processing parameters such as the number of the positioning objects, the number of the acquisition objects and the positioning acquisition ratio, so that the underground network access equipment included in all the network access equipment can be determined, wherein for the underground network access equipment, the underground network access equipment has the characteristic that the number of the positioning objects is greater than the number of the acquisition objects due to an application scene that the underground network access equipment is located underground.

1.2, identifying the adjacent equipment and the position information of the adjacent equipment.

When the terminal device sends a positioning request to the identification device, for one positioning request, multiple network access devices can be scanned at the same time, and the network access devices are close to each other in space, so that devices adjacent to the network access devices can be determined, and position information of the adjacent devices can be determined.

Specifically, determining the implementation manner of the neighboring device may include:

(1) and the direct adjacent equipment acquires the network positioning request and determines the network access equipment which simultaneously appears in the network positioning request.

(2) The time proximity device acquires a first network positioning request and a second network positioning request input in different time periods, and determines network access devices appearing in the first network positioning request and the second network positioning request.

(3) Indirectly adjacent to the device, i.e. directly adjacent to a directly adjacent device.

It is noted that the priority of the direct neighboring device is greater than the priority of the temporal neighboring device, which is greater than the priority of the indirect neighboring device. And then different weights are set for the three different types of adjacent equipment, wherein the weight of the direct adjacent equipment is greater than that of the time adjacent equipment, and the weight of the time adjacent equipment is greater than that of the indirect adjacent equipment.

Then, clustering processing is carried out on the network access equipment type of the network access equipment and the weight information corresponding to the network access equipment type by adopting a DBSCAN clustering algorithm, and multiple rounds of iteration can be carried out on the network access equipment type of the network access equipment and the weight information corresponding to the network access equipment type, so that the iteration position of the network access equipment can be obtained finally.

1.3, determining the network access equipment.

And analyzing and processing the adjacent devices, the positioning object quantity and the acquisition object quantity by using a machine learning model, and determining network access devices corresponding to the positioning logs and the acquisition logs, wherein the network access devices are included in all the network access devices, and the machine learning model is trained to be used for identifying the network access devices included in all the network access devices, so that the network access devices can be determined as a network access device result.

Network access equipment position mining based on subway line data

And 2.1, extracting, filtering and cutting subway line data.

The subway line data are acquired from the positioning logs, when the quality of the subway line data does not meet the preset requirement, the subway line data can be filtered, and when subway stations are omitted from the subway line data, the subway line data can be cut based on the omitted subway stations.

2.2, identification of the network access device.

The matching hidden Markov model of subway station is a probability model about time sequence, and describes the process of generating a non-observable state sequence (hidden state) by a hidden Markov chain randomly and then generating an observation by each state so as to generate an observation random sequence (apparent state). The sequence of hidden state generations becomes a sequence of states, each state producing an observation, and the resulting sequence of observations is referred to as an observation sequence. Each position of the sequence can in turn be regarded as a time instant. The current position of the terminal equipment is regarded as an explicit state-observation sequence, and both the initial state probability vector pi and the observation probability matrix B can be defined as a Gaussian distribution.

Specifically, it can be considered as a hidden state that the terminal device is located at which subway station, the state transition probability matrix a between stations can be designed and modeled through the expected running time and the actual running time of the terminal device, and what to be solved is on which subway station the terminal device is located, and specifically, it can be solved by using a viterbi algorithm.

The initial state probability vector pi and the observation probability matrix B may be expressed by a gaussian distribution, and the parameters in the gaussian distribution may be determined by preset conditions, for example, the preset conditions may include: 95% of the positioning results are within 500m of the true value result (correct subway station), the unique Gaussian distribution parameter can be determined through the preset conditions, and the unique Gaussian distribution function can be determined through the Gaussian distribution parameter.

In addition, the probability transition matrix is related to the expected running time, and specifically, the expected running time and the probability transition information included in the probability transition matrix can be determined according to the following rules:

(1) expected run time: the expected running time between different stations of the same subway line data can be obtained in length/speed. For an application scene of transferring different subway line data, the expected running time can need to be increased by 180 s; for an application scenario of the opposite direction of transferring the same subway line data, the expected running time may be increased by 120s, i.e. the time for walking or waiting in the middle needs to be increased in consideration of the actual situation in the expected running time.

(2) The transition probability matrix (here a piecewise probability distribution function) is determined based on the actual runtime and the expected runtime:

a. determining transition probability information in the transition probability matrix as 1 if the actual runtime is equal to the expected runtime or within a certain range of the expected runtime;

b. determining transition probability information in the transition probability matrix as a gaussian distribution if the actual runtime is less than the expected runtime;

c. if the actual runtime is greater than the expected runtime, the transition probability information in the transition probability matrix is determined to be a gaussian distribution, but the attenuation of the gaussian distribution is slow because there is a possibility that the terminal device may be delayed in the middle of the run.

After the preset original station information, the initial state probability vector pi, the state transition probability matrix A and the observation probability matrix B are obtained, the information can be analyzed and processed by utilizing a Viterbi algorithm, so that the subway station corresponding to the subway line data can be determined.

2.3 identification of network access devices

The total network access devices scanned by the terminal device can be identified through the subway line data, the total network access devices may include a main network access device, and specifically, when each terminal device performs a positioning request, one main network access device may be scanned based on the positioning request, for example: the positioning request of the mobile terminal device can scan a mobile main network access device, the positioning request of the communication terminal device can scan a communication main network access device, the positioning request of the telecommunication terminal device can scan a telecommunication main network access device, after the main network access device is obtained, the position of the main network access device can be scanned, and the position of the subway station can be determined based on the position of the main network access device.

In addition, the total network access equipment not only comprises underground network access equipment, but also comprises above-ground network access equipment. In the continuous iteration process, if the network access equipment on the terminal equipment is not refreshed, the scanned network access equipment is actually ground network access equipment scanned before entering the subway, namely the total network access equipment at the moment can comprise the ground network access equipment; in addition, the subway line data is not all the track of the terminal equipment in the subway, and possibly, as the track is not cut, part of the track can be located on the ground, even the whole track is not located in the subway; for another example, there are some ground network access devices, and terminal devices on the subway can scan network access devices in the ground station, and people outside the subway can scan the network access devices. In summary, it is not negligible for these non-underground network access devices, and if the terminal device is not located in the subway, but the terminal device is located near the subway station, the user experience for the terminal device is poor, and therefore, the above-mentioned part of the non-underground network access devices needs to be filtered out.

Specifically, the non-underground network access device may be filtered by using a supervised learning algorithm. The method comprises the steps of using a pre-configured truth sample (network access equipment) in the subway as a positive sample, then lifting the ground near a subway station to obtain a part of acquisition logs as a negative sample, and then performing feature training by using the positive sample, the negative sample, the positioning acquisition ratio of the network access equipment, the positioning object quantity, the acquisition object quantity, the type of the network access equipment (ground network access equipment and underground network access equipment) and other parameters, thereby obtaining a classification model, wherein the classification model can identify the whole amount of network access equipment, and then determining the part of network access equipment information as a second access equipment result.

Of course, besides obtaining the classification model based on the above features, some artificial rules may be added, such as: within a week, the positioning object quantity and the acquisition object quantity of the network access equipment can be identified only after reaching certain values. Or, the identification of the network access device may also be implemented by other classification models, and besides the characteristics formed by the information such as the positioning acquisition ratio, the positioning object amount, the acquisition object amount, and the like of the network access device, more network access device characteristics may be acquired. In addition, except that the DBSCAN clustering algorithm performs correlation iteration on the position of the network access device, other clustering methods can be used to perform correlation iteration on the position of the network access device.

Finally, after the network access device result and the second access device result are obtained, the target network access device corresponding to the current position can be determined according to the network access device result and the second access device result, and specifically, when the network access device range of the network access device result is larger than the network access device range of the second access device result, the network access device result is determined as the target network access device corresponding to the current position; or when the network access equipment range of the network access equipment result is different from the network access equipment range of the second access equipment result, determining the sum of the network access equipment ranges formed by the network access equipment result and the second access equipment result as the target network access equipment corresponding to the current position; or when the network access device range of the network access device result is smaller than the network access device range of the second access device result, determining the second access device result as the target network access device corresponding to the current position.

The method for identifying the network access equipment provided by the application embodiment does not need to depend on related equipment manually laid, all data sources can be acquired through the terminal equipment (or the client), effective identification of the network access equipment can be realized, and the method has the advantages of low cost and wide coverage range.

In addition, the present embodiment provides a positioning method, where the positioning method may perform positioning of a subway scene based on the network access device deployed at a subway station determined by the identification method of the network access device in the embodiments shown in fig. 1 to fig. 10.

Specifically, the network access device deployed in the subway station can be identified by the network access device identification method shown in the embodiments in fig. 1 to 10, and after the network access device is acquired, the positioning operation of the subway scene can be realized based on the acquired network access device, so that the accurate and effective positioning operation of the terminal device located in the subway scene is effectively realized, and the practicability of the positioning method is further improved.

Fig. 11 is a schematic structural diagram of an identification apparatus of a network access device according to an embodiment of the present invention; referring to fig. 11, the present embodiment provides an identification apparatus of a network access device, which can perform the identification method of the network access device shown in fig. 1. Specifically, the identification means may include:

the acquisition module 11 is configured to acquire a positioning log and an acquisition log of the terminal device, where the positioning log includes network positioning data; the acquisition log comprises satellite positioning data;

the processing module 12 is configured to obtain subway line data of the terminal device and a subway station corresponding to the subway line data based on the positioning log;

and the determining module 13 is configured to determine the network access device deployed in the subway station according to the subway line data, the positioning log and the acquisition log.

In some examples, after acquiring the subway line data corresponding to the terminal device, the processing module 12 in this embodiment is further configured to execute: acquiring standard station information corresponding to subway line data; detecting whether subway stations are omitted in subway line data or not based on standard station information and the subway stations; when the subway station is omitted in the subway line data, the subway line data is divided into a plurality of new subway line data based on the omitted subway station.

In some examples, when the determining module 13 determines the network access device deployed at the subway station according to the subway line data, the positioning log and the collection log, the determining module 13 may perform: determining all network access devices in the positioning log and the acquisition log, and a positioning object quantity and an acquisition object quantity corresponding to each network access device; and analyzing and processing the subway line data, the subway station, the positioning object quantity and the acquisition object quantity corresponding to each network access device by using a first machine learning model, determining first access devices which are arranged in the subway station and are included in all the network access devices, wherein the first machine learning model is trained to be used for identifying the information of the subway devices included in all the network access devices.

In some examples, when the determining module 13 determines the positioning object quantity and the acquisition object quantity corresponding to each network access device, the determining module 13 may perform: determining a positioning object quantity corresponding to each network access device based on the positioning log; and determining the collection object quantity corresponding to each network access device based on the collection log.

In some examples, the positioning object quantity of the network access device is greater than the acquisition object quantity.

In some examples, when the processing module 12 obtains a subway station corresponding to subway line data, the processing module 12 may perform: acquiring original station information which is not matched with subway line data; determining initial state probability information, observation probability information and state transition probability information between stations in the subway line data, wherein the initial state probability information comprises transition probabilities corresponding to a first station in the subway line data, and the observation probability information comprises transition probabilities corresponding to all stations in the subway line data; and analyzing and processing the original station information, the initial state probability information, the observation probability information and the state transition probability information by using a Viterbi algorithm, and determining the subway station corresponding to the subway line data.

In some examples, the initial state probability information and the observation probability information conform to a gaussian distribution.

In some examples, when the processing module 12 determines initial state probability information corresponding to subway line data, the processing module 12 may perform: acquiring a first station included in subway line data; determining the station longitude and latitude of a first station and the track longitude and latitude corresponding to subway line data; and determining initial state probability information corresponding to subway line data according to the station longitude and latitude and the track longitude and latitude.

In some examples, when the processing module 12 determines state transition probability information between stations in the subway line data, the processing module 12 may perform: acquiring the actual running time of the terminal equipment based on subway line data; determining an expected running time corresponding to the subway line data; and determining state transition probability information corresponding to the subway line data according to the actual running time and the expected running time.

In some examples, when the processing module 12 determines the state transition probability information corresponding to the subway line data according to the actual runtime and the expected runtime, the processing module 12 may perform: when the actual running time is the same as the expected running time, determining that the state transition probability information corresponding to the subway line data is 1; or when the actual running time is less than the expected running time, determining that the state transition probability information corresponding to the subway line data meets the first Gaussian distribution; or when the actual running time is greater than the expected running time, determining that the state transition probability information corresponding to the subway line data meets a second Gaussian distribution, wherein the attenuation characteristic of the second Gaussian distribution is slower than that of the first Gaussian distribution.

In some examples, when the processing module 12 determines the expected runtime corresponding to the subway line data, the processing module 12 may perform: determining the distance between stations and the subway running speed in the subway line data; and determining the ratio of the distance between the stations to the subway running speed as the expected running time corresponding to the subway line data.

In some examples, after determining all the network access devices included in the location log and the collection log and the location object quantity and the collection object quantity corresponding to each network access device, the processing module 12 in this embodiment may be configured to perform: identifying neighboring devices and location information of the neighboring devices included in all network access devices; analyzing and processing the adjacent equipment, the position information of the adjacent equipment, the positioning object quantity and the acquisition object quantity by using a second machine learning model, and determining second access equipment corresponding to the positioning log and the acquisition log in all the network access equipment, wherein the second access equipment is deployed in a subway station, and the second machine learning model is trained to be used for identifying the subway equipment in all the network access equipment; and determining target access equipment deployed at the subway station according to the first access equipment and the second access equipment.

In some examples, when the processing module 12 identifies neighboring devices included in all network access devices, the processing module 12 is operable to perform: acquiring a first network positioning request; and scanning based on the first network positioning request, and determining all the scanned network access devices as first adjacent devices.

In some examples, when the processing module 12 identifies neighboring devices included in all network access devices, the processing module 12 is operable to perform: acquiring a second network positioning request, wherein the request time of the first network positioning request is different from that of the second network positioning request; and scanning based on the second network positioning request, and determining all the scanned second access equipment and all the scanned network access equipment as second adjacent equipment.

In some examples, when the processing module 12 identifies neighboring devices included in all network access devices, the processing module 12 is operable to perform: acquiring a coincidence device between first equipment and second equipment; determining third equipment for removing overlapped equipment in the first equipment and fourth equipment for removing overlapped equipment in the second equipment; the third device and the fourth device are determined to be third neighboring devices.

In some examples, the priority of the first neighboring device is greater than the priority of the second neighboring device, which is greater than the priority of the third neighboring device.

In some examples, when the processing module 12 identifies location information of proximate devices, the processing module 12 is operable to perform: acquiring the type and weight information of network access equipment of each adjacent equipment; and clustering the adjacent equipment and the weight information corresponding to the adjacent equipment by using a clustering algorithm to obtain the position information of the adjacent equipment.

In some examples, the network access device types of the neighboring devices include a first neighboring device, a second neighboring device, and a third neighboring device; the weight of the first neighboring device is greater than the weight of the second neighboring device, which is greater than the weight of the third neighboring device.

In some examples, when the processing module 12 determines the target access device deployed at the subway station according to the first access device and the second access device, the processing module 12 may be configured to perform: when the range of the network access equipment of the first access equipment is larger than that of the second access equipment, determining the first access equipment as target access equipment deployed in a subway station; or when the range of the network access equipment of the first access equipment is partially different from that of the second access equipment, determining the sum of the ranges of the network access equipment formed by the first access equipment and the second access equipment as target access equipment deployed in the subway station; or when the range of the network access equipment of the first access equipment is smaller than that of the second access equipment, determining the second access equipment as target access equipment deployed in the subway station.

In some examples, after determining the target access device deployed at the subway station, the processing module 12 in this embodiment may be configured to perform: identifying the number of target access devices; and when the number of the target access devices is larger than a preset threshold value, determining that the terminal device is positioned in the subway station.

The apparatus shown in fig. 11 can perform the method of the embodiment shown in fig. 1 to 10, and for the parts not described in detail in this embodiment, reference may be made to the related description of the embodiment shown in fig. 1 to 10. The implementation process and technical effect of the technical solution refer to the descriptions in the embodiments shown in fig. 1 to fig. 10, and are not described herein again.

In one possible design, the structure of the identification apparatus of the network access device shown in fig. 11 may be implemented as an electronic device, which may be a mobile phone, a tablet computer, a server, or other devices. As shown in fig. 12, the electronic device may include: a first processor 21 and a first memory 22. Wherein the first memory 22 is used for storing a program that supports the electronic device to execute the method for identifying a network access device provided in at least some embodiments shown in fig. 1-10, and the first processor 21 is configured to execute the program stored in the first memory 22.

The program comprises one or more computer instructions, wherein the one or more computer instructions, when executed by the first processor 21, are capable of performing the steps of:

Optionally, the first processor 21 is further configured to perform all or part of the steps in at least some of the embodiments shown in fig. 1-10.

The electronic device may further include a first communication interface 23 for communicating with other devices or a communication network.

In addition, an embodiment of the present invention provides a computer storage medium for storing computer software instructions for an electronic device, which includes a program for executing the method for identifying a network access device in at least some embodiments shown in fig. 1 to 10.

Fig. 13 is a schematic structural diagram of a positioning apparatus according to an embodiment of the present invention, and referring to fig. 13, the positioning apparatus may include: a second processor 31 and a second memory 32. Wherein the second memory 32 is used for storing programs that support the positioning device to execute the positioning method shown in the above-mentioned embodiments, and the second processor 31 is configured for executing the programs stored in the second memory 32.

The program comprises one or more computer instructions which, when executed by the second processor 31, are capable of: and determining the network access equipment deployed in the subway station to position the subway scene.

Optionally, the second processor 31 is further configured to perform all or part of the steps shown in the foregoing embodiments.

The electronic device may further include a second communication interface 33, which is used for the electronic device to communicate with other devices or a communication network.

In addition, an embodiment of the present invention provides a computer storage medium for storing computer software instructions for an electronic device, which includes a program for executing the positioning method in the embodiment.

The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.

Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by adding a necessary general hardware platform, and of course, can also be implemented by a combination of hardware and software. With this understanding in mind, the above-described aspects and portions of the present technology which contribute substantially or in part to the prior art may be embodied in the form of a computer program product, which may be embodied on one or more computer-usable storage media having computer-usable program code embodied therein, including without limitation disk storage, CD-ROM, optical storage, and the like.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.

Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.

Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims

1. An identification method of a network access device, which is used for identifying a base station deployed at a subway station, comprises the following steps:

acquiring a positioning log and an acquisition log of terminal equipment, wherein the positioning log comprises network positioning data; the acquisition log comprises satellite positioning data;

2. The method of claim 1, wherein after acquiring the subway line data corresponding to the terminal device based on the positioning log, the method further comprises:

acquiring standard station information corresponding to the subway line data;

detecting whether subway stations are omitted in the subway line data or not based on the standard station information and the subway stations;

and when the subway station is missed in the subway line data, dividing the subway line data into a plurality of new subway line data based on the missed subway station.

3. The method of claim 1 or 2, wherein determining the network access equipment deployed at the subway station according to the subway line data, the positioning log and the acquisition log comprises:

determining all network access devices in the positioning log and the acquisition log, and a positioning object quantity and an acquisition object quantity corresponding to each network access device;

and analyzing and processing the subway line data, the subway station and the positioning object quantity and the acquisition object quantity corresponding to each network access device by utilizing a first machine learning model, determining first access devices which are arranged in the subway station and are included in all the network access devices, and training the first machine learning model to be used for identifying the subway device information included in all the network access devices.

4. The method of claim 3, wherein determining the positioning object quantity and the acquisition object quantity corresponding to each network access device comprises:

determining a positioning object quantity corresponding to each network access device based on the positioning log;

and determining the collection object quantity corresponding to each network access device based on the collection log.

5. The method of claim 3, wherein the first access device has a larger number of located objects than the number of acquired objects.

6. The method of claim 1, wherein obtaining a subway station corresponding to the subway line data comprises:

acquiring original station information which is not matched with the subway line data;

determining initial state probability information, observation probability information and state transition probability information between stations in the subway line data, wherein the initial state probability information comprises a transition probability corresponding to a first station in the subway line data, and the observation probability information comprises a transition probability corresponding to each station in the subway line data;

and analyzing and processing the original station information, the initial state probability information, the observation probability information and the state transition probability information by using a Viterbi algorithm, and determining the subway station corresponding to the subway line data.

7. The method of claim 6, wherein the initial state probability information and the observed probability information conform to a Gaussian distribution.

8. The method of claim 6, wherein determining initial state probability information corresponding to the subway line data comprises:

acquiring a first station included in the subway line data;

determining the station longitude and latitude of the first station and the track longitude and latitude corresponding to the subway line data;

and determining initial state probability information corresponding to the subway line data according to the station longitude and latitude and the track longitude and latitude.

9. The method of claim 6, wherein determining state transition probability information between stations and stations in the subway line data comprises:

acquiring the actual running time of the terminal equipment based on the subway line data;

determining an expected running time corresponding to the subway line data;

and determining state transition probability information corresponding to the subway line data according to the actual running time and the expected running time.

10. The method of claim 9, wherein determining state transition probability information corresponding to the subway line data based on the actual run time and the expected run time comprises:

determining that state transition probability information corresponding to the subway line data is 1 when the actual running time is the same as the expected running time; alternatively, the first and second electrodes may be,

determining that state transition probability information corresponding to the subway line data satisfies a first Gaussian distribution when the actual running time is less than the expected running time; alternatively, the first and second electrodes may be,

and when the actual running time is greater than the expected running time, determining that the state transition probability information corresponding to the subway line data meets a second Gaussian distribution, wherein the attenuation characteristic of the second Gaussian distribution is slower than that of the first Gaussian distribution.

11. The method of claim 9, wherein determining an expected run time corresponding to the subway line data comprises:

determining the distance between stations and the subway running speed between the stations in the subway line data;

and determining the ratio of the distance between the stations to the subway running speed as the expected running time corresponding to the subway line data.

12. The method of claim 3, wherein after determining all network access devices included in the location log and acquisition log and the location object quantity and acquisition object quantity corresponding to each network access device, the method further comprises:

identifying neighboring devices included in all network access devices and location information of the neighboring devices;

analyzing and processing the neighboring equipment, the position information of the neighboring equipment, the positioning object quantity and the acquisition object quantity by using a second machine learning model, and determining second access equipment corresponding to the positioning log and the acquisition log, wherein the second access equipment is arranged in the subway station, and the second machine learning model is trained to be used for identifying subway equipment in all network access equipment;

and determining target access equipment deployed at the subway station according to the first access equipment and the second access equipment.

13. The method of claim 12, wherein identifying neighboring devices included in all network access devices comprises:

acquiring a first network positioning request;

and scanning based on the first network positioning request, and determining all the scanned first devices as first adjacent devices.

14. The method of claim 13, wherein identifying neighboring devices included in all network access devices comprises:

acquiring a second network positioning request, wherein the request time of the first network positioning request is different from that of the second network positioning request;

and performing scanning based on the second network positioning request, and determining all the scanned second devices and all the first devices as second adjacent devices.

15. The method of claim 14, wherein identifying neighboring devices included in all network access devices comprises:

acquiring a coincidence device between the first device and the second device;

determining third equipment which removes the overlapped equipment in the first equipment and fourth equipment which removes the overlapped equipment in the second equipment;

determining the third device and the fourth device as third neighboring devices.

16. The method of claim 15, wherein the priority of the first neighboring device is greater than the priority of the second neighboring device, which is greater than the priority of the third neighboring device.

17. The method of claim 12, wherein identifying location information of the proximate device comprises:

acquiring the type and weight information of network access equipment of each adjacent equipment;

and clustering the types of the network access equipment and the weight information corresponding to the adjacent equipment by using a clustering algorithm to obtain the position information of the adjacent equipment.

18. The method of claim 17, wherein the network access device type of the neighboring device comprises at least one of: a first proximity device, a second proximity device, and a third proximity device; the weight of the first neighboring device is greater than the weight of the second neighboring device, which is greater than the weight of the third neighboring device.

19. The method of claim 12, wherein determining, from the first access device and the second access device, a target access device deployed at a subway station comprises:

when the range of the network access equipment of the first access equipment is larger than that of the network access equipment of the second access equipment, determining the first access equipment as target access equipment deployed in a subway station; alternatively, the first and second electrodes may be,

when the range of the network access equipment of the first access equipment is partially different from that of the second access equipment, determining the sum of the ranges of the network access equipment formed by the first access equipment and the second access equipment as target access equipment deployed in a subway station; alternatively, the first and second electrodes may be,

and when the range of the network access equipment of the first access equipment is smaller than that of the second access equipment, determining the second access equipment as target access equipment deployed in the subway station.

20. The method of any of claims 1-11, wherein after determining the target access device deployed at the subway station, the method further comprises:

identifying the number of the target access devices;

and when the number of the target access devices is larger than a preset threshold value, determining that the terminal device is located in the subway station.

21. An apparatus for identifying a network access device, comprising:

the processing module is used for acquiring subway line data corresponding to the terminal equipment based on the positioning log;

22. An electronic device, comprising: a memory, a processor; wherein the content of the first and second substances,

the memory is configured to store one or more computer instructions, wherein the one or more computer instructions, when executed by the processor, implement the method of identifying a network access device of any of claims 1-20.

23. A positioning method, wherein a network access device deployed in a subway station is determined based on any one of claims 1 to 20, and positioning of a subway scene is performed.

24. A positioning apparatus, comprising: a memory, a processor; wherein the content of the first and second substances,

the memory is configured to store one or more computer instructions, wherein the one or more computer instructions, when executed by the processor, implement the positioning method of claim 23.