Business processing method, apparatus, and storage medium

Abstract

This application provides a service processing method, apparatus, and storage medium. The method includes: a Location Management Function (LMF) network element receiving a user terminal's location prediction request and historical trajectory information sent by an Access and Mobility Management Function (AMF) network element; the location prediction request includes a target prediction time point; the LMF network element determining the user terminal's location prediction result; the location prediction result being location information obtained by the LMF network element through location prediction of the user terminal's current location information and historical trajectory information at the target prediction time point; the user terminal's current location information being determined by the LMF network element through positioning the user terminal. This method solves the problem that existing positioning methods can only determine the UE's current location information but cannot determine the UE's location information at future time points.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to a service processing method, apparatus and storage medium. Background Technology

[0002] 5G location services refer to the services provided by 5G mobile networks to users (UEs) by acquiring their geographic location information through specific positioning methods, and then providing this information to the UE user or other legitimate organizations and individuals who request it. Location services can be applied in areas such as vehicle navigation, intelligent transportation, and public safety.

[0003] Currently, the 5G positioning services defined by the 3rd Generation Partnership Project (3GPP) mainly use two positioning methods: Mobile Originated Location Request (MO-LR) and Mobile Terminated Location Request (MT-LR). For example... Figure 1 As shown, the existing positioning system architecture includes: UE 11, RAN 12, 5G core network, and LCS client 17 or Application Function (AF) network element 18. RAN 12 can be a radio access network (NG-RAN). In the MO-LR positioning method, UE 11 initiates an MO-LR positioning request to the 5G core network through RAN 12 and receives the positioning result of UE 11 returned by the 5G core network. The positioning result includes the current location information of UE 11 determined by the 5G core network after positioning UE 11. In the MT-LR positioning method, UE 11 accesses LCS client 17 or AF network element 18 in advance through RAN 12 and the 5G core network. LCS client 17 or AF network element 18 then initiates an MT-LR positioning request to the 5G core network on behalf of UE 11. After receiving the positioning result of UE 11 returned by the 5G core network, LCS client 17 or AF network element 18 returns the positioning result to UE 11.

[0004] Existing positioning methods can only determine the current location information of the UE, but cannot determine the location information of the UE at future points in time. Summary of the Invention

[0005] This application provides a service processing method, apparatus, and storage medium to solve the problem that existing positioning methods can only determine the current location information of the UE, but cannot determine the location information of the UE at future time points.

[0006] Firstly, this application provides a business processing method, including:

[0007] The Location Management Function (LMF) network element receives a location prediction request and historical trajectory information of a user terminal sent by the Access and Mobility Management Function (AMF) network element; the location prediction request includes a target prediction time point.

[0008] The LMF network element determines the location prediction result of the user terminal; the location prediction result is the location information obtained by the LMF network element through the target prediction time point prediction of the current location information of the user terminal and the historical trajectory information of the user terminal; the current location information of the user terminal is determined by the LMF network element through positioning of the user terminal.

[0009] Optionally, the LMF network element determines the location prediction result of the user terminal, including:

[0010] The LMF network element uses a preset location prediction model to predict the target prediction time point based on the current location information and historical trajectory information of the user terminal, thereby obtaining the location information of the user terminal at the target prediction time point.

[0011] The location prediction model is trained using training samples composed of historical trajectory information from multiple user terminals.

[0012] Optionally, after the LMF network element determines the location prediction result of the user terminal, the method further includes:

[0013] The LMF network element returns a location prediction request response message containing the location prediction result to the AMF network element.

[0014] Optionally, before the LMF network element determines the location prediction result of the user terminal, the method further includes:

[0015] The LMF network element receives the user terminal's location prediction request sent by the AMF network element and the user terminal's historical trajectory information sent by the NWDAF network element, which is part of the network data analysis function.

[0016] Optionally, before the LMF network element receives the historical trajectory information of the user terminal sent by the NWDAF network element, the method further includes:

[0017] The LMF network element sends a historical trajectory request of the user terminal to the NWDAF network element; the historical trajectory request includes the identifier of the user terminal and the target prediction time point.

[0018] Optionally, before the LMF network element sends the user terminal's historical trajectory request to the NWDAF network element, the method further includes:

[0019] The LMF network element determines the NWDAF network element with the historical trajectory information of the user terminal from multiple NWDAF network elements based on the identifier of the user terminal.

[0020] Secondly, this application provides a service processing system, which includes: an Access and Mobility Management Function (AMF) network element and a Location Management Function (LMF) network element;

[0021] The LMF network element receives the location prediction request and historical trajectory information of the user terminal sent by the AMF network element; the location prediction request includes the target prediction time point.

[0022] The LMF network element determines the location prediction result of the user terminal; the location prediction result is the location information obtained by the LMF network element through the target prediction time point prediction of the current location information of the user terminal and the historical trajectory information of the user terminal; the current location information of the user terminal is determined by the LMF network element through positioning of the user terminal.

[0023] Optionally, the service processing system further includes: a network data analysis function (NWDAF) network element;

[0024] Before the LMF network element determines the location prediction result of the user terminal, the LMF network element receives the location prediction request of the user terminal sent by the AMF network element and the historical trajectory information of the user terminal sent by the NWDAF network element.

[0025] Thirdly, this application provides a location management function (LMF) network element, the network element comprising:

[0026] Send / receive module, processing module;

[0027] The transceiver module is used to receive location prediction requests and historical trajectory information of user terminals sent by the Access and Mobility Management Function (AMF) network element; the location prediction request includes the target prediction time point;

[0028] The processing module is used to determine the location prediction result of the user terminal; the location prediction result is the location information obtained by the processing module through the current location information of the user terminal and the historical trajectory information of the user terminal at the target prediction time point; the current location information of the user terminal is determined by the processing module through positioning the user terminal.

[0029] Fourthly, this application provides a location management function (LMF) network element, the network element comprising:

[0030] Processor and memory;

[0031] The memory stores executable instructions that the processor can execute;

[0032] The processor executes the executable instructions stored in the memory, causing the processor to perform the method described above.

[0033] Fifthly, this application provides a storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the method described above.

[0034] The service processing method, apparatus, and storage medium provided in this application predict the location of the user terminal at a target prediction time point by using the current location information and historical trajectory information of the user terminal through the LMF network element, thereby determining the location information of the user terminal at a future time point (such as the target prediction time point). This application solves the problem that existing positioning methods can only determine the current location information of the UE, but cannot determine the location information of the UE at a future time point. Attached Figure Description

[0035] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0036] Figure 1 Diagram of the existing positioning system architecture;

[0037] Figure 2 Flowchart of the existing MO-LR localization method;

[0038] Figure 3 Flowchart of the existing MT-LR localization method;

[0039] Figure 4 This application provides a business processing system architecture diagram for its embodiments.

[0040] Figure 5 The business processing method flow provided in the embodiments of this application Figure 1 ;

[0041] Figure 6 The business processing method flow provided in the embodiments of this application Figure 2 ;

[0042] Figure 7 The business processing method flow provided in the embodiments of this application Figure 3 ;

[0043] Figure 8 Structure of LMF network element provided in the embodiments of this application Figure 1 ;

[0044] Figure 9Structure of LMF network element provided in the embodiments of this application Figure 2 .

[0045] The accompanying drawings have illustrated specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to specific embodiments. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this invention, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0047] Location services have wide applications in vehicle navigation, intelligent transportation, and public safety. Currently, the main location methods defined by 3GPP, the international standards organization, for 5G location services are MO-LR and MT-LR. For example... Figure 1 As shown, the existing positioning system architecture includes: UE 11, Access Network (RAN) 12, 5G Core Network, Access Location Service (LCS) Client 17 or Application Function (AF) Network Element 18. The 5G Core Network includes Access and Mobility Management Function (AMF) Network Element 13, Location Management Function (LMF) Network Element 14, Unified Data Management (UDM) Network Element 15, Location Gateway (GMLC) Network Element 16, and Network Capability Opening Function (NEF) Network Element 19. The Access Network (RAN) 12 can be a Radio Access Network (NG-RAN).

[0048] In this application, UE 11 is a user terminal that has completed network registration in the mobile network. MO-LR location request can be abbreviated as MO-LR. MT-LR location request can be abbreviated as MT-LR.

[0049] The specific process of the MO-LR localization method is as follows: Figure 2 As shown in steps 1.1-1.13:

[0050] 1.1 UE 11 initiates a location service request (UE Triggered Service Request) to establish a signaling channel with AMF network element 13.

[0051] 1.2 UE 11 initiates an MO-LR location request, requesting the 5G core network to locate it.

[0052] For example, UE 11 sends an MO-LR location request (such as UL NAS TRANSPORT (MO-LR Request)) to AMF network element 13 via RAN 12.

[0053] 1.3. Select LMF network element 14 for AMF network element 13.

[0054] For example, AMF network element 13 can select LMF network element 14 from multiple LMF network elements 14 in the core network to perform subsequent steps.

[0055] 1.4. AMF network element 13 sends a location determination request (e.g., Nlmf_Location_Determin).

[0056]

[0057] To locate.

[0058] 1.5. LMF network element 14 performs UE Positioning on UE 11 and obtains the positioning result.

[0059] For example, LMF network element 14 executes one or more positioning procedures to locate UE 11 and obtain the positioning result, that is, to determine the current location information of UE 11.

[0060] The location results include the current location information of UE 11.

[0061] 1.6 The LMF network element 14 returns the positioning result to the AMF network element 13.

[0062] LMF element 14 returns a location determination request response message (such as Nlmf_Location_DeterminLocation_Response) carrying the location result to AMF element 13.

[0063] 1.7. AMF network element 13 sends the positioning result to VGMLC network element 161 in GMLC network element 16.

[0064] For example, AMF network element 13 sends a location update request (such as Ngmlc_Location_LocationUpdate Request) carrying the positioning results to VGMLC network element 161.

[0065] 1.8 VGMLC network element 161 sends a location update request (such as Ngmlc_Location_LocationUpdate Request) carrying the positioning results to HGMLC network element 162.

[0066] 1.9a. HGMLC network element 162 performs location update on UE 11, obtains the current updated location information of UE 11, and sends the current updated location information to LCS client 17.

[0067] For example, HGMLC network element 162 updates the map location of UE 11, obtains the current map location information of UE 11, and sends the current map location information to LCS client 17.

[0068] 1.10a. The LCS client 17 returns a confirmation message to the HGMLC network element 162 to acknowledge receipt of the currently updated location information.

[0069] 1.11 HGMLC network element 162 returns a location update request response message (such as Ngmlc_Location_LocationUpdate Response) to VGMLC network element 161.

[0070] The location update request response message includes the current updated location information of UE11.

[0071] 1.12. VGMLC network element 161 returns a location update request response message (such as Ngmlc_Location_LocationUpdate Response) to AMF network element 13.

[0072] 1.13. AMF network element 13 returns an MO-LR location request response message (such as UL NASTRANSPORT (MO-LR Response)) to UE 11 through RAN 12.

[0073] The MO-LR location request response message includes UE11's current updated location information.

[0074] Optionally, after step 1.7, VGMLC network element 161 can perform a location update on UE 11 to obtain the current updated location information of UE 11. For example, VGMLC network element 161 can perform a map location update on UE 11 to obtain the current map location information of UE 11. VGMLC network element 161 executes step 1.12 and returns a location update request response message including the current updated location information of UE 11 to AMF network element 13. AMF network element 13 executes step 1.13.

[0075] Optionally, after step 1.8 and before step 1.11, HGMLC network element 162, NEF network element 19, and AF network element 18 can also perform information exchange according to steps 1.9b-1 to 1.10b-2 as follows.

[0076] 1.9b-1. HGMLC network element 162 sends a location update notification (such as Ngmlc_Location_LocationUpdate Notify) carrying the positioning results to NEF network element 19.

[0077] 1.9b-2. NEF network element 19 sends a location update notification (such as Nnef_Location_LocationUpdate Notify) carrying the positioning results to AF network element 18.

[0078] 1.10b-1. AF network element 18 performs location update on UE 11, obtains the current updated location information of UE 11, and returns a location update notification response message (such as Nnef_Location_LocationUpdate Notify Response) carrying the current updated location information of UE 11 to NEF network element 19.

[0079] 1.10b-2. NEF network element 19 returns a location update notification response message (such as Ngmlc_Location_LocationUpdate Notify Response) carrying the current updated location information of UE11 to HGMLC network element 162.

[0080] The specific process of the MT-LR positioning method is as follows: Figure 3 As shown in steps 2.1-2.11:

[0081] Typically, in the MT-LR positioning method, UE 11 usually accesses LCS client 17 or AF network element 18 via RAN 12 and core network using SMS, MMS, or WAP browsing. Then, LCS client 17 or AF network element 18 initiates the MT-LR positioning request on its behalf. Steps 2.1-2.11 below are described using LCS client 17 as the initiator of the MT-LR positioning request.

[0082] 2.1. LCS client 17 initiates a location service request.

[0083] For example, LCS client 17 sends an LCS Service Request to GMLC network element 16 to initiate an MT-LR request process, requesting the 5G mobile network to locate UE 11. The LCS Service Request includes the identifier of UE 11.

[0084] 2.2 GMLC network element 16 sends a query request (such as Nudm_UECM_Get Request) to UDM network element 15 so that UDM network element 15 can authenticate UE 11 and determine the identifier of AMF network element 13 serving UE 11.

[0085] 2.3 UDM network element 15 returns a query request response message (such as Nudm_UECM_GetResponse) to GMLC network element 16. The query request response message contains the identifier of the AMF network element 13 currently serving the UE 11.

[0086] The identifier of AMF network element 13 can be the address of AMF network element 13.

[0087] 2.4 GMLC network element 16 sends the MT-LR positioning request to AMF network element 13.

[0088] For example, GMLC element 16 sends an MT-LR positioning request (such as Namf_Location_ProvidePositioningInfo_Request) to the AMF element 13 corresponding to UE 11.

[0089] 2.5 Optionally, the 5G mobile network triggers the MT-LR location service (Network Triggered Service Request).

[0090] 2.6. Select LMF network element 14 for AMF network element 13.

[0091] For example, AMF network element 13 can select LMF network element 14 from multiple LMF network elements 14 in the core network to perform subsequent steps.

[0092] 2.7. AMF network element 13 sends a location determination request (such as Nlmf_Location_Determin Location_Request) to LMF network element 14 to request LMF network element 14 to locate UE 11.

[0093] 2.8. LMF network element 14 performs UE Positioning on UE 11 and obtains the positioning result.

[0094] For example, LMF network element 14 executes one or more positioning procedures to locate UE 11 and obtain the positioning result.

[0095] The location results include the current location information of UE 11.

[0096] 2.9. The LMF network element 14 returns a location determination request response message (such as Nlmf_Location_DeterminLocation_Response) carrying the location result to the AMF network element 13.

[0097] 2.10. AMF network element 13 returns an MT-LR positioning request response message (such as Namf_Location_ProvidePositioningInfo_Response) carrying the positioning result to GMLC network element 16.

[0098] 2.11. The GMLC network element 16 returns an LCS location service request response message (such as LCS ServiceResponse) carrying the location result to the LCS client 17.

[0099] Optionally, if the MT-LR positioning request is initiated by AF network element 18, its specific implementation method and technical effect are similar to those in steps 2.1-2.11.

[0100] As can be seen from the above MO-LR and MT-LR positioning methods, the existing positioning methods can only determine the current location information of UE 11, but cannot determine the location information of UE 11 at future time points.

[0101] To address this issue, this application proposes a service processing method. The method involves the Location Management Function (LMF) network element receiving a location prediction request and historical trajectory information of a user terminal from the Access and Mobility Management Function (AMF) network element. The location prediction request includes a target prediction time point. The LMF network element determines the location prediction result of the user terminal. The location prediction result is the location information obtained by the LMF network element through location prediction of the target prediction time point using the user terminal's current location information and historical trajectory information. The user terminal's current location information is determined by the LMF network element through positioning the user terminal. This application enables the determination of the user terminal's location information at a future time point (such as the target prediction time point), solving the problem that existing positioning methods can only determine the UE's current location information but cannot determine the UE's location information at future time points.

[0102] The business processing method provided in this application will be described below with reference to some embodiments.

[0103] Figure 4 This is a diagram illustrating the architecture of a business processing system provided in an embodiment of this application. Figure 4As shown, the system architecture includes: UE 11, RAN 12, 5G core network, LCS client 17, and / or Application Function (AF) network element 18. The 5G core network includes Access and Mobility Management Function (AMF) network element 13, Location Management Function (LMF) network element 14, Unified Data Management (UDM) network element 15, Location Gateway (GMLC) network element 16, Network Capability Opening Function (NEF) network element 19, and Network Data Analysis Function (NWDAF) network element 21. RAN 12 can be a Radio Access Network (NG-RAN).

[0104] UE 11 adopts as follows Figure 2 The MO-LR location request is initiated in the manner shown, or, LCS client 17 or AF network element 18 uses the following method: Figure 3 After initiating an MT-LR positioning request as shown, AMF network element 13 obtains the UE 11 identifier and target prediction time point carried in the positioning request from the MT-LR or MT-LR positioning request. AMF network element 13 generates a location prediction request based on the UE 11 identifier and target prediction time point, or it can generate a historical trajectory request. The location prediction request may include the UE 11's historical trajectory request. AMF network element 13 sends the UE 11's location prediction request or historical trajectory request to NWDAF network element 21. NWDAF network element 21 returns the UE 11's historical trajectory information to AMF network element 13 based on the historical trajectory request. AMF network element 13 sends the UE 11's location prediction request and historical trajectory information to LMF network element 14. For example, AMF network element 13 may select any LMF network element 14 with a location prediction function identifier from among multiple LMF network elements 14 in the core network, and send the UE 11's location prediction request and historical trajectory information to the selected LMF network element 14. LMF element 14 performs location prediction for the target prediction time point of UE 11 as follows: LMF element 14 receives a location prediction request and historical trajectory information of user terminal (UE) 11 sent by AMF element 13. The location prediction request includes the target prediction time point. The target prediction time point is a future time point. LMF element 14 determines the location prediction result of user terminal (UE) 11. The location prediction result is the location information obtained by LMF element 14 from the current location information and historical trajectory information of user terminal (UE) 11 to predict the location of the target prediction time point. The current location information of the user terminal is determined by LMF element 14 in locating user terminal (UE) 11. LMF element 14 can locate user terminal (UE) 11 by using... Figure 2 or Figure 3The positioning method shown is used for positioning. After LMF network element 14 determines the location prediction result of user terminal (UE) 11, LMF network element 14 returns a location prediction response message containing the location prediction result to AMF network element 13.

[0105] The service processing method provided in this application uses LMF network elements to predict the location of the user terminal at the target prediction time point based on the current location information and historical trajectory information of the user terminal, thereby determining the location information of the user terminal at a future time point (such as the target prediction time point). This solves the problem that existing positioning methods can only determine the current location information of the user terminal, but cannot determine the location information of the user terminal at a future time point.

[0106] The following is combined with Figure 5 This application provides a detailed description of the business processing methods provided. Figure 5 The business processing method flow provided in the embodiments of this application Figure 1 . Figure 5 The execution subject of the embodiment shown is Figure 4 The LMF network element 14 in the illustrated embodiment. For example... Figure 5 As shown, the method includes steps S101-S102:

[0107] S101, The Location Management Function (LMF) network element receives the location prediction request and historical trajectory information of the user terminal sent by the Access and Mobility Management Function (AMF) network element; the location prediction request includes the target prediction time point.

[0108] For example, LMF network element 14 receives a location prediction request and historical trajectory information of UE 11 sent by AMF network element 13. The location prediction request includes the target prediction time point.

[0109] Optionally, the location prediction request may include one or more target prediction time points.

[0110] S102, the LMF network element determines the location prediction result of the user terminal; the location prediction result is the location information obtained by the LMF network element from the target prediction time point based on the current location information and the historical trajectory information of the user terminal; the current location information of the user terminal is determined by the LMF network element for positioning the user terminal.

[0111] For example, LMF network element 14 determines the location prediction result of UE 11. The location prediction result is the location information obtained by LMF network element 14 through target prediction time point prediction based on the current location information of UE 11 and the historical trajectory information of UE 11. The current location information of UE 11 is determined by LMF network element 14 in locating UE 11. For example, the current location information of UE 11 is determined by LMF network element 14 according to... Figure 2The MO-LR positioning method shown or Figure 3 The MT-LR positioning method shown is used to determine the positioning of UE 11.

[0112] For example, the LMF network element 14 can determine the location prediction result of UE 11 in the following manner: The LMF network element 14 uses a preset location prediction model to perform model prediction of the current location information and historical trajectory information of UE 11 at the target prediction time point, thereby obtaining the location information of UE 11 at the target prediction time point. The location prediction model is trained using training samples composed of historical trajectory information of multiple UEs 11. Optionally, the location prediction model can be a hybrid network model composed of a Long Short-Term Memory (LSTM) neural network sub-model, a convolutional neural network sub-model, and a random forest sub-model. The location prediction model can also be other computational models with location prediction functions.

[0113] Optionally, after the LMF network element 14 determines the location prediction result of UE 11, the LMF network element 14 returns a location prediction request response message containing the location prediction result to the AMF network element 13, so that the AMF network element 13 can return the location prediction result to the UE 11 that initiated MO-LR, or return the location prediction result to the AF network element 18 or LCS client 17 that initiated MT-LR.

[0114] Optionally, before the LMF network element 14 determines the location prediction result of UE 11 in step S102, the LMF network element 14 may also receive the location prediction request of UE 11 sent by the AMF network element 13 and the historical trajectory information of UE 11 sent by the NWDAF network element 21.

[0115] Optionally, before LMF network element 14 receives the historical trajectory information of UE 11 sent by NWDAF network element 21, LMF network element 14 sends a historical trajectory request for UE 11 to NWDAF network element 21. The historical trajectory request includes the identifier of user terminal (UE) 11 and the target predicted time point.

[0116] Optionally, before the LMF network element 14 sends the historical trajectory request of UE 11 to the NWDAF network element 21, the LMF network element 14 determines the NWDAF network element 21 with the historical trajectory information of UE 11 from multiple NWDAF network elements 21 based on the identifier of UE 11.

[0117] The following are combined with Figure 6 and Figure 7 The specific examples further illustrate the business processing methods provided in this application. Figure 6 The business processing method flow provided in the embodiments of this application Figure 2 . Figure 7The business processing method flow provided in the embodiments of this application Figure 3 .

[0118] like Figure 6 As shown, UE 11 obtains its location information at the target prediction time point through the following steps S301-S309:

[0119] S301 and AMF network element 13 receive positioning requests.

[0120] The location request can be an MO-LR sent by UE11 in a manner similar to steps 1.1-1.2, or an MT-LR sent by LCS client 17 or AF network element 18 in a manner similar to steps 2.1-2.4. The location request may include (or carry) the identifier of UE11 and the location prediction requirement; it may also include the location prediction identifier.

[0121] Location prediction requirements can include the target prediction time point, prediction accuracy requirements, and the method of representing the predicted location. Methods of representing the predicted location include latitude and longitude, and relative location representation.

[0122] S302, AMF network element 13 selects NWDAF network element 21.

[0123] For example, AMF network element 13 selects an NWDAF network element 21 with historical trajectory information of UE 11 from multiple NWDAF network elements 21 in the core network. For instance, AMF network element 13 selects an NWDAF network element 21 with historical trajectory information of UE 11 from multiple NWDAF network elements 21 based on the identifier of UE 11.

[0124] S303 and AMF network element 13 send historical trajectory requests to NWDAF network element 21.

[0125] Specifically, AMF network element 13 sends a historical trajectory request to NWDAF network element 21 as determined in step S302. The historical trajectory request may include the UE 11's identifier and location prediction requirements.

[0126] S304 and NWDAF network element 21 return historical trajectory information to AMF network element 13.

[0127] For example, NWDAF network element 21 returns the historical trajectory information of UE 11 to AMF network element 13. The historical trajectory information of UE 11 returned by NWDAF network element 21 can be the historical trajectory information of UE 11 within a preset time period before the target prediction time point. Moreover, the representation method of the historical trajectory information is the same as the prediction position representation method in the historical trajectory request, and the accuracy of the historical trajectory information meets the prediction accuracy requirements in the historical trajectory request.

[0128] S305, AMF network element 13 selects LMF network element 14.

[0129] For example, AMF element 13 selects an LMF element 14 with location prediction function from multiple LMF elements 14 in the core network.

[0130] For example, AMF element 13 selects one LMF element 14 from multiple LMF elements 14 in the core network that are marked with location prediction function identifiers.

[0131] S306, AMF network element 13 sends a location prediction request and UE 11's historical trajectory information to LMF network element 14.

[0132] The location prediction request includes the UE 11's identifier and location prediction requirements.

[0133] S307 and LMF network element 14 locate UE 11.

[0134] For example, LMF network element 14 uses a method similar to step 1.5 or step 2.8 to locate UE 11 and obtain the current location information of UE 11.

[0135] S308 and LMF network element 14 perform location prediction on UE 11 and obtain the location prediction result.

[0136] Specifically, the LMF network element 14 performs location prediction for UE 11 in the following manner to determine the location prediction result of UE 11: the LMF network element 14 performs location prediction for the target prediction time point based on the current location information and historical trajectory information of UE 11 to obtain the location information of UE 11 at the target prediction time point.

[0137] For example, the LMF network element 14 uses a hybrid network model to predict the location of UE 11 at the target prediction time point based on the current location information and historical trajectory information of UE 11, thereby obtaining the location information of UE 11 at the target prediction time point.

[0138] S309, Return the position prediction result.

[0139] For example, LMF network element 14 returns the location prediction result to AMF network element 13. AMF network element 13 returns the location prediction result to UE 11 in a manner similar to steps 1.7-1.13; or, AMF network element 13 returns the location prediction result to LCS client 17 or AF network element 18 in a manner similar to steps 2.7-2.11.

[0140] like Figure 7 As shown, UE 11 can also obtain the location information of UE 11 at the target prediction time point by following the steps S401-S409:

[0141] S401, AMF network element 13 receives the positioning request. This step is similar to step S301 and will not be described again here.

[0142] S402, AMF network element 13 selects LMF network element 14. This step is similar to step S305, and will not be described again here.

[0143] S403, AMF network element 13 sends a location prediction request to LMF network element 14.

[0144] The location prediction request includes the UE 11's identifier and location prediction requirements.

[0145] S404, LMF network element 14 selects NWDAF network element 21.

[0146] For example, LMF network element 14 selects an NWDAF network element 21 with historical trajectory information of UE 11 from multiple NWDAF network elements 21 in the core network. For instance, LMF network element 14 selects an NWDAF network element 21 with historical trajectory information of UE 11 from multiple NWDAF network elements 21 based on the identifier of UE 11.

[0147] S405 and LMF network element 14 send historical trajectory requests to NWDAF network element 21.

[0148] Specifically, LMF network element 14 sends a historical trajectory request to NWDAF network element 21, which has historical trajectory information of UE 11. The historical trajectory request may include the identifier of UE 11 and location prediction requirements.

[0149] S406, NWDAF network element 21 returns the historical trajectory information of UE 11 to LMF network element 14.

[0150] For example, NWDAF network element 21 returns the historical trajectory information of UE 11 to LMF network element 14. The historical trajectory information of UE 11 returned by NWDAF network element 21 can be the historical trajectory information of UE 11 within a preset time period before the target prediction time point. Moreover, the representation method of this historical trajectory information is the same as the prediction position representation method in the historical trajectory request, and the accuracy of this historical trajectory information meets the prediction accuracy requirements in the historical trajectory request.

[0151] Steps S407-S409 are similar to steps S307-S309, and will not be repeated here.

[0152] from Figure 6 and Figure 7As illustrated in the example, the service processing method provided by this invention enables location prediction of a user terminal (UE) 11, determining the location information of UE 11 at one or more future time points (one or more target prediction time points). This service processing method is of great significance in the field of public safety, such as personnel monitoring and search and rescue, and object monitoring, such as geological disaster early warning and animal migration.

[0153] The service processing method provided in this embodiment uses the LMF network element to predict the location of the user terminal at one or more target prediction time points based on the user terminal's current location information and historical trajectory information. This achieves the determination of the user terminal's future location information, solving the problem that existing positioning methods can only determine the UE's current location information but cannot determine the UE's future location information. Furthermore, the service processing method provided by this invention is of great significance in the field of public safety, such as personnel monitoring and search and rescue, and object monitoring, such as geological disaster early warning and animal migration.

[0154] This application also provides a business processing system, such as... Figure 4 As shown, the service processing system 20 includes: AMF network element 13 and LMF network element 14.

[0155] LMF network element 14 receives the user terminal's location prediction request and historical trajectory information sent by AMF network element 13. The location prediction request includes the target prediction time point.

[0156] LMF network element 14 determines the location prediction result of the user terminal. The location prediction result is the location information obtained by LMF network element 14 through target prediction time point prediction based on the current location information and historical trajectory information of the user terminal. The current location information of the user terminal is determined by LMF network element 14 through positioning the user terminal.

[0157] Optionally, the service processing system also includes: NWDAF network element 21.

[0158] Before the LMF network element 14 determines the location prediction result of the user terminal, the LMF network element 14 receives the location prediction request of the user terminal sent by the AMF network element 13 and the historical trajectory information of the user terminal sent by the NWDAF network element 21.

[0159] For example, AMF network element 13 sends a historical trajectory request for UE 11 to NWDAF network element 21 and receives historical trajectory information returned by NWDAF network element 21 in response to the historical trajectory request. The historical trajectory request includes the identifier of UE 11 and the target prediction time point. The identifier of UE 11 and the target prediction time point are obtained by AMF network element 13 from the terminal originating location request MO-LR or the terminal terminating location request MT-LR. MO-LR is sent by UE 11. MT-LR is sent by application function AF network element 18 or location service LCS client 17.

[0160] AMF network element 13 sends UE 11's location prediction request and historical trajectory information to LMF network element 14. The location prediction request includes the target prediction time point.

[0161] LMF network element 14 determines the location prediction result of UE 11 based on the location prediction request and historical trajectory information. The location prediction result is the location information obtained by LMF network element 14 through target prediction time point prediction using the current location information and historical trajectory information of UE 11. The current location information of UE 11 is determined by LMF network element 14 through positioning of UE 11.

[0162] LMF element 14 returns a location prediction request response message containing the location prediction results to AMF element 13.

[0163] Further, AMF network element 13 returns the location prediction result to UE 11, which initiated MO-LR, or returns the location prediction result to AF network element 18 or LCS client 17, which initiated MT-LR. For example, AMF network element 13 returns the location prediction result to UE 11 in a manner similar to steps 1.7-1.13; or, AMF network element 13 returns the location prediction result to LCS client 17 or AF network element 18 in a manner similar to steps 2.7-2.11.

[0164] The implementation principle and technical effects of this embodiment are similar to Figure 5 The implementation principle and technical effect of the embodiments shown are similar, and will not be repeated here.

[0165] This application also provides a location management function (LMF) network element. Figure 8 Structure of LMF network element provided in the embodiments of this application Figure 1 .like Figure 8 As shown, the LMF network element includes:

[0166] 41. Receive / transmit module; 42. Processing module.

[0167] The transceiver module 41 is used to receive location prediction requests and historical trajectory information of user terminals sent by the Access and Mobility Management Function (AMF) network elements. The location prediction request includes the target prediction time point.

[0168] Processing module 42 is used to determine the location prediction result of the user terminal. The location prediction result is the location information obtained by the processing module through target prediction time point prediction using the user terminal's current location information and historical trajectory information. The user terminal's current location information is determined by the processing module through positioning the user terminal.

[0169] The implementation principle and technical effects of this embodiment are similar to Figure 5 The implementation principle and technical effect of the embodiments shown are similar, and will not be repeated here.

[0170] This application also provides a location management function LMF network element. Figure 9 Structure of LMF network element provided in the embodiments of this application Figure 2 .like Figure 9 As shown, the LMF network element includes a processor 51 and a memory 52. ​​The memory 52 stores executable instructions for the processor 51, enabling the processor 51 to execute the technical solutions of the above-described method embodiments. The implementation principle and technical effects are similar, and will not be repeated here. It should be understood that the processor 51 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules in the processor. The memory 52 may include high-speed random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device, or a USB flash drive, external hard drive, read-only memory, disk, or optical disc, etc.

[0171] This application embodiment also provides a storage medium storing computer-executable instructions. When these computer-executable instructions are executed by a processor, they implement the aforementioned business processing method. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random-Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The storage medium can be any available medium accessible by a general-purpose or special-purpose computer.

[0172] An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Alternatively, the storage medium can be an integral part of the processor. Both the processor and the storage medium can reside in an application-specific integrated circuit (ASIC). Alternatively, the processor and storage medium can exist as discrete components in an electronic device or host device.

[0173] This application also provides a program product, such as a computer program, which, when executed by a processor, implements the business processing methods covered by this application.

[0174] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0175] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A business processing method, characterized in that, include: The Location Management Function (LMF) network element receives a location prediction request from the Access and Mobility Management Function (AMF) network element and historical trajectory information of the user terminal from the Network Data Analysis Function (NWDAF) network element; the location prediction request includes a target prediction time point. The LMF network element determines the location prediction result of the user terminal; the location prediction result is the location information obtained by the LMF network element through the target prediction time point prediction of the current location information of the user terminal and the historical trajectory information of the user terminal; the current location information of the user terminal is determined by the LMF network element through positioning of the user terminal. The LMF network element determines the location prediction result of the user terminal, including: The LMF network element uses a preset location prediction model to predict the target prediction time point based on the current location information and historical trajectory information of the user terminal, thereby obtaining the location information of the user terminal at the target prediction time point. The location prediction model is trained using training samples composed of historical trajectory information from multiple user terminals; the location prediction model is a hybrid network model composed of a long short-term memory neural network sub-model, a convolutional neural network sub-model, and a random forest sub-model. Before the LMF network element receives the historical trajectory information of the user terminal sent by the NWDAF network element, the method further includes: The LMF network element determines the NWDAF network element with the historical trajectory information of the user terminal from multiple NWDAF network elements based on the identifier of the user terminal. The LMF network element sends a historical trajectory request of the user terminal to the NWDAF network element; the historical trajectory request includes the identifier of the user terminal and the target prediction time point.

2. The method according to claim 1, characterized in that, After the LMF network element determines the location prediction result of the user terminal, the method further includes: The LMF network element returns a location prediction request response message containing the location prediction result to the AMF network element.

3. A business processing system, characterized in that, The service processing system includes: an Access and Mobility Management (AMF) network element, a Location Management (LMF) network element, and a Network Data Analysis (NWDAF) network element; The LMF network element receives the location prediction request of the user terminal sent by the AMF network element and the historical trajectory information of the user terminal sent by the NWDAF network element; the location prediction request includes the target prediction time point; The LMF network element determines the location prediction result of the user terminal; the location prediction result is the location information obtained by the LMF network element through the target prediction time point prediction of the current location information of the user terminal and the historical trajectory information of the user terminal; the current location information of the user terminal is determined by the LMF network element through positioning of the user terminal. The LMF network element determines the location prediction result of the user terminal, including: The LMF network element uses a preset location prediction model to predict the target prediction time point based on the current location information and historical trajectory information of the user terminal, thereby obtaining the location information of the user terminal at the target prediction time point. The location prediction model is trained using training samples composed of historical trajectory information from multiple user terminals; the location prediction model is a hybrid network model composed of a long short-term memory neural network sub-model, a convolutional neural network sub-model, and a random forest sub-model. Before the LMF network element receives the historical trajectory information of the user terminal sent by the NWDAF network element, the LMF network element determines the NWDAF network element with the historical trajectory information of the user terminal from multiple NWDAF network elements based on the identifier of the user terminal; the LMF network element sends the historical trajectory request of the user terminal to the NWDAF network element; the historical trajectory request includes the identifier of the user terminal and the target prediction time point.

4. A positioning management function LMF network element, characterized in that, The network element includes: Send / receive module, processing module; The transceiver module is used to receive location prediction requests from user terminals sent by Access and Mobility Management Function (AMF) network elements and historical trajectory information of the user terminals sent by Network Data Analysis Function (NWDAF) network elements; the location prediction request includes a target prediction time point; The processing module is used to determine the location prediction result of the user terminal; the location prediction result is the location information obtained by the processing module through the current location information of the user terminal and the historical trajectory information of the user terminal at the target prediction time point; the current location information of the user terminal is determined by the processing module through positioning the user terminal; The processing module is specifically used for: Using a preset location prediction model, the current location information and historical trajectory information of the user terminal are used to predict the target prediction time point, thereby obtaining the location information of the user terminal at the target prediction time point; The location prediction model is trained using training samples composed of historical trajectory information from multiple user terminals; the location prediction model is a hybrid network model composed of a long short-term memory neural network sub-model, a convolutional neural network sub-model, and a random forest sub-model; before the LMF network element receives the historical trajectory information of the user terminal sent by the NWDAF network element, the transceiver module is further configured to: The LMF network element sends a historical trajectory request of the user terminal to the NWDAF network element; the historical trajectory request includes the identifier of the user terminal and the target prediction time point; Before the LMF network element sends the user terminal's historical trajectory request to the NWDAF network element, the processing module is further configured to: The LMF network element determines the NWDAF network element with the historical trajectory information of the user terminal from multiple NWDAF network elements based on the identifier of the user terminal.

5. A positioning management function LMF network element, characterized in that, The network element includes: Processor and memory; The memory stores the execution instructions that are executed by the processor; The processor executes the execution instructions stored in the memory, causing the processor to perform the method as described in claim 1 or 2.

6. A storage medium, characterized in that, The storage medium stores computer execution instructions, which, when executed by a processor, are used to implement the method as described in claim 1 or 2.

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