Method, apparatus, electronic device and readable storage medium for determining waypoints

Abstract

The method, apparatus, electronic device and readable storage medium for determining trip points provided by this application include: determining multiple positions experienced by a user during a sampling period according to the network information of the user during the sampling period; in response to the multiple positions belonging to at least two candidate areas, obtaining the user location information of the user during the sampling period; obtaining the local area network location information and base station location information of the user during the sampling period; determining the target trajectory information of the user during the sampling period according to the user location information, local area network location information and base station location information, and determining the target trip points of the user during the sampling period according to the target trajectory information. The accuracy of trip point positioning is improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of computer technology, and in particular, to a method, apparatus, electronic device, and readable storage medium for determining trip points. Background Art

[0002] The communication big data trip card can be used to obtain the location information of a user at one or more trip points, and present these one or more trip points in the form of a trip code to facilitate the user's passage in public places.

[0003] Compared with the Global Positioning System (GPS) positioning technology and Wireless-Fidelity (WIFI) positioning technology, the base station positioning technology has advantages such as low energy consumption and few blind spots. Therefore, in the related art, the communication big data trip card usually uses the base station positioning technology to determine the user's trip points. That is, when the user connects to the cellular network, the location update can be performed by sending a Tracking Area Update (TAU) signaling to the network side. The city where the base station in the location update signaling belongs will be determined as the current city where the user is located. When the cumulative duration of the user in this city reaches the preset duration, this city will be used as the user's trip point and added to the communication big data trip card. However, the accuracy of the base station positioning technology is affected by factors such as network coverage, base station location, user environment, and user behavior. For example, when the user works or lives at the boundary between two cities, if the user only moves within one of the cities, the user's trip points obtained by the base station positioning technology may be two cities, resulting in inaccurate positioning of the location information of the user's trip points. Summary of the Invention

[0004] The present application provides a method, apparatus, electronic device, and readable storage medium for determining trip points, which improves the accuracy of trip point positioning.

[0005] In a first aspect, the present application provides a method for determining trip points, including:

[0006] Determine multiple locations experienced by the user during the sampling period according to the network information of the user during the sampling period;

[0007] In response to the multiple locations belonging to at least two candidate areas, obtain the user location information of the user during the sampling period, where the user location information includes at least one first moment and the user location corresponding to each first moment;

[0008] Obtain the local network location information and base station location information of the user during the sampling period. The local network location information includes multiple second moments and the corresponding local network locations at each second moment. The base station location information includes multiple third moments and the corresponding base station locations at each third moment;

[0009] Determine the target trajectory information of the user during the sampling period according to the user location information, the local network location information, and the base station location information, and determine the target travel points of the user during the sampling period according to the target trajectory information.

[0010] In a possible implementation manner, obtaining the user location information of the user during the sampling period includes:

[0011] Obtain at least one network interoperation record of the user during the sampling period. The network interoperation record includes the occurrence time, the first location under the 4G network, and the second location under the 5G network;

[0012] Respectively determine the first moment corresponding to each network interoperation record and the user location corresponding to the first moment to obtain the user location information.

[0013] In a possible implementation manner, for any network interoperation record; determining the first moment corresponding to the network interoperation record and the user location corresponding to the first moment includes:

[0014] Determine the occurrence time in the network interoperation record as the first moment;

[0015] Determine the user location corresponding to the first moment according to the first location and the second location in the network interoperation record.

[0016] In a possible implementation manner, determining the user location corresponding to the first moment according to the first location and the second location in the network interoperation record includes:

[0017] Determine the longitude mean, latitude mean, longitude variance, and latitude variance according to the first location and the second location;

[0018] Determine the longitude prediction function according to the longitude mean and the longitude variance;

[0019] Determine the latitude prediction function according to the latitude mean and the latitude variance;

[0020] Determine the user location corresponding to the first moment according to the longitude prediction function and the latitude prediction function.

[0021] In a possible implementation manner, determining the target trajectory information of the user within the sampling period according to the user location information, the local network location information, and the base station location information includes:

[0022] Determining a set of moments according to the user location information, the local network location information, and the base station location information, where the set of moments includes a plurality of the first moments, a plurality of the second moments, and a plurality of the third moments;

[0023] Arranging the moments in the set of moments in ascending order of time to obtain a moment sequence;

[0024] Determining the target trajectory information according to the moment sequence and the positions corresponding to the moments in the moment sequence.

[0025] In a possible implementation manner, determining the target trajectory information according to the moment sequence and the positions corresponding to the moments in the moment sequence includes:

[0026] Performing a sorting process on the positions corresponding to the moments in the moment sequence to obtain initial trajectory information, where the initial trajectory information includes the positions corresponding to the moments in the moment sequence;

[0027] Obtaining road network data and user behavior characteristics corresponding to the area where the user is located;

[0028] Processing the initial trajectory information, the road network data, and the user behavior characteristics through a preset model to obtain the target trajectory information.

[0029] In a possible implementation manner, determining the target trip points of the user within the sampling period according to the target trajectory information includes:

[0030] Determining sub-trajectory information corresponding to each candidate area in the target trajectory information;

[0031] Determining the proportion corresponding to each sub-trajectory information according to the target trajectory information and each sub-trajectory information;

[0032] Determining a target candidate area among the at least two candidate areas according to the proportion corresponding to each sub-trajectory information, where the proportion corresponding to the sub-trajectory information corresponding to the target candidate area is greater than or equal to a preset proportion;

[0033] Determining the trip points corresponding to the target candidate area as the target trip points.

[0034] In a second aspect, the present application provides a device for determining trip points, including a first determination module, an acquisition module, and a second determination module, where,

[0035] The first determination module is configured to determine a plurality of positions experienced by the user during the sampling period according to the network information of the user during the sampling period;

[0036] The obtaining module is configured to, in response to the plurality of positions belonging to at least two candidate regions, obtain the user position information of the user during the sampling period, where the user position information includes at least one first moment and the user position corresponding to each first moment;

[0037] The obtaining module is further configured to obtain the local area network position information and the base station position information of the user during the sampling period, where the local area network position information includes a plurality of second moments and the local area network position corresponding to each second moment, and the base station position information includes a plurality of third moments and the base station position corresponding to each third moment;

[0038] The second determination module is configured to determine the target trajectory information of the user during the sampling period according to the user position information, the local area network position information, and the base station position information, and determine the target travel points of the user during the sampling period according to the target trajectory information.

[0039] In a possible implementation manner, the obtaining module is specifically configured to:

[0040] Obtain at least one network interoperability record of the user during the sampling period, where the network interoperability record includes an occurrence time, a first position under a 4G network, and a second position under a 5G network;

[0041] Respectively determine the first moment corresponding to each network interoperability record and the user position corresponding to the first moment to obtain the user position information.

[0042] In a possible implementation manner, the obtaining module is specifically configured to:

[0043] Determine the occurrence time in the network interoperability record as the first moment;

[0044] Determine the user position corresponding to the first moment according to the first position and the second position in the network interoperability record.

[0045] In a possible implementation manner, the obtaining module is specifically further configured to:

[0046] Determine the longitude mean, the latitude mean, the longitude variance, and the latitude variance according to the first position and the second position;

[0047] Determine a longitude prediction function according to the longitude mean and the longitude variance;

[0048] Determine a latitude prediction function according to the mean value of the latitudes and the variance of the latitudes;

[0049] Determine the user location corresponding to the first moment according to the longitude prediction function and the latitude prediction function.

[0050] In a possible implementation manner, the second determination module is further specifically configured to:

[0051] Determine a set of moments according to the user location information, the local network location information, and the base station location information, where the set of moments includes a plurality of the first moments, a plurality of the second moments, and a plurality of the third moments;

[0052] Arrange the moments in the set of moments in ascending order of time to obtain a moment sequence;

[0053] Determine the target trajectory information according to the moment sequence and the locations corresponding to the moments in the moment sequence.

[0054] In a possible implementation manner, the second determination module is further specifically configured to:

[0055] Perform a sorting process on the locations corresponding to the moments in the moment sequence to obtain initial trajectory information, where the initial trajectory information includes the locations corresponding to the moments in the moment sequence;

[0056] Obtain road network data and user behavior characteristics corresponding to the area where the user is located;

[0057] Process the initial trajectory information, the road network data, and the user behavior characteristics through a preset model to obtain the target trajectory information.

[0058] In a possible implementation manner, the second determination module is specifically configured to:

[0059] Determine sub-trajectory information corresponding to each candidate area in the target trajectory information;

[0060] Determine the proportion corresponding to each sub-trajectory information according to the target trajectory information and each sub-trajectory information;

[0061] Determine a target candidate area among the at least two candidate areas according to the proportion corresponding to each sub-trajectory information, where the proportion corresponding to the sub-trajectory information corresponding to the target candidate area is greater than or equal to a preset proportion;

[0062] Determine the travel point corresponding to the target candidate area as the target travel point.

[0063] In a third aspect, the present application provides an electronic device, including: a processor and a memory;

[0064] The memory is used to store a computer program;

[0065] The processor is used to execute the computer program stored in the memory to implement the method according to any one of the first aspect.

[0066] In a fourth aspect, the present application provides a computer-readable storage medium, in which computer-executable instructions are stored, and when the computer-executable instructions are executed by a processor, they are used to implement the method according to any one of the first aspect.

[0067] In a fifth aspect, the present application provides a computer program product, including a computer program, and when the computer program is executed by a processor, it implements the method according to any one of the first aspect.

[0068] The method, device, electronic device and readable storage medium for determining a travel point provided by the present application can obtain multiple positions of a user within a sampling period according to the network information of the user within the sampling period. In response to the multiple positions belonging to at least two candidate regions, the user position information, local area network information and base station position information of the user within the sampling period are obtained. Based on the user position information, local area network position information and base station position information, the target trajectory information of the user within the sampling period is determined. The target travel point of the user within the sampling period is determined according to the target trajectory information of the user, improving the accuracy of travel point positioning. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] The drawings herein are incorporated into the specification and constitute a part of this specification, showing embodiments consistent with the present application and used together with the specification to explain the principles of the present application.

[0070] Figure 1 It is a schematic diagram of an application scenario provided by an embodiment of the present application;

[0071] Figure 2 It is a schematic flowchart of a method for determining a travel point provided by an embodiment of the present application;

[0072] Figure 3 It is a schematic flowchart of another method for determining a travel point provided by an embodiment of the present application;

[0073] Figure 4 It is a schematic structural diagram of a device for determining a travel point provided by an embodiment of the present application;

[0074] Figure 5 It is a schematic hardware structure diagram of an electronic device provided by an embodiment of the present application.

[0075] Through the above-mentioned accompanying drawings, specific embodiments of the present application have been shown, and there will be a more detailed description hereinafter. These drawings and written descriptions are not intended to limit the scope of the concept of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. Detailed Embodiments

[0076] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0077] Figure 1 It is a schematic diagram of the application scenario provided for the embodiments of the present application. Please refer to Figure 1 , which includes two cities, City A and City B, and City A and City B are adjacent. Among them, Base Station 1 is set in City A, and Base Station 2 is set in City B. For Base Station 1 or Base Station 2, it has a corresponding service area, and within the service area, it can obtain the position information of multiple trajectory points of a user.

[0078] Please refer to Figure 1 , Trajectory Point 1 is within the service area of Base Station 1, so Base Station 1 can obtain the position information of Trajectory Point 1. Trajectory Points 1 to 5 are all within the service area of Base Station 2, so Base Station 2 can obtain the position information of Trajectory Points 1 to 5. Based on the position information obtained by Base Station 1 and Base Station 2, the communication big data travel card determines and displays City A and City B, and the display interface is as shown in Interface 101. However, the user only moves in City B and does not move in City A, which will cause the communication big data travel card to display the target city of the user incorrectly. At this time, the method for determining travel points proposed in the present application can be adopted to further determine the target travel points of the user during the sampling period by obtaining the target trajectory information of the user, so as to correct the information of the communication big data travel card. The display interface of the corrected communication big data travel card is as shown in Interface 102.

[0079] Compared with GPS positioning technology and WIFI positioning technology, base station positioning technology has advantages such as low energy consumption and few blind spots. Therefore, in related technologies, communication big data travel cards usually use base station positioning technology to determine the travel points of users. That is, when a user connects to a cellular network, location updates can be performed by sending location update signaling to the network side. The city where the base station belongs in the location update signaling will be determined as the current location city of the user. When the cumulative duration of the user in this location city reaches a preset duration, this location city will be used as the travel point of the user and added to the communication big data travel card. However, the accuracy of base station positioning technology is affected by factors such as network coverage, base station location, user environment, and user behavior. For example, when a user works or lives at the border between two cities, if the user only moves within one of the cities, the travel points of the user obtained through base station positioning technology may be two cities, resulting in inaccurate positioning of the location information of the user's travel points.

[0080] In the embodiments of the present application, multiple positions of a user within a sampling period can be obtained according to the network information of the user within the sampling period. In response to the multiple positions belonging to at least two candidate regions, the user location information, local area network information, and base station location information of the user within the sampling period are obtained. Based on the user location information, local area network location information, and base station location information, the target trajectory information of the user within the sampling period is determined. The target travel point of the user within the sampling period is determined according to the target trajectory information of the user, improving the accuracy of travel point positioning.

[0081] Next, the method shown in the present application will be described through specific embodiments. It should be noted that the following several embodiments can exist independently or be combined with each other. For the same or similar content, it will not be repeated in different embodiments.

[0082] Figure 2 It is a schematic flowchart of a method for determining a travel point provided by an embodiment of the present application. Please refer to Figure 2 The method may include:

[0083] S201. Determine multiple positions experienced by the user within the sampling period according to the network information of the user within the sampling period.

[0084] The execution subject of the embodiments of the present application can be an electronic device or a travel point determination device provided in the electronic device. The travel point determination device can be implemented by software or by a combination of software and hardware.

[0085] The duration of the sampling period can be any length, and this application does not make a mandatory regulation on the duration of the sampling period. Optionally, the sampling period can be 4 hours, and these 4 hours can be selected as the 4 hours before the location update signaling is generated, or the 4 hours after the location update signaling is generated.

[0086] The network information can include user identification, the subscribed data of the user, and multiple location update signals. The network information can include the network information of the 4G network and the network information of the 5G network. The network information of the 5G network can be recorded by the UDM network element and saved to the network management platform; the network information of the 4G network can be recorded by the HSS network element and saved to the network management platform. Optionally, the network information can be obtained from the network management platform of the operator where the user is located through the user identification.

[0087] The user identification can be a user identification code. The user identification code can be the International Mobile Equipment Identity (IMEI) of the electronic device used by the user, or the user identification code can be the International Mobile Subscriber Identification (IMSI) of the user.

[0088] The location update signaling can include user identification, network interoperability records, and the Cell Global Identifier (CGI).

[0089] Assume there are multiple users. For any one of the multiple users, by executing S201, it is possible to determine multiple locations experienced by the any one user during the sampling period.

[0090] S202. In response to the multiple locations belonging to at least two candidate areas, obtain the user location information of the user during the sampling period.

[0091] The candidate area can be any one city, and there can be an adjacent relationship between two candidate areas.

[0092] The user location information can include at least one first moment and the user location corresponding to each first moment.

[0093] For any one of the multiple users, if the multiple locations experienced by the user during the sampling period belong to at least two candidate areas, then the user is determined as a marked user and can be marked as TAG doubt . Optionally, multiple marked users can be screened out from the multiple users, and the user location information of these marked users during the sampling period can be obtained.

[0094] Optionally, the user location information during the sampling period can be obtained in the following manner: obtain at least one network interoperation record of the user during the sampling period, where the network interoperation record includes the occurrence time, the first location under the 4G network, and the second location under the 5G network; respectively determine the first time corresponding to each network interoperation record and the user location corresponding to the first time, so as to obtain the user location information.

[0095] S203. Obtain the local network location information and the base station location information of the user during the sampling period.

[0096] The local network location information may include multiple second times and the local network location corresponding to each second time. The local network may be a WIFI network.

[0097] Optionally, the multiple second times and the local network location corresponding to each second time can be determined in the following manner: obtain at least one local network access record of the user during the sampling period, where the local area network access record includes the user identifier, the access time, the wireless access point (AP) identifier, and the hardware address (MAC) of the wireless AP. For any local network access record, the access time in the local network access record can be determined as the second time; the local network location corresponding to the second time can be determined through the MAC address of the wireless AP in the local network access record.

[0098] The base station location information may include multiple third times and the base station location corresponding to each third time.

[0099] Optionally, the multiple third times and the base station location information corresponding to each third time can be determined in the following manner: obtain the CGI of at least one resident cell of the user during the sampling period, where the CGI may include operator information, the location city, the base station identifier, the cell number, and the access time of the base station. For the CGI of any resident cell, the access time of the base station in the CGI can be determined as the third time; the base station location corresponding to the third time can be determined through the base station identifier in the CGI.

[0100] S204. Determine the target trajectory information of the user during the sampling period according to the user location information, the local network location information, and the base station location information, and determine the target travel points of the user during the sampling period according to the target trajectory information.

[0101] Optionally, the target trajectory information of the user within the adoption period can be obtained in the following manner: Determine a set of moments based on the user location information, local network location information, and base station location information. The set of moments includes multiple first moments, multiple second moments, and multiple third moments; Arrange the moments in the set of moments in ascending order of time to obtain a moment sequence; Determine the target trajectory information based on the moment sequence and the positions corresponding to each moment in the moment sequence.

[0102] The method for determining a travel point provided in the embodiments of the present application can obtain multiple positions of a user within a sampling period according to the network information of the user within the sampling period. In response to the multiple positions belonging to at least two candidate areas, obtain the user location information, local area network information, and base station location information of the user within the sampling period. Based on the user location information, local network location information, and base station location information, determine the target trajectory information of the user within the sampling period. Determine the target travel point of the user within the sampling period according to the target trajectory information of the user, improving the accuracy of positioning the travel point.

[0103] Next, in combination with Figure 3 , the method for determining a travel point provided in the embodiments of the present application will be described in detail.

[0104] Figure 3 It is a schematic flowchart of another method for determining a travel point provided in the embodiments of the present application. Please refer to Figure 3 , this method may include:

[0105] S301. Determine multiple positions experienced by the user within the sampling period according to the network information of the user within the sampling period.

[0106] It should be noted that the execution process of S301 can refer to the execution process of S201, and will not be elaborated here.

[0107] S302. In response to the multiple positions belonging to at least two candidate areas, obtain at least one network interoperability record of the user within the sampling period.

[0108] In the actual operation process, when the wireless communication system propagates under non-line-of-sight (NLOS) conditions, coverage shadows will be generated, resulting in weak network coverage, thus causing network interoperability between the 4G network and the 5G network; Or when the user uses Voice over Long-Term Evolution (VOLTE) service to make a call, it will also cause network interoperability between the 4G network and the 5G network.

[0109] Each network interoperability between the 4G network and the 5G network generates a network interoperability record. The network interoperability record may include the occurrence time, the first location under the 4G network, and the second location under the 5G network. Optionally, the network interoperability record may further include the network type and the CGI of the resident cell.

[0110] For example, the network interoperability record may be as shown in Table 1:

[0111] Table 1

[0112]

[0113] Optionally, the occurrence time in the network interoperability record can be accurate to milliseconds. For example, the occurrence time can be 18:11:02.1.

[0114] For any network interoperability record, the first time corresponding to the network interoperability record can be determined by performing step S303.

[0115] S303. Determine the occurrence time in the network interoperability record as the first time.

[0116] For example, in the network interoperability record shown in Table 1, the occurrence time of the network interoperability is 18:11:02, and this occurrence time is the first time.

[0117] The base station positioning technology mainly adopts the Time of Arrival (TOA) positioning method. This method can calculate the distance between the user's location and the base station through the following formula:

[0118] δ = ct s

[0119] In the formula, δ is the distance between the user's location and the base station, and the user's location can be on a circular arc δ meters away from the base station; c is the propagation speed of electromagnetic waves; t s is the time duration from when the base station transmits the signal to when the user receives the signal.

[0120] In actual use, the longitude and latitude of the user's location can be determined by three base stations. However, due to the large number of buildings in the city, the electromagnetic waves will have a multipath effect during propagation (i.e., the electromagnetic waves propagate in a refracted and scattered manner), and the propagation signal of the electromagnetic waves is easily interfered by other signals, resulting in a decrease in the Signal to Interference plus Noise Ratio (SINR) value, thereby causing additional time delay during the propagation of the electromagnetic waves. At this time, the distance between the user's location and the base station can be calculated through the following formula:

[0121] δ = cts +cτ

[0122] Where δ is the distance between the user's location and the base station; c is the propagation speed of electromagnetic waves; t s is the time duration from the base station transmitting the signal to the user receiving the signal; τ represents the additional time delay.

[0123] When determining the user's location through three base stations, due to different additional time delays, the calculated longitude and latitude of the user's location may be multiple. In addition, when determining the user's location through base station positioning technology, when the cell connected by the user's mobile terminal changes or the user's mobile terminal generates a location update signaling, the location of the user saved in the core network will change, which also leads to the inconsistency between the location of the user recorded in the operator's network management platform and the actual location of the user. For the above reasons, other means are also needed to cooperate to obtain more accurate user location information.

[0124] In this application, for any network interoperability record, the accurate user location information within the sampling period can be obtained by executing S304 to S307.

[0125] S304. Determine the longitude mean, latitude mean, longitude variance, and latitude variance according to the first location and the second location.

[0126] Assume that the longitude and latitude of the first location are (latitude NR , longitude NR ), and the longitude and latitude of the second location are (latitude LTE , longitude LTE ). Then, the average value of the longitude NR of the first location and the longitude LTE of the second location can be determined as the longitude mean; the average value of the latitude NR of the first location and the latitude LTE of the second location can be determined as the latitude mean. Furthermore, the longitude variance can be calculated through the longitude NR of the first location, the longitude LTE of the second location, and the longitude mean; the latitude variance can be calculated through the latitude NR of the first location, the latitude LTE of the second location, and the latitude mean.

[0127] Optionally, the latitude mean can be calculated according to the following formula:

[0128]

[0129] Where μ x is the latitude mean, x NR is the latitude value of the first location, xLTE is the latitude value of the second position.

[0130] Optionally, the longitude mean can be calculated respectively according to the following formula:

[0131]

[0132] In the formula, μ y is the longitude mean, y NR is the longitude value of the first position, y LTE is the longitude value of the second position.

[0133] Optionally, the latitude variance can be calculated respectively according to the following formula:

[0134]

[0135] In the formula, σ x is the latitude variance, μ x is the latitude mean, x NR is the latitude value of the first position, x LTE is the latitude value of the second position.

[0136] Optionally, the latitude variance can be calculated respectively according to the following formula:

[0137]

[0138] In the formula, σ y is the longitude variance, μ y is the longitude mean, y NR is the longitude value of the first position, y LTE is the longitude value of the second position.

[0139] S305. Determine the longitude prediction function according to the longitude mean and the longitude variance.

[0140] Optionally, the longitude prediction function can be expressed by the following formula:

[0141]

[0142] In the formula, σ y is the longitude variance, μ y is the longitude mean, y is the longitude value, and f(y) is the probability distribution function corresponding to the longitude value y.

[0143] S306. Determine the latitude prediction function according to the latitude mean and the latitude variance.

[0144] Optionally, the latitude prediction function can be expressed by the following formula:

[0145]

[0146] Wherein, σ x is the latitude variance, μ x is the latitude mean value, x is the latitude value, and f(x) is the probability distribution function corresponding to the latitude value x.

[0147] S307. Determine the user location corresponding to the first moment according to the longitude prediction function and the latitude prediction function.

[0148] The maximum value of the latitude prediction function f(x) can be determined as the latitude value of the user location, denoted as x True ; the maximum value of the longitude prediction function f(y) is determined as the longitude value of the user location, denoted as y True . Then, the coordinates of the user location can be expressed as (x True , y True ), where x True and y True each follow a Gaussian distribution: x True ∈(μ x , σ x 2 ), y True ∈(μ y , σ y 2 ). Optionally, each city can determine x True and y True according to the actual network deployment situation. When determining x True and y True , the confidence interval can be selected as 95%.

[0149] S308. Obtain the local network location information and base station location information of the user during the sampling period.

[0150] The local network location information includes multiple second moments and the corresponding local network locations for each second moment. The base station location information includes multiple third moments and the corresponding base station locations for each third moment.

[0151] It should be noted that the execution process of S308 can refer to the execution process of S203, which will not be elaborated here.

[0152] S309. Determine the moment set according to the user location information, local network location information, and base station location information.

[0153] The moment set includes multiple first moments, multiple second moments, and multiple third moments.

[0154] S310. Arrange the moments in the moment set in ascending order of time to obtain a moment sequence.

[0155] S311. Sort the positions corresponding to each moment in the moment sequence to obtain initial trajectory information.

[0156] The initial trajectory information includes the positions corresponding to each moment in the moment sequence.

[0157] Optionally, the positions corresponding to each moment can be marked on the map first, and then the positions corresponding to each moment marked on the map are connected in the order of the moments in the moment sequence to obtain the initial trajectory information.

[0158] S312. Obtain road network data and user behavior characteristics corresponding to the user's location area.

[0159] Optionally, the road network data can be obtained from the database of the Internet map. The user behavior characteristics corresponding to the user's location area can be obtained according to the tidal movement characteristics of the population in the user's location area, the change of relative population density within a day, and the prediction results of the group behavior by the big data platform.

[0160] S313. Process the initial trajectory information, road network data, and user behavior characteristics through a preset model to obtain target trajectory information.

[0161] The preset model can use a machine learning algorithm to process the initial trajectory information, road network data, and user behavior characteristics to obtain the target trajectory information. Optionally, the machine learning algorithm can be the Viterbi algorithm.

[0162] In a possible implementation manner, the prediction model can be a hidden Markov model. The hidden Markov model adopts a double stochastic process, which includes a set of directly observable state sequence sets and a set of non-directly observable state sequence sets. The hidden Markov model can be described by the following five-tuple:

[0163] {S, T, π, A, B}

[0164] Among them, S is the set of non-directly observable state sequences, denoted as S = {S0, S1, S2,......, S N}; T is the set of directly observable state sequences, denoted as T = {T1, T2, T3,......, T N}; π is the initial state probability matrix; A is the hidden state transition probability matrix; B is the observation state transition probability matrix.

[0165] In this application, S can be determined according to the tidal movement characteristics of the current city population or the prediction results of the group behavior by the big data platform. T can be determined according to the initial trajectory information. A can be determined according to the travel habits of the current city population. Specifically, A can be determined by the following formula:

[0166]

[0167] In the formula, A is the implicit state transition probability matrix; ρ is the population travel density corresponding to the corresponding street and corresponding time period; d is the actual travel distance between the positions corresponding to any two adjacent moments on the map, rather than the straight-line distance on the map; t is the time interval between two adjacent moments.

[0168] According to the parameters in the above five-tuple, the Viterbi algorithm in the prediction model can be used to determine the trajectory information with the highest travel probability of the user, and this trajectory information is determined as the target trajectory information.

[0169] S314. Determine the sub-trajectory information corresponding to each candidate area in the target trajectory information.

[0170] Optionally, for any candidate area, the area range of the candidate area can be determined on the map first, and the trajectory information in the target trajectory information that is within the area range is determined as the sub-trajectory information corresponding to the candidate area.

[0171] S315. Determine the proportion corresponding to each sub-trajectory information according to the target trajectory information and each sub-trajectory information.

[0172] Optionally, the ratio of each sub-trajectory information to the target trajectory information can be determined as the proportion corresponding to each sub-trajectory information.

[0173] S316. Determine the target candidate area among at least two candidate areas according to the proportion corresponding to each sub-trajectory information.

[0174] The proportion corresponding to the sub-trajectory information corresponding to the target candidate area is greater than or equal to the preset proportion.

[0175] Optionally, the preset proportion can be 75%. For example, assume there are two candidate areas, namely City A and City B. Among them, the proportion corresponding to the sub-trajectory information corresponding to City A is 20%, and the proportion corresponding to the sub-trajectory information corresponding to City B is 80%. Then, City B can be determined as the target candidate area.

[0176] S317. Determine the travel points corresponding to the target candidate area as the target travel points.

[0177] The method for determining a travel point provided by an embodiment of the present application can obtain multiple positions of a user during a sampling period according to the network information of the user during the sampling period. In response to the multiple positions belonging to at least two candidate areas, the user position information, local area network information, and base station position information of the user during the sampling period are obtained. Based on the user position information, local area network position information, and base station position information, the target trajectory information of the user during the sampling period is determined. The target travel point of the user during the sampling period is determined according to the target trajectory information of the user, improving the accuracy of travel point positioning.

[0178] Figure 4 It is a schematic structural diagram of a device for determining a travel point provided by an embodiment of the present application. Please refer to Figure 4 The travel point determination device 10 includes a first determination module 11, an acquisition module 12, and a second determination module 13, where

[0179] The first determination module 11 is configured to determine multiple positions experienced by the user during the sampling period according to the network information of the user during the sampling period;

[0180] The acquisition module 12 is configured to, in response to the multiple positions belonging to at least two candidate areas, acquire the user position information of the user during the sampling period, where the user position information includes at least one first moment and the user position corresponding to each first moment;

[0181] The acquisition module 12 is further configured to acquire the local area network position information and the base station position information of the user during the sampling period, where the local area network position information includes multiple second moments and the local area network position corresponding to each second moment, and the base station position information includes multiple third moments and the base station position corresponding to each third moment;

[0182] The second determination module 13 is configured to determine the target trajectory information of the user during the sampling period according to the user position information, the local area network position information, and the base station position information, and determine the target travel point of the user during the sampling period according to the target trajectory information.

[0183] The travel point determination device provided by the embodiment of the present application can execute the technical solutions shown in the above method embodiments, and its implementation principle and beneficial effects are similar, and will not be elaborated here.

[0184] In a possible implementation manner, the acquisition module 12 is specifically configured to:

[0185] Acquire at least one network interoperability record of the user during the sampling period, where the network interoperability record includes an occurrence moment, a first position under a 4G network, and a second position under a 5G network;

[0186] Determine the first moment corresponding to each network interoperability record and the user location corresponding to the first moment respectively, so as to obtain the user location information.

[0187] In a possible implementation manner, the obtaining module 12 is specifically configured to:

[0188] Determine the occurrence moment in the network interoperability record as the first moment;

[0189] Determine the user location corresponding to the first moment according to the first location and the second location in the network interoperability record.

[0190] In a possible implementation manner, the obtaining module 12 is specifically further configured to:

[0191] Determine the longitude mean, latitude mean, longitude variance and latitude variance according to the first location and the second location;

[0192] Determine a longitude prediction function according to the longitude mean and the longitude variance;

[0193] Determine a latitude prediction function according to the latitude mean and the latitude variance;

[0194] Determine the user location corresponding to the first moment according to the longitude prediction function and the latitude prediction function.

[0195] In a possible implementation manner, the second determining module 13 is specifically further configured to:

[0196] Determine a moment set according to the user location information, the local area network location information and the base station location information, where the moment set includes a plurality of the first moments, a plurality of the second moments and a plurality of the third moments;

[0197] Arrange the moments in the moment set in ascending order of time to obtain a moment sequence;

[0198] Determine the target trajectory information according to the moment sequence and the positions corresponding to the moments in the moment sequence.

[0199] In a possible implementation manner, the second determining module 13 is specifically further configured to:

[0200] Perform a sorting process on the positions corresponding to the moments in the moment sequence to obtain initial trajectory information, where the initial trajectory information includes the positions corresponding to the moments in the moment sequence;

[0201] Obtain road network data and user behavior characteristics corresponding to the area where the user is located;

[0202] The initial trajectory information, the road network data and the user behavior characteristics are processed by a preset model to obtain the target trajectory information.

[0203] In a possible implementation manner, the second determining module 13 is specifically configured to:

[0204] Determining sub-trajectory information corresponding to each to-be-selected area in the target trajectory information;

[0205] According to the target trajectory information and each sub-trajectory information, determine the corresponding proportion of each sub-trajectory information;

[0206] Determine a target area to be selected from the at least two areas to be selected according to the proportion corresponding to each sub-trajectory information, wherein the proportion corresponding to the sub-trajectory information corresponding to the target area to be selected is greater than or equal to a preset proportion;

[0207] The travel point corresponding to the target area to be selected is determined as the target travel point.

[0208] The device for determining travel points provided in the embodiment of the present application can implement the technical solution shown in the above method embodiment. Its implementation principles and beneficial effects are similar and will not be repeated here.

[0209] Figure 5 This is a hardware structure diagram of the electronic device provided in the embodiment of this application. Figure 5 The electronic device 20 may include a processor 21 and a memory 22 . The processor 21 and the memory 22 may communicate with each other; illustratively, the processor 21 and the memory 22 communicate with each other via a communication bus 23 .

[0210] The memory 22 is used to store computer-executable instructions;

[0211] The processor 21 is configured to execute the computer-executable instructions stored in the memory 22 , so that the processor 21 executes the method for determining a travel point as shown in the above method embodiment.

[0212] Optionally, the electronic device 20 may further include a communication interface, which may include a transmitter and / or a receiver.

[0213] Optionally, the above-mentioned processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present application may be directly implemented by a hardware processor, or may be implemented by a combination of hardware and software modules in the processor.

[0214] The electronic device provided in the embodiments of the present application can execute the technical solutions shown in the above method embodiments. The implementation principles and beneficial effects are similar, and will not be elaborated here.

[0215] The embodiments of the present application provide a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the method for determining waypoints as described in any of the above embodiments.

[0216] The embodiments of the present application provide a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the method for determining waypoints as described in any of the above embodiments.

[0217] All or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions. The foregoing program can be stored in a readable memory. When the program is executed, it executes the steps including the above method embodiments; and the foregoing memory (storage medium) includes: read-only memory (abbreviation: ROM), RAM, flash memory, hard disk, solid-state drive, magnetic tape, floppy disk, optical disc, and any combination thereof.

[0218] The embodiments of the present application are described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, and the combination of processes and / or blocks in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to the processing unit of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processing unit of the computer or other programmable data processing devices generate for implementation in the processFigure 1 a process or multiple processes and / or blocks Figure 1 a device for the functions specified in a block or multiple blocks.

[0219] It can be understood that the various numerical numbers involved in the embodiments of the present application are only for the convenience of description and are not used to limit the scope of the embodiments of the present application.

[0220] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or fully authorized by all parties. Moreover, the collection, use, and processing of relevant data need to comply with the relevant laws, regulations, and standards of relevant countries and regions, and corresponding operation entrances are provided for users to choose to authorize or reject.

[0221] In the present application, the term "including" and its variants may refer to non-limiting inclusion; the term "or" and its variants may refer to "and / or". In the present application, terms such as "first" and "second" are used to distinguish similar objects and do not necessarily need to describe a specific order or sequence. In the present application, "multiple" means two or more. "And / or" describes the association relationship of associated objects and indicates that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally indicates that the associated objects before and after are in an "or" relationship.

[0222] Obviously, those skilled in the art can make various changes and modifications to the embodiments of the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the embodiments of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application also intends to include these changes and modifications.

Claims

1. A method for determining a travel point, characterized in that, Including: Determine a plurality of positions experienced by the user during the sampling period according to the user's network information during the sampling period; In response to the plurality of positions belonging to at least two candidate areas, obtain the user position information of the user during the sampling period, where the user position information includes at least one first moment and the user position corresponding to each first moment; Obtain the local network position information and the base station position information of the user during the sampling period, where the local network position information includes a plurality of second moments and the local network position corresponding to each second moment, and the base station position information includes a plurality of third moments and the base station position corresponding to each third moment; Determine the target trajectory information of the user during the sampling period according to the user position information, the local network position information, and the base station position information, and determine the target trip points of the user during the sampling period according to the target trajectory information; The determining the target trip points of the user during the sampling period according to the target trajectory information includes: Determine the sub-trajectory information corresponding to each candidate area in the target trajectory information; Determine the proportion corresponding to each sub-trajectory information according to the target trajectory information and each sub-trajectory information; Determine the target candidate area among the at least two candidate areas according to the proportion corresponding to each sub-trajectory information, where the proportion corresponding to the sub-trajectory information corresponding to the target candidate area is greater than or equal to a preset proportion; Determine the trip points corresponding to the target candidate area as the target trip points.

2. The method according to claim 1, characterized in that, Obtaining the user position information of the user during the sampling period includes: Obtain at least one network interoperation record of the user during the sampling period, where the network interoperation record includes the occurrence time, the first position under the 4G network, and the second position under the 5G network; Respectively determine the first moment corresponding to each network interoperation record and the user position corresponding to the first moment to obtain the user position information.

3. The method according to claim 2, wherein For any one network interoperation record; Determining the first moment corresponding to the network interoperation record and the user position corresponding to the first moment includes: Determine the occurrence time in the network interoperation record as the first moment; Determine the user position corresponding to the first moment according to the first position and the second position in the network interoperation record.

4. The method according to claim 3, characterized in that, Determining the user position corresponding to the first moment according to the first position and the second position in the network interoperation record includes: Determine the longitude mean, latitude mean, longitude variance, and latitude variance according to the first position and the second position; Determine the longitude prediction function according to the longitude mean and the longitude variance; Determine the latitude prediction function according to the latitude mean and the latitude variance; Determine the user position corresponding to the first moment according to the longitude prediction function and the latitude prediction function.

5. The method according to any one of claims 1-4, characterized in that, Determining the target trajectory information of the user during the sampling period according to the user position information, the local network position information, and the base station position information includes: Determine a set of moments according to the user location information, the local network location information, and the base station location information, where the set of moments includes a plurality of the first moments, a plurality of the second moments, and a plurality of the third moments; Arrange the moments in the set of moments in ascending order of time to obtain a moment sequence; Determine the target trajectory information according to the moment sequence and the positions corresponding to each moment in the moment sequence.

6. The method according to claim 5, characterized in that, Determining the target trajectory information according to the moment sequence and the positions corresponding to each moment in the moment sequence includes: Sort the positions corresponding to each moment in the moment sequence to obtain initial trajectory information, where the initial trajectory information includes the positions corresponding to each moment in the moment sequence; Obtain road network data and user behavior characteristics corresponding to the area where the user is located; Process the initial trajectory information, the road network data, and the user behavior characteristics through a preset model to obtain the target trajectory information.

7. A device for determining a travel point, characterized in that, Including: A first determination module, an acquisition module, and a second determination module, where The first determination module is configured to determine a plurality of positions experienced by the user during the sampling period according to the network information of the user during the sampling period; The acquisition module is configured to, if the plurality of positions belong to at least two candidate areas, acquire the user location information of the user during the sampling period, where the user location information includes at least one first moment and the user location corresponding to each first moment; The acquisition module is further configured to acquire the local network location information and the base station location information of the user during the sampling period, where the local network location information includes a plurality of second moments and the local network location corresponding to each second moment, and the base station location information includes a plurality of third moments and the base station location corresponding to each third moment; The second determination module is configured to determine the target trajectory information of the user during the sampling period according to the user location information, the local network location information, and the base station location information, and determine the target travel point of the user during the sampling period according to the target trajectory information; Specifically, the second determination module is configured to: Determine sub-trajectory information corresponding to each candidate area in the target trajectory information; Determine the proportion corresponding to each sub-trajectory information according to the target trajectory information and each sub-trajectory information; Determine a target candidate area among the at least two candidate areas according to the proportion corresponding to each sub-trajectory information, where the proportion corresponding to the sub-trajectory information corresponding to the target candidate area is greater than or equal to a preset proportion; Determine the travel point corresponding to the target candidate area as the target travel point.

8. An electronic device, characterized in that, Including: A processor and a memory; The memory is configured to store a computer program; The processor is configured to execute the computer program stored in the memory to implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, Wherein, Computer-executable instructions are stored in the computer-readable storage medium, and when the computer-executable instructions are executed by the processor, they are used to implement the method according to any one of claims 1 to 6.

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