Method, device and network element for predicting terminal information

Abstract

The present application discloses a method, apparatus and network element for predicting terminal information, which belongs to the field of communications. An embodiment of the method of the present application includes: a first network element obtains terminal positioning data in a first time zone through a location service LCS architecture; and the first network element inputs the terminal positioning data in the first time zone into a traffic model or a supervised learning model, and obtains terminal prediction information in a second time zone output by the traffic model or the supervised learning model.

Description

[Technical Field]

[0001] (CROSS-REFERENCE TO RELATED APPLICATIONS) This application claims priority from Chinese Patent Application No. 202210314449.X filed in China on March 28, 2022, and from Chinese Patent Application No. 202210920037.0 filed in China on August 1, 2022, the entire contents of which are incorporated herein by reference.

[0002] The present application relates to the field of communication technology, and specifically to a method, apparatus and network element for predicting terminal information. [Background technology]

[0003] The Network Data Analytics Function (NWDAF) network element supports the collection of network element or terminal-related data and provides information such as statistics and forecasts. The NWDAF can collect data from network elements such as the Access and Mobility Management Function (AMF) and Session Management Function (SMF) or from the Operation, Administration, and Maintenance (OAM). In the conventional flow, the NWDAF obtains terminal location information collected by the AMF. The terminal location information is coarse-grained, for example, at the Tracking Area (TA) level or cell level, with a range of approximately 1000 meters. That is, the NWDAF can learn from the AMF which cell or TA the terminal is currently in.

[0004] When an NWDAF user initiates a service request to the NWDAF, requesting statistics or forecasts of information related to the NWDAF, the NWDAF will collect information from different network elements based on the parameters in the request message, and after the NWDAF performs statistics and analysis, it will return the results to the NWDAF user. The results of these statistics and analysis may be provided to other 5GC network elements for network optimization, or to third-party applications for services such as personalized recommendations, smart transportation planning, etc.

[0005] The accuracy of statistical and forecasting services provided by conventional architecture NWDAF is limited to the TA or cell level, making it impossible to realize more accurate statistical and forecasting services.If NWDAF obtains more accurate positioning information, including geographic location, movement rate, and orientation, through the Location Services (LCS) architecture, how to use this information to provide more accurate location information to users remains to be studied. Summary of the Invention [Problem to be solved by the invention]

[0006] The embodiments of the present application provide a method, apparatus, and network element for predicting terminal information that can solve the problem in the prior art that NWDAF cannot predict terminal location using positioning data of the LCS architecture. [Means for solving the problem]

[0007] According to a first aspect, there is provided a method for predicting terminal information, the method comprising: A first network element obtains terminal positioning data within a first time period via a location service LCS architecture; The first network element inputs terminal positioning data within a first time period into a traffic model or a supervised learning model, and obtains terminal prediction information within a second time period output by the traffic model or the supervised learning model.

[0008] According to a second aspect, there is provided a device for predicting terminal information, the device comprising: an acquisition module for acquiring terminal positioning data within a first time period via a location services LCS architecture; and a prediction module for inputting terminal positioning data within a first time period into a traffic model or a supervised learning model, and obtaining terminal prediction information within a second time period output by the traffic model or the supervised learning model.

[0009] According to a third aspect, there is provided a first network element, the first network element including a processor and a memory, the memory storing a program or instructions operable to run on the processor, the program or instructions, when executed by the processor, implementing the steps of the method of the first aspect.

[0010] According to a fourth aspect, there is provided a first network element, the first network element including a processor and a communication interface, wherein the communication interface is used to obtain terminal positioning data within a first time period via a location services LCS architecture, and the processor is used to input the terminal positioning data within the first time period to a traffic model or a supervised learning model, and obtain terminal prediction information within a second time period output by the traffic model or the supervised learning model.

[0011] According to a fifth aspect, there is provided a readable storage medium having stored thereon a program or instructions which, when executed by a processor, implements the steps of the method according to the first aspect.

[0012] According to a sixth aspect, there is provided a chip, the chip including a processor and a communication interface, the communication interface coupled to the processor, the processor being adapted to run a program or instructions to implement the method of the first aspect.

[0013] According to a seventh aspect, there is provided a computer program product stored on a storage medium, the computer program product being configured to implement the steps of the method according to the first aspect when executed by at least one processor. [Effects of the Invention]

[0014] In an embodiment of the present application, the first network element obtains terminal positioning data within a first time period through an LCS architecture, and predicts terminal prediction information within a certain future time period based on a traffic model or supervised learning model corresponding to the terminal positioning data, thereby improving the service performance of the first network element and realizing trajectory prediction of the interaction between the first network element and drones, vehicle-to-everything architectures, and thereby providing smart transportation planning for urban bus systems. [Brief explanation of the drawings]

[0015] [Figure 1] 1 illustrates a block diagram of a wireless communication system to which an embodiment of the present application can be applied. [Figure 2] 1 illustrates a step flowchart of a method for predicting terminal information according to an embodiment of the present application; [Figure 3] 1 depicts a flow chart of Example 1 according to an embodiment of the present application. [Figure 4] 1 depicts a flow chart of Example 2 according to an embodiment of the present application. [Figure 5] 1 illustrates a structural schematic diagram of a terminal information prediction device according to an embodiment of the present application; [Figure 6]1 illustrates a structural schematic diagram of a first network element according to an embodiment of the present application; [Figure 7] 1 illustrates a structural schematic diagram of a network-side device according to an embodiment of the present application; DETAILED DESCRIPTION OF THE INVENTION

[0016] The following clearly describes the technical solutions in the embodiments of the present application in conjunction with the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only some of the embodiments of the present application, and not all of the embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present application fall within the scope of protection of the present application.

[0017] The terms "first," "second," etc. in the specification and claims of this application are intended to distinguish between similar objects and are not intended to describe a particular order or sequence. It should be understood that terms used in this manner are interchangeable where appropriate, so that embodiments of this application may be performed in orders other than those illustrated or described herein, and that objects distinguished by "first" and "second" are generally of the same type and do not limit the number of objects; for example, a first object may be one or more. Furthermore, "and / or" in the specification and claims indicates at least one of the connected objects, and the character " / " generally indicates an "or" relationship between the related objects.

[0018] It should be noted that the techniques described in the embodiments of the present application are not limited to Long Term Evolution (LTE) / LTE-Advanced (LTE-A) systems, but can also be applied to other wireless communication systems, such as Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Frequency Division Multiple Access (FDMA), Orthogonal Frequency Division Multiple Access (OFDMA), Single-Carrier Frequency Division Multiple Access (SC-FDMA), and other systems. The terms "system" and "network" in the embodiments of the present application are always used interchangeably, and the described techniques may be used in the above-mentioned systems and radio technologies as well as other systems and radio technologies. Although the following description describes a New Radio (NR) system for illustrative purposes and uses NR terminology in most of the description below, these techniques may also be applied to applications other than NR system applications, such as 6th Generation (6G) communication systems.

[0019] 1 shows a block diagram of a wireless communication system to which an embodiment of the present application can be applied. The wireless communication system includes a terminal 11 and a network side device 12. Here, the terminal 11 may be a mobile phone, a tablet personal computer (TPC), a laptop computer (LC) (also called a notebook computer), a personal digital assistant (PDA), a palmtop computer, a netbook, an ultra-mobile personal computer (UMPC), a mobile internet device (MID), an augmented reality (AR) / virtual reality (VR) device, a robot, a wearable device, a vehicle user equipment (VUE), a pedestrian user equipment (PUE), a smart home (home devices with wireless communication capabilities, such as a refrigerator, a television, a washing machine, or furniture), a game console, a personal computer (Personal The network side device 12 may be a terminal side device such as a personal computer (PC), a teller machine or a self-service machine, and the wearable device may be a smart watch, a smart wristband, a smart earphone, a smart glasses, a smart accessory (a smart bracelet, a smart hand chain, a smart ring, a smart necklace, a smart ankle bracelet, a smart anklet, etc.), a smart band, a smart clothing, etc. It should be noted that the terminal 11 in the embodiments of the present application is not limited to a specific type. The network side device 12 may include an access network device or a core network device, where the access network device may be referred to as a radio access network device, a radio access network (RAN), a radio access network function, or a radio access network unit.The access network equipment may include a base station, a wireless local area network (WLAN), an access point, or a wireless fidelity (WiFi) node, and the base station may be called a Node B, an evolved Node B (eNB), an access point, a base transceiver station (BTS), a radio base station, a radio transceiver, a basic service set (BSS), an extended service set (ESS), a home B node, a home evolved B node, a transmitting receiving point (TRP), or any other suitable term in the art. As long as the same technical effect is achieved, the base station is not limited to a specific technical term. It should be noted that the embodiments of this application only take the base station in an NR system as an example, and do not limit the specific type of base station.Core network devices include core network nodes, core network functions, mobility management entities (MMEs), access and mobility management functions (AMFs), session management functions (SMFs), user plane functions (UPFs), policy control functions (PCFs), policy and charging rules functions (PCRFs), edge application server discovery functions (EASDFs), unified data management (UDMs), unified data repository (UDRs), home subscriber servers (HSSs), centralized network configuration (CNCs), network repository functions (NRFs), network exposure functions (NEFs), local NEFs (or L-NEFs), binding support functions (BSFs), and application functions (Application Node Functions). It should be noted that the embodiments of the present application only take core network equipment in an NR system as an example, and do not limit the specific type of core network equipment.

[0020] The following describes in detail the method for predicting terminal information according to the embodiments of the present application through several examples and application scenarios in conjunction with the drawings.

[0021] As shown in FIG. 2, an embodiment of the present application further provides a method for predicting terminal information, which includes the following steps:

[0022] Step 201: a first network element obtains terminal positioning data within a first time period through a location service LCS architecture; Optionally, the first network element mentioned in the embodiments of the present application is an NWDAF network element. For example, the NWDAF obtains terminal positioning data within a first time period through an interface with the LCS architecture. This terminal positioning data may be stored historical positioning information from the NWDAF, or may be terminal positioning data just obtained from the Access and Mobility Management Function (AMF) or Location Management Function (LMF) architecture. This terminal positioning data has a relatively fine granularity, that is, the horizontal accuracy meets the specific positioning requirements of ordinary traffic, for example, at the 1-10m level.

[0023] Step 202: The first network element inputs terminal positioning data within a first time period into a traffic model or a supervised learning model, and obtains terminal prediction information within a second time period output by the traffic model or the supervised learning model.

[0024] In one alternative embodiment, the terminal positioning data within the first time period is Terminal identifier information (e.g., UE ID), A group identifier (e.g., group UE ID) of the group to which the terminal belongs; The geographic location of the device; a motion rate at the device's current geographic location; a motion orientation at the device's current geographic location; Positioning accuracy information including horizontal accuracy and vertical accuracy; A timestamp and The age of location of the positioning data, and first indication information indicating that the terminal is in an outdoor area or an indoor area.

[0025] Here, the external area includes at least one of the ground, the outside, and the outside of the vehicle, and the internal area includes at least one of the basement, the inside, and the inside of the vehicle, and accordingly, the first instruction information is indicating that the terminal is on the ground; Indicating that the terminal is underground; and Indicating that the device is in the room; Indicating that the terminal is outdoors; indicating that the device is in the vehicle; and indicating that the terminal is outside the vehicle.

[0026] In one alternative embodiment, the terminal prediction information within the second time period is: location prediction information of the terminal within a second time period; Rate prediction information for the terminal within a second time period; Direction prediction information of the terminal within a second time period; Prediction indication information indicating that the terminal is in an external area or an internal area during a second time period; a total number of people located within the second area during the second time period; a total number of terminals located within the second region during the second time period; the number of terminals located in the second area and in the first traffic environment during the second time period; a ratio of the number of terminals located in the second area and in the first traffic environment during the second time period; an average moving speed of a terminal located in the second area and in the first traffic environment during a second time period; a direction of the first transportation means located within the second region during the second time period; and the number of terminals of the first transportation means located in the second area within the second time period and having the same orientation; the number of terminals located in the second area during the second time period and whose moving speed exceeds a preset value; a ratio of the number of terminals that are located in the second area during the second time period and whose moving speed exceeds a preset value; the number of terminals located in the second region within the second time period and having the first orientation; a ratio of the number of terminals located in the second region and having the first orientation within the second time period; The first transportation means is one of all transportation means located in a second region during a second time period.

[0027] It should be noted that the first transportation environment includes at least one of walking, cycling, bus, subway, and car, and the first transportation means is at least one of walking, cycling, bus, subway, car, etc.

[0028] It should be noted that the proportion of the number of terminals located in the second area during the second time period and in the first traffic environment is the proportion of the number of terminals located in the second area during the second time period and in the first traffic environment to the total number of terminals located in the second area during the second time period.

[0029] It should be noted that the proportion of the number of terminals located within the second area during the second time period and whose movement speed exceeds a predetermined value is the proportion of the number of terminals located within the second area during the second time period and whose movement speed exceeds a predetermined value to the total number of terminals located within the second area during the second time period.

[0030] It should be noted that the proportion of the number of terminals located in the second region during the second time period and having the first orientation is the proportion of the number of terminals located in the second region during the second time period and having the first orientation to the total number of terminals located in the second region during the second time period.

[0031] Optionally, the method further includes the first network element calculating statistical information within a first time period based on the terminal positioning data within the first time period, wherein the statistical information within the first time period includes: the total number of people located within the second area during the first time period; a total number of terminals located within the second area during the first time period; the number of terminals located in the second area and in the first traffic environment during the first time period; a ratio of the number of terminals located in the second area and in the first traffic environment during the first time period; an average moving speed of a terminal located in the second region and in the first traffic environment during the first time period; a heading of a first mode of transportation located within a second region during a first time period; the number of terminals of the first transportation means located in the second area within the first time period and having the same orientation; the number of terminals located in the second area during the first time period and whose moving speed exceeds a preset value; a ratio of the number of terminals that are located in the second area during the first time period and whose moving speed exceeds a preset value; the number of terminals located in the second region within the first time period and having the first orientation; a ratio of the number of terminals located in the second region and having the first orientation within the first time period; The first transportation means is one of all transportation means located in a second region within a first time period.

[0032] Optionally, the first network element is a network element having a network data analysis function NWDAF; Optionally, the method further includes the first network element sending terminal prediction information within the second time period or statistical information within the first time period to a user of the NWDAF.

[0033] It should be noted that the proportion of the number of terminals located in the second area and in the first traffic environment during the first time period is the proportion of the number of terminals located in the second area and in the first traffic environment during the first time period to the total number of terminals located in the second area during the first time period.

[0034] It should be noted that the percentage of the number of terminals located within the second area during the first time period and whose movement speed exceeds a predetermined value is the proportion of the number of terminals located within the second area during the first time period and whose movement speed exceeds a predetermined value to the total number of terminals located within the second area during the first time period.

[0035] It should be noted that the proportion of the number of terminals located in the second region and having the first orientation within the first time period is the proportion of the number of terminals located in the second region and having the first orientation within the first time period to the total number of terminals located in the second region within the first time period.

[0036] Here, the external area includes at least one of the ground, the outside, and the outside of the vehicle, and the internal area includes at least one of the basement, the inside, and the inside of the vehicle, and accordingly, the predicted instruction information is indicating that the terminal is on the ground; Indicating that the terminal is underground; and Indicating that the device is in the room; Indicating that the terminal is outdoors; indicating that the device is in the vehicle; and indicating that the terminal is outside the vehicle.

[0037] In at least one embodiment of the present application, when the terminal positioning data in the first time period includes first indication information indicating that the terminal is in an external area or an internal area, the predicted indication information included in the terminal prediction information in the second time period is different from the first indication information, and in this case, the predicted indication information output by the traffic model or the supervised learning model may be referred to as modified indication information; Alternatively, when the terminal positioning data within the first time period does not include first instruction information indicating that the terminal is in an external area or an internal area, the terminal prediction information for the second time period includes the prediction instruction information.

[0038] In one alternative embodiment, the terminal prediction information in the second time period output by the traffic model is: Further included is a predicted transportation mode for the terminal within a second time period.

[0039] In another optional embodiment, the terminal prediction information for the second time period output by the supervised learning model is: and a regional hotspot map within the second time period, wherein the regional hotspot map comprises: the probability that the terminal is in the first region during the second time period; and A change trend of the terminal rate within the second time period; the number of terminals within the first area during the second time period; and and a trend in the number of terminals in the first area within the second time period.

[0040] In at least one embodiment of the present application, before the first network element inputs terminal positioning data within a first time period into a traffic model, the method includes: The first network element performs data filtering on the terminal positioning data within a first time period to obtain positioning data that satisfies a predetermined condition; The first network element performs an environment judgment for the terminal based on the positioning data and the terminal history usage model that satisfy a predetermined condition, and determines a traffic environment in which the terminal is currently located, wherein the traffic environment includes at least one of walking, cycling, bus, subway, and car; The first network element further includes determining, based on a traffic environment in which the terminal is currently located, a traffic model corresponding to the traffic environment in which the terminal is currently located; wherein the first network element inputs terminal positioning data within a first time period into a traffic model, The first network element inputs terminal positioning data that satisfies a preset condition within a first time period into a traffic model corresponding to a traffic environment in which the terminal is currently located.

[0041] It should be noted that the data filtering function is to determine the accuracy of the reported terminal positioning data and delete erroneous positioning data, such as data whose movement rate exceeds that of normal transportation (exceeding 400 km / h), whose positioning information life cycle has timed out, whose accuracy does not meet the requirements, whose timestamp is within the allowable range of input data, etc. Here, the terminal positioning data that satisfies the preset condition may be understood as the terminal positioning data that satisfies the preset condition after data filtering is performed on the terminal positioning data within the first time period and the remaining terminal positioning data obtained after deleting the erroneous positioning data.

[0042] For example, when first indication information is provided in the terminal positioning data, the environment judgment process is relatively simple. That is, if it indicates that the terminal is underground and the vertical coordinate of the positioning is at a relatively deep position, it is judged that the terminal's current traffic environment is in the subway and the probability of taking the subway is relatively high; if it indicates that the terminal is above ground and outdoors and the speed is 2-5 km / h, it is judged that the user's current traffic environment is walking and the probability of commuting on foot is relatively high.

[0043] Also, for example, if the first instruction information is not provided in the terminal positioning data, the environment judgment is made based on the current geographical position, speed and direction, that is, if the user's direction has not changed for a long time and the speed is maintained at 20-40 km / h, it is judged that the terminal's current traffic environment is driving a car, and if there is actually no road (residential area, bridge) at the terminal's current geographical position and the terminal speed is about 80 km / h, it is judged with a high probability that the terminal is riding on a subway.

[0044] In at least one embodiment of the present application, before the first network element inputs terminal positioning data within a first time period into a traffic model, the method includes: The first network element further includes making training corrections to a traffic model based on the positioning data within the first time period.

[0045] wherein the first network element performs a training correction to a traffic model based on terminal positioning data within a first time period; The first network element performs data filtering on the terminal positioning data within a first time period to obtain positioning data that satisfies a predetermined condition; The first network element performs an environment judgment for the terminal based on the positioning data and the terminal history usage model that satisfy a predetermined condition, and determines a traffic environment in which the terminal is currently located, wherein the traffic environment includes at least one of walking, cycling, bus, subway, and car; The first network element inputs the positioning data of the terminal that satisfies the preset condition into a traffic model corresponding to the traffic environment in which the terminal is currently located, and obtains terminal prediction information within a third time period output by the traffic model; The first network element compares terminal prediction information within a third time period with positioning data for the third time period, and makes training corrections to a traffic model based on the comparison result, wherein the third time period is before the second time period.

[0046] Here, the data filtering function is to determine the accuracy of the reported terminal positioning data and delete incorrect positioning data, such as data whose movement rate exceeds the normal speed of transportation (exceeding 400 km / h), data whose positioning information life cycle has timed out, data whose accuracy does not meet the requirements, and data whose timestamp is outside the allowable range of the input data.

[0047] For example, the first network element uses a traffic model to predict its location at a fixed time in the future, compares the terminal positioning information at this time with the predicted information, and continuously corrects the discrepancy. If the model discrepancy is too large, the first network element uses the terminal positioning information at this time to correct the traffic model.

[0048] In at least one embodiment of the present application, the first network element inputting terminal positioning data within a first time period into a supervised learning model includes: The first network element inputs terminal positioning data that satisfies an aging requirement into the supervised learning model.

[0049] Wherein, before the first network element inputs terminal positioning data that meets an aging requirement into the supervised learning model, the method includes: The first network element uses historical positioning data of a terminal to train the supervised learning model; The first network element is further used to verify the output accuracy of the supervised learning model based on terminal positioning data that meets the aging requirement.

[0050] Optionally, the terminal positioning data that satisfies the aging requirement includes terminal positioning data before the second time period.

[0051] In an embodiment of the present application, the NWDAF determines the traffic environment (including but not limited to walking, cycling, buses, subways, cars, etc.) in which the terminal will be traveling based on geographical location, speed, and orientation information, establishes a traffic model, and predicts the terminal's location and status at a certain time in the future. Alternatively, the NWDAF's supervised learning model may use the terminal positioning data as a dataset to provide a prediction service for predicting the terminal's location and status at a certain time in the future.

[0052] In order to more clearly describe the method for predicting terminal information according to the embodiment of the present application, the following will combine two examples to describe the traffic model and the supervised learning model respectively.

[0053] Example 1 is shown in FIG.

[0054] Step 1: The NWDAF acquires terminal positioning data within a first time period through an interface with the LCS architecture, and performs data filtering on the terminal positioning data; Here, the data filtering function is to determine the accuracy of the terminal positioning data and delete incorrect positioning information, such as information whose movement rate exceeds the normal transportation rate (exceeding 400 km / h), information whose life cycle has timed out, information whose accuracy does not meet the requirements, and information whose timestamp is within the allowable range of the input data.

[0055] Step 2: Using the filtered terminal positioning data, the terminal performs environment judgment. The environment judgment here mainly involves determining whether the user is on the ground or underground, inside or outside the vehicle, and combining the rate and orientation information to finally determine the traffic environment in which the user is currently located.

[0056] For example, when the first instruction information is provided in the terminal positioning data, the environment judgment process is relatively simple: if it indicates that the terminal is underground and the vertical coordinate of the positioning is relatively deep, it is determined that the terminal's current traffic environment is in the subway and the probability of taking the subway is relatively high; if it indicates that the terminal is above ground and outdoors and the speed is 2-5 km / h, it is determined that the user's current traffic environment is walking and the probability of walking to work is relatively high. The final output of the traffic model optionally provides instruction information to modify the traffic model.

[0057] For example, if the first instruction information is not provided in the terminal positioning data, the environment judgment is made based on the current geographical position, speed and direction, i.e., if the user's direction has not changed for a long time and the speed is maintained at 20-40 km / h, the current traffic environment of the terminal is judged to be driving a car, and if there is no actual road (residential area, bridge) at the current geographical position of the terminal and the terminal speed is about 80 km / h, it is judged with a high probability that the terminal is riding on a subway.The final output of the traffic model optionally provides predicted instruction information of this traffic model.

[0058] Step 3: Select different transportation models based on the environment judgment, including but not limited to walking model, bicycle model, bus model, subway model, car model, etc. Optionally, NWDAF's environment judgment records the user's transportation model, and the transportation model that is frequently applied in different seasons and at different times is used as a weighting factor during judgment. That is, the factors for environment judgment mainly include historical usage model, current time, terminal geographic location, speed, orientation, indoor / outdoor indication, etc.

[0059] Here, relatively complex and difficult-to-distinguish models may be distinguished by pre-set model parameters. For example, bus models and car models may be determined by the actual lanes of relatively high-precision location information, or by the degree of overlap of routes, where the degree of overlap of bus routes is relatively high, or by the number of terminals in a unit area, where the terminal density on the bus is relatively high.

[0060] Step 4: Input the terminal positioning data into the corresponding traffic model; Step 5: Predict the terminal information for a fixed time in the future using the corresponding traffic model.

[0061] Step 6: Compare the terminal positioning data at the above time with the terminal information predicted by the traffic model, and continuously correct the difference.

[0062] Step 7: If the model discrepancy is too large, the first network element modifies the traffic model based on the terminal positioning data at the time. During this process, it may modify or confirm the indoors / outdoors indication and send it to the NWDAF user as the final output information.

[0063] In step 8, the traffic model outputs data such as the transportation mode, location prediction, rate prediction, direction prediction, and indoor / outdoor instructions required for the NWDAF user at a certain time in the future. Up to this point, the environment judgment and model optimization are constantly improved, and all the terminal positioning data used is the data obtained up to this point.

[0064] In this example, we propose a method in which NWDAF predicts changes in terminal position based on terminal positioning data provided by LCS. It determines the terminal's commuting environment in collaboration with the terminal's current rate information, combined with orientation and reported indoor / outdoor indicator information, and determines the user's future transportation route for a certain period of time based on historical rate information and historical stay location information, providing a reasonable terminal trajectory prediction map.

[0065] Example 2 is shown in FIG.

[0066] Step 1: The NWDAF obtains terminal positioning data within a first time period through an interface with the LCS architecture, and the terminal positioning data is past historical positioning information, which may be positioning data from last week, yesterday, or one hour ago, which is used to train and continuously improve the accuracy of the model; Step 2: The difference between the current terminal positioning data provided and the positioning information provided in step 1 is the time lag, i.e., the terminal positioning data from the previous second or meeting the time lag requirement. The terminal positioning data in step 2 is used to verify the accuracy of the supervised learning model, and this terminal positioning data is also used as input information in step 3 to provide a more accurate prediction for a certain time period in the future.

[0067] Step 3: The data output by the supervised learning model provides information such as the location, rate, and orientation of the terminal at a certain time in the future, as well as a regional hotspot map for a certain time in the future. The output may be in the form of the probability that the terminal will be in a certain area at a certain time in the future, the trend of terminal rate changes at a certain time in the future, the number and trend of terminals in a certain area at a certain time in the future, the indoor / outdoor correction of the terminal (if provided in step 1), the indoor / outdoor indication of the terminal (if not provided in step 1), etc.

[0068] In this example, a supervised learning model of NWDAF is adopted, and the NWDAF is trained in supervised learning using collected positioning data, including information such as geographic location information, accuracy information, rate, orientation, and indoor / outdoor indication (including whether the UE is above ground or underground, indoors or outdoors, inside or outside the vehicle, etc.).

[0069] To summarize, in an embodiment of the present application, the NWDAF determines the traffic environment (including but not limited to walking, cycling, buses, subways, cars, etc.) in which the terminal will be traveling based on geographic location, speed, and orientation information, establishes a traffic model, and predicts the terminal's location and status at a certain time in the future. Alternatively, the NWDAF's supervised learning model may use the terminal positioning data as a dataset to provide a prediction service for predicting the terminal's location and status at a certain time in the future.

[0070] In the terminal information prediction method according to the embodiment of the present application, the execution body may be a terminal information prediction device. In the embodiment of the present application, the terminal information prediction device according to the embodiment of the present application will be described by taking the terminal information prediction device executing the terminal information prediction method as an example.

[0071] As shown in FIG. 5 , an embodiment of the present application further provides a terminal information prediction device 500, an acquisition module 501 for acquiring terminal positioning data within a first time period via a location service LCS architecture; and a prediction module 502 for inputting terminal positioning data within a first time period into a traffic model or a supervised learning model, and obtaining terminal prediction information within a second time period output by the traffic model or the supervised learning model.

[0072] In one alternative embodiment, the terminal prediction information within the second time period is: location prediction information of the terminal within a second time period; Rate prediction information for the terminal within a second time period; Direction prediction information of the terminal within a second time period; Prediction indication information indicating that the terminal is in an external area or an internal area during a second time period; a total number of people located within the second area during the second time period; a total number of terminals located within the second region during the second time period; the number of terminals located in the second area and in the first traffic environment during the second time period; a ratio of the number of terminals located in the second area and in the first traffic environment during the second time period; an average moving speed of a terminal located in the second area and in the first traffic environment during a second time period; a direction of the first transportation means located within the second region during the second time period; and the number of terminals of the first transportation means located in the second area within the second time period and having the same orientation; the number of terminals located in the second area during the second time period and whose moving speed exceeds a preset value; a ratio of the number of terminals that are located in the second area during the second time period and whose moving speed exceeds a preset value; the number of terminals located in the second region within the second time period and having the first orientation; a ratio of the number of terminals located in the second region and having the first orientation within the second time period; The first transportation means is one of all transportation means located in a second region during a second time period.

[0073] Optionally, the device further includes a statistics module for collecting statistical information within a first time period; Here, the statistical information within the first time period is the total number of people located within the second area during the first time period; a total number of terminals located within the second area during the first time period; the number of terminals located in the second area and in the first traffic environment during the first time period; a ratio of the number of terminals located in the second area and in the first traffic environment during the first time period; an average moving speed of a terminal located in the second region and in the first traffic environment during the first time period; a heading of a first mode of transportation located within a second region during a first time period; the number of terminals of the first transportation means located in the second area within the first time period and having the same orientation; the number of terminals located in the second area during the first time period and whose moving speed exceeds a preset value; a ratio of the number of terminals that are located in the second area during the first time period and whose moving speed exceeds a preset value; the number of terminals located in the second region within the first time period and having the first orientation; a ratio of the number of terminals located in the second region and having the first orientation within the first time period; The first transportation means is one of all transportation means located in a second region within a first time period.

[0074] Optionally, the device is a network element having a network data analysis function NWDAF; Optionally, the device further includes a transmission module for transmitting terminal forecast information within the second time period or statistical information within the first time period to a user of the NWDAF.

[0075] As one alternative embodiment, when the terminal positioning data in the first time period includes first indication information indicating that the terminal is in an external area or an internal area, the terminal prediction information in the second time period includes the prediction indication information, and the prediction indication information is different from the first indication information, Or, When the terminal positioning data within the first time period does not include first indication information indicating that the terminal is in an external area or an internal area, the terminal prediction information for the second time period includes the prediction indication information.

[0076] In one alternative embodiment, the terminal prediction information in the second time period output by the traffic model is: Further included is a predicted transportation mode for the terminal within a second time period.

[0077] In one alternative embodiment, the terminal prediction information for the second time period output by the supervised learning model is: and a regional hotspot map within the second time period, wherein the regional hotspot map comprises: the probability that the terminal is in the first region during the second time period; and A change trend of the terminal rate within the second time period; the number of terminals within the first area during the second time period; and and a trend in the number of terminals in the first area within the second time period.

[0078] In one alternative embodiment, the device comprises: a first filtering module for performing data filtering on terminal positioning data within a first time period to acquire positioning data that satisfies a predetermined condition; a first determination module for determining an environment for the terminal based on positioning data that meets a predetermined condition and a terminal history usage model, and determining a traffic environment in which the terminal is currently located, the traffic environment including at least one of walking, cycling, bus, subway, and car; a first model determination module for determining, based on the traffic environment in which the terminal is currently located, a traffic model corresponding to the traffic environment in which the terminal is currently located; wherein the prediction module: The system includes a first prediction sub-module for inputting terminal positioning data that satisfies a preset condition within a first time period into a traffic model corresponding to a traffic environment in which the terminal is currently located.

[0079] In one alternative embodiment, the device comprises: The system further includes a training modification module for performing a training modification to the traffic model based on the positioning data within the first time period.

[0080] In one alternative embodiment, the training modification module comprises: a filtering submodule for performing data filtering on terminal positioning data within a first time period to acquire positioning data that satisfies a predetermined condition; a judgment submodule for making an environment judgment for the terminal based on positioning data that meets a predetermined condition and a terminal history usage model, and determining a traffic environment in which the terminal is currently located, the traffic environment including at least one of walking, cycling, bus, subway, and car; an acquisition submodule for inputting positioning data that meets a predetermined condition into a traffic model corresponding to the traffic environment in which the terminal is currently located, and acquiring terminal prediction information within a third time period output by the traffic model; and a correction sub-module for comparing the terminal prediction information within a third time period with the positioning data of the third time period and making training corrections to the traffic model based on the comparison result, where the third time period is before the second time period.

[0081] In one alternative embodiment, the prediction module: A second prediction sub-module is included for inputting terminal positioning data that meets a time requirement into the supervised learning model.

[0082] In one alternative embodiment, the device comprises: a training module for training the supervised learning model using historical positioning data of the terminal; The method further includes a verification module for verifying the accuracy of the output of the supervised learning model based on the terminal positioning data that meets the aging requirement.

[0083] In one alternative embodiment, the terminal positioning data that satisfies the aging requirement includes terminal positioning data before the second time period.

[0084] In one alternative embodiment, the terminal positioning data within the first time period is Terminal identifier information; A group identifier of a group to which the terminal belongs; The geographic location of the device; a motion rate at the device's current geographic location; a motion orientation at the device's current geographic location; Positioning accuracy information, A timestamp and The survival cycle of positioning data, and first indication information indicating that the terminal is in an external area or an internal area.

[0085] In one alternative embodiment, the external region includes at least one of a ground level, an exterior, and an exterior of a vehicle, and the internal region includes at least one of a basement level, an interior of a vehicle, and an interior of a vehicle; Here, the first instruction information or the predicted instruction information is: indicating that the terminal is on the ground; Indicating that the terminal is underground; and Indicating that the device is in the room; Indicating that the terminal is outdoors; indicating that the device is in the vehicle; and indicating that the terminal is outside the vehicle.

[0086] In an embodiment of the present application, the first network element obtains terminal positioning data within a first time period through an LCS architecture, and predicts terminal prediction information within a certain future time period based on a traffic model or supervised learning model corresponding to the terminal positioning data, thereby improving the service performance of the first network element and realizing trajectory prediction of the interaction between the first network element and drones, vehicle-to-everything architectures, and thereby providing smart transportation planning for urban bus systems.

[0087] It should be noted that the terminal information prediction device according to the embodiments of the present application is a device for a prediction method that can execute the above terminal information, and all embodiments of the above terminal information prediction method can be applied to this device, and all can achieve the same or similar beneficial effects.

[0088] The terminal information prediction device in the embodiments of the present application may be an electronic device, for example, an electronic device having an operating system, or a component of an electronic device, for example, an integrated circuit or a chip. The electronic device may be a terminal or other device other than a terminal. Exemplarily, the terminal may include, but is not limited to, the types of terminals 11 listed above. The other device may be a server, a network-attached storage (NAS), etc., and the embodiments of the present application are not specifically limited thereto.

[0089] The terminal information prediction device according to the embodiment of the present application can realize each process realized by the method embodiments of Figures 1 to 4 and achieve the same technical effects, and will not be further described here to avoid repetition.

[0090] Optionally, as shown in FIG. 6, an embodiment of the present application further provides a first network element 600, which includes a processor 601 and a memory 602, and the memory 602 includes a program or instruction that can run on the processor 601, and when the program or instruction is executed by the processor 601, it can realize each step of the embodiment of the terminal information prediction method and achieve the same technical effect, and in order to avoid repetition, it will not be further described here.

[0091] An embodiment of the present application further provides a first network element, including a processor and a communication interface, where the communication interface is used to obtain terminal positioning data within a first time period through a location service LCS architecture, and the processor is used to input the terminal positioning data within the first time period into a traffic model or a supervised learning model, and obtain terminal prediction information within a second time period output by the traffic model or the supervised learning model. The first network element may be a network-side device, and an embodiment of the network-side device corresponds to the above-mentioned embodiment of the network-side device method, and the implementation processes and realization manners of the above-mentioned embodiment of the method can be applied to the embodiment of the network-side device, and the same technical effects can be achieved.

[0092] Specifically, an embodiment of the present application further provides a network side device. As shown in Fig. 7, the network side device 700 includes an antenna 71, a radio frequency device 72, a baseband device 73, a processor 74, and a memory 75. The antenna 71 and the radio frequency device 72 are connected. In the uplink direction, the radio frequency device 72 receives information through the antenna 71 and transmits the received information to the baseband device 73 for processing. In the downlink direction, the baseband device 73 processes the information to be transmitted and transmits it to the radio frequency device 72, which processes the received information and then transmits it through the antenna 71.

[0093] The methods performed by the network side equipment in the above embodiments may be implemented in the baseband device 73, which includes a baseband processor.

[0094] The baseband device 73 may include, for example, at least one baseband board, on which multiple chips are installed, and as shown in FIG. 7, one of the chips is, for example, a baseband processor, which is connected to a memory 75 via a bus interface, calls a program in the memory 75, and performs the network equipment operations shown in the above method embodiments.

[0095] The network side equipment may further include a network interface 76, which may be, for example, a Common Public Radio Interface (CPRI).

[0096] Specifically, the network side device 700 of the embodiment of the present invention further includes instructions or programs stored in the memory 75 and operable on the processor 74, and the processor 74 can call the instructions or programs in the memory 75 to execute the methods performed by the modules shown in FIG. 5 and achieve the same technical effects, which will not be further described here to avoid repetition.

[0097] The embodiments of the present application further provide a readable storage medium, which stores a program or instruction, and when the program or instruction is executed by a processor, it can realize each process of the embodiment of the terminal information prediction method and achieve the same technical effect, and in order to avoid repetition, it will not be further described here.

[0098] Wherein, the processor is the processor in the terminal described in the above embodiment. The readable storage medium includes a computer readable storage medium, such as a computer read only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0099] An embodiment of the present application further provides a chip, the chip including a processor and a communication interface, the communication interface coupled to the processor, the processor running a program or instruction to realize each process of the embodiment of the terminal information prediction method, and can achieve the same technical effect, and in order to avoid repetition of description, no further description will be given here.

[0100] It should be understood that the chips referred to in the embodiments of this application may be referred to as system level chips, system chips, chip systems, or system-on-chips.

[0101] An embodiment of the present application further provides a computer program product, which is stored in a storage medium, and which can be executed by at least one processor to realize each process of the embodiment of the terminal information prediction method and achieve the same technical effects, and will not be further described here to avoid repetition.

[0102] It should be noted that, in this specification, the terms "comprise," "include," "includes," or any other variations thereof are intended to cover the non-exclusive "comprise," whereby a process, method, article, or apparatus comprising a set of elements not only includes those elements, but also other elements not expressly listed or inherent in such process, method, article, or apparatus. Absent further limitations, an element defined by the phrase "comprises one of" does not preclude the presence of other identical elements in the process, method, article, or apparatus comprising that element. It should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may include performing functions in an essentially simultaneous manner or in the reverse order based on the functions involved. For example, the described method may be performed in a different order than described, and various steps may be added, omitted, or combined. Furthermore, features described with reference to some examples may be combined in other examples.

[0103] As will be apparent to those skilled in the art from the above description of the embodiments, the methods of the above embodiments can be realized in the form of software and a necessary general-purpose hardware platform. Of course, they can also be realized in hardware, but in many cases the former is a more preferred embodiment. Based on this understanding, the technical proposal of the present application, in substance or in part contributing to the prior art, may be embodied in the form of a computer software product, which is stored in a storage medium (e.g., ROM / RAM, magnetic disk, optical disk) and includes a number of instructions for causing a terminal (which may be a mobile phone, computer, server, air conditioner, network device, etc.) to execute the methods described in each embodiment of the present application.

[0104] Although the embodiments of the present application have been described above in conjunction with the drawings, the present application is not limited to the above specific embodiments. The above specific embodiments are merely illustrative and not limiting. Those skilled in the art can take the teachings of the present application into account and implement many forms without departing from the spirit and scope of the claims, all of which fall within the scope of protection of the present application.

Claims

1. A method for predicting terminal information, comprising: A first network element, which is a network element having a network data analysis function NWDAF, obtains terminal positioning data within a first time period via a location service LCS architecture; The first network element inputs terminal positioning data within a first time period into a traffic model or a supervised learning model, and obtains terminal prediction information within a second time period output by the traffic model or the supervised learning model; The terminal prediction information within the second time period is a total number of terminals located within the second region during the second time period; an average moving speed of a terminal located in the second area and in the first traffic environment during a second time period; the number of terminals located in the second area during the second time period and whose moving speed exceeds a preset value; a ratio of the number of terminals that are located in the second area during the second time period and whose moving speed exceeds a preset value; the number of terminals located in the second region within the second time period and having the first orientation; a ratio of the number of terminals located in the second region and having the first orientation within the second time period; The terminal positioning data within the first time period is Terminal identifier information; The geographic location of the device; a motion rate at the device's current geographic location; a motion orientation at the device's current geographic location; Positioning accuracy information, A timestamp and The survival cycle of positioning data, and first indication information indicating that the terminal is in an external area or an internal area.

2. The terminal prediction information for the second time period output by the traffic model is: The method of claim 1 further comprising predicting transportation of the terminal within a second time period.

3. The terminal prediction information for the second time period output by the supervised learning model is: and a regional hotspot map within the second time period, wherein the regional hotspot map comprises: the probability that the terminal is in the first region during the second time period; and A change trend of the terminal rate within the second time period; the number of terminals within the first area during the second time period; and and a trend in the number of terminals in the first area within a second time period.

4. Before the first network element inputs terminal positioning data within a first time period into a traffic model, the method includes: The first network element performs data filtering on the terminal positioning data within a first time period to obtain positioning data that satisfies a predetermined condition; The first network element performs an environment judgment for the terminal based on the positioning data and the terminal history usage model that satisfy a predetermined condition, and determines a traffic environment in which the terminal is currently located, wherein the traffic environment includes at least one of walking, cycling, bus, subway, and car; The first network element further includes determining, based on a traffic environment in which the terminal is currently located, a traffic model corresponding to the traffic environment in which the terminal is currently located; wherein the first network element inputs terminal positioning data within a first time period into a traffic model, 2. The method of claim 1, further comprising: inputting terminal positioning data that satisfies a predetermined condition within a first time period into a traffic model corresponding to a traffic environment in which the terminal is currently located.

5. Before the first network element inputs terminal positioning data within a first time period into a traffic model, the method includes: The method of claim 1 , further comprising the first network element making training corrections to a traffic model based on positioning data within a first time period.

6. The first network element performing a training correction to a traffic model based on terminal positioning data within a first time period, The first network element performs data filtering on the terminal positioning data within a first time period to obtain positioning data that satisfies a predetermined condition; The first network element performs an environment judgment for the terminal based on the positioning data and the terminal history usage model that satisfy a predetermined condition, and determines a traffic environment in which the terminal is currently located, wherein the traffic environment includes at least one of walking, cycling, bus, subway, and car; The first network element inputs the positioning data of the terminal that satisfies the preset condition into a traffic model corresponding to the traffic environment in which the terminal is currently located, and obtains terminal prediction information within a third time period output by the traffic model; 6. The method of claim 5, further comprising: the first network element comparing terminal prediction information within a third time period with positioning data for a third time period, and making training corrections to a traffic model based on a comparison result, wherein the third time period is before the second time period.

7. the external area includes at least one of a ground area, an exterior area, and an exterior area of ​​the vehicle, and the internal area includes at least one of a basement area, an interior area, and an interior area of ​​the vehicle, Here, the first instruction information is indicating that the terminal is on the ground; Indicating that the terminal is underground; and Indicating that the device is in the room; Indicating that the terminal is outdoors; indicating that the device is in the vehicle; and indicating that the terminal is outside the vehicle.

8. The method comprises: The first network element further includes calculating statistical information within a first time period based on the terminal positioning data within the first time period, where the statistical information within the first time period includes: the total number of people located within the second area during the first time period; a total number of terminals located within the second area during the first time period; the number of terminals located in the second area and in the first traffic environment during the first time period; a ratio of the number of terminals located in the second area and in the first traffic environment during the first time period; an average moving speed of a terminal located in the second region and in the first traffic environment during the first time period; a heading of a first mode of transportation located within a second region during a first time period; the number of terminals of the first transportation means located in the second area within the first time period and having the same orientation; the number of terminals located in the second area during the first time period and whose moving speed exceeds a preset value; a ratio of the number of terminals that are located in the second area during the first time period and whose moving speed exceeds a preset value; the number of terminals located in the second region within the first time period and having the first orientation; a ratio of the number of terminals located in the second region and having the first orientation within the first time period; 2. The method of claim 1, wherein the first transportation mode is one of a total number of transportation modes located within a second region during a first time period.

9. The method comprises: The method of claim 1 , further comprising: the first network element transmitting terminal prediction information within the second time period or statistical information within the first time period to a user of the NWDAF.

10. A terminal information prediction device having a network data analysis function NWDAF, an acquisition module for acquiring terminal positioning data within a first time period via a location services LCS architecture; a prediction module for inputting terminal positioning data within a first time period into a traffic model or a supervised learning model, and obtaining terminal prediction information within a second time period output by the traffic model or the supervised learning model; The terminal prediction information within the second time period is a total number of terminals located within the second region during the second time period; an average moving speed of a terminal located in the second area and in the first traffic environment during a second time period; the number of terminals located in the second area during the second time period and whose moving speed exceeds a preset value; a ratio of the number of terminals that are located in the second area during the second time period and whose moving speed exceeds a preset value; the number of terminals located in the second region within the second time period and having the first orientation; a ratio of the number of terminals located in the second region and having the first orientation within the second time period; The terminal positioning data within the first time period is Terminal identifier information; The geographic location of the device; a motion rate at the device's current geographic location; a motion orientation at the device's current geographic location; Positioning accuracy information, A timestamp and The survival cycle of positioning data, and first indication information indicating that the terminal is in an external area or an internal area.

11. The terminal prediction information for the second time period output by the traffic model comprises: The device for predicting terminal information according to claim 10 , further comprising a predicted mode of transportation of the terminal within a second time period.

12. The external area includes at least one of the ground, the outside, and the outside of the vehicle, and the internal area includes at least one of the basement, the inside of the vehicle, and the inside of the vehicle; Here, the first instruction information is indicating that the terminal is on the ground; Indicating that the terminal is underground; and Indicating that the device is in the room; Indicating that the terminal is outdoors; indicating that the device is in the vehicle; The device for predicting terminal information according to claim 10, wherein the device is used to indicate at least one of the following: indicating that the terminal is outside the vehicle;

13. A first network element comprising a processor and a memory, the memory storing a program or instructions operable on the processor, the program or instructions, when executed by the processor, realizing the steps of the terminal information prediction method described in any one of claims 1 to 9.

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