Terminal positioning method and device, access network equipment and storage medium

Abstract

The invention discloses a terminal positioning method and device, access network equipment and a storage medium, and relates to the technical field of communication. According to the implementation scheme, the method comprises the following steps: sorting a plurality of first measurement values sent by a terminal according to corresponding timestamp information to obtain a measurement data sequence; determining an abnormal second measurement value from a plurality of first measurement values in the measurement data sequence; according to a third measurement value adjacent to the second measurement value in the measurement data sequence, updating the second measurement value in the measurement data sequence; and positioning the terminal according to the updated measurement data sequence. Therefore, the abnormal point detection is performed on the plurality of measurement values sent by the terminal to obtain the abnormal measurement value, and the abnormal measurement value is updated or corrected based on the normal measurement value adjacent to the abnormal measurement value, so that the deviation generated by the measurement value reported by the terminal due to the difference of the external environment can be corrected; therefore, the terminal is positioned based on the corrected measurement value, and the positioning precision can be improved.

Description

Technical Field

[0001] This application relates to the field of communication technologies, and in particular, to a positioning method, apparatus, access network device, and storage medium for a terminal. Background Art

[0002] Currently, an access network device can determine the location information of a terminal according to the measurement data in the MRO (Measurement Report Original, measurement report sample, or measurement report sample data file) reported by the terminal. Among them, in order to improve the accuracy of location information calculation, when generating the MRO, the terminal can optimize the measurement data in the MRO (such as denoising, filtering, etc.), so that the access network device can locate the terminal according to the optimized measurement data in the MRO reported by the terminal, and the positioning accuracy can be improved.

[0003] However, the measurement data is related to the external environment (for example, different propagation paths such as reflection and refraction will result in different reported measurement data). The method of pre-optimizing the measurement data by the terminal cannot correct the deviation caused by the difference in the external environment in the measurement data reported by the terminal, thus resulting in low positioning accuracy. Summary of the Invention

[0004] This application provides a positioning method, apparatus, access network device, and storage medium for a terminal.

[0005] According to one aspect of this application, a positioning method for a terminal is provided, which is applied to an access network device. The method includes: sorting a plurality of first measurement values sent by the terminal according to the corresponding timestamp information to obtain a measurement data sequence; wherein, the first measurement value and the corresponding timestamp information are carried in the measurement report sample MRO; determining an abnormal second measurement value from the plurality of first measurement values in the measurement data sequence; updating the second measurement value in the measurement data sequence according to a third measurement value adjacent to the second measurement value in the measurement data sequence; and positioning the terminal according to the updated measurement data sequence.

[0006] As a possible implementation, determining an abnormal second measurement value from the plurality of first measurement values in the measurement data sequence includes: sliding and intercepting the measurement data sequence according to a sliding window to obtain subsequences intercepted by each sliding; wherein, the window length of the sliding window is a first set duration, and the sliding step length is a second set duration; for any first measurement value in any subsequence, determining the k-distance neighborhood of any first measurement value from any subsequence; determining the k-local outlier factor of any first measurement value according to the k-distance neighborhood; and determining an abnormal second measurement value from the plurality of first measurement values according to the k-local outlier factors of the plurality of first measurement values.

[0007] As a possible implementation manner, determining the k-th local outlier factor of any first measurement value according to the k-th distance neighborhood includes: determining the k-th reachable distance and the local k-th local reachable density of any first measurement value according to any first measurement value and the k-th distance neighborhood; determining the k-th local outlier factor of any first measurement value according to the k-th reachable distance and the local k-th local reachable density.

[0008] As a possible implementation manner, determining the abnormal second measurement values from multiple first measurement values according to the k-th local outlier factors of the multiple first measurement values includes: determining a first abnormal data set from the multiple first measurement values according to the k-th local outlier factors of the multiple first measurement values; generating a difference data sequence according to the difference data between adjacent first measurement values in any subsequence; determining the abnormal target difference data from each difference data in the difference data sequence; determining a second abnormal data set from the multiple first measurement values according to the target difference data; taking the intersection of the first abnormal data set and the second abnormal data set as the abnormal second measurement values.

[0009] As a possible implementation manner, when there are multiple target difference data, determining the second abnormal data set from the multiple first measurement values according to the target difference data includes: sorting the multiple target difference data according to the timestamp information corresponding to the multiple target difference data to obtain a first sorted sequence; where the timestamp information corresponding to the target difference data is determined according to the timestamp information of the first measurement value for generating the target difference data; determining at least one difference data pair from the first sorted sequence; where the difference data pair includes the (2i + 1)-th target difference data and the (2i + 2)-th target difference data in the first sorted sequence, i = 0, 1, 2,...; determining the second abnormal data set from the multiple first measurement values according to the difference data pair.

[0010] As a possible implementation manner, determining the first abnormal data set from the multiple first measurement values according to the k-th local outlier factors of the multiple first measurement values includes: determining a target number n according to the number of first measurement values included in the measurement data sequence and the application scenario where the terminal is located; where n is a positive integer; sorting the multiple first measurement values in descending order according to the values of the corresponding k-th local outlier factors to obtain a second sorted sequence; generating a first abnormal data set according to the first n first measurement values sorted at the front in the second sorted sequence; or generating a first abnormal data set according to the first measurement values with the k-th local outlier factor higher than the set threshold.

[0011] As a possible implementation, to determine an abnormal second measurement value from multiple first measurement values in a measurement data sequence, it includes: obtaining the mean and standard deviation of the multiple first measurement values in the measurement data sequence; determining a reference value range according to the mean and standard deviation; and taking the first measurement values not within the reference value range in the measurement data sequence as the abnormal second measurement values.

[0012] As a possible implementation, to determine an abnormal second measurement value from multiple first measurement values in a measurement data sequence, it includes: clustering the multiple first measurement values in the measurement data sequence to obtain at least one cluster; for any cluster, obtaining the distances between each first measurement value in any cluster and the clustering center point of any cluster; and taking the first measurement values in any cluster whose distances from the cluster center point are greater than a first distance threshold as the abnormal second measurement values.

[0013] As a possible implementation, to determine an abnormal second measurement value from multiple first measurement values in a measurement data sequence, it includes: classifying the multiple first measurement values in the measurement data sequence to obtain the classification probabilities of the multiple first measurement values; where the classification probability is used to indicate the probability that the first measurement value belongs to abnormal data; and determining the abnormal second measurement value from the multiple first measurement values based on the classification probabilities of the multiple first measurement values.

[0014] As a possible implementation, to determine an abnormal second measurement value from multiple first measurement values in a measurement data sequence, it includes: obtaining the information entropy of the multiple first measurement values in the measurement data sequence; determining a reference value according to the information entropy of the multiple first measurement values; and determining the abnormal second measurement value from the multiple first measurement values according to the reference value, where the information entropy of the second measurement value is higher than the reference value.

[0015] As a possible implementation, to determine an abnormal second measurement value from multiple first measurement values in a measurement data sequence, it includes: constructing a distance matrix according to the distances between the multiple first measurement values in the measurement data sequence; determining a second distance threshold according to the distance matrix; and determining the abnormal second measurement value from the multiple first measurement values according to the second distance threshold, where the distance between the second measurement value and the remaining first measurement values is greater than the second distance threshold.

[0016] As a possible implementation, to update the second measurement value in the measurement data sequence according to the third measurement value adjacent to the second measurement value in the measurement data sequence, it includes: updating the second measurement value in the measurement data sequence according to the mean of the third measurement value; or updating the second measurement value in the measurement data sequence according to the weighted sum value of the third measurement value.

[0017] As a possible implementation manner, multiple MROs sent by a terminal are obtained according to first identification information allocated by a core network device for the terminal, second identification information of a serving cell accessed by the terminal, and third identification information of an access network device accessed by the terminal; the first measurement values in the multiple MROs are sorted according to the timestamp information in the multiple MROs to obtain a measurement data sequence.

[0018] According to another aspect of the present application, an access network device is provided, including a memory, a transceiver, and a processor;

[0019] The memory is used for storing a computer program; the transceiver is used for transceiving data under the control of the processor; the processor is used for reading the computer program in the memory and performing the following operations: sorting multiple first measurement values sent by the terminal according to corresponding timestamp information to obtain a measurement data sequence; wherein the first measurement values and the corresponding timestamp information are carried in a measurement report sample MRO; determining abnormal second measurement values from the multiple first measurement values in the measurement data sequence; updating the second measurement values in the measurement data sequence according to third measurement values adjacent to the second measurement values in the measurement data sequence; and positioning the terminal according to the updated measurement data sequence.

[0020] As a possible implementation manner, when the processor executes to determine abnormal second measurement values from the multiple first measurement values in the measurement data sequence, specifically: the measurement data sequence is slidably intercepted according to a sliding window to obtain subsequences intercepted by each sliding; wherein the window length of the sliding window is a first set duration, and the sliding step length is a second set duration; for any first measurement value in any subsequence, a k-distance neighborhood of any first measurement value is determined from any subsequence; according to the k-distance neighborhood, a k-local outlier factor of any first measurement value is determined; and abnormal second measurement values are determined from the multiple first measurement values according to the k-local outlier factors of the multiple first measurement values.

[0021] As a possible implementation manner, when the processor executes to determine the k-local outlier factor of any first measurement value according to the k-distance neighborhood, specifically: according to any first measurement value and the k-distance neighborhood, a k-reachability distance and a local k-local reachability density of any first measurement value are determined; and the k-local outlier factor of any first measurement value is determined according to the k-reachability distance and the local k-local reachability density.

[0022] As a possible implementation, the processor executes the k-th local outlier factor based on multiple first measurement values to determine abnormal second measurement values from the multiple first measurement values. Specifically: determine a first abnormal data set from the multiple first measurement values according to the k-th local outlier factor of the multiple first measurement values; generate a difference data sequence according to the difference data between adjacent first measurement values in any subsequence; determine abnormal target difference data from each difference data in the difference data sequence; determine a second abnormal data set from the multiple first measurement values according to the target difference data; use the intersection of the first abnormal data set and the second abnormal data set as the abnormal second measurement value.

[0023] As a possible implementation, there are multiple target difference data. The processor executes to determine a second abnormal data set from the multiple first measurement values according to the target difference data. Specifically: sort the multiple target difference data according to the timestamp information corresponding to the multiple target difference data to obtain a first sorted sequence; wherein, the timestamp information corresponding to the target difference data is determined according to the timestamp information of the first measurement value that generates the target difference data; determine at least one difference data pair from the first sorted sequence; wherein, the difference data pair includes the (2i + 1)-th target difference data and the (2i + 2)-th target difference data in the first sorted sequence, i = 0, 1, 2,...; determine a second abnormal data set from the multiple first measurement values according to the difference data pair.

[0024] As a possible implementation, the processor executes to determine a first abnormal data set from the multiple first measurement values according to the k-th local outlier factor of the multiple first measurement values. Specifically: determine a target number n according to the number of first measurement values included in the measurement data sequence and the application scenario where the terminal is located; wherein, n is a positive integer; sort the multiple first measurement values in descending order according to the values of the corresponding k-th local outlier factor to obtain a second sorted sequence; generate a first abnormal data set according to the first n first measurement values sorted at the front in the second sorted sequence; or generate a first abnormal data set according to the first measurement values whose k-th local outlier factor is higher than a set threshold.

[0025] As a possible implementation, the processor executes to determine abnormal second measurement values from the multiple first measurement values in the measurement data sequence. Specifically: obtain the mean and standard deviation of the multiple first measurement values in the measurement data sequence; determine a reference value range according to the mean and standard deviation; use the first measurement values not within the reference value range in the measurement data sequence as the abnormal second measurement values.

[0026] As a possible implementation, the processor executes to determine an abnormal second measurement value from multiple first measurement values in a measurement data sequence, specifically: clustering the multiple first measurement values in the measurement data sequence to obtain at least one cluster; for any cluster, obtaining the distances between each of the first measurement values in any cluster and the clustering center point of any cluster; taking the first measurement values in any cluster whose distances from the cluster center point are greater than a first distance threshold as the abnormal second measurement values.

[0027] As a possible implementation, the processor executes to determine an abnormal second measurement value from multiple first measurement values in a measurement data sequence, specifically: classifying the multiple first measurement values in the measurement data sequence to obtain the classification probabilities of the multiple first measurement values; wherein, the classification probability is used to indicate the probability that the first measurement value belongs to abnormal data; based on the classification probabilities of the multiple first measurement values, determining the abnormal second measurement value from the multiple first measurement values.

[0028] As a possible implementation, the processor executes to determine an abnormal second measurement value from multiple first measurement values in a measurement data sequence, specifically: obtaining the information entropy of the multiple first measurement values in the measurement data sequence; determining a reference value according to the information entropy of the multiple first measurement values; determining the abnormal second measurement value from the multiple first measurement values according to the reference value, wherein the information entropy of the second measurement value is higher than the reference value.

[0029] As a possible implementation, the processor executes to determine an abnormal second measurement value from multiple first measurement values in a measurement data sequence, specifically: constructing a distance matrix according to the distances between the multiple first measurement values in the measurement data sequence; determining a second distance threshold according to the distance matrix; determining the abnormal second measurement value from the multiple first measurement values according to the second distance threshold, wherein the distance between the second measurement value and the remaining first measurement values is greater than the second distance threshold.

[0030] As a possible implementation, the processor executes to update the second measurement value in the measurement data sequence according to a third measurement value adjacent to the second measurement value in the measurement data sequence, specifically: updating the second measurement value in the measurement data sequence according to the mean value of the third measurement value; or updating the second measurement value in the measurement data sequence according to the weighted sum value of the third measurement value.

[0031] As a possible implementation, the processor executes sorting multiple first measurement values sent by the terminal according to the corresponding timestamp information to obtain a measurement data sequence. Specifically: obtaining multiple MROs sent by the terminal according to the first identification information assigned by the core network device to the terminal, the second identification information of the serving cell accessed by the terminal, and the third identification information of the access network device accessed by the terminal; sorting the first measurement values in the multiple MROs according to the timestamp information in the multiple MROs to obtain a measurement data sequence.

[0032] According to another aspect of the present application, a positioning device for a terminal is provided, which is applied to an access network device. The device includes: a sorting unit for sorting multiple first measurement values sent by the terminal according to the corresponding timestamp information to obtain a measurement data sequence, where the first measurement values and the corresponding timestamp information are carried in a measurement report sample MRO; a determination unit for determining an abnormal second measurement value from the multiple first measurement values in the measurement data sequence; an update unit for updating the second measurement value in the measurement data sequence according to a third measurement value adjacent to the second measurement value in the measurement data sequence; and a positioning unit for positioning the terminal according to the updated measurement data sequence.

[0033] As a possible implementation, the determination unit is specifically configured to: perform a sliding intercept on the measurement data sequence according to a sliding window to obtain subsequences intercepted by each sliding, where the window length of the sliding window is a first set duration and the sliding step is a second set duration; for any first measurement value in any subsequence, determine the k-distance neighborhood of any first measurement value from any subsequence; determine the k-local outlier factor of any first measurement value according to the k-distance neighborhood; and determine an abnormal second measurement value from the multiple first measurement values according to the k-local outlier factors of the multiple first measurement values.

[0034] As a possible implementation, the determination unit is specifically configured to: determine the k-reachability distance and the local k-local reachability density of any first measurement value according to any first measurement value and the k-distance neighborhood; and determine the k-local outlier factor of any first measurement value according to the k-reachability distance and the local k-local reachability density.

[0035] As a possible implementation, the determination unit is specifically configured to: determine a first abnormal data set from the multiple first measurement values according to the k-local outlier factors of the multiple first measurement values; generate a difference data sequence according to the difference data between adjacent first measurement values in any subsequence; determine an abnormal target difference data from each difference data in the difference data sequence; determine a second abnormal data set from the multiple first measurement values according to the target difference data; and use the intersection of the first abnormal data set and the second abnormal data set as the abnormal second measurement value.

[0036] As a possible implementation, there are multiple target differential data. The determining unit is specifically configured to: sort the multiple target differential data according to the timestamp information corresponding to the multiple target differential data to obtain a first sorted sequence, where the timestamp information corresponding to the target differential data is determined according to the timestamp information of the first measurement value for generating the target differential data; determine at least one pair of differential data from the first sorted sequence, where a pair of differential data includes the (2i + 1)-th target differential data and the (2i + 2)-th target differential data in the first sorted sequence, and i = 0, 1, 2, …; and determine a second set of abnormal data from the multiple first measurement values according to the pair of differential data.

[0037] As a possible implementation, the determining unit is specifically configured to: determine a target number n according to the number of first measurement values included in the measurement data sequence and the application scenario where the terminal is located, where n is a positive integer; sort the multiple first measurement values in descending order according to the values of the corresponding k-th local outlier factor to obtain a second sorted sequence; generate a first set of abnormal data according to the first n first measurement values sorted at the front in the second sorted sequence; or generate a first set of abnormal data according to the first measurement values with the k-th local outlier factor higher than a set threshold.

[0038] As a possible implementation, the determining unit is specifically configured to: obtain the mean value and standard deviation of the multiple first measurement values in the measurement data sequence; determine a reference value range according to the mean value and standard deviation; and use the first measurement values not within the reference value range in the measurement data sequence as abnormal second measurement values.

[0039] As a possible implementation, the determining unit is specifically configured to: cluster the multiple first measurement values in the measurement data sequence to obtain at least one cluster; for any cluster, obtain the distances between each first measurement value in any cluster and the clustering center point of any cluster; and use the first measurement values with distances greater than a first distance threshold from the cluster center point in any cluster as abnormal second measurement values.

[0040] As a possible implementation, the determining unit is specifically configured to: classify the multiple first measurement values in the measurement data sequence to obtain the classification probabilities of the multiple first measurement values, where the classification probability is used to indicate the probability that the first measurement value belongs to abnormal data; and determine abnormal second measurement values from the multiple first measurement values based on the classification probabilities of the multiple first measurement values.

[0041] As a possible implementation manner, the determination unit is specifically configured to: obtain the information entropy of multiple first measurement values in the measurement data sequence; determine a reference value according to the information entropy of the multiple first measurement values; determine, according to the reference value, abnormal second measurement values from the multiple first measurement values, where the information entropy of the second measurement values is higher than the reference value.

[0042] As a possible implementation manner, the determination unit is specifically configured to: construct a distance matrix according to the distances between multiple first measurement values in the measurement data sequence; determine a second distance threshold according to the distance matrix; determine, according to the second distance threshold, abnormal second measurement values from the multiple first measurement values, where the distance between the second measurement values and the remaining first measurement values is greater than the second distance threshold.

[0043] As a possible implementation manner, the update unit is specifically configured to: update the second measurement values in the measurement data sequence according to the mean value of the third measurement values; or update the second measurement values in the measurement data sequence according to the weighted sum value of the third measurement values.

[0044] As a possible implementation manner, the sorting unit is specifically configured to: obtain multiple MROs sent by the terminal according to the first identification information assigned by the core network device to the terminal, the second identification information of the serving cell accessed by the terminal, and the third identification information of the access network device accessed by the terminal; sort the first measurement values in the multiple MROs according to the timestamp information in the multiple MROs to obtain a measurement data sequence.

[0045] According to another aspect of the present application, there is provided a processor-readable storage medium storing a computer program for causing a processor to execute any one of the foregoing terminal positioning methods.

[0046] According to another aspect of the present application, there is provided a computer program product which, when the instructions in the computer program product are executed by a processor, executes any one of the foregoing terminal positioning methods.

[0047] The present application has the following technical effects: The access network device performs outlier detection or identification on multiple first measurement values sent by the terminal to obtain abnormal second measurement values, and updates or corrects the abnormal second measurement values based on the third measurement values that are adjacent to and normal to the second measurement values, so as to correct the deviation caused by the difference in the external environment in the measurement values reported by the terminal, and thus perform positioning on the terminal based on the corrected measurement values, which can improve the positioning accuracy, that is, improve the accuracy of calculating the location information of the terminal.

[0048] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The drawings are used to better understand the solution of the present application and do not constitute a limitation to the present application. Among them:

[0050] Figure 1 is a schematic flowchart of a positioning method for a terminal provided by an embodiment of the present application;

[0051] Figure 2 is a schematic flowchart of another positioning method for a terminal provided by an embodiment of the present application;

[0052] Figure 3 is a schematic flowchart of another positioning method for a terminal provided by an embodiment of the present application;

[0053] Figure 4 is a schematic flowchart of another positioning method for a terminal provided by an embodiment of the present application;

[0054] Figure 5 is a schematic flowchart of another positioning method for a terminal provided by an embodiment of the present application;

[0055] Figure 6 is a schematic flowchart of another positioning method for a terminal provided by an embodiment of the present application;

[0056] Figure 7 is a schematic flowchart of another positioning method for a terminal provided by an embodiment of the present application;

[0057] Figure 8 is a schematic flowchart of another positioning method for a terminal provided by an embodiment of the present application;

[0058] Figure 9 is a schematic flowchart of another positioning method for a terminal provided by an embodiment of the present application;

[0059] Figure 10 is a schematic diagram of the sliding mode of a sliding window provided by an embodiment of the present application;

[0060] Figure 11 is a schematic structural diagram of an access network device provided by an embodiment of the present application;

[0061] Figure 12 is a schematic structural diagram of a positioning device for a terminal provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0062] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all of the embodiments. 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.

[0063] That is, the term "and / or" in the embodiments of the present application describes the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally represents an "or" relationship between the associated objects before and after.

[0064] Currently, the measurement data in MRO (such as AOA (Angle of Arrival) / TA (Time Advance)) can be widely used for positioning, coverage evaluation, etc. The calculation of the terminal's location information relies on the original measurement data (such as AOA / TA) in the MRO reported by the terminal. If the accuracy of the original measurement data is slightly poor and the deviation of some reported measurement data is large, it will affect the application.

[0065] In the related art, when generating a measurement report, the terminal can adopt the following several methods to optimize the reporting of measurement data:

[0066] First, the signal preprocessing method: including steps such as denoising and filtering, which can help eliminate the noise in the measurement data and improve the signal-to-noise ratio. This usually involves some digital signal processing techniques.

[0067] Second, the channel consistency calibration method: For the MRO data or AOA data received by multiple channels (i.e., antenna channels), there may be inconsistencies between channels. It is necessary to calibrate the data received by multiple channels to ensure the consistency between the data received by multiple channels. This usually involves calibrating and equalizing the response of each channel.

[0068] Third, the time synchronization calibration method: For the MRO data or AOA data received by multiple channels, there may be time synchronization problems between channels. It is necessary to perform time synchronization calibration to ensure the synchronization of the data received by each channel.

[0069] Fourth, the space calibration method: This involves aligning the data received by each channel to the same spatial coordinate system. For example, for AOA data, this usually involves aligning different receiving antennas to the same coordinate system.

[0070] Fifth, phase calibration method: For measurement data, phase calibration is a very important step. Due to the possible phase differences between channels, phase calibration is required to eliminate such differences.

[0071] However, none of the above methods can correct the deviation of the reported measurement data caused by differences in the external environment. That is, the existing correction of measurement data relies on the measurement algorithm. The basis of this correction is the measurement data, and the measurement data is related to the external environment (for example, different propagation paths such as reflection and refraction will result in different AOA / TA in the reported measurement data). Only using the measurement algorithm to optimize the measurement data cannot truly reflect the real position information of the measurement terminal.

[0072] To address at least one of the above problems, this application provides a positioning method, device, access network device, and storage medium for a terminal.

[0073] The following describes the positioning method, device, access network device, and storage medium for the terminal in this embodiment with reference to the accompanying drawings. Before specifically describing the embodiments of the present application, for the convenience of understanding, first introduce common technical terms:

[0074] The first measurement value refers to the measurement data used for positioning the terminal in MRO. For example, the first measurement value may include, but is not limited to, AOA and TA.

[0075] The second measurement value refers to the outlier (or called outlier point, abnormal data, or outlier) among multiple first measurement values. Among them, the number of second measurement values can be one, or it can also be multiple. The embodiments of the present application do not limit this.

[0076] The third measurement value refers to the first measurement value adjacent to the second measurement value. Among them, in order to improve the positioning accuracy, the third measurement value can be a normal first measurement value (that is, a non-outlier or non-outlier point).

[0077] It should be noted that in actual application, there may be a situation where at least two second measurement values are adjacent. At this time, in order to improve the positioning accuracy, the normal first measurement value closest to the second measurement value can be used as the third measurement value.

[0078] Sliding interception means sliding the sliding window according to the sliding step size, and taking the data within the sliding window after each sliding as the data intercepted by sliding.

[0079] The k-distance neighborhood: For a data point p, the k-distance neighborhood of this point p is the set of all points within the k-distance of point p. For example, the k points closest to this point p (i.e., the nearest neighbors).

[0080] k-th reachable distance: For a data point p, the k-th reachable distance of point p can be calculated as follows: calculate the distances between each point in the k-th distance neighborhood and point p, and take the minimum value among the above distances as the k-th reachable distance of point p.

[0081] Local k-local reachability density: For a point P, the local k-local reachability density of point p can be the ratio of the mutual reachable distances between each point in the k-th distance neighborhood.

[0082] For example, assume that the points in the k-th distance neighborhood of point p include the following points: N1, N2,..., N k , for point p, the distances from point p to these points are D(p, N1), D(p, N2),..., D(p, N k ). The reachable distances from point p to these points can be calculated, that is, the k-th reachable distance of point p is: min(D(p, N1), D(p, N2),..., D(p, N k ). Then, the mutual reachable distances between points N1, N2,..., N k can be calculated, that is, for any two points N i and N j , calculate min(D(N i , N j ), D(N j , N i ), and calculate the ratio of all these mutual reachable distances, that is, for all (N i , N j ), calculate min(D(N i , N j ), D(N j , N i )) / D(p, N i ), and this ratio is the local k-local reachability density of point p.

[0083] k-th local outlier factor (or local anomaly factor): For a data point p, the k-th local outlier factor of point p is used to measure the degree of outlier of point p. Among them, the higher the degree of outlier of point p, the larger the value of the k-th local outlier factor of point p. Among them, the k-th local outlier factor of point p can be the ratio of the local k-local reachability density of point p to the k-th reachable distance of point p.

[0084] Differential data refers to the difference between two adjacent first measurement values.

[0085] The timestamp information corresponding to the target differential data is determined based on the timestamp information of the two first measurement values used to calculate the target differential data. For example, any one of the timestamp information of the two first measurement values used to calculate the target differential data can be used as the timestamp information corresponding to the target differential data. For another example, the average value of the timestamp information of the two first measurement values used to calculate the target differential data can be used as the timestamp information corresponding to the target differential data.

[0086] The application scenarios where the terminal is located include, but are not limited to, the following scenarios: highways, railways, urban arterial roads, etc.

[0087] Clustering refers to dividing a data set into different classes or clusters (denoted as clusters in this application) according to a specific criterion (such as distance), so that the similarity between data objects within the same cluster (or cluster) is as large as possible, and at the same time, the difference between data objects not in the same cluster (or cluster) is also as large as possible. That is, after clustering, data objects belonging to the same class are gathered together as much as possible, and data objects of different classes are separated as much as possible.

[0088] The clustering center point refers to the center of the cluster, that is, the most representative point in the cluster, which can make any object in the cluster closer to the clustering center point of the cluster than the clustering center points of other clusters. For example, the clustering center point of the cluster can be the average value or the center point of all points in the cluster, etc.

[0089] Information entropy is a quantity used to determine the uncertainty of data.

[0090] The first identification information refers to the identification information assigned by the core network device to the terminal. For example, taking the core network device as the AMF (Access and Mobility Management Function) for illustration, the first identification information can be the AMF UE NGAP ID (AMF UE Next Generation Application Identity, the identification of the user's next-generation application protocol interface within the AMF device) of the terminal.

[0091] The second identification information refers to the identification information of the serving cell (cell) accessed by the terminal. For example, the second identification information can be the cell ID.

[0092] The third identification information refers to the third identification information of the access network device accessed by the terminal. Taking the access network device as the gNB (gNB) in the 5G network architecture (next generation system) for illustration, the third identification information can be the gNB ID.

[0093] Figure 1 It is a schematic flow chart of a positioning method for a terminal provided by an embodiment of the present application.

[0094] The positioning method for the terminal in the embodiment of the present application can be applied to an access network device.

[0095] Among them, taking the access network device as a base station for example. The base station may include multiple cells that provide services for terminals. According to different specific application scenarios, the base station may also be referred to as an access point, or may be a device that communicates with a wireless terminal through one or more sectors on the air interface in the access network, or other names. The access network device can be used to mutually replace the received air frame and Internet Protocol (IP) packets, and act as a router between the wireless terminal and the rest of the access network, where the rest of the access network may include an Internet Protocol (IP) communication network. The access network device can also coordinate the attribute management of the air interface. For example, the access network device involved in the embodiment of the present application may be an access network device (Base Transceiver Station, BTS) in a Global System for Mobile communications (GSM) or Code Division Multiple Access (CDMA), or may be an access network device (NodeB) in a Wide-band Code Division Multiple Access (WCDMA), or may also be an evolved access network device (evolutional Node B, eNB or e-NodeB) in a Long Term Evolution (LTE) system, a 5G base station (gNB) in a 5G network architecture (next generation system), or may also be a Home evolved Node B (HeNB), a relay node, a femto, a pico, etc. The embodiment of the present application does not limit this. In some network structures, the access network device may include a Centralized Unit (CU) node and a Distributed Unit (DU) node, and the centralized unit and the distributed unit may also be geographically separated.

[0096] Among them, the terminal can be a device that provides voice and / or data connectivity to users, such as a handheld device with wireless connection capabilities, or other processing devices connected to a wireless modem. In different systems, the name of the terminal may also be different. For example, in a 5G system, the terminal can be called a User Equipment (UE). Among them, the wireless terminal can communicate with one or more Core Networks (CNs) via a Radio Access Network (RAN). The wireless terminal can be a mobile terminal, such as a mobile phone (or a "cellular" phone) and a computer with a mobile terminal. For example, it can be a portable, pocket-sized, handheld, computer-integrated, or vehicle-mounted mobile device that exchanges voice and / or data with the wireless access network. For example, devices such as Personal Communication Service (PCS) phones, cordless phones, Session Initiated Protocol (SIP) phones, Wireless Local Loop (WLL) stations, and Personal Digital Assistants (PDAs). The wireless terminal can also be called a system, a subscriber unit, a subscriber station, a mobile station, a mobile, a remote station, an access point, a remote terminal, an access terminal, a user terminal, a user agent, a user device, which is not limited in the embodiments of this application.

[0097] As Figure 1 shown, the positioning method of the terminal may include the following steps:

[0098] Step S101, sort the multiple first measurement values sent by the terminal according to the corresponding timestamp information to obtain a measurement data sequence; among them, the first measurement value and the corresponding timestamp information are carried in the MRO.

[0099] Among them, each first measurement value has corresponding timestamp information (TimeStamp), and each first measurement value and the corresponding timestamp information are carried in the MRO sent by the terminal.

[0100] In the embodiments of the present application, the access network device may receive multiple MROs sent by the terminal. For example, the terminal may periodically send MROs to the access network device (for example, the periodic duration may be 4 s (seconds), 5 s, 6 s, etc.). After that, the access network device may sort the first measurement values in the multiple received MROs according to the timestamp information in the corresponding MROs to obtain a measurement data sequence.

[0101] For example, multiple first measurement values may be sorted according to the timestamp information from earliest to latest to obtain a measurement data sequence. Alternatively, multiple first measurement values may also be sorted according to the timestamp information from latest to earliest to obtain a measurement data sequence.

[0102] Step S102: Determine an abnormal second measurement value from the multiple first measurement values in the measurement data sequence.

[0103] In the embodiments of the present application, the access network device may perform abnormal point or outlier detection on the multiple first measurement values in the measurement data sequence to determine an abnormal second measurement value from the multiple first measurement values in the measurement data sequence.

[0104] As an example, based on statistical abnormal point detection algorithms, clustering-based abnormal point detection algorithms, classification-based abnormal point detection algorithms, information theory-based abnormal point detection algorithms, distance-based abnormal point detection algorithms, density-based abnormal point detection algorithms, etc., abnormal point or outlier detection may be performed on the multiple first measurement values in the measurement data sequence to obtain an abnormal second measurement value.

[0105] Step S103: Update the second measurement value in the measurement data sequence according to the third measurement value adjacent to the second measurement value in the measurement data sequence.

[0106] In the embodiments of the present application, for any second measurement value, the access network device may update the second measurement value in the measurement data sequence according to the third measurement value adjacent to the second measurement value in the measurement data sequence.

[0107] As an example, when the number of third measurement values is one, for example, the second measurement value is the first element or the last element in the measurement data sequence. At this time, the third measurement value may be directly assigned to the second measurement value.

[0108] As another example, when the number of third measurement values is multiple, for example, the third measurement values may include: the first measurement value on the left side of the second measurement value and the first measurement value on the right side of the second measurement value. At this time, the second measurement value may be updated according to the multiple third measurement values.

[0109] For example, the second measurement value in the measurement data sequence can be updated according to the mean value of multiple third measurement values, that is, the above mean value is assigned to the second measurement value.

[0110] For another example, the second measurement value in the measurement data sequence can be updated according to the weighted sum value of multiple third measurement values, that is, the above weighted sum value can be assigned to the second measurement value.

[0111] Step S104: Locate the terminal according to the updated measurement data sequence.

[0112] In the embodiment of the present application, the access network device can locate the terminal according to the updated measurement data sequence.

[0113] In the terminal location method of the embodiment of the present application, the access network device performs outlier detection or identification on multiple first measurement values sent by the terminal to obtain abnormal second measurement values, and updates or corrects the abnormal second measurement values based on the normal third measurement values adjacent to the second measurement values, which can correct the deviation caused by the difference in the external environment in the measurement values reported by the terminal. Thus, based on the corrected measurement values, the terminal can be located, and the location accuracy can be improved, that is, the accuracy of calculating the location information of the terminal can be improved.

[0114] To clearly illustrate how to determine the abnormal second measurement values from multiple first measurement values in the measurement data sequence in the above embodiments of the present application, the present application also proposes a terminal location method.

[0115] Figure 2 It is a schematic flowchart of another terminal location method provided by the embodiment of the present application.

[0116] As Figure 2 shown, the terminal location method may include the following steps:

[0117] Step S201: Sort multiple first measurement values sent by the terminal according to the corresponding timestamp information to obtain a measurement data sequence.

[0118] Among them, the first measurement value and the corresponding timestamp information are carried in the measurement report sample MRO.

[0119] For the explanation of step S201, reference can be made to the relevant description in any embodiment of the present application, and details are not described herein again.

[0120] Step S202: Slide and intercept the measurement data sequence according to a sliding window to obtain subsequences intercepted by each slide.

[0121] Among them, the window length of the sliding window is a first set duration, and the sliding step of the sliding window is a second set duration.

[0122] Wherein, both the first set duration and the second set duration are preset duration parameters. It should be noted that, in order to avoid missing detection of abnormal points, the value of the second set duration should not be greater than the value of the first set duration. For example, the first set duration can be equal to the second set duration, or the first set duration can be greater than the second set duration.

[0123] In the embodiment of the present application, the measurement data sequence may have a corresponding time length and time range, wherein the time length and time range are determined according to the timestamp information corresponding to each first measurement value in the measurement data sequence.

[0124] For example, taking the sorting of multiple first measurement values in ascending order of the corresponding timestamp information to obtain the measurement data sequence as an example, marking the timestamp information of the first measurement value at the first position in the measurement data sequence as TimeStamp1, and the timestamp information of the first measurement value at the last position as TimeStamp2, then the time length corresponding to the measurement data sequence can be: TimeStamp2 - TimeStamp1, and the time range can be: [TimeStamp1, TimeStamp2].

[0125] In the embodiment of the present application, the measurement data sequence can be slidably intercepted according to a sliding window to obtain the subsequences intercepted by the sliding window after each slide, that is, the subsequence is a partial sequence within the sliding window in the measurement data sequence.

[0126] For example, marking the window length of the sliding window as T, the sliding step of the sliding window as t1, and the timestamp information of the first measurement value at the first position in the measurement data sequence as TimeStamp1, then the subsequence intercepted by the first slide is: the subsequence in the measurement data sequence with the timestamp information within [TimeStamp1, TimeStamp1 + T], the subsequence intercepted by the second slide is: the subsequence in the measurement data sequence with the timestamp information within [TimeStamp1 + t1, TimeStamp1 + t1 + T], the subsequence intercepted by the third slide is: the subsequence in the measurement data sequence with the timestamp information within [TimeStamp1 + 2t1, TimeStamp1 + 2t1 + T], the subsequence intercepted by the fourth slide is: the subsequence in the measurement data sequence with the timestamp information within [TimeStamp1 + 3t1, TimeStamp1 + 3t1 + T], and so on, which will not be listed one by one here.

[0127] Step S203, for any first measurement value in any subsequence, determine the k-distance neighborhood of any first measurement value from any subsequence.

[0128] In an embodiment of the present application, for any first measurement value in any subsequence among the subsequences intercepted by sliding, the k-distance neighborhood of the first measurement value can be determined from the any subsequence.

[0129] Step S204: Determine the k-local outlier factor of any first measurement value according to the k-distance neighborhood.

[0130] In an embodiment of the present application, based on the LOF (Local Outlier Factor) algorithm, the k-local outlier factor of the above-mentioned first measurement value can be determined according to the k-distance neighborhood.

[0131] As an example, the calculation method of the k-local outlier factor of the first measurement value can be, for example:

[0132] 1. The k-reachability distance and the local k-local reachability density of the first measurement value can be calculated according to the above-mentioned first measurement value and its k-distance neighborhood.

[0133] As an example, mark the first measurement value as point p. Then, the distances between each point in the k-distance neighborhood of point p and point p can be calculated, and the minimum value among the above distances is used as the k-reachability distance of point p. For example, assume that the points in the k-distance neighborhood of point p include the following points: N1, N2,..., N k , then for point p, the distances from point p to these points are D(p, N1), D(p, N2),..., D(p, N k ). The reachability distances from point p to these points can be calculated, that is, the k-reachability distance of point p is: min(D(p, N1), D(p, N2),..., D(p, N k ))

[0134] As an example, still taking the above example, the calculation method of the local k-local reachability density of point p is: the mutual reachability distances between points N1, N2,..., N k can be calculated, that is, for any two points N i and N j , calculate min(D(N i , N j ), D(N j , N i ), and take min(D(N i , N j ), D(N j , N i )) / D(p, N i ) as the local k-local reachability density of point p.

[0135] 2. Determine the k-th local outlier factor of the first measurement value according to the k-th reachable distance and the local k-th local reachability density of the first measurement value.

[0136] For example, the ratio of the local k-th local reachability density of the first measurement value to the k-th reachable distance of point p can be used as the k-th local outlier factor of the first measurement value.

[0137] Step S205. Determine the abnormal second measurement value from multiple first measurement values according to the k-th local outlier factors of the multiple first measurement values.

[0138] In the embodiment of the present application, the abnormal second measurement value can be determined from multiple first measurement values according to the k-th local outlier factors of the multiple first measurement values in the measurement data sequence.

[0139] As a possible implementation manner, the abnormal second measurement value can be directly determined according to the k-th local outlier factors of the multiple first measurement values. For example, the first measurement value with a relatively large k-th local outlier factor can be used as the abnormal second measurement value.

[0140] In one example, the first measurement value with a k-th local outlier factor higher than a set threshold can be used as the abnormal second measurement value.

[0141] In another example, the first measurement value with a relatively large k-th local outlier factor and a target number n can be selected from multiple first measurement values and used as the abnormal second measurement value. Wherein, the target number n is a positive integer, and n can be determined according to the number of first measurement values included in the measurement data sequence and the application scenario where the terminal is located. For example, the mapping relationship between the number of elements included in the measurement data sequence, n, and the application scenario can be preset in advance. Therefore, in the present application, according to the application scenario where the terminal is located and the number of first measurement values (i.e., the number of elements included in the measurement data sequence), the above mapping relationship can be queried to obtain the target number n.

[0142] For example, multiple first measurement values in the measurement data sequence can be sorted in descending order according to the corresponding values of the k-th local outlier factors to obtain a sorted sequence, and the first n first measurement values sorted at the front in the second sorted sequence can be used as the abnormal second measurement values.

[0143] As another possible implementation manner, the abnormal second measurement value can also be indirectly determined according to the k-th local outlier factors of the multiple first measurement values, and the embodiment of the present application does not limit this.

[0144] Step S206. Update the second measurement value in the measurement data sequence according to the third measurement value adjacent to the second measurement value in the measurement data sequence.

[0145] Step S207: Locate the terminal according to the updated measurement data sequence.

[0146] For the explanations of steps S206 to S207, reference can be made to the relevant descriptions in any embodiment of this application, which will not be elaborated here.

[0147] The terminal positioning method according to the embodiment of this application can implement a density-based outlier detection algorithm to detect outliers in the measurement data sequence, improving the effectiveness and accuracy of outlier detection.

[0148] To clearly illustrate how to indirectly determine the abnormal second measurement value according to the k-th local outlier factor of multiple first measurement values in any of the above embodiments of this application, this application also proposes a terminal positioning method.

[0149] Figure 3 It is a schematic flowchart of another terminal positioning method provided by the embodiment of this application.

[0150] As Figure 3 , the terminal positioning method may include the following steps:

[0151] Step S301: Sort the multiple first measurement values sent by the terminal according to the corresponding timestamp information to obtain a measurement data sequence.

[0152] Among them, the first measurement value and the corresponding timestamp information are carried in the measurement report sample MRO.

[0153] Step S302: Slide and intercept the measurement data sequence according to a sliding window to obtain the subsequences intercepted by each slide.

[0154] Among them, the window length of the sliding window is a first set duration, and the sliding step is a second set duration.

[0155] Step S303: For any first measurement value in any subsequence, determine the k-distance neighborhood of any first measurement value from any subsequence.

[0156] Step S304: Determine the k-th local outlier factor of any first measurement value according to the k-distance neighborhood.

[0157] For the explanations of steps S301 to S304, reference can be made to the relevant descriptions in any embodiment of this application, which will not be elaborated here.

[0158] Step S305: Determine a first abnormal data set from the multiple first measurement values according to the k-th local outlier factor of the multiple first measurement values.

[0159] In an embodiment of the present application, the access network device may determine a first abnormal data set from multiple first measurement values in a measurement data sequence according to the k-th local outlier factor of the multiple first measurement values.

[0160] As a possible implementation manner, the determination manner of the first abnormal data set may be, for example:

[0161] 1. Determine a target number n according to the number of first measurement values included in the measurement data sequence and the application scenario where the terminal is located; where n is a positive integer.

[0162] For example, a mapping relationship between the number of elements included in the measurement data sequence, n, and the application scenario may be preset in advance. Thus, in the present application, according to the application scenario where the terminal is located and the number of first measurement values (i.e., the number of elements included in the measurement data sequence), the above mapping relationship can be queried to obtain the target number n.

[0163] Among them, n has a positive correlation with the number of first measurement values.

[0164] 2. Sort the multiple first measurement values in descending order according to the values of the corresponding k-th local outlier factors to obtain a sorted sequence (denoted as the second sorted sequence in the present application).

[0165] 3. Generate a first abnormal data set according to the first n first measurement values sorted at the front in the second sorted sequence, that is, the first abnormal data set includes n first measurement values, and these n first measurement values are sorted at the front in the second sorted sequence.

[0166] As another possible implementation manner, the determination manner of the first abnormal data set may be, for example: Generate a first abnormal data set according to the first measurement values whose k-th local outlier factors are higher than a set threshold. That is, the first abnormal data set includes all the first measurement values whose k-th local outlier factors are higher than the set threshold.

[0167] Thus, it is possible to determine the first abnormal data set based on different methods, improving the flexibility and applicability of the method.

[0168] Step S306: Generate a difference data sequence according to the difference data between adjacent first measurement values in any subsequence.

[0169] Among them, the number of difference data sequences is the same as the number of subsequences, that is, a difference data sequence can be generated according to one subsequence.

[0170] In an embodiment of the present application, for any one subsequence, a difference data sequence may also be generated according to the difference data between two adjacent first measurement values in the subsequence.

[0171] For example, the difference between the first measurement value at the second position in the subsequence and the first measurement value at the first position is used as the differential data at the first position in the differential data sequence. The difference between the first measurement value at the third position in the subsequence and the first measurement value at the second position is used as the differential data at the second position in the differential data sequence. The difference between the first measurement value at the fourth position in the subsequence and the first measurement value at the third position is used as the differential data at the third position in the differential data sequence, and so on. Details are not enumerated herein.

[0172] Step S307: Determine the target differential data with anomalies from each differential data in the differential data sequence.

[0173] Among them, the number of target differential data can be at least one.

[0174] In the embodiments of the present application, anomaly or outlier detection can be performed on all differential data in the differential data sequence to determine the target differential data with anomalies from the differential data in the differential data sequence.

[0175] As an example, based on statistical anomaly detection algorithms, clustering-based anomaly detection algorithms, classification-based anomaly detection algorithms, information theory-based anomaly detection algorithms, distance-based anomaly detection algorithms, density-based anomaly detection algorithms, etc., anomaly or outlier detection can be performed on all differential data in the differential data sequence to obtain the target differential data with anomalies.

[0176] Step S308: Determine the second set of abnormal data from multiple first measurement values according to the target differential data.

[0177] In the embodiments of the present application, the second set of abnormal data can be determined from multiple first measurement values according to the target differential data.

[0178] As an example, when the number of target differential data is one, the second set of abnormal data can be generated according to the two adjacent first measurement values that generate the target differential data.

[0179] As another example, when there are multiple target differential data, the determination method of the second set of abnormal data can be, for example:

[0180] 1. The multiple target differential data can be sorted according to the timestamp information corresponding to the multiple target differential data to obtain the first sorted sequence.

[0181] Among them, the timestamp information corresponding to the target differential data is determined according to the timestamp information of the first measurement value that generates the target differential data.

[0182] For example, multiple target differential data can be sorted in ascending order according to the corresponding timestamp information to obtain a first sorted sequence.

[0183] 2. Determine at least one pair of differential data from the first sorted sequence; wherein, the pair of differential data includes the target differential data at the (2i + 1)-th position and the target differential data at the (2i + 2)-th position in the first sorted sequence, where i = 0, 1, 2, …, that is, i is a natural number.

[0184] 3. Determine a second set of abnormal data from the multiple first measurement values according to the pair of differential data.

[0185] For example, for any pair of differential data, the first measurement values used to generate the two target differential data in the pair of differential data can be determined to obtain 4 first measurement values, and the earliest timestamp information and the latest timestamp information are determined from the timestamp information of these 4 first measurement values. According to each first measurement value whose timestamp information is within [the earliest timestamp information, the latest timestamp information], a second set of abnormal data is generated.

[0186] Step S309: Use the intersection of the first set of abnormal data and the second set of abnormal data as the abnormal second measurement value.

[0187] In the embodiments of the present application, the intersection of the first set of abnormal data and the second set of abnormal data can be used as the abnormal second measurement value.

[0188] Step S310: Update the second measurement value in the measurement data sequence according to the third measurement value adjacent to the second measurement value in the measurement data sequence.

[0189] Step S311: Locate the terminal according to the updated measurement data sequence.

[0190] For the explanations of steps S310 to S311, reference can be made to the relevant descriptions in any embodiment of the present application, and details are not described herein again.

[0191] In the positioning method of the terminal in the embodiments of the present application, considering that when the terminal is in a moving state, directly detecting abnormal points in the first measurement values will misidentify some normal points as abnormal points. Therefore, in the present application, to solve the above problem, abnormal points can also be identified based on the differential data between adjacent first measurement values, which can further improve the accuracy and reliability of abnormal point identification.

[0192] To clearly illustrate how to determine the abnormal second measurement value from the multiple first measurement values in the measurement data sequence in any embodiment of the present application, the present application also proposes a positioning method for a terminal.

[0193] Figure 4It is a schematic flowchart of another positioning method for a terminal provided by an embodiment of the present application.

[0194] As Figure 4 , the positioning method of the terminal may include the following steps:

[0195] Step S401: Sort multiple first measurement values sent by the terminal according to the corresponding timestamp information to obtain a measurement data sequence.

[0196] Among them, the first measurement value and the corresponding timestamp information are carried in the measurement report sample MRO.

[0197] For the explanation of step S401, reference can be made to the relevant description in any embodiment of the present application, and details will not be elaborated herein.

[0198] Step S402: Obtain the mean and standard deviation of multiple first measurement values in the measurement data sequence.

[0199] In the embodiment of the present application, the mean μ and standard deviation σ of multiple first measurement values in the measurement data sequence can be calculated.

[0200] Step S403: Determine a reference value range according to the mean and standard deviation.

[0201] In the embodiment of the present application, the reference value range can be determined according to the mean μ and a set multiple of the standard deviation σ. For example, the reference value range can be: [μ - σ * set multiple, μ + σ * set multiple], where the set multiple is a positive number. For example, the set multiple can be 3.

[0202] Step S404: Take the first measurement values that are not within the reference value range in the measurement data sequence as abnormal second measurement values.

[0203] In the embodiment of the present application, the first measurement values within the reference value range in the measurement data sequence can be taken as normal points. Correspondingly, the first measurement values not within the reference value range in the measurement data sequence can be taken as abnormal points (i.e., second measurement values).

[0204] Step S405: Update the second measurement values in the measurement data sequence according to the third measurement values adjacent to the second measurement values in the measurement data sequence.

[0205] Step S406: Locate the terminal according to the updated measurement data sequence.

[0206] For the explanation of steps S405 to S406, reference can be made to the relevant description in any embodiment of the present application, and details will not be elaborated herein.

[0207] The positioning method of the terminal in the embodiment of the present application can implement a statistical-based outlier detection algorithm to detect outliers in the measurement data sequence, improving the effectiveness and accuracy of outlier detection.

[0208] To clearly illustrate how to determine the abnormal second measurement value from multiple first measurement values in the measurement data sequence in any embodiment of the present application, the present application also proposes a positioning method for a terminal.

[0209] Figure 5 It is a schematic flowchart of another positioning method for a terminal provided by an embodiment of the present application.

[0210] Such as Figure 5 The positioning method of the terminal may include the following steps:

[0211] Step S501: Sort the multiple first measurement values sent by the terminal according to the corresponding timestamp information to obtain a measurement data sequence.

[0212] Among them, the first measurement value and the corresponding timestamp information are carried in the measurement report sample MRO.

[0213] For the explanation of step S501, reference can be made to the relevant description in any embodiment of the present application, which will not be elaborated here.

[0214] Step S502: Cluster the multiple first measurement values in the measurement data sequence to obtain at least one cluster.

[0215] In the embodiment of the present application, based on a clustering algorithm (such as the K-means clustering algorithm, the DBSCAN clustering algorithm (Density-Based Spatial Clustering of Applications with Noise, density-based clustering algorithm), the hierarchical clustering algorithm, etc.), the multiple first measurement values in the measurement data sequence are clustered to obtain at least one cluster.

[0216] Step S503: For any cluster, obtain the distances between each first measurement value in the cluster and the clustering center point of the cluster.

[0217] In the embodiment of the present application, for any one cluster, the distances between each first measurement value in the cluster and the clustering center point of the cluster can be calculated.

[0218] Step S504: Use the first measurement value whose distance from the cluster center point in any cluster is greater than the first distance threshold as the abnormal second measurement value.

[0219] Among them, the first distance threshold can be preset or determined according to a clustering algorithm, and the embodiments of the present application do not limit this.

[0220] In the embodiments of the present application, for the above clusters, the first measurement values whose distances from the cluster center points of the clusters are less than or equal to the first distance threshold can be used as normal points. Correspondingly, for the above clusters, the first measurement values whose distances from the cluster center points of the clusters are greater than the first distance threshold can be used as abnormal points (i.e., the second measurement values).

[0221] Step S505: Update the second measurement value in the measurement data sequence according to the third measurement value adjacent to the second measurement value in the measurement data sequence.

[0222] Step S506: Locate the terminal according to the updated measurement data sequence.

[0223] For the explanatory descriptions of steps S505 to S506, reference can be made to the relevant descriptions in any embodiment of the present application, and details are not described herein again.

[0224] The positioning method of the terminal in the embodiments of the present application can implement an outlier detection algorithm based on clustering to detect outliers in the measurement data sequence, improving the effectiveness and accuracy of outlier detection.

[0225] To clearly illustrate how to determine the abnormal second measurement value from multiple first measurement values in the measurement data sequence in any embodiment of the present application, the present application also proposes a positioning method for a terminal.

[0226] Figure 6 It is a schematic flowchart of another positioning method for a terminal provided by the embodiments of the present application.

[0227] Such as Figure 6 , the positioning method of the terminal may include the following steps:

[0228] Step S601: Sort the multiple first measurement values sent by the terminal according to the corresponding timestamp information to obtain a measurement data sequence.

[0229] Among them, the first measurement value and the corresponding timestamp information are carried in the measurement report sample MRO.

[0230] For the explanatory description of step S601, reference can be made to the relevant descriptions in any embodiment of the present application, and details are not described herein again.

[0231] Step S602: Classify the multiple first measurement values in the measurement data sequence to obtain the classification probabilities of the multiple first measurement values; among them, the classification probability is used to indicate the probability that the first measurement value belongs to abnormal data.

[0232] In an embodiment of the present application, a classification algorithm (such as a classification model in the field of artificial intelligence) can be used to classify multiple first measurement values in a measurement data sequence, and obtain classification probabilities of the multiple first measurement values, where the classification probability is used to indicate the probability that the first measurement value belongs to abnormal data (or is referred to as an abnormal point, an outlier).

[0233] Step S603: Determine abnormal second measurement values from the multiple first measurement values based on the classification probabilities of the multiple first measurement values.

[0234] In an embodiment of the present application, abnormal second measurement values can be determined from the multiple first measurement values based on the classification probabilities of the multiple first measurement values. For example, the first measurement values with classification probabilities higher than a set probability threshold (such as 0.5) can be used as the abnormal second measurement values.

[0235] Step S604: Update the second measurement values in the measurement data sequence according to third measurement values adjacent to the second measurement values in the measurement data sequence.

[0236] Step S605: Locate the terminal according to the updated measurement data sequence.

[0237] For the explanation of steps S604 to S605, reference can be made to the relevant descriptions in any embodiment of the present application, and details are not described herein again.

[0238] The positioning method of the terminal in the embodiment of the present application can implement an outlier detection algorithm based on classification to detect outliers in the measurement data sequence, and improve the effectiveness and accuracy of outlier detection.

[0239] To clearly illustrate how to determine abnormal second measurement values from multiple first measurement values in any embodiment of the present application, the present application also proposes a positioning method for a terminal.

[0240] Figure 7 It is a schematic flowchart of another positioning method for a terminal provided by an embodiment of the present application.

[0241] Such as Figure 7 , the positioning method of the terminal may include the following steps:

[0242] Step S701: Sort multiple first measurement values sent by the terminal according to the corresponding timestamp information to obtain a measurement data sequence.

[0243] Among them, the first measurement value and the corresponding timestamp information are carried in the measurement report sample MRO.

[0244] For the explanation of step S701, reference can be made to the relevant descriptions in any embodiment of the present application, and details are not described herein again.

[0245] Step S702: Obtain the information entropy of multiple first measurement values in the measurement data sequence.

[0246] In the embodiments of the present application, for each first measurement value in the measurement data sequence, the information entropy of the first measurement value can be calculated. Among them, the information entropy is a measure of the uncertainty of the measurement data points, and outliers or abnormal points are usually regarded as points with higher information entropy compared with the surrounding data points.

[0247] As an example, the information entropy of each first measurement value can be calculated according to the probability distribution of each first measurement value. For example, first, each first measurement value in the measurement data sequence can be discretized, for example, converting continuous numerical values into discrete categories. After that, according to the discretization result, the probability distribution of each first measurement value in the measurement data sequence can be calculated, so that the information entropy of each first measurement value can be calculated according to the probability distribution of each first measurement value.

[0248] Step S703: Determine a reference value according to the information entropy of multiple first measurement values.

[0249] In the embodiments of the present application, a threshold (denoted as the reference value in the present application) can be determined according to the calculated information entropy of multiple first measurement values, which is used to distinguish normal points from abnormal points (or outliers).

[0250] As an example, the mean value of the information entropy of multiple first measurement values can be used as the reference value.

[0251] As another example, the median of the information entropy of multiple first measurement values can be used as the reference value.

[0252] Step S704: Determine abnormal second measurement values from multiple first measurement values according to the reference value, where the information entropy of the second measurement values is higher than the reference value.

[0253] In the embodiments of the present application, the first measurement values with information entropy higher than the reference value can be used as the abnormal second measurement values.

[0254] Step S705: Update the second measurement values in the measurement data sequence according to the third measurement values adjacent to the second measurement values in the measurement data sequence.

[0255] Step S706: Locate the terminal according to the updated measurement data sequence.

[0256] For the explanations of steps S705 to S706, reference can be made to the relevant descriptions in any embodiment of the present application, which will not be elaborated here.

[0257] The positioning method of the terminal according to the embodiments of the present application can implement an outlier detection algorithm based on information theory to detect outliers in the measurement data sequence, improving the effectiveness and accuracy of outlier detection.

[0258] To clearly illustrate how to determine the abnormal second measurement value from multiple first measurement values in the measurement data sequence in any embodiment of the present application, the present application also proposes a positioning method for the terminal.

[0259] Figure 8 It is a schematic flowchart of another positioning method for the terminal provided by the embodiments of the present application.

[0260] Such as Figure 8 , the positioning method of the terminal may include the following steps:

[0261] Step S801: Sort the multiple first measurement values sent by the terminal according to the corresponding timestamp information to obtain a measurement data sequence.

[0262] Among them, the first measurement value and the corresponding timestamp information are carried in the measurement report sample MRO.

[0263] For the explanation of step S801, reference can be made to the relevant description in any embodiment of the present application, and details will not be elaborated here.

[0264] Step S802: Construct a distance matrix according to the distances between multiple first measurement values in the measurement data sequence.

[0265] In the embodiments of the present application, the distances between multiple measurement values in the measurement data sequence can be calculated, and a distance matrix can be constructed according to the distances between the multiple measurement values, that is, the distance matrix includes the distances between multiple measurement values.

[0266] Step S803: Determine a second distance threshold according to the distance matrix.

[0267] In the embodiments of the present application, a second distance threshold can be determined according to multiple elements in the distance matrix.

[0268] For example, the median of multiple elements in the distance matrix can be used as the second distance threshold, or the mean of multiple elements in the distance matrix can be used as the second distance threshold, or the mean of multiple elements in the distance matrix plus or minus a certain proportion of the standard deviation can be used as the second distance threshold, where the standard deviation refers to the standard deviation of multiple elements in the distance matrix.

[0269] Step S804: Determine the abnormal second measurement value from multiple first measurement values according to the second distance threshold, where the distance between the second measurement value and the remaining first measurement values is greater than the second distance threshold.

[0270] In an embodiment of the present application, abnormal second measurement values can be determined from multiple first measurement values according to a second distance threshold, where the distance between a second measurement value and the remaining first measurement values in the measurement data sequence except this second measurement value is greater than the second distance threshold.

[0271] Step S805: Update the second measurement value in the measurement data sequence according to a third measurement value adjacent to the second measurement value in the measurement data sequence.

[0272] Step S806: Locate the terminal according to the updated measurement data sequence.

[0273] For the explanatory description of steps S805 to S806, reference can be made to the relevant descriptions in any embodiment of the present application, and details are not described herein again.

[0274] The positioning method of the terminal in the embodiment of the present application can implement a distance-based outlier detection algorithm to detect outliers in the measurement data sequence, improving the effectiveness and accuracy of outlier detection.

[0275] To clearly illustrate how to obtain the measurement data sequence in any embodiment of the present application, the present application also proposes a positioning method for a terminal.

[0276] Figure 9 It is a schematic flowchart of another positioning method for a terminal provided by an embodiment of the present application.

[0277] As Figure 9 , the positioning method of the terminal may include the following steps:

[0278] Step S901: Obtain multiple MROs sent by the terminal according to the first identification information assigned by the core network device to the terminal, the second identification information of the serving cell accessed by the terminal, and the third identification information of the access network device accessed by the terminal.

[0279] In an embodiment of the present application, multiple MROs sent by the terminal can be determined from the received MROs according to the first identification information assigned by the core network device to the terminal, the second identification information of the serving cell accessed by the terminal, and the third identification information of the access network device accessed by the terminal.

[0280] Among them, the first identification information in multiple MROs sent by the same terminal is the same, and the second identification information in multiple MROs sent by the same terminal is also the same, and the third identification information in multiple MROs sent by the same terminal is also the same.

[0281] Step S902: Sort the first measurement values in multiple MROs according to the timestamp information in the multiple MROs to obtain a measurement data sequence.

[0282] In the embodiments of the present application, the first measurement values in multiple MROs sent by a terminal can be sorted according to the timestamp information in the multiple MROs in ascending or descending order to obtain a measurement data sequence.

[0283] Step S903: Determine abnormal second measurement values from the multiple first measurement values in the measurement data sequence.

[0284] Step S904: Update the second measurement values in the measurement data sequence according to the third measurement values adjacent to the second measurement values in the measurement data sequence.

[0285] Step S905: Locate the terminal according to the updated measurement data sequence.

[0286] For the explanations of steps S903 to S905, reference can be made to the relevant descriptions in any embodiment of the present application, which will not be elaborated here.

[0287] The positioning method of the terminal in the embodiments of the present application can accurately identify each MRO sent by the same terminal based on the identification information in the MRO, improving the accuracy of the identification result.

[0288] In any embodiment of the present application, abnormal point identification and correction can be performed on the first measurement values (such as AOA or TA) in the MRO reported by the terminal to improve the positioning accuracy of the terminal. For example, on the basis that the terminal continuously reports MROs (for example, the reporting time interval is about 5 seconds) and the position of the terminal changes relatively stably, the first measurement values in the MRO can be corrected.

[0289] As an example, the MRO used can be the original XML (eXtensible Markup Language) file on the access network device side. The detection and correction of abnormal data are finally completed mainly through the following steps: data processing, abnormal data identification, abnormal data correction, and result data output.

[0290] Step 1: Data processing - Generate a measurement data sequence of the same terminal.

[0291] Taking the access network device as gNB as an example, since the field "AMF UENGAPID" generated by the same terminal in the MRO remains basically unchanged within a certain time in the same cell, the MROs sent by the same terminal can be determined according to the three fields "gnbid", "cellid", and "AMFUENGAPID" in the MRO. Then, according to the field "TimeStamp", the first measurement values in each MRO sent by the same terminal are sorted to generate a measurement data sequence of the same terminal.

[0292] Attention should be paid to the continuity of the timestamp information here. If the three fields of "gnbid", "cellid", and "AMFUENGAPID" in multiple MROs are the same, but the "TimeStamp" in multiple MROs is not continuous, then the first measurement value in the MROs sent by the terminal in the front and back time periods cannot be used as an element in the measurement data sequence either.

[0293] Step 2: Abnormal data identification: Double outlier identification.

[0294] Double outlier identification is to identify double outliers through the original data (i.e., the first measurement value) and differential data in the MRO. It is possible to identify abnormal data for the measurement data sequence of the same terminal within a certain time period generated based on Step 1. The identification methods include but are not limited to: outlier detection algorithms based on statistics, clustering, classification, information theory, distance, and density.

[0295] As an example, taking the LOF algorithm as an example, the identification of abnormal data is introduced as follows:

[0296] Step 2-1: Set a sliding window and take the subsequence within the corresponding sliding window. For example, the window length of the sliding window can be T (minutes), and the window length can be t1 (minutes). At this time, if the time duration of the measurement data sequence of the same terminal does not meet T, no analysis will be performed this time. If the time length of the measurement data sequence of the same terminal exceeds T, then take t1 minutes as the sliding step of the sliding window, and perform sliding processing on the measurement data sequence, always keeping the time length of the intercepted subsequence as T.

[0297] For example, the sliding method of the sliding window can be as Figure 10 shown, where T1 refers to the time length of the first intercepted subsequence, and T2 refers to the time length of the second intercepted subsequence. When both T1 and T2 can meet the T time requirement, that is, T1 = T, T2 = T, proceed to Step 2-2 for processing. When T n does not meet the T time requirement (i.e., T n < T), do not proceed to the next stage of processing.

[0298] Step 2-2: Discrete point identification. The LOF algorithm (not limited to) can be used for discrete point identification. By calculating the local reachability density of each point (i.e., the first measurement value), and then calculating the local outlier factor of each point, select and output the n points with the highest outlier degree as follows:

[0299] 1) Calculate the k-reachable distance of each point within the k-distance neighborhood of each point;

[0300] 2) Calculate the local k-local reachability density of each point;

[0301] 3) Calculate the k-th local outlier factor of each point according to the k-th reachable distance and the local k-th local reachability density;

[0302] 4) Output the outlier set n1 (denoted as the first abnormal data set in this application) for the data points belonging to the largest n k-th local outlier factors.

[0303] Step 2-3: Differential discrete point identification. At this time, if the terminal is a continuously moving terminal, some normal points will be identified as abnormal points. To solve this problem, further identification can be performed through differential discrete point calculation.

[0304] 1. Differential data: For a subsequence in the measurement data sequence of the same terminal, taking the first measurement value as TA as an example, assuming that two adjacent TAs in the time stamp are D n and D n-1 , then calculate the differential data between D n and D n-1 as: D n - D n-1 .

[0305] 2. Repeat the algorithm in Step 2-2 to calculate the outlier set n 2-1 .

[0306] 3. When calculating outliers based on the differential data in n 2-1 , all data points between the first outlier and the second outlier are marked as outliers in chronological order; all data points between the (2i + 1)-th outlier and the (2i + 2)-th outlier are marked as outliers. Thus, the outlier set n2 (denoted as the second abnormal data set in this application) can be obtained. If there is no (2i + 2)-th outlier, this part of the data points is not processed.

[0307] Step 2-4: Process the obtained outlier sets n1 and n2. The second measurement value in the final outlier set is: the points that belong to both n1 and n2, denoted as the final outliers, that is, the final second measurement value ∈ (n1 ∩ n2).

[0308] Step Three: Abnormal data correction.

[0309] For the final outlier (i.e., the second measurement value) D m , according to the nearest principle, find the nearest normal point D m-1 forward and the nearest normal point D m+1 backward according to the time stamp information for these two data points, and then replace the original second measurement value with the average of these two normal points, that is:

[0310]

[0311] Step 4: Correct data output.

[0312] After correcting the abnormal data, output the full amount of data after final correction.

[0313] In summary, the solution provided by this application has at least the following advantages:

[0314] 1. By combining the two methods of discrete point recognition of the original data and discrete point recognition of the differential data of the original data, accurate detection of abnormal points or discrete points in MRO can be achieved. That is, based on double outlier determination, abnormal points or discrete points in MRO can be identified more accurately and comprehensively. Among them, the recognition algorithms for abnormal points or discrete points include, but are not limited to, outlier detection algorithms based on statistics, clustering, classification, information theory, distance, and density.

[0315] 2. For the outlier processing of differential data, backfill the anomalies between continuous differential data points to fill the missed judgment of continuous abnormal points.

[0316] 3. Applying the division of time windows to this method and integrating the terminal behavior characteristics into this method can achieve more accurate outlier recognition, thereby improving the positioning accuracy of the terminal.

[0317] The technical solutions provided by the embodiments of this application can be applied to a variety of systems, especially 5G systems. For example, the applicable systems can be the Global System of Mobile communication (GSM for short), Code Division Multiple Access (CDMA for short), Wideband Code Division Multiple Access (WCDMA for short), General Packet Radio Service (GPRS for short), Long Term Evolution (LTE for short), LTE Frequency Division Duplex (FDD for short), LTE Time Division Duplex (TDD for short), Long Term Evolution Advanced (LTE-A for short), Universal Mobile Telecommunication System (UMTS for short), Worldwide Interoperability for Microwave Access (WiMAX for short), 5G New Radio (NR for short) systems, etc. Both terminals and network devices are included in these various systems. The system may also include a core network part, such as the Evolved Packet System (EPS for short), 5G System (5GS for short), etc.

[0318] To implement the above embodiments, this application also provides an access network device.

[0319] Figure 11 It is a schematic structural diagram of an access network device provided according to the embodiments of this application.

[0320] As Figure 11 shown, the access network device may include a transceiver 1100, a processor 1110, and a memory 1120, where:

[0321] The transceiver 1100 is used to receive and send data under the control of the processor 1110.

[0322] Among them, in Figure 11Among them, the bus architecture may include any number of interconnected buses and bridges, specifically various circuits represented by one or more processors represented by processor 1110 and memory represented by memory 1120 are linked together. The bus architecture can also link together various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art, so they will not be further described herein. The bus interface provides an interface. The transceiver 1100 can be multiple components, that is, including a transmitter and a receiver, and provides a unit for communicating with various other devices on a transmission medium, and these transmission media include transmission media such as wireless channels, wired channels, and optical cables. The processor 1110 is responsible for managing the bus architecture and general processing, and the memory 1120 can store data used by the processor 1110 when executing operations.

[0323] The processor 1110 can be a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or a Complex Programmable Logic Device (CPLD). The processor can also adopt a multi-core architecture.

[0324] The processor 1110 calls the computer program stored in the memory and performs the following operations: sorting multiple first measurement values sent by the terminal according to the corresponding timestamp information to obtain a measurement data sequence; wherein, the first measurement values and the corresponding timestamp information are carried in the measurement report sample MRO; determining abnormal second measurement values from the multiple first measurement values in the measurement data sequence; updating the second measurement values in the measurement data sequence according to the third measurement values adjacent to the second measurement values in the measurement data sequence; and positioning the terminal according to the updated measurement data sequence.

[0325] As a possible implementation manner, when the processor 1110 determines abnormal second measurement values from the multiple first measurement values in the measurement data sequence, specifically: slidingly intercepting the measurement data sequence according to a sliding window to obtain subsequences intercepted by each sliding; wherein, the window length of the sliding window is a first set duration, and the sliding step is a second set duration; for any first measurement value in any subsequence, determining the k-distance neighborhood of any first measurement value from any subsequence; determining the k-local outlier factor of any first measurement value according to the k-distance neighborhood; and determining abnormal second measurement values from the multiple first measurement values according to the k-local outlier factors of the multiple first measurement values.

[0326] As a possible implementation, the processor 1110 executes to determine the k-th local outlier factor of any first measurement value according to the k-th distance neighborhood, specifically: according to any first measurement value and the k-th distance neighborhood, determine the k-th reachable distance and the local k-th local reachable density of any first measurement value; according to the k-th reachable distance and the local k-th local reachable density, determine the k-th local outlier factor of any first measurement value.

[0327] As a possible implementation, the processor 1110 executes to determine the abnormal second measurement values from multiple first measurement values according to the k-th local outlier factors of the multiple first measurement values, specifically: according to the k-th local outlier factors of the multiple first measurement values, determine the first abnormal data set from the multiple first measurement values; generate a difference data sequence according to the difference data between adjacent first measurement values in any subsequence; determine the abnormal target difference data from each difference data in the difference data sequence; according to the target difference data, determine the second abnormal data set from the multiple first measurement values; use the intersection of the first abnormal data set and the second abnormal data set as the abnormal second measurement values.

[0328] As a possible implementation, there are multiple target difference data, and the processor 1110 executes to determine the second abnormal data set from the multiple first measurement values according to the target difference data, specifically: sort the multiple target difference data according to the timestamp information corresponding to the multiple target difference data to obtain a first sorted sequence; wherein, the timestamp information corresponding to the target difference data is determined according to the timestamp information of the first measurement value that generates the target difference data; determine at least one pair of difference data from the first sorted sequence; wherein, the pair of difference data includes the (2i + 1)-th target difference data and the (2i + 2)-th target difference data in the first sorted sequence, i = 0, 1, 2,...; determine the second abnormal data set from the multiple first measurement values according to the pair of difference data.

[0329] As a possible implementation, the processor 1110 executes to determine the first abnormal data set from the multiple first measurement values according to the k-th local outlier factors of the multiple first measurement values, specifically: determine the target number n according to the number of first measurement values included in the measurement data sequence and the application scenario where the terminal is located; wherein, n is a positive integer; sort the multiple first measurement values in descending order according to the values of the corresponding k-th local outlier factors to obtain a second sorted sequence; generate the first abnormal data set according to the first n first measurement values sorted at the front in the second sorted sequence; or, generate the first abnormal data set according to the first measurement values whose k-th local outlier factors are higher than the set threshold.

[0330] As a possible implementation, the processor 1110 determines abnormal second measurement values from multiple first measurement values in the measurement data sequence, specifically: obtaining the mean and standard deviation of the multiple first measurement values in the measurement data sequence; determining a reference value range based on the mean and standard deviation; and taking the first measurement values not within the reference value range in the measurement data sequence as the abnormal second measurement values.

[0331] As a possible implementation, the processor 1110 determines abnormal second measurement values from multiple first measurement values in the measurement data sequence, specifically: clustering the multiple first measurement values in the measurement data sequence to obtain at least one cluster; for any cluster, obtaining the distances between each first measurement value in any cluster and the clustering center point of any cluster; and taking the first measurement values in any cluster whose distances from the cluster center point are greater than the first distance threshold as the abnormal second measurement values.

[0332] As a possible implementation, the processor 1110 determines abnormal second measurement values from multiple first measurement values in the measurement data sequence, specifically: classifying the multiple first measurement values in the measurement data sequence to obtain the classification probabilities of the multiple first measurement values; where the classification probability is used to indicate the probability that the first measurement value belongs to abnormal data; and determining the abnormal second measurement values from the multiple first measurement values based on the classification probabilities of the multiple first measurement values.

[0333] As a possible implementation, the processor 1110 determines abnormal second measurement values from multiple first measurement values in the measurement data sequence, specifically: obtaining the information entropy of the multiple first measurement values in the measurement data sequence; determining a reference value according to the information entropy of the multiple first measurement values; and determining the abnormal second measurement values from the multiple first measurement values according to the reference value, where the information entropy of the second measurement value is higher than the reference value.

[0334] As a possible implementation, the processor 1110 determines abnormal second measurement values from multiple first measurement values in the measurement data sequence, specifically: constructing a distance matrix according to the distances between the multiple first measurement values in the measurement data sequence; determining a second distance threshold according to the distance matrix; and determining the abnormal second measurement values from the multiple first measurement values according to the second distance threshold, where the distance between the second measurement value and the remaining first measurement values is greater than the second distance threshold.

[0335] As a possible implementation, the processor 1110 updates the second measurement value in the measurement data sequence according to the third measurement value adjacent to the second measurement value in the measurement data sequence. Specifically, the second measurement value in the measurement data sequence is updated according to the mean value of the third measurement value; or the second measurement value in the measurement data sequence is updated according to the weighted sum value of the third measurement value.

[0336] As a possible implementation, the processor 1110 sorts multiple first measurement values sent by the terminal according to the corresponding timestamp information to obtain a measurement data sequence. Specifically, according to the first identification information assigned by the core network device to the terminal, the second identification information of the serving cell accessed by the terminal, and the third identification information of the access network device accessed by the terminal, multiple MROs sent by the terminal are obtained; the first measurement values in the multiple MROs are sorted according to the timestamp information in the multiple MROs to obtain a measurement data sequence.

[0337] It should be noted here that the access network device provided in the embodiments of the present application can implement all the method steps implemented by the above Figures 1 to 9 method embodiments and can achieve the same technical effects. The same parts and beneficial effects as those in the method embodiments will not be specifically described in this embodiment.

[0338] Corresponding to the terminal positioning method provided in the above Figures 1 to 9 embodiment, the present application further provides a terminal positioning device. Since the terminal positioning device provided in the embodiments of the present application corresponds to the terminal positioning method provided in the above Figures 1 to 9 embodiment, the implementation manners of the terminal positioning method are also applicable to the terminal positioning device provided in the embodiments of the present application and will not be described in detail in the embodiments of the present application.

[0339] To implement the above embodiment, the present application further proposes a terminal positioning device.

[0340] Figure 12 It is a schematic structural diagram of a terminal positioning device provided in the embodiments of the present application.

[0341] As Figure 12 shown, the terminal positioning device 1200 can be applied to an access network device and includes: a sorting unit 1210, a determination unit 1220, an update unit 1230, and a positioning unit 1240.

[0342] Among them, the sorting unit 1210 is configured to sort multiple first measurement values sent by the terminal according to the corresponding timestamp information to obtain a measurement data sequence; among them, the first measurement value and the corresponding timestamp information are carried in the measurement report sample MRO.

[0343] A determination unit 1220, configured to determine an abnormal second measurement value from a plurality of first measurement values in a measurement data sequence.

[0344] An update unit 1230, configured to update the second measurement value in the measurement data sequence according to a third measurement value adjacent to the second measurement value in the measurement data sequence.

[0345] A positioning unit 1240, configured to position the terminal according to the updated measurement data sequence.

[0346] As a possible implementation manner, the determination unit 1220 is specifically configured to: perform sliding interception on the measurement data sequence according to a sliding window to obtain subsequences intercepted by each sliding; wherein, the window length of the sliding window is a first set duration, and the sliding step is a second set duration; for any first measurement value in any subsequence, determine the k-distance neighborhood of any first measurement value from any subsequence; determine the k-local outlier factor of any first measurement value according to the k-distance neighborhood; determine an abnormal second measurement value from the plurality of first measurement values according to the k-local outlier factors of the plurality of first measurement values.

[0347] As a possible implementation manner, the determination unit 1220 is specifically configured to: determine the k-reachability distance and the local k-local reachability density of any first measurement value according to any first measurement value and the k-distance neighborhood; determine the k-local outlier factor of any first measurement value according to the k-reachability distance and the local k-local reachability density.

[0348] As a possible implementation manner, the determination unit 1220 is specifically configured to: determine a first abnormal data set from the plurality of first measurement values according to the k-local outlier factors of the plurality of first measurement values; generate a difference data sequence according to the difference data between adjacent first measurement values in any subsequence; determine an abnormal target difference data from each difference data in the difference data sequence; determine a second abnormal data set from the plurality of first measurement values according to the target difference data; use the intersection of the first abnormal data set and the second abnormal data set as the abnormal second measurement value.

[0349] As a possible implementation, there are multiple target differential data. The determination unit 1220 is specifically configured to: sort the multiple target differential data according to the timestamp information corresponding to the multiple target differential data to obtain a first sorted sequence, where the timestamp information corresponding to the target differential data is determined according to the timestamp information of the first measurement value for generating the target differential data; determine at least one pair of differential data from the first sorted sequence, where the pair of differential data includes the (2i + 1)-th target differential data and the (2i + 2)-th target differential data in the first sorted sequence, and i = 0, 1, 2, …; determine a second set of abnormal data from the multiple first measurement values according to the pair of differential data.

[0350] As a possible implementation, the determination unit 1220 is specifically configured to: determine a target number n according to the number of first measurement values included in the measurement data sequence and the application scenario where the terminal is located, where n is a positive integer; sort the multiple first measurement values in descending order according to the values of the corresponding k-th local outlier factor to obtain a second sorted sequence; generate a first set of abnormal data according to the first n first measurement values sorted at the front in the second sorted sequence; or generate a first set of abnormal data according to the first measurement values whose k-th local outlier factor is higher than a set threshold.

[0351] As a possible implementation, the determination unit 1220 is specifically configured to: obtain the mean value and standard deviation of the multiple first measurement values in the measurement data sequence; determine a reference value range according to the mean value and the standard deviation; use the first measurement values not within the reference value range in the measurement data sequence as abnormal second measurement values.

[0352] As a possible implementation, the determination unit 1220 is specifically configured to: cluster the multiple first measurement values in the measurement data sequence to obtain at least one cluster; for any cluster, obtain the distances between each first measurement value in any cluster and the clustering center point of any cluster; use the first measurement values in any cluster whose distances from the cluster center point are greater than a first distance threshold as abnormal second measurement values.

[0353] As a possible implementation, the determination unit 1220 is specifically configured to: classify the multiple first measurement values in the measurement data sequence to obtain the classification probabilities of the multiple first measurement values, where the classification probability is used to indicate the probability that the first measurement value belongs to abnormal data; determine abnormal second measurement values from the multiple first measurement values based on the classification probabilities of the multiple first measurement values.

[0354] As a possible implementation manner, the determining unit 1220 is specifically configured to: obtain the information entropy of multiple first measurement values in the measurement data sequence; determine a reference value according to the information entropy of the multiple first measurement values; and determine an abnormal second measurement value from the multiple first measurement values according to the reference value, where the information entropy of the second measurement value is higher than the reference value.

[0355] As a possible implementation manner, the determining unit 1220 is specifically configured to: construct a distance matrix according to the distances between multiple first measurement values in the measurement data sequence; determine a second distance threshold according to the distance matrix; and determine an abnormal second measurement value from the multiple first measurement values according to the second distance threshold, where the distance between the second measurement value and the remaining first measurement values is greater than the second distance threshold.

[0356] As a possible implementation manner, the updating unit 1230 is specifically configured to: update the second measurement value in the measurement data sequence according to the mean value of the third measurement value; or update the second measurement value in the measurement data sequence according to the weighted sum value of the third measurement value.

[0357] As a possible implementation manner, the sorting unit 1210 is specifically configured to: obtain multiple MROs sent by a terminal according to a first identification information assigned by a core network device to the terminal, a second identification information of a serving cell accessed by the terminal, and a third identification information of an access network device accessed by the terminal; and sort the first measurement values in the multiple MROs according to the timestamp information in the multiple MROs to obtain a measurement data sequence.

[0358] It should be noted here that the positioning device of the terminal provided in the embodiments of the present application can implement all the method steps implemented by the above Figures 1 to 9 method embodiments, and can achieve the same technical effects. Therefore, the same parts and beneficial effects as those in the method embodiments will not be specifically described in this embodiment.

[0359] It should be noted that the division of units in the embodiments of the present application is illustrative, and is only a logical function division. In actual implementation, there may be other division methods. In addition, in each embodiment of the present application, the functional units may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit. The above integrated units may be implemented in the form of hardware or in the form of software functional units.

[0360] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a processor-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network-side device, etc.) or a processor to execute all or part of the steps of the methods of various embodiments of this application. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks, or optical discs that can store program codes.

[0361] On the other hand, an embodiment of this application also provides a processor-readable storage medium. The processor-readable storage medium stores a computer program, and the computer program is used to cause the processor to execute this application Figures 1 to 9 the method shown in any of the embodiments.

[0362] Among them, the above-mentioned processor-readable storage medium can be any available medium or data storage device that the processor can access, including but not limited to magnetic memories (such as floppy disks, hard disks, magnetic tapes, magneto-optical discs (MO), etc.), optical memories (such as CDs, DVDs, BDs, HVDs, etc.), and semiconductor memories (such as ROM, EPROM, EEPROM, non-volatile memories (NANDFLASH), solid-state drives (SSD)).

[0363] To implement the above embodiments, this application also proposes a computer program product.

[0364] Among them, this computer program product includes a computer program, and when the computer program is executed by the processor, it implements this application Figures 1 to 9 the method shown in any of the embodiments.

[0365] Those skilled in the art should understand that the embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, this application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories and optical memories, etc.) that contain computer-usable program codes.

[0366] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as combinations of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer-executable instructions. These computer-executable instructions can be provided to the processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing device produce a means for realizing the functions specified in the Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0367] These processor-executable instructions can also be stored in a processor-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the processor-readable memory produce a manufacture including instruction means that implement the functions specified in the Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0368] These processor-executable instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operational steps are performed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for realizing the functions specified in the Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0369] Obviously, those skilled in the art can make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalent technologies, this application is also intended to include these changes and modifications.

Claims

1. A positioning method for a terminal, characterized in that, Applied to access network devices, including: Sorting a plurality of first measurement values sent by a terminal according to corresponding timestamp information to obtain a measurement data sequence; wherein, the first measurement values and the corresponding timestamp information are carried in a measurement report sample MRO; Determining abnormal second measurement values from the plurality of first measurement values in the measurement data sequence; Updating the second measurement values in the measurement data sequence according to third measurement values adjacent to the second measurement values in the measurement data sequence; Locating the terminal according to the updated measurement data sequence.

2. The method according to claim 1, characterized in that, The determining abnormal second measurement values from the plurality of first measurement values in the measurement data sequence includes: Slidingly intercepting the measurement data sequence according to a sliding window to obtain subsequences intercepted by each sliding; wherein, the window length of the sliding window is a first set duration, and the sliding step is a second set duration; For any first measurement value in any subsequence, determining the k-distance neighborhood of the any first measurement value from the any subsequence; Determining the k-local outlier factor of the any first measurement value according to the k-distance neighborhood; Determining abnormal second measurement values from the plurality of first measurement values according to the k-local outlier factors of the plurality of first measurement values.

3. The method according to claim 2, characterized in that, The determining the k-local outlier factor of the any first measurement value according to the k-distance neighborhood includes: Determining the k-reachability distance and the local k-local reachability density of the any first measurement value according to the any first measurement value and the k-distance neighborhood; Determining the k-local outlier factor of the any first measurement value according to the k-reachability distance and the local k-local reachability density.

4. The method according to claim 2, characterized in that, The determining abnormal second measurement values from the plurality of first measurement values according to the k-local outlier factors of the plurality of first measurement values includes: Determining a first abnormal data set from the plurality of first measurement values according to the k-local outlier factors of the plurality of first measurement values; Generating a differential data sequence according to the differential data between adjacent first measurement values in any subsequence; Determining abnormal target differential data from each of the differential data in the differential data sequence; Determining a second abnormal data set from the plurality of first measurement values according to the target differential data; Taking the intersection of the first abnormal data set and the second abnormal data set as the abnormal second measurement values.

5. The method according to claim 4, characterized in that, There are a plurality of the target differential data, and the determining a second abnormal data set from the plurality of first measurement values according to the target differential data includes: Sorting the plurality of target differential data according to the timestamp information corresponding to the plurality of target differential data to obtain a first sorted sequence; wherein, the timestamp information corresponding to the target differential data is determined according to the timestamp information of the first measurement value generating the target differential data; Determine at least one differential data pair from the first sorted sequence; wherein, the differential data pair includes the (2i + 1)-th target differential data and the (2i + 2)-th target differential data in the first sorted sequence, where i = 0, 1, 2, …; Determine the second set of abnormal data from the multiple first measurement values according to the differential data pair.

6. The method according to claim 4, characterized in that, Determining the first set of abnormal data from the multiple first measurement values according to the k-th local outlier factor of the multiple first measurement values includes: Determine a target number n according to the number of first measurement values included in the measurement data sequence and the application scenario where the terminal is located; where n is a positive integer; Sort the multiple first measurement values in descending order according to the values of the corresponding k-th local outlier factor to obtain a second sorted sequence; Generate the first set of abnormal data according to the first n first measurement values sorted at the front in the second sorted sequence; Or, Generate the first set of abnormal data according to the first measurement values with the k-th local outlier factor higher than a set threshold.

7. The method according to claim 1, characterized in that, Determining the abnormal second measurement value from the multiple first measurement values in the measurement data sequence includes: Obtain the mean and standard deviation of the multiple first measurement values in the measurement data sequence; Determine a reference value range according to the mean and the standard deviation; Take the first measurement values not within the reference value range in the measurement data sequence as the abnormal second measurement values.

8. The method according to claim 1, wherein Determining the abnormal second measurement value from the multiple first measurement values in the measurement data sequence includes: Cluster the multiple first measurement values in the measurement data sequence to obtain at least one cluster; For any cluster, obtain the distance between each first measurement value in the any cluster and the cluster center point of the any cluster; Take the first measurement values in the any cluster whose distance from the cluster center point is greater than a first distance threshold as the abnormal second measurement values.

9. The method according to claim 1, wherein Determining the abnormal second measurement value from the multiple first measurement values in the measurement data sequence includes: Classify the multiple first measurement values in the measurement data sequence to obtain the classification probabilities of the multiple first measurement values; where the classification probability is used to indicate the probability that a first measurement value belongs to abnormal data; Based on the classification probabilities of the multiple first measurement values, determine the abnormal second measurement value from the multiple first measurement values.

10. The method according to claim 1, wherein Determining the abnormal second measurement value from the multiple first measurement values in the measurement data sequence includes: Obtain the information entropy of the multiple first measurement values in the measurement data sequence; Determine a reference value according to the information entropy of the multiple first measurement values; Determine the abnormal second measurement value from the multiple first measurement values according to the reference value, where the information entropy of the second measurement value is higher than the reference value.

11. The method according to claim 1, wherein Determining the abnormal second measurement value from the multiple first measurement values in the measurement data sequence includes: Construct a distance matrix according to the distances between the multiple first measurement values in the measurement data sequence; Determine a second distance threshold according to the distance matrix; Determine an abnormal second measurement value from the multiple first measurement values according to the second distance threshold, where the distance between the second measurement value and the remaining first measurement values is greater than the second distance threshold.

12. The method according to any one of claims 1-11, wherein Updating the second measurement value in the measurement data sequence according to a third measurement value adjacent to the second measurement value in the measurement data sequence includes: Updating the second measurement value in the measurement data sequence according to the mean value of the third measurement value; Or, Updating the second measurement value in the measurement data sequence according to the weighted sum value of the third measurement value.

13. The method according to any one of claims 1-11, wherein Sorting the multiple first measurement values sent by the terminal according to the corresponding timestamp information to obtain a measurement data sequence, including: Obtain multiple MROs sent by the terminal according to the first identification information assigned by the core network device to the terminal, the second identification information of the serving cell accessed by the terminal, and the third identification information of the access network device accessed by the terminal; Sort the first measurement values in the multiple MROs according to the timestamp information in the multiple MROs to obtain the measurement data sequence.

14. An access network device, wherein Including a memory, a transceiver, and a processor; The memory is used to store computer programs; The transceiver is used to send and receive data under the control of the processor; The processor is used to read the computer program in the memory and perform the following operations: Sort the multiple first measurement values sent by the terminal according to the corresponding timestamp information to obtain a measurement data sequence; wherein, the first measurement value and the corresponding timestamp information are carried in the measurement report sample MRO; Determine an abnormal second measurement value from the multiple first measurement values in the measurement data sequence; Update the second measurement value in the measurement data sequence according to a third measurement value adjacent to the second measurement value in the measurement data sequence; Locate the terminal according to the updated measurement data sequence.

15. The access network device according to claim 14, wherein When the processor executes to determine an abnormal second measurement value from the multiple first measurement values in the measurement data sequence, specifically: Perform a sliding intercept on the measurement data sequence according to a sliding window to obtain subsequences intercepted by each sliding; wherein, the window length of the sliding window is a first set duration, and the sliding step is a second set duration; For any first measurement value in any subsequence, determine the k-distance neighborhood of the any first measurement value from the any subsequence; Determine the k-local outlier factor of the any first measurement value according to the k-distance neighborhood; Determine an abnormal second measurement value from the multiple first measurement values according to the k-local outlier factors of the multiple first measurement values.

16. The access network device according to claim 15, wherein When the processor executes to determine the k-local outlier factor of the any first measurement value according to the k-distance neighborhood, specifically: Determine the k-reachability distance and the local k-local reachability density of the any first measurement value according to the any first measurement value and the k-distance neighborhood; Determine the k-local outlier factor of the any first measurement value according to the k-reachability distance and the local k-local reachability density.

17. The access network device according to claim 15, wherein The processor executes the k-th local outlier factor based on the multiple first measurement values, and determines the abnormal second measurement values from the multiple first measurement values, specifically as follows: Determine a first abnormal data set from the multiple first measurement values according to the k-th local outlier factor of the multiple first measurement values; Generate a difference data sequence according to the difference data between adjacent first measurement values in any one of the subsequences; Determine the target difference data with anomalies from each of the difference data in the difference data sequence; Determine a second abnormal data set from the multiple first measurement values according to the target difference data; Use the intersection of the first abnormal data set and the second abnormal data set as the abnormal second measurement values.

18. The access network device according to claim 17, wherein There are multiple pieces of the target difference data, and the processor executes to determine a second abnormal data set from the multiple first measurement values according to the target difference data, specifically as follows: Sort the multiple target difference data according to the timestamp information corresponding to the multiple target difference data to obtain a first sorted sequence; wherein, the timestamp information corresponding to the target difference data is determined according to the timestamp information of the first measurement value that generates the target difference data; Determine at least one pair of difference data from the first sorted sequence; wherein, the pair of difference data includes the (2i + 1)-th target difference data and the (2i + 2)-th target difference data in the first sorted sequence, i = 0, 1, 2,...; Determine the second abnormal data set from the multiple first measurement values according to the pair of difference data.

19. The access network device according to claim 17, wherein The processor executes to determine a first abnormal data set from the multiple first measurement values according to the k-th local outlier factor of the multiple first measurement values, specifically as follows: Determine a target number n according to the number of first measurement values included in the measurement data sequence and the application scenario where the terminal is located; where n is a positive integer; Sort the multiple first measurement values in descending order according to the values of the corresponding k-th local outlier factor to obtain a second sorted sequence; Generate the first abnormal data set according to the first n first measurement values sorted at the front in the second sorted sequence; Or, Generate the first abnormal data set according to the first measurement values with the k-th local outlier factor higher than a set threshold.

20. The access network device according to claim 14, wherein The processor executes to determine the abnormal second measurement values from the multiple first measurement values in the measurement data sequence, specifically as follows: Obtain the mean and standard deviation of the multiple first measurement values in the measurement data sequence; Determine a reference value range according to the mean and the standard deviation; Use the first measurement values not within the reference value range in the measurement data sequence as the abnormal second measurement values.

21. The access network device according to claim 14, wherein The processor executes to determine the abnormal second measurement values from the multiple first measurement values in the measurement data sequence, specifically as follows: Cluster the multiple first measurement values in the measurement data sequence to obtain at least one cluster; For any one of the clusters, obtain the distances between each of the first measurement values in the any one of the clusters and the clustering center point of the any one of the clusters; Take the first measurement value in any of the clusters whose distance from the cluster center point is greater than the first distance threshold as the abnormal second measurement value.

22. The access network device according to claim 14, wherein The processor executes to determine the abnormal second measurement value from multiple first measurement values in the measurement data sequence, specifically: Classify multiple first measurement values in the measurement data sequence to obtain the classification probabilities of the multiple first measurement values; wherein, the classification probability is used to indicate the probability that the first measurement value belongs to abnormal data; Based on the classification probabilities of the multiple first measurement values, determine the abnormal second measurement value from the multiple first measurement values.

23. The access network device according to claim 14, characterized in that, The processor executes to determine the abnormal second measurement value from multiple first measurement values in the measurement data sequence, specifically: Obtain the information entropy of multiple first measurement values in the measurement data sequence; Determine a reference value according to the information entropy of the multiple first measurement values; Determine the abnormal second measurement value from the multiple first measurement values according to the reference value, wherein the information entropy of the second measurement value is higher than the reference value.

24. The access network device according to claim 14, characterized in that, The processor executes to determine the abnormal second measurement value from multiple first measurement values in the measurement data sequence, specifically: Construct a distance matrix according to the distances between multiple first measurement values in the measurement data sequence; Determine a second distance threshold according to the distance matrix; Determine the abnormal second measurement value from the multiple first measurement values according to the second distance threshold, wherein the distance between the second measurement value and the remaining first measurement values is greater than the second distance threshold.

25. The access network device according to any one of claims 14 - 24, characterized in that, The processor executes to update the second measurement value in the measurement data sequence according to the third measurement value adjacent to the second measurement value in the measurement data sequence, specifically: Update the second measurement value in the measurement data sequence according to the mean value of the third measurement value; Or, Update the second measurement value in the measurement data sequence according to the weighted sum value of the third measurement value.

26. The access network device according to any one of claims 14 - 24, characterized in that, The processor executes to sort multiple first measurement values sent by the terminal according to the corresponding timestamp information to obtain a measurement data sequence, specifically: Obtain multiple MROs sent by the terminal according to the first identification information assigned by the core network device to the terminal, the second identification information of the serving cell accessed by the terminal, and the third identification information of the access network device accessed by the terminal; Sort the first measurement values in the multiple MROs according to the timestamp information in the multiple MROs to obtain the measurement data sequence.

27. A positioning device for a terminal, characterized in that, Applied to an access network device, it includes: A sorting unit, configured to sort multiple first measurement values sent by the terminal according to the corresponding timestamp information to obtain a measurement data sequence; wherein, the first measurement value and the corresponding timestamp information are carried in the measurement report sample MRO; A determination unit, configured to determine the abnormal second measurement value from multiple first measurement values in the measurement data sequence; An update unit, configured to update the second measurement value in the measurement data sequence according to the third measurement value adjacent to the second measurement value in the measurement data sequence; A positioning unit for positioning the terminal according to the updated measurement data sequence.

28. A processor-readable storage medium, characterized in that, The processor-readable storage medium stores a computer program for causing the processor to execute the method according to any one of claims 1 to 13.

Images