METHOD AND DEVICE FOR DETERMINING EMISSION PROBABILITY, TRANSMISSION PROBABILITY AND SEQUENCE POSITIONING

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the field of telecommunication positioning, and in particular, to a method and an apparatus for obtaining an emission probability used for sequence positioning.BACKGROUND

[0002] Telecommunication positioning means obtaining a location of a mobile device through calculation by using data sent by the mobile device to a pipeline side (such as a telecommunications operator) and data on a base station side. Currently, common telecommunication positioning technologies include a range-based method, a fingerprint method, and a sequence positioning method. A main idea of the sequence positioning method (or a sequence method) is to describe a positioning process as a model of matching from an observed sequence to a hidden sequence, where a latitude and longitude location is used as a hidden state, and signal strength is used as an observed value. During positioning, an observed value sequence is used as input, and an optimal hidden state sequence in a one-to-one correspondence with the input observed value sequence is output as a positioning result. A core of the positioning method is to define an emission probability (emission probability) and a transition probability (transition probability). The emission probability means mapping from a hidden state to an observed value. The transition probability means transition between different hidden states.

[0003] The sequence positioning method has the following main advantage: Context information of a location is used, so that a prediction result of each location can be restricted by a previous location, and a relatively smooth track is obtained, thereby effectively avoiding a case in which a prediction result obtained by using a method such as the range-based method and the fingerprint method "jumps". Obtaining of the emission probability and the transition probability directly affects a positioning capability of the sequence positioning method. In the prior art, the emission probability is obtained by modeling a mean square error of signal strength. The emission probability obtained by using the method cannot describe complex observed information. This directly affects positioning precision and reliability of the sequence positioning method.

[0004] US2015373501 relates to method of analysing data collected in a cellular network and system thereof. CN106709606 relates to personalized scene prediction method and apparatus. US2013079039 relates to method and system for refining accuracy of location positioning. US2005136944 relates to sequence-based positioning technique.SUMMARY

[0005] To resolve a technical problem in the prior art, this application provides a method for obtaining an emission probability, so that an obtained emission probability can express complex observed information, and using the emission probability for sequence positioning can improve positioning precision and reliability of a sequence positioning method.

[0006] The invention has been defined in the independent claims. Further specific technical features have been defined in the dependent claims.

[0007] Specifically, the target region may be obtained through division based on a population quantity and an administrative region. For example, a suburb of a city is a target region or an urban region of a city is a target region. An area size, a geographical location, and the like of the target region are not limited in this application. Generally, an emission probability obtained based on an MR of a terminal in a region is applied to the region. It should be noted that, the obtaining a plurality of MRs of a terminal in a target region may be obtaining a plurality of MRs of one terminal in the target region, or may be obtaining a plurality of MRs of a plurality of terminals in the target region. In addition, a quantity of MRs of one terminal is not limited, and the plurality of terminals may have one or more MRs. Each of the plurality of MRs includes location information and parameter information. The location information is used to indicate a location that is in the target region and that is of a terminal corresponding to the MR including the location information. Parameter information in an MR is not limited in this application. Generally, information other than location information in the MR may be parameter information.

[0008] The environment parameter is used to indicate an environment in which a terminal corresponding to an MR including the environment parameter is located, for example, time period information, weather information, and event information (a holiday, a celebration day, a sports meeting, or the like). Different emission probabilities are obtained based on different environments, and the environment parameter is used as a feature, so that positioning in different environments can be more accurately supported. According to the claimed invention, the environment parameter includes time period information and weather information. Any implementation in which the environment parameter does not include time period information and weather information is not specifically claimed in the present application.

[0009] It should be noted that, an MR that is input into the single-point positioning model and an MR that is used to train the single-point positioning model may not be limited to MRs of a same terminal, and may alternatively be MRs of a terminal other than the terminal in the target region, that is, the MR that is used to obtain the single-point positioning model and the MR that is used to be input into the single-point positioning model to calculate the emission probability may be MRs uploaded by different terminals in the target region. The single-point positioning model is obtained through training by using MRs of a terminal in the target region, then MRs of the terminal in the target region is input into the single-point positioning model, and a correspondence between a feature vector and location information is obtained through statistics collection by using a spatial model of the single-point positioning model, so that the correspondence is more reliable.

[0010] Actually, one MR may include information about a plurality of base stations to which a corresponding terminal is connected. The at least one base station includes at least base stations indicated by base station IDs included in the plurality of MRs, so that the base station ID in each MR corresponds to an engineering parameter used to obtain a feature vector. The obtaining, based on the parameter information in each of the plurality of MRs and the engineering parameter of the at least one base station, a feature vector corresponding to each of the plurality of MRs includes: matching the plurality of MRs with the engineering parameter of the at least one base station based on the base station IDs, to obtain an associated engineering parameter of each of the plurality of MRs, where an associated engineering parameter of any MR includes an engineering parameter of a base station indicated by each base station ID in the any MR; and obtaining, based on the associated engineering parameter and the parameter information of each of the plurality of MRs, the feature vector corresponding to each of the plurality of MRs, where any feature vector includes an associated engineering parameter and parameter information of one MR. It should be noted that a quantity of base stations indicated by a base station ID in a corresponding MR generally determines that engineering parameters of how many base stations can be included in a feature vector.

[0011] Optionally, feature vectors corresponding to MRs that are input into the single-point positioning model and location information in the MRs may not be the feature vectors corresponding to the MRs of the terminal and the location information in the MRs, and may be MRs of a plurality of other terminals in a same target region as the terminal. In addition, a method for obtaining the feature vectors is the same as a method for obtaining the feature vectors corresponding to the MRs of the terminal. Details are not described herein again.

[0012] Specifically, the target region may be obtained through division based on a population quantity and an administrative region. For example, a suburb of a city is a target region or an urban region of a city is a target region. An area size, a geographical location, and the like of the target region are not limited in this application. Generally, an emission probability obtained based on an MR of a terminal in a region is applied to the region. Each of the plurality of pieces of track data includes at least two pieces of location information. The location information is used to indicate a location that is in the target region and that is of a terminal corresponding to track data including the location information. Each of a plurality of pieces of location information included in the plurality of pieces of track data corresponds to a time stamp. The method further includes: calculating a transition probability based on the plurality of pieces of track data, where the transition probability includes at least one transition probability value, and the transition probability value is used to indicate a probability that movement is performed from a piece of location information to another piece of location information after a time interval T. Optionally, the plurality of pieces of track data of the terminal in the target region are from a third-party platform, for example, a third-party app such as Didi Chuxing or a bicycle-sharing platform.

[0013] The environment parameter is used to indicate an environment in which a terminal corresponding to track data including the environment parameter is located, for example, at least one of time period information, weather information, and event information. Different transition probabilities are obtained based on different environments, and the environment parameter is used as an identifier, so that positioning in different environments can be more accurately supported. According to the claimed invention, the environment parameter includes time period information and weather information. Any implementation in which the environment parameter does not include time period information and weather information is not specifically claimed in the present application.

[0014] As an example, it is determined that a quantity of all combination sequences that include location information A and a time interval T1 is M, and the combination sequences may be represented as [location information A, time interval T1, and location information X n ]. A quantity of pieces of each of location information X 1 to X n is counted in all the combination sequences that include the location information A and the time interval T1, to obtain a probability value that each of the location information X 1 to X n occupies in M. The probability values that correspond to the location information X 1 to X n constitute a transition probability corresponding to the condition including the location information A and the time interval T1. Optionally, the preset time interval is a preset time interval range, and the preset time interval may be specific duration or a duration range. For example, the preset time interval is 2 seconds, or the preset time interval is 2 seconds to 4 seconds. In this way, a transition probability corresponding to a duration range can be obtained. Because different terminals may obtain MRs at different frequencies, time intervals in combination sequences obtained by using track data of different terminals may be different. The time interval in the preset condition is set to a value range, so that combination sequences corresponding to different time intervals may be fused to use existing data to a maximum extent.

[0015] In practice, unreliable data, namely, defective track data, may exist in track data obtained from a third party. The defective track data is removed from the obtained plurality of pieces of track data, and then further processing continues to be performed on track data obtained after the removal, to obtain the transition probability, so that a movement track of the terminal recovered / predicted by using the transition probability can be more reliable and smooth.

[0016] Interpolation processing is performed, so that location information in obtained track data can be denser. A transition probability obtained based on the track data obtained after the interpolation processing is used to recover / predict the movement track of the terminal, so that the recovered / predicted movement track can be smoother. In addition, transition probabilities corresponding to more different time intervals can be obtained. In a possible implementation of the second aspect, the obtaining a plurality of pieces of track data of a terminal in a target region includes: obtaining the plurality of pieces of track data of the terminal in the target region in a peak traffic time period or a non-peak traffic time period. In practice, a movement track of the terminal in the peak traffic time period is usually different from a movement track of the terminal in the non-peak traffic time period. Different transition probabilities obtained in the peak traffic time period and the non-peak traffic period are used to recover / predict a movement track of the terminal in a corresponding time period, so that accuracy and reliability of recovering / predicting the movement track of the terminal can be improved.

[0017] According to the method for obtaining a transition probability provided in this application, a transition probability calculated based on movement track data that is of a terminal in a target region and that is provided by a third-party platform is used to recover or predict a movement track of a terminal in the target region, so that the movement track is smoother, and track jumping can effectively be avoided.

[0018] According to the sequence positioning method provided in this application, an emission probability obtained based on feature vectors obtained based on a plurality of pieces of parameter information in MRs and engineering parameters of corresponding base stations can express more complex observed information, thereby further improving accuracy and reliability of a movement track recovered / predicted through sequence positioning. Alternatively, a transition probability obtained by using real track data from a third party is used for sequence positioning, so that smoothness of a recovered / predicted movement track can be improved, and the obtained movement track is more reliable.BRIEF DESCRIPTION OF DRAWINGS

[0019] FIG. 1 is a schematic structural diagram of a sequence positioning system according to this application; FIG. 2 is a schematic diagram of an application scenario of a sequence positioning system according to this application; FIG. 3 is a flowchart of a method for obtaining an emission probability according to this application; FIG. 4 is a schematic diagram of calculating an emission probability according to this application; FIG. 5 is a flowchart of a method for obtaining a transition probability according to this application; FIG. 6 is a schematic diagram of a map matching and interpolation method according to this application; FIG. 7 is a schematic diagram of a method for calculating a transition probability in an offline index manner according to this application; FIG. 8 is a schematic diagram of a method for calculating a transition probability in an online index manner according to this application; FIG. 9 is a flowchart of a sequence positioning method according to this application; FIG. 10 is a schematic diagram of recovering a track based on a sequence positioning method according to this application; FIG. 11 is a schematic diagram of a particle-filtering-based sequence positioning method according to this application; FIG. 12 is a schematic diagram of an apparatus for calculating an emission probability according to this application; FIG. 13 is a schematic diagram of an apparatus for calculating a transition probability according to this application; FIG. 14 is a schematic diagram of a sequence positioning apparatus according to this application; FIG. 15 is a schematic diagram of a device for calculating an emission probability and / or a transition probability according to this application; and FIG. 16 is an experiment setting and a result of an actual testing experiment according to this application. DESCRIPTION OF ASPECTS

[0020] The implementations described below are not all claimed, they are included to help understanding the context of the disclosure. While the description often refers to aspects, the aspects of the disclosure are those which comprise at least all the features of an independent claim. Any aspect which does not fall within the scope of the claims does not form part of the disclosure, but rather included as an illustrative example that is useful for understanding the disclosure.

[0021] The following describes the technical solutions of the aspects of this application with reference to the accompanying drawings.

[0022] An aspect of this application describes a positioning system. The system is configured to position a telecommunication user to recover a movement track of the telecommunication user. As shown in FIG. 1, the positioning system includes an apparatus for calculating an emission probability (emission probability), an apparatus for calculating a transition probability (transition probability), and a sequence positioning apparatus. The apparatus for calculating an emission probability, the apparatus for calculating a transition probability, and the sequence positioning apparatus perform data communication with each other by using a data link. The apparatus for calculating an emission probability is configured to calculate an emission probability used for sequence positioning, where input is measurement report (Measurement Report, MR) data with a location label, and output is an emission probability. The apparatus for calculating a transition probability is configured to calculate a transition probability used for sequence positioning, where input is movement track data, and output is a transition probability. The sequence positioning apparatus is configured to recover a movement track of a to-be-positioned terminal / device based on the emission probability, the transition probability, and MR data of the to-be-positioned terminal / device, where input is a string of MRs (without location information) of the to-be-positioned target terminal, and output is the movement track of the to-be-positioned target terminal. It should be noted that the sequence positioning apparatus may be alternatively used for single-point positioning, that is, input is one MR, and output is one corresponding location. The following further describes specific structures and functions of the apparatus for calculating an emission probability, the apparatus for calculating a transition probability, and the sequence positioning apparatus with reference to the accompanying drawings. Details are not described herein. It should be noted that the apparatus for calculating an emission probability, the apparatus for calculating a transition probability, and the sequence positioning apparatus may alternatively form an integral device. The apparatus for calculating an emission probability is an emission probability calculation module of the integral device, the apparatus for calculating a transition probability is a transition probability calculation module of the integral device, and the sequence positioning apparatus is a sequence positioning module of the integral device. Functions of each module are the same as functions of a corresponding apparatus, and data may be transmitted between the modules. Optionally, functions implemented by each of the emission probability calculation module, the transition probability calculation module, and the sequence positioning module or implemented by the integral device formed by the foregoing three function modules are implemented by using software or software and hardware.

[0023] Usually, the positioning system may be deployed on a big data analytics platform. When daily MRs are stored on the platform, the MRs are first input into the positioning system. The positioning system extracts features based on the MRs; predicts, by using a model obtained through offline training, a latitude and longitude location recorded in each MR; and adds the latitude and longitude location into the MR. The MR with the latitude and longitude location may be used for further analysis and modeling.

[0024] An aspect of this application describes an application scenario. As shown in FIG. 2, in this application scenario, the positioning system described in the foregoing aspect may be used as a component of a big data analytics platform of an operator, to hourly or daily input, into the positioning system, MRs uploaded by a mobile device to a pipeline of the operator. The positioning system obtains corresponding location information for each MR that is input into the positioning system, and adds the corresponding location information into the MR. Subsequently, these MRs into which location information has been added may be used by the operator to perform user profiling, traffic prediction, outdoor advertisement placement policy optimization, and the like.

[0025] An aspect of this application describes a method for obtaining an emission probability. As shown in FIG. 3, the method includes the following steps:

[0026] S101. Obtain a plurality of measurement reports MRs of a terminal in a target region and an engineering parameter of a base station in the target region. The target region is a specific geographical region such as a suburb, an urban region, a city, or a rural region, and an area size, an administrative region, and a geographical location are not limited. Each MR includes location information and parameter information, and the location information is used to label a location of a corresponding terminal in the target region.

[0027] The parameter information includes an environment parameter. The environment parameter is used to indicate a real environment in which a corresponding terminal generates an MR, for example, at least one of time period information, weather information, and event information. The environment parameter is used as a feature, and different emission probabilities are obtained based on different environments, so that positioning in different environments can be more accurately supported. In actual implementation, a peak time period and a non-peak time period are used as examples. An environment parameter corresponding to the peak time period is represented by 1, and an environment parameter corresponding to the non-peak time period is represented by 0. Positioning a terminal in the target region in the peak time period by using an emission probability obtained based on an MR with the environment parameter 1 is more accurate. Similarly, positioning a terminal in the target region in the non-peak time period by using an emission probability obtained based on an MR with the environment parameter 0 is more accurate. The implementation based on event information is not specifically claimed in the present application.

[0028] S102. Obtain, based on the parameter information and the engineering parameter, a feature vector corresponding to each MR. Specifically, matching is performed between the MRs and the engineering parameter of the base station based on base station IDs in the parameter information, to obtain an associated engineering parameter of each MR. The associated engineering parameter of each MR and the information parameter in the MR are combined to generate the feature vector corresponding to the MR. In specific implementation, an information parameter in an MR may include one or more base station IDs. This also indicates that a terminal corresponding to the MR is connected to one or more base stations at a same time. When parameter information in an MR includes a plurality of base station IDs, an association engineering parameter of the MR is a set of engineering parameters of base stations indicated by the plurality of base station IDs.

[0029] S103. Process the location information and the feature vector by using a machine learning model, to obtain a single-point positioning model. Specifically, the location information in each MR and the feature vector corresponding to the MR constitute a training set, and the training set corresponding to each MR is input to the machine learning model for training, to obtain the single-point positioning model. Optionally, the single-point positioning model may be directly used to position a to-be-positioned terminal in the target region, where input of the single-point positioning model is a feature vector including parameter information in an MR of the to-be-positioned terminal and the engineering parameter of the base station, and output is location information of the to-be-positioned terminal. Optionally, the machine learning model is a regression model such as linear regression or a random forest.

[0030] S104. Calculate an emission probability based on the single-point positioning model, the location information, and the feature vector. The emission probability includes at least one emission probability value, and the emission probability value is used to indicate a mapping relationship in which a feature vector used as an observed value corresponds to a piece of location information used as a hidden value. Specifically, the location information in each MR and the feature vector corresponding to the MR are input into the single-point positioning model to obtain a mapping relationship, where the mapping relationship is used to indicate a correspondence between a feature vector and location information; and the emission probability of the feature vector corresponding to each MR is calculated based on the mapping relationship. It should be noted that the location information and the feature vector that are used in S104 and the location information and the feature vector that may be used in S101 to S103 are not necessarily obtained based on a same MR. A reason lies in that a large quantity of MRs need to be obtained, and using a same MR avoids implementation of a step of obtaining data again. In addition, in terms of result, when an MR base is large enough, whether to use different MRs has relatively small impact on a result.

[0031] A possible implementation of calculating an emission probability is as follows: A single-point positioning model is trained, and then spatial model of the single-point positioning model is used to calculate an emission probability. Specifically, the following steps are included.

[0032] First, MRs of a plurality of mobile terminals in a preset geographical region are obtained. The MRs each include a location label, for example, GPS information carried in the MR. In addition, some other data in a telecommunications network is obtained, for example, an engineering parameter of a base station. The engineering parameter mainly includes information such as a base station ID, a latitude and longitude location, an antenna height, and an antenna azimuth.

[0033] Then, a positioning-related feature is extracted, as a feature vector of a corresponding MR, from the obtained data by using a feature engineering method. The feature vector may include an engineering parameter of a connected base station, an engineering parameter of a base station in a neighboring cell, signal strength of a connection, and the like. Specifically, matching is performed between the MR that carries the GPS information and the engineering parameter of the base station based on a base station ID, to find corresponding parameter information in the engineering parameter for each base station in the MR. Further, some simple feature engineering parameters are added into data obtained after the matching, for example, a quantity of connected base stations in each MR, where different base stations are at different latitude and longitude locations, or a quantity of sectors, where a plurality of sectors may be at a same latitude and longitude location. In this way, a feature vector may be created for each MR, and a feature vector and a location label corresponding to a same MR form a training set corresponding to the MR, to train a single-point positioning model.

[0034] Table 1 provides some telecommunication features for training a positioning model, including some original fields in the MR and some fields in the engineering parameter of the base station. Features with * indicate that these fields correspond to a connected base station and a base station in a neighboring cell that correspond to a same MR. Therefore, a same field appears in a feature vector a plurality of times, and corresponds to different base stations. Table 1 List of features used in a positioning model Feature nameDescriptionRNCID*ID of an RNC deviceCellID*Cell IDRSCP*Received signal code powerEc / No*Signal-to-noise ratioRSSI*Received signal strength indicatorAntenna height*Antenna heightAntenna azimuth*Antenna orientationMechanical downtilt angle*Mechanical downtilt angleElectrical downtilt angle*Electrical downtilt angleSector latitude*Latitude of a location of an antennaSector longitude*Longitude of a location of an antennaBase station type*Base station type (a macro base station or an indoor distributed base station)Base station manufacturer*Base station device manufacturer (Huawei, Nokia Siemens Networks, or the like)Quantity of sectors to which a device is connectedQuantity of connected sectors in an MRQuantity of base stations to which a device is connectedQuantity of connected sectors at different locations

[0035] Further, environment information is extracted from the MR as an environment parameter, for example, weather information (a sunny day, a rainy day, a snowy day, or the like), a time period (a peak time period, a non-peak time period, a work day, a weekend, or an official holiday), and event information (a sports meeting, a concert, a national celebration, and the like). The environment parameter and the telecommunication feature in Table 1 form a feature vector, and the feature vector and a corresponding location label form a training set. According to the claimed invention, the environment parameter includes time period information and weather information. Any implementation in which the environment parameter does not include time period information and weather information is not specifically claimed in the present application.

[0036] Next, all training sets obtained in the foregoing step are input to a machine learning regression model for training, to obtain a corresponding model, namely, the single-point positioning model. There may be a plurality of types of machine learning regression models such as linear regression and a random forest. Usually, for training of a single-point positioning model in a relatively large region, the region is divided into blocks, and a single-point positioning model is trained for each block. In this way, different features in different regions can be learned, for example, two models are trained for an urban region and a suburb.

[0037] Finally, the feature vector obtained in the foregoing step or a feature sequence obtained based on another MR sample with a location label is used as an observed value, and the location label is used as a hidden state. The observed value and the hidden state are input into the single-point positioning model obtained in the foregoing step. In this case, spatial model of the single-point positioning model may be analyzed to obtain a correspondence between a location label and a feature vector, so as to obtain P (feature vector|location).

[0038] Spatial model of different models corresponds to different calculation manners. The following uses a random forest as an example to describe a spatial model analysis method and an emission probability calculation manner. Details are described as follows:

[0039] After a sample (including a feature vector and a location label) is input to a random forest, a feature value of the sample and a splitting feature value of a decision node are continuously compared, to select a left child node or a right child node, until a leaf node is finally selected. Therefore, each leaf node in the random forest may be considered as a series of feature vectors (which are obtained by using splitting features of a series of decision nodes), and one leaf node is considered as one observed value. In this case, an emission probability is changed to a probability P (leaf node|label) of obtaining a leaf node by using a given label. As shown in FIG. 4, labeled samples are input into a trained tree model, and a corresponding leaf node in the tree can be found for each labeled sample (as shown in a third part in FIG. 4). In this way, an emission probability value may be obtained by dividing a total quantity of labeled samples with a same label by a quantity of the labeled samples falling on the leaf node. For example, there are 10 dot samples in total, and only one dot sample falls on a leaf node on the rightmost side of the third picture in FIG. 4. In this case, an emission probability value is 1 / 10. This indicates that a degree of matching between a location of a labeled sample and an observed value (a probability that a similar observed value may be obtained at the location). A higher matching degree indicates that a predicted location is more accurate.

[0040] An aspect of this application describes a method for obtaining a transition probability. As shown in FIG. 5, the method includes the following steps: S201. Obtain a plurality of pieces of track data of one or more terminals in a target region, where the target region is a specific geographical region such as a suburb, an urban region, a city, or a rural region, and an area size, an administrative region, and a geographical location are not limited. Each piece of track data includes at least two pieces of location information. The location information is used to label a location of a terminal in the target region. Each piece of location information corresponds to a time stamp. The time stamp is used to indicate a moment at which the terminal generates corresponding location information. In specific implementation, the track data may be obtained from a third-party platform, for example, Didi Chuxing or a traffic data publishing platform.

[0041] Optionally, the obtaining a plurality of pieces of track data of one or more terminals in a target region includes: obtaining track data of the terminal in the target region in a peak traffic time period, or obtaining track data of the one or more terminals in the target region in a non-peak traffic time period, or obtaining track data of the one or more terminals in a preset time period. Certainly, an optimal effect is achieved by applying a transition probability obtained based on track data of the terminal obtained in a time period to sequence positioning in the same time period, and the transition probability may also be applied to sequence positioning in a time period similar to the time period.

[0042] S202. Calculate a transition probability based on the plurality of pieces of track data, where the transition probability includes at least one transition probability value, and the transition probability value is used to indicate a probability that movement is performed from a piece of location information (a start location) to another piece of location information (an arrival location) after a time interval T. Specifically, the obtained plurality of pieces of track data are processed to obtain a combination sequence of each piece of track data. Any two pieces of location information in one piece of track data and a time interval between the any two pieces of location information form a combination sequence. One piece of track data may have one or more combination sequences. For two pieces of location information in one combination sequence, one piece of location information is used as a start location, and the other piece of location information is used as an arrival location. In this case, the combination sequence indicates that movement is performed from the start location to the arrival location after a time interval. In all the obtained combination sequences, combination sequences including a preset condition are obtained through screening. The preset condition is a preset start location and a preset time interval. Different combination sequences are obtained through screening based on different preset conditions. In the combination sequences that meet the preset condition and that are obtained through screening, the arrival location is used as an object to count quantities of different arrival locations and calculate corresponding probability values that the quantities of different arrival locations occupy in all the combination sequences that meet the preset condition, namely, transition probability values. A set of all the calculated probability values is a transition probability corresponding to the preset condition. Optionally, the preset time interval in the preset condition may be a time interval or a time interval range. For example, the preset time interval is 2 seconds, or the preset time interval is 2 seconds to 4 seconds, that is, if a time interval meets the range of 2 seconds to 4 seconds, the time interval meets the preset time interval in the preset condition.

[0043] Optionally, before the transition probability is calculated based on the plurality of pieces of track data, defective track data is removed from the plurality of pieces of track data. The defective track data is track data in which location information deviates from a road in the target region by a distance greater than a threshold, or is track data in which a distance between two pieces of adjacent location information is greater than a threshold. The plurality of pieces of track data obtained after the defective track data is removed are used to calculate the transition probability. This can improve reliability of the transition probability or smoothness of transition between two adjacent locations.

[0044] Optionally, before the transition probability is calculated based on the plurality of pieces of track data, sparse track data in the track data is obtained through interpolation for densification. The sparse track data is track data in which a distance between any two pieces of adjacent location information is greater than a third threshold. Interpolation means adding one or more pieces of location information between adjacent pieces of location information based on map information and track data, so that the location information in the track data is dense. Specifically, the location information may be added based on a time interval. For example, a track includes only two locations, and a time interval between the two locations is 6 seconds. To obtain a time interval of 3 seconds, one location is inserted between the two locations, so that a time interval between locations in the track may be 3 seconds. Based on the foregoing example, to obtain a time interval of 1 second, five locations are inserted between the two locations, that is, one location is inserted every 1 second, so that a time interval between locations in the track may be 1 second. Specific geographic location information of the inserted location may be relatively accurately determined based on map information and the track.

[0045] Optionally, the plurality of pieces of track data each include an environment parameter indicating an environment in which a corresponding terminal moves to generate a corresponding track, for example, at least one of time period information, weather information, and event information. The track data can be classified based on the environment parameter. Track data including a same environment parameter is obtained from existing tracks including different environment parameters, for example, track data in a peak time period or track data during raining. Different transition probabilities can be obtained based on track data including different environment parameters, for example, an obtained transition probability corresponding to a raining environment is used to position a terminal in the target region during raining. The method for obtaining a transition probability described in this aspect of this application is to use a transition concept to apply a motion pattern learned from real track data to calculating the transition probability. According to the claimed invention, the environment parameter includes time period information and weather information. Any implementation in which the environment parameter does not include time period information and weather information is not specifically claimed in the present application.

[0046] A possible implementation of calculating a transition probability is as follows: First, a batch of third-party track data in a to-be-positioned region is obtained. Then, a track with a relatively large deviation is removed from the data. A large deviation is mainly reflected in that a point in the track is relatively far from a road or a point that frequently appears in the track immediately jumps to a far place. Subsequently, less dense track data is densified by using a map matching and interpolation method, so that a transition probability at a relatively fine granularity (a time interval between two adjacent points in the track is as small as possible) can be obtained. Finally, track-point coordinates need to be discretized, and the entire to-be-positioned region is evenly divided into rectangular grids (a grid size is approximately 20 m * 20 m), so that each coordinate can uniquely correspond to one grid ID.

[0047] A transition probability calculation process may be divided into two processes: track densification and transition probability learning.

[0048] The track densification process is to learn a transition probability corresponding to any time interval. Specifically, first, each track is mapped to a road network by using a map matching algorithm, so that a road through which each track passes can be estimated. Then, interpolation is evenly performed along the road through which the track passes, so that a time interval between two adjacent points after the interpolation is 1s. In this way, the transition probability corresponding to any time interval at a granularity of second can be learned.

[0049] FIG. 6 shows a method for implementing map matching and interpolation. There are a plurality of map matching methods. For example, a map matching method for a track with a low sampling frequency is used. A mapping probability from an original track point to a nearby road and a transition probability between roads are calculated, to obtain a road sequence with a maximum probability. After a matched path is obtained, interpolation is evenly performed between adjacent points in the track, until a time interval between two adjacent points is equal to 1s.

[0050] The transition probability learning process is mainly to learn, from a track, a transition probability from each location to another location, and there are many specific learning manners.

[0051] An aspect of this application describes a method for learning a transition probability in an offline index manner. As shown in FIG. 7, the manner is divided into two parts. An upper part is offline index establishment, and a lower part is online query.

[0052] A total of three steps are required during offline index establishment. In a first step, track data is processed into data in a form of a table in FIG. 7. The table includes a total of three columns (a track ID, a time stamp, and a grid ID), and each row represents a record of a track point. Next, in a second step, triplets 〈Δt, i, j) are extracted from the table, where Δt is a difference between time stamps corresponding to two records, and i and j are respectively grid IDs corresponding to the two records. Every two of records corresponding to a same track ID can be used to generate a triplet, and this indicates that movement can be performed from a grid i to a grid j within the time Δt. In actual implementation, only records meeting Δt<60s need to be extracted. In a third step, statistics about the triplets generated in the second step are collected, to obtain a transition probability matrix. For example, for a probability of arriving at another grid from a grid 1 within 1s, only all triplets (1, 1, j) meeting Δt=1 and i=1 need to be found, and then a transition probability vector may be obtained by collecting statistics about frequencies that different j appears, namely, a transition probability that meets the condition Δt=1 and i=1. The transition probability matrix (a same transition time interval) may be obtained based on different start grids i, and then different transition probability matrices may be obtained based on different transition time intervals. In addition, for more in line with reality, transition probabilities in a peak time period and a non-peak time period are differentiated. Track data in the peak time period (for example, 7:00 to 9:00 or 17:00 to 19:00) is used to generate a transition matrix in the peak time period, and track data in another time period is used to generate a transition matrix in the non-peak time period.

[0053] The online query process is mainly as follows: During sequence positioning, under a condition of a given transition time interval and a given start grid, a probability distribution vector of arriving at another grid is obtained. First, a corresponding offline index is selected based on whether a current time is a peak time period. Then, a corresponding transition probability matrix is selected based on a time interval Δt. Finally, a corresponding row in the transition probability matrix is found based on a start grid i, namely, a required transition probability vector.

[0054] An aspect of this application describes a method for calculating a transition probability in an online index manner. As shown in FIG. 8, the manner may also be divided into three steps, and the first two steps are the same as those in the offline index. First, track data is processed into data in a form of a table of three columns (a track ID, a time stamp, and a grid ID). Then, triplets 〈Δt, i, j) are extracted from the table. Next, RTree is used to establish three-dimensional indexes for all the extracted triplets 〈Δt, i, j) (three elements in the triplet respectively correspond to three dimensions of indexes).

[0055] During online query, (Range Query) is queried by using a range of RTree. Ranges of Δt and i are given, for example, 1≤Δt≤2 and 1≤i≤1, RTree can return all triplets that meet the condition. Subsequently, all the third elements j are extracted from the triplets, and transition probability distribution is obtained based on value distribution of j. Different from the offline index, in the online index, a time interval may be set to a range, for example, 1s to 2s specified in the foregoing example.

[0056] An aspect of this application describes a sequence positioning method. As shown in FIG. 9, the method includes the following steps:

[0057] S301. Obtain a plurality of target MRs of a target terminal in a target region and an engineering parameter of a base station in the target region, where each target MR includes parameter information, and the parameter information includes an environment parameter. For details, refer to the foregoing descriptions of a corresponding aspect.

[0058] S302. Obtain a target feature vector based on the parameter information in the target MR and the engineering parameter of the base station, where each target MR corresponds to a target feature vector, and the target feature vector is used as an observed value, and is used to be input into a sequence positioning model to obtain a corresponding hidden location.

[0059] S303. Input the obtained target feature vector into the sequence positioning model, to obtain a movement track of the target terminal. An emission probability and a transition probability applied to the sequence positioning model are calculated by using the method for obtaining an emission probability and the method for obtaining a transition probability that are described in the foregoing aspects. Details are not described herein again. It should be noted that a target region for obtaining the emission probability and the transition probability and the target region for sequence positioning are a same geographical region. Similarly, a time period for obtaining the emission probability and the transition probability and a time period for sequence positioning are also a same time period. In this way, a better effect can be achieved.

[0060] After both the emission probability and the transition probability are obtained, the sequence positioning method can be used to recover a track of a user. As shown in FIG. 10, same as a method for obtaining a feature vector when the emission probability is calculated, herein, an MR of a to-be-positioned terminal also needs to be processed to generate a corresponding feature vector. A series of feature vectors of the same to-be-positioned terminal and the previously obtained emission probability and transition probability are input into the sequence positioning method, so that the algorithm can be used to predict a movement track of the to-be-positioned terminal based on a feature sequence.

[0061] There are a plurality of sequence positioning methods. As shown in FIG. 11, an aspect of this application describes a particle-filtering-based sequence positioning method. An idea of particle filtering is to find a particle sequence with a length T (the length is the same as a length of a to-be-recovered track), so that the sequence is most consistent with feature vectors corresponding to MRs.

[0062] In a first step, particles are initialized in state space to generate a particle set P={p^((1)), p^(2)), ..., p^((N))}. Each particle corresponds to a state and an importance weight 〈x_1^((i)), w_1^((i))〉 (the superscript i indicates a sequence number of a particle, and the subscript 1 indicates that the particle corresponds to the first point in a track). Usually, there are hundreds to thousands of particles. Each initialized particle forms a particle sequence in a subsequent step through state transition, and random initialization is performed on an initial state in a suitable range (for example, within coverage of hundreds of meters of a connected base station). The importance weight is an emission probability p(y|x), namely, a probability of obtaining an observed value by using a given state. According to the foregoing method for calculating an emission probability, a state (equivalent to a label) of a particle is input to obtain a corresponding emission probability value.

[0063] In a second step, sampling is performed. Then, a next state is sampled based on a current state x_j^((i)) of each particle and a time interval Δt_j between two adjacent points in an MR. Herein, the offline index in the foregoing aspect is used. After Δt_j and x_j^((i)) are input by using the foregoing online query method, state transition probability distribution p(x_(j+1)^((i))|x_j^((i))) is obtained. A state is sampled from the distribution as a state x_(j+1)^((i)) of an i th< particle at a (j+1) th< moment.

[0064] In a third step, decision making is performed. A corresponding importance weight w_(j+1)^((i))=w_j^((i))p(y_(j+1)|x_(j+1)^((i))) is calculated based on x_(j+1)^((i)) and w_j^((i)). Normalization is performed on importance weights of all particle sequences, so that importance distribution can be obtained. After the second and third steps are completed, lengths of all the particle sequences in the particle set are increased by 1.

[0065] In a fourth step, resampling is performed. If distribution of the importance weights of all the particle sequences meets a specific condition, resampling is performed for the particles. Resampling is a process in which sampling with replacement is performed. Sampling is performed based on values of the weights. If a particle sequence has a larger weight, a probability that sampling is performed on the particle sequence is higher (sampling may be performed on the particle sequence a plurality of times). The particle sequences existing before resampling is performed are replaced with the particle sequences existing after resampling is performed (quantities of sequences before and after sampling are the same). If a current particle sequence length is less than T, the importance weights of all the particle sequences need to be reset to 1 / N.

[0066] The second, third, and fourth steps are repeated, until the particle sequence length is equal to T. In this case, a particle sequence with a largest importance weight is output as a predicted track, and one particle sequence corresponds to a string of states, namely, a string of latitude and longitude locations.

[0067] An aspect of this application describes a Viterbi sequence positioning method. A dynamic planning idea is used in the method. A matrix V_(t,k) is continuously updated, and the matrix indicates a probability that final states of the first t sequences are a state sequence k. Each time when V_(t+1,k) is calculated, a maximum value of V_(t,x)*a_(x,k) needs to be found, where x is a variable, that is, a most suitable state prior to k needs to be found, and a_(x,k) is a calculated transition probability, namely, a probability that movement is performed from a grid x to a grid k. Then, V_(t+1,k)=b_y(k)*max((V)_(t,x)*a_(x,k)), where b represents a calculated emission probability. In this way, after all V matrix values are updated, a maximum value can be found in a row V_(T,k), and then the previous state (a state meeting the maximum value in the foregoing formula) is found through tracing, until a state transition sequence is obtained.

[0068] An aspect of this application describes an apparatus for calculating an emission probability. As shown in FIG. 12, the apparatus 100 for calculating an emission probability includes an MR obtaining module 110, a feature vector module 120, a regression processing module 130, and an emission probability calculation module 140. The MR obtaining module 110 is configured to obtain a plurality of measurement reports MRs of a terminal in a target region and an engineering parameter of at least one base station in the target region, where the target region is a predetermined geographical region, each of the plurality of MRs includes location information and parameter information, and the location information is used to indicate a location of a corresponding terminal in the target region. The feature vector module 120 is configured to obtain, based on the parameter information in each of the plurality of MRs and the engineering parameter of the at least one base station that are obtained by the MR obtaining module 110, a feature vector corresponding to each of the plurality of MRs. The regression processing module 130 is configured to obtain a single-point positioning model based on the location information in each of the plurality of MRs obtained by the MR obtaining module 110 and the feature vector that is corresponding to each of the plurality of MRs and that is obtained by the feature vector module 120. The emission probability calculation module 140 is configured to calculate, based on the single-point positioning model obtained by the regression processing module 130, the location information in each of the plurality of MRs obtained by the MR obtaining module 110, and the feature vector that is corresponding to each of the plurality of MRs and that is obtained by the feature vector module 120, an emission probability of the feature vector corresponding to each of the plurality of MRs, where the emission probability includes at least one emission probability value, and the emission probability value is used to indicate a probability that a feature vector corresponds to a piece of location information.

[0069] Further, the parameter information in each of the plurality of MRs includes at least one base station ID, the base station ID is used to indicate a base station to which a terminal corresponding to an MR including the base station ID is connected, and the at least one base station includes at least base stations indicated by base station IDs included in the plurality of MRs. The feature vector module 120 is specifically configured to: match, based on the base station IDs, the plurality of MRs obtained by the MR obtaining module 110 with the engineering parameter of the at least one base station obtained by the MR obtaining module 110, to obtain an associated engineering parameter of each of the plurality of MRs, where an associated engineering parameter of any MR includes an engineering parameter of a base station indicated by each base station ID in the any MR; and obtain, based on the associated engineering parameter and the parameter information of each of the plurality of MRs obtained by the MR obtaining module, the feature vector corresponding to each of the plurality of MRs, where any feature vector includes an associated engineering parameter and parameter information of one MR.

[0070] Further, the regression processing module 130 is specifically configured to: obtain, based on the location information in each of the plurality of MRs obtained by the MR obtaining module 110 and the feature vector that is corresponding to each of the plurality of MRs and that is obtained by the feature vector module 120, a training set corresponding to each of the plurality of MRs, where any training set includes a feature vector and location information that correspond to one MR; and input, into a machine learning model for training, the training set corresponding to each of the plurality of MRs, to obtain the single-point positioning model.

[0071] Further, the emission probability calculation module 140 is specifically configured to: input, into the single-point positioning model obtained by the regression processing module 130, the location information in each of the plurality of MRs obtained by the MR obtaining module 110 and the feature vector that is corresponding to each of the plurality of MRs and that is obtained by the feature vector module 120, to obtain a mapping relationship, where the mapping relationship is used to indicate a correspondence between a feature vector and location information; and calculate, based on the mapping relationship, the emission probability of the feature vector corresponding to each of the plurality of MRs.

[0072] The apparatus for calculating an emission probability described in this aspect is configured to implement the method described in the aspect corresponding to FIG. 3. For more detailed descriptions, refer to the aspect corresponding to FIG. 3. Details are not described herein again.

[0073] According to the apparatus for calculating an emission probability provided in this aspect of this application, feature vectors obtained by using a plurality of pieces of parameter information in MRs and engineering parameters of corresponding base stations are used as observed values, and then a single-point positioning model is trained by using the feature vectors and location information that correspond to the MRs, so that an emission probability obtained by using a spatial model of the single-point positioning model can express complex observed information, and a correspondence between a feature vector (an observed value) and location information is more reliable.

[0074] An aspect of this application describes an apparatus for calculating a transition probability. As shown in FIG. 13, the apparatus 200 for calculating a transition probability includes a track obtaining module 210 and a transition probability calculation module 220. The track obtaining module 210 is configured to obtain a plurality of pieces of track data of a terminal in a target region, where the target region is a predetermined geographical region, each of the plurality of pieces of track data includes at least two pieces of location information, the location information is used to indicate a location of a corresponding terminal in the target region, and each of a plurality of pieces of location information included in the plurality of pieces of track data corresponds to a time stamp. The transition probability calculation module 220 is configured to calculate a transition probability based on the plurality of pieces of track data obtained by the track obtaining module 210, where the transition probability includes at least one transition probability value, and the transition probability value is used to indicate a probability that movement is performed from a piece of location information to another piece of location information after a time interval T. Optionally, the plurality of pieces of track data of the terminal in the target region in a peak traffic time period or a non-peak traffic time period are obtained.

[0075] Optionally, the plurality of pieces of track data each include an environment parameter indicating an environment in which a corresponding terminal moves to generate a corresponding track, for example, at least one of time period information, weather information, and event information. The track data can be classified based on the environment parameter. Track data including a same environment parameter is obtained from existing tracks including different environment parameters, for example, track data in a peak time period or track data during raining. Different transition probabilities can be obtained based on track data including different environment parameters, for example, an obtained transition probability corresponding to a raining environment is used to position a terminal in the target region during raining. The implementation based on event information is not specifically claimed in the present application.

[0076] Further, the transition probability calculation module 220 includes a preprocessing unit 221 and a transition probability calculation unit 222. The preprocessing unit 221 is configured to process the plurality of pieces of track data obtained by the track obtaining module 210, to obtain at least one combination sequence of each of the plurality of pieces of track data, where the combination sequence includes any two pieces of location information in one piece of track data and a time interval between the any two pieces of location information. The transition probability calculation unit 222 is configured to obtain, based on a first preset condition and the at least one combination sequence that is of each of the plurality of pieces of track data and that is obtained by the preprocessing unit 221, a transition probability corresponding to the first preset condition, where the first preset condition is any one of a plurality of preset conditions, each of the plurality of preset conditions includes a preset time interval and preset location information, the preset time interval corresponds to the time interval T, and the preset location information corresponds to the piece of location information. Optionally, the preset time interval is a preset time interval range.

[0077] Further, the transition probability calculation unit 222 is specifically configured to: determine combination sequences that include a preset time interval and preset location information in the first preset condition and that are in all combination sequences that are included in the plurality of pieces of track data and that are obtained by the preprocessing unit 221; and collect statistics about the combination sequences that include the preset time interval and the preset location information in the preset condition, and calculate the transition probability corresponding to the first preset condition.

[0078] Optionally, the apparatus 200 for calculating a transition probability further includes a first track processing module 230. The first track processing module 230 is configured to remove defective track data from the plurality of pieces of track data obtained by the track obtaining module 210, where the defective track data is track data in which at least one piece of location information deviates from a road in the target region by a distance greater than a first threshold, or is track data in which a distance between two pieces of adjacent location information is greater than a second threshold.

[0079] Optionally, the apparatus 200 for calculating a transition probability further includes a second track processing module 240. The second track processing module 240 is configured to: determine sparse track data in the plurality of pieces of track data obtained by the track obtaining module 210, where the sparse track data is track data in which a distance between any two pieces of adjacent location information in the at least two pieces of location information included in the track data is greater than a third threshold; and insert one or more pieces of location information between the any two pieces of adjacent location information in the sparse track data based on map information of the target region.

[0080] The apparatus for calculating a transition probability described in this aspect is configured to implement the method described in the aspect corresponding to FIG. 5. For more detailed descriptions, refer to the aspect corresponding to FIG. 5. Details are not described herein again.

[0081] According to the apparatus for calculating a transition probability provided in this aspect of this application, a transition probability calculated based on movement track data that is of a terminal in a target region and that is provided by a third-party platform is used to recover or predict a movement track of a terminal in the target region, so that the movement track is smoother, and track jumping can effectively be avoided.

[0082] An aspect of this application provides a sequence positioning apparatus. As shown in FIG. 14, the sequence positioning apparatus 300 includes an emission probability calculation module 310, a transition probability calculation module 320, and a sequence positioning module 330. Application parameters of a sequence positioning model 330 include an emission probability and a transition probability. The emission probability calculation module 310 is configured to calculate the emission probability. The transition probability calculation module 320 is configured to calculate the transition probability. The sequence positioning module 330 is configured to obtain a movement track of a target terminal.

[0083] Specifically, the emission probability calculation module 310 includes a measurement report MR obtaining unit 311, a feature vector unit 312, a regression processing unit 313, and an emission probability calculation unit 314. The MR obtaining unit 311 is configured to obtain a plurality of measurement reports MRs of a first terminal in a target region and an engineering parameter of at least one base station in the target region, where the target region is a predetermined geographical region, each of the plurality of MRs includes location information and parameter information, and the location information is used to indicate a location of a corresponding first terminal in the target region. The feature vector unit 312 is configured to obtain, based on the parameter information in each of the plurality of MRs and the engineering parameter of the at least one base station that are obtained by the MR obtaining unit 311, a feature vector corresponding to each of the plurality of MRs. The regression processing unit 313 is configured to obtain a single-point positioning model based on the location information in each of the plurality of MRs obtained by the MR obtaining unit 311 and the feature vector that is corresponding to each of the plurality of MRs and that is obtained by the feature vector unit 312. The emission probability calculation unit 314 is configured to: calculate, based on the single-point positioning model obtained by the regression processing unit 313, the location information in each of the plurality of MRs obtained by the MR obtaining unit 311, and the feature vector that is corresponding to each of the plurality of MRs and that is obtained by the feature vector unit 312, an emission probability of the feature vector corresponding to each of the plurality of MRs, where the emission probability includes at least one emission probability value, and the emission probability value is used to indicate a probability that a feature vector corresponds to a piece of location information. The emission probability calculation module 310 described in this aspect has same functions as the apparatus for calculating an emission probability described in the aspect corresponding to FIG. 12. For detailed descriptions of the emission probability calculation module 310, refer to descriptions of the aspect corresponding to FIG. 12. Details are not described herein again.

[0084] The transition probability calculation module 320 includes a track obtaining unit 321, a first track processing unit 322, a second track processing unit 323, and a transition probability calculation unit 324. The track obtaining unit 321 is configured to obtain a plurality of pieces of track data of a second terminal in the target region, where each of the plurality of pieces of track data includes at least two pieces of location information, the location information is used to indicate a location of a corresponding second terminal in the target region, and each of a plurality of pieces of location information included in the plurality of pieces of track data corresponds to a time stamp. The first track processing unit 322 is configured to remove defective track data from the plurality of pieces of track data obtained by the track obtaining unit 321, where the defective track data is track data in which at least one piece of location information deviates from a road in the target region by a distance greater than a first threshold, or is track data in which a distance between two pieces of adjacent location information is greater than a second threshold. The second track processing module 323 is configured to determine sparse track data in the plurality of pieces of track data obtained by the track obtaining unit 321, where the sparse track data is track data in which a distance between any two pieces of adjacent location information in the at least two pieces of location information included in the track data is greater than a third threshold; and insert one or more pieces of location information between the any two pieces of adjacent location information in the sparse track data based on map information of the target region. The transition probability calculation unit 324 is configured to calculate a transition probability based on the plurality of pieces of track data processed by the first track processing unit 322 and / or the second track processing unit 323, where the transition probability includes at least one transition probability value, and the transition probability value is used to indicate a probability that movement is performed from a piece of location information to another piece of location information after a time interval T. Optionally, the plurality of pieces of track data obtained by the track obtaining unit 321 are not processed by the first track processing unit 322 and the second track processing unit 323, and the transition probability calculation unit 324 calculates a transition probability based on the plurality of pieces of track data obtained by the track obtaining unit 321. The transition probability calculation module 320 described in this aspect has same functions as the apparatus for calculating a transition probability described in the aspect corresponding to FIG. 13. For detailed descriptions of the transition probability calculation module 320, refer to descriptions of the aspect corresponding to FIG. 13. Details are not described herein again.

[0085] The sequence positioning module 330 includes a target measurement report MR obtaining unit 331, a target feature vector unit 332, and a track prediction unit 333. The target MR unit 331 is configured to obtain a plurality of target measurement reports MRs of a target terminal in the target region and the engineering parameter of the at least one base station in the target region, where the target region is a predetermined geographical region, and each of the plurality of target MRs includes parameter information. The target feature vector unit 332 is configured to obtain, based on the parameter information in each of the plurality of target MRs and the engineering parameter of the at least one base station that are obtained by the target MR unit 331, a target feature vector corresponding to each of the plurality of target MRs. The track prediction unit 333 is configured to obtain a movement track of the target terminal based on the target feature vector that is corresponding to each of the plurality of target MRs and that is obtained by the target feature vector unit 332. According to the sequence positioning apparatus provided in this aspect of this application, an emission probability obtained based on feature vectors obtained based on a plurality of pieces of parameter information in MRs and engineering parameters of corresponding base stations can express more complex observed information, thereby further improving accuracy and reliability of a movement track recovered / predicted through sequence positioning. Alternatively, a transition probability obtained by using real track data from a third party is used for sequence positioning, so that smoothness of a recovered / predicted movement track can be improved, and the obtained movement track is more reliable.

[0086] An aspect of this application provides a device. As shown in FIG. 15, the device 400 includes a memory 410, a processor 420, an input / output port 430, and a power supply 440.

[0087] The memory 410 is configured to store a programmable instruction.

[0088] The processor 420 may invoke the programmable instruction stored in the memory 410, to perform the method for obtaining an emission probability described in the aspect corresponding to FIG. 3 and / or the method for obtaining a transition probability described in the aspect corresponding to FIG. 5. For a specific method, refer to descriptions of a corresponding aspect. Details are not described herein again.

[0089] The input / output port 430 is used by the processor 420 to exchange data with a device or an apparatus outside the device 400. Specifically, the processor 420 obtains MR and / or track data from the outside by using the input / output port 430, and outputs a calculation result by using the input / output port 430.

[0090] The power supply 440 is configured to supply required power for the device 400.

[0091] FIG. 16 shows an experiment setting and a result of an actual testing experiment according to a solution of this application. Compared with a current mainstream single-point positioning method in the industry such as fingerprint (Fingerprint) positioning, range-based (Range-Based) positioning, and other sequence positioning, in the sequence positioning method based on machine learning and feature engineering described in the aspects of this application, precision is greatly improved. In the sequence positioning method described in the aspects of this application, a median of errors obtained by using drive test data may reach 22 meters, and precision is improved by more than 20%.

[0092] Compared with a case in which only single signal strength is considered in a definition of an observed value in the prior art, in the present disclosure, the definition is extended to a combination of features in any dimension.

[0093] In the prior art, there are mainly two manners of calculating a transition probability. One manner is to directly perform transition from a current grid to a neighboring grid based on an equal probability. The other manner is to assume an equation of a motion pattern, to calculate the transition probability according to the equation. According to the method described in the aspects of this application, a transition probability at each time granularity is calculated by using real data. This is more realistic than the prior art and the probability is at a finer granularity.

[0094] The scope of protection shall be defined by the appended claims.

Claims

1. A computer-implemented method for obtaining a movement track of a target terminal in a target region, wherein the method is performed by a sequence positioning apparatus and the method comprises: obtaining (S301) a plurality of target measurement reports, MRs, of the target terminal in the target region and an engineering parameter of at least one base station in the target region, wherein the target region is a predetermined geographical region, each target MR comprises target parameter information, the target parameter information comprises a target environment parameter, wherein the target environment parameter is used to indicate an environment in which the target terminal is located; obtaining (S302), based on the target parameter information in each target MR and the engineering parameter of the at least one base station, a target feature vector corresponding to each target MR; and inputting (S303), into a sequence positioning model, the target feature vector corresponding to each target MR, to obtain the movement track of the target terminal, wherein application parameters of the sequence positioning model comprise an emission probability, wherein before the obtaining a plurality of target measurement reports, MRs, of the target terminal in the target region and an engineering parameter of at least one base station in the target region, the method further comprises obtaining the emission probability by: obtaining (S101) a plurality of measurement reports MRs of a first terminal in the target region and an engineering parameter of at least one base station in the target region, wherein each of the plurality of MRs of the first terminal comprises first location information and parameter information, the first location information is used to indicate a location that is in the target region and that is of a first terminal corresponding to the MR comprising the first location information, the parameter information comprised in each of the plurality of MRs of the first terminal comprises a first environment parameter, and the first environment parameter is used to indicate an environment in which the first terminal corresponding to the MR comprising the first environment parameter is located; obtaining (S102), based on the parameter information in each of the plurality of MRs of the first terminal and the engineering parameter of the at least one base station, a feature vector corresponding to each of the plurality of MRs of the first terminal; processing (S103), by using a machine learning model, the first location information in each of the plurality of MRs of the first terminal and the feature vector corresponding to each of the plurality of MRs of the first terminal, to obtain a single-point positioning model; and calculating (S 104), based on the single-point positioning model, the first location information in each of the plurality of MRs of the first terminal, and the feature vector corresponding to each of the plurality of MRs of the first terminal, an emission probability of the feature vector corresponding to each of the plurality of MRs of the first terminal, wherein the emission probability comprises at least one emission probability value, the emission probability value is used to indicate a probability that a feature vector corresponds to a piece of first location information, and the machine learning model is a regression model; wherein the target environment parameter comprises time period information and weather information; and wherein the first environment parameter comprises time period information and weather information; wherein the engineering parameter comprises information related to a base station ID, a latitude and longitude location, an antenna height, and an antenna azimuth; wherein the application parameters of the sequence positioning model comprise a transition probability, wherein before the obtaining a plurality of target measurement reports, MRs, of the target terminal in the target region and an engineering parameter of at least one base station in the target region, the method further comprises obtaining the transition probability by: obtaining (S201) a plurality of pieces of track data of a second terminal in the target region from a third-party platform, wherein the plurality of pieces of track data comprise a same second environment parameter, the second environment parameter is used to indicate an environment in which a second terminal corresponding to track data comprising the second environment parameter is located, each of the plurality of pieces of track data of the second terminal comprises at least two pieces of second location information, the second location information is used to indicate a location that is in the target region and that is of a second terminal corresponding to track data comprising the second location information, and each piece of second location information in a plurality of pieces of location information comprised in the plurality of pieces of track data of the second terminal corresponds to a time stamp; and calculating (S202) a transition probability based on the plurality of pieces of track data of the second terminal, wherein the transition probability comprises at least one transition probability value, and the transition probability value is used to indicate a probability that movement is performed from a piece of second location information to another piece of second location information after a time interval T; wherein the second environment parameter comprises time period information and weather information.

2. The method according to claim 1, wherein the parameter information in each of the plurality of MRs of the first terminal comprises at least one base station ID, the base station ID is used to indicate a base station to which a first terminal corresponding to an MR comprising the base station ID is connected, and the at least one base station comprises at least base stations indicated by all base station IDs comprised in the plurality of MRs of the first terminal; and the obtaining, based on the parameter information in each of the plurality of MRs of the first terminal and the engineering parameter of the at least one base station, a feature vector corresponding to each of the plurality of MRs of the first terminal comprises: matching the plurality of MRs of the first terminal with the engineering parameter of the at least one base station based on the base station IDs, to obtain an associated engineering parameter of each of the plurality of MRs of the first terminal, wherein an associated engineering parameter of any MR of the first terminal comprises an engineering parameter of a base station indicated by each base station ID in the any MR of the first terminal; and obtaining, based on the associated engineering parameter and the parameter information of each of the plurality of MRs of the first terminal, the feature vector corresponding to each of the plurality of MRs of the first terminal, wherein any feature vector comprises an associated engineering parameter and parameter information of one MR of the first terminal.

3. The method according to claim 1, wherein the processing, by using a machine learning model, the first location information in each of the plurality of MRs of the first terminal and the feature vector corresponding to each of the plurality of MRs of the first terminal, to obtain a single-point positioning model comprises: obtaining, based on the first location information in each of the plurality of MRs of the first terminal and the feature vector corresponding to each of the plurality of MRs of the first terminal, a training set corresponding to each of the plurality of MRs of the first terminal, wherein any training set comprises a feature vector and first location information that correspond to one MR of the first terminal; and inputting, into the machine learning model for training, the training set corresponding to each of the plurality of MRs of the first terminal, to obtain the single-point positioning model.

4. The method according to claim 1, wherein the calculating, based on the single-point positioning model, the first location information in each of the plurality of MRs of the first terminal, and the feature vector corresponding to each of the plurality of MRs of the first terminal, an emission probability of the feature vector corresponding to each of the plurality of MRs of the first terminal comprises: inputting, into the single-point positioning model, the first location information in each of the plurality of MRs of the first terminal and the feature vector corresponding to each of the plurality of MRs of the first terminal, to obtain a mapping relationship, wherein the mapping relationship is used to indicate a correspondence between a feature vector and first location information; and calculating, based on the mapping relationship, the emission probability of the feature vector corresponding to each of the plurality of MRs of the first terminal.

5. The method according to any one of claims 1 to 4, wherein the calculating a transition probability based on the plurality of pieces of track data of the second terminal comprises: processing the plurality of pieces of track data of the second terminal to obtain at least one combination sequence of each of the plurality of pieces of track data of the second terminal, wherein the combination sequence comprises any two pieces of second location information in one piece of track data of the second terminal and a time interval between the any two pieces of second location information; and obtaining, based on a first preset condition and the at least one combination sequence of each of the plurality of pieces of track data of the second terminal, a transition probability corresponding to the first preset condition, wherein the first preset condition is any one of a plurality of preset conditions, each of the plurality of preset conditions comprises a preset time interval and preset second location information, the preset time interval corresponds to the time interval T, and the preset second location information corresponds to the piece of second location information.

6. The method according to claim 5, wherein the obtaining, based on a first preset condition and the at least one combination sequence of each of the plurality of pieces of track data of the second terminal, a transition probability corresponding to the first preset condition comprises: determining combination sequences that comprise a preset time interval and preset second location information in the first preset condition and that are in all combination sequences comprised in the plurality of pieces of track data of the second terminal; and collecting statistics about the combination sequences that comprise the preset time interval and the preset second location information in the preset condition, and calculating the transition probability corresponding to the first preset condition.

7. The method according to any one of claims 4 to 6, wherein before the calculating a transition probability based on the plurality of pieces of track data of the second terminal, the method further comprises: removing defective track data from the plurality of pieces of track data, wherein the defective track data is track data in which at least one piece of second location information deviates from a road in the target region by a distance greater than a first threshold, or is track data in which a distance between two pieces of adjacent second location information is greater than a second threshold.

8. The method according to any one of claims 4 to 6, wherein before the calculating a transition probability based on the plurality of pieces of track data of the second terminal, further comprising: determining sparse track data in the plurality of pieces of track data, wherein the sparse track data is track data in which a distance between any two pieces of adjacent second location information in the at least two pieces of second location information comprised in the track data is greater than a third threshold; and inserting one or more pieces of second location information between the any two pieces of adjacent second location information in the sparse track data based on map information of the target region.

9. The method according to any one of claims 4 to 6, wherein the obtaining a plurality of pieces of track data of a second terminal in a target region comprises: obtaining the plurality of pieces of track data of the second terminal in the target region in a peak traffic time period or a non-peak traffic time period.

10. A sequence positioning apparatus (400), comprising a memory (410) and a processor (420), wherein the memory is configured to store a set of instructions, which when executed by the processor causes the sequence positioning apparatus to perform the method according to any one of claims 1 to 9.

11. A computer program product comprising a set of instructions, which when executed by a computer of a sequence positioning apparatus, causes the computer to perform the method according to any one of claims 1 to 9.