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 to Chinese Patent Application No. 202210314449.X filed in China on March 28, 2022 and 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 in particular 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), Session Management Function (SMF) or Operation Administration Maintenance (OAM). In the conventional flow, the NWDAF obtains terminal location information collected by the AMF, and the terminal location information is coarse-grained, for example, at the Tracking Area (TA) level or cell level, with a range of about 1000m level, i.e., the NWDAF can know 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 in 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 analyses may be provided to other 5GC network elements for network optimization, or to third-party applications for services such as personalized recommendations, smart traffic planning, etc.

[0005] The accuracy of statistical and prediction services provided by conventional architecture NWDAF is limited to the TA or cell level, and cannot realize more accurate statistical and prediction services. If NWDAF obtains more accurate positioning information including geographic location, movement rate, and orientation through the Location Services (LCS) architecture, it is still awaiting research on how to use this information to provide more accurate location to users. Summary of the Invention [Problem to be solved by the invention]

[0006] The embodiments of the present application provide a method, device, 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 service 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 being operable when executed by the processor to implement 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, where the communication interface is used for obtaining terminal positioning data within a first time period via a location service LCS architecture, and the processor is used for inputting the terminal positioning data within the 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.

[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, effectuates 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 operable, when executed by at least one processor, to implement the steps of the method according to the first aspect. Effect 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 the drone, vehicle-to-everything architecture, and thereby providing smart transportation planning for the urban bus system. [Brief description 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. [Diagram 2] 3 illustrates a step flowchart of a terminal information prediction method according to an embodiment of the present application; [Diagram 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. [Diagram 5] 1 illustrates a structural schematic diagram of a terminal information prediction device according to an embodiment of the present application; [Figure 6]FIG. 2 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 PREFERRED EMBODIMENTS

[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 are all within the scope of protection of the present application.

[0017] The terms "first," "second," etc. in the specification and claims of the present application are intended to distinguish between similar objects and are not intended to describe a particular order or sequence. It is to be understood that the terms so used are interchangeable where appropriate, such that the embodiments of the present application may be performed in an order other than that shown or described herein, and that the objects distinguished by "first" and "second" are generally of the same type and do not limit the number of objects, e.g., the first object may be one or more. It is to be noted that "and / or" in the specification and claims represents at least one of the objects connected, and the character " / " generally represents 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 techniques described may be used in the above-mentioned systems and radio technologies, or in 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 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 equipment having a wireless communication function, such as a refrigerator, a television, a washing machine, or furniture), a game machine, a personal computer (PUE), a mobile phone (Mobile Internet Device), a mobile user equipment (Mobile Internet Device), a mobile phone ... The terminal 11 may be a terminal side device such as a personal computer (PC), a deposit and withdrawal machine or a self-service machine, and the wearable device includes a smart watch, a smart wristband, a smart earphone, a smart glasses, a smart accessory (smart bracelet, smart hand chain, smart finger ring, smart necklace, smart ankle bracelet, smart anklet, etc.), a smart band, a smart clothing, etc. It should be noted that the terminal 11 in the embodiment 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 called a radio access network device, a radio access network (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, and it should be explained that in the embodiments of the present application, only a base station in an NR system is introduced as an example, and the specific type of the base station is not limited.The core network devices are: Core Network Node, Core Network Function, Mobility Management Entity (MME), Access and Mobility Management Function (AMF), Session Management Function (SMF), User Plane Function (UPF), Policy Control Function (PCF), Policy and Charging Rules Function (PCRF), Edge Application Server Discovery Function (EASDF), Unified Data Management (UDM), Unified Data Repository (UDR), Home Subscriber Server (HSS), Centralized Network Configuration (CNC), Network Repository Function (NRF), Network Exposure Function (NEF), Local NEF (Local NEF or L-NEF), Binding Support Function (BSF), Application Function (Application Node Function (ADN)), and Application Node Function (ADN). It should be noted that the embodiments of the present application only take the core network device in the NR system as an example, and do not limit the specific type of the core network device.

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

[0021] As shown in FIG. 2, an embodiment of the present application further provides a terminal information prediction method, 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 embodiment of the present application is an NWDAF network element. For example, the NWDAF obtains terminal positioning data in a first time period through an interface with the LCS architecture. The terminal positioning data may be stored historical positioning information from the NWDAF, or may be terminal positioning data just obtained from an Access and Mobility Management Function (AMF) or Location Management Function (LMF) architecture. The terminal positioning data is relatively fine-grained, that is, the horizontal accuracy meets the specific positioning requirements of normal traffic, for example, 1-10m level.

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

[0024] As one optional embodiment, the terminal positioning data within the first time period is Identifier information of the terminal (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, 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 of the vehicle, and the inside of the vehicle. Accordingly, the first instruction information is indicating that the terminal is on the ground; Indicating that the terminal is underground; and Indicating that the terminal is in the room; Indicating that the terminal is outdoors; indicating that the terminal is in the vehicle; and indicating that the terminal is outside the vehicle.

[0026] In one alternative embodiment, the terminal prediction information in the second time period is: Position prediction information of the terminal within a second time period; and rate prediction information for the terminal within a second time period; and Direction prediction information of the terminal within a second time period; and 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 a second time period; and A total amount of terminals located in the second area during the second time period; and a number of terminals located in the second area during the second time period and in the first traffic environment; a ratio of a number of terminals located within the second region and in the first traffic environment during the second time period; an average moving speed of a terminal located in the second region during a second time period and in the first traffic environment; a heading of a first vehicle located within a second region during a second time period; and A number of terminals having the same orientation in the first transportation means located in the second area during the second time period; and the number of terminals located in the second area during the second time period and having a moving speed exceeding 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; and a number of terminals located in the second region during the second time period and having the first orientation; a ratio of a 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 within 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 automobile, and the first transportation mode is at least one of walking, cycling, bus, subway, automobile, etc.

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

[0029] It should be explained that the proportion of the number of terminals located within the second area during the second time period and whose moving 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 moving 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 explained 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 collecting 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: A total number of people located within the second area during the first time period; and A total amount of terminals located in the second area during the first time period; and A number of terminals located in the second area during the first time period and in the first traffic environment; A ratio of a number of terminals located in a second region and in a first traffic environment during a first time period; an average moving speed of a terminal located in a second region and in a first traffic environment during a first time period; a heading of a first vehicle located within a second region during a first time period; A number of terminals having the same orientation in the first transportation means located in the second area during the first time period; the number of terminals located in the second area during the first time period and having a moving speed exceeding 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; and A number of terminals located in the second region during the first time period and having the first orientation; a ratio of a 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 within a second region during 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 transmitting 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 during the first time period and in the first traffic environment is the proportion of the number of terminals located in the second area during the first time period and in the first traffic environment to the total number of terminals located in the second area during the first time period.

[0034] It should be explained that the percentage of the number of terminals located in the second area during the first time period and whose moving speed exceeds a predetermined value is the proportion of the number of terminals located in the second area during the first time period and whose moving speed exceeds a predetermined value to the total number of terminals located in 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 during the first time period and having the first orientation is the proportion of the number of terminals located in the second region during the first time period and having the first orientation to the total number of terminals located in the second region during the first time period.

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

[0038] In one optional embodiment, the terminal prediction information in the second time period output by the traffic model is: Further included is a predicted transportation mode of 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: a 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; and the number of terminals located 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 the terminal positioning data within the 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 preset 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 preset condition, and determines a traffic environment in which the terminal is currently located, the traffic environment including at least one of walking, cycling, bus, subway, and car; The first network element further includes determining a traffic model corresponding to a traffic environment in which the terminal is currently located based on a traffic environment in which the terminal is currently located; Here, the first network element inputting terminal positioning data within a first time period into a traffic model includes: 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 realize the accuracy judgment of the reported terminal positioning data, and to delete erroneous positioning data, such as data whose motion rate exceeds the rate of normal transportation (exceeding 400km / h), whose life cycle of positioning information has timed out, whose accuracy does not meet the requirements, whose time stamp 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 above-mentioned preset condition, which is the remaining terminal positioning data obtained after performing data filtering on the terminal positioning data in the first time period and deleting the erroneous positioning data.

[0042] For example, when the first indication information is provided in the terminal positioning data, the process of determining the environment is relatively simple, that is, when it indicates that the terminal is underground and the vertical coordinate of the positioning is at a relatively deep position, it is determined that the terminal's current traffic environment is in the subway, and the probability of taking the subway is relatively high; when it indicates that the terminal is above ground and outdoors, and the rate 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.

[0043] Also, for example, when the first instruction information is not provided in the terminal positioning data, the environment judgment is made based on the current geographical position, rate and orientation, that is, when the user's orientation has not changed for a long time and the rate is maintained at 20-40 km / h, it is judged that the current traffic environment of the terminal is driving a car, and when there is no actual road (residential area, bridge) at the current geographical position of the terminal and the terminal rate 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 the terminal positioning data within the 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 preset 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 preset condition, and determines a traffic environment in which the terminal is currently located, the traffic environment including at least one of walking, cycling, bus, subway, and car; The first network element inputs the positioning data of the terminal that satisfies a preset condition into a traffic model corresponding to a 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 the positioning data for a third time period, and makes a training correction to a traffic model based on a comparison result, wherein the third time period is before the second time period.

[0046] Here, the data filtering function is to judge the accuracy of the reported terminal positioning data, and to delete erroneous positioning data, such as data whose motion rate exceeds the normal traffic rate (exceeding 400km / h), whose positioning information life cycle has timed out, whose accuracy does not meet the requirements, and whose time stamp is within 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 difference. If the model difference is too large, the terminal positioning information at this time is used 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 satisfies an aging requirement into the supervised learning model, the method further comprises: 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 the terminal positioning data that satisfies the aging requirement.

[0050] Optionally, the terminal positioning data that satisfies the aging requirement includes terminal positioning data prior to 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, bus, subway, car, etc.) in which the terminal rides according to the geographical location, rate, and orientation information, establishes a traffic model, and predicts the terminal's position and state at a certain time in the future. Alternatively, the NWDAF may use the terminal positioning data as a data set to provide a prediction service in the supervised learning model of the NWDAF to predict the terminal's position and state 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 combines two examples to describe the traffic model and the supervised learning model respectively.

[0053] Example 1 is as shown in FIG.

[0054] Step 1: the NWDAF obtains 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 realize the accuracy judgment of the terminal positioning data, and to delete erroneous positioning information, such as the movement rate exceeding the normal transportation rate (exceeding 400km / h), the positioning information life cycle timeout, the accuracy does not meet the requirements, and the time stamp is within the allowable range of the input data.

[0055] Step 2: Use the filtered terminal positioning data to perform environment judgment for the terminal. The environment judgment here mainly refers to judging whether the user is on the ground or underground, inside or outside the vehicle, and combining the rate and orientation information to finally judge 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 process of environment judgment is relatively simple, that is, when 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, when it indicates that the terminal is above ground and outdoors and the rate is 2-5km / h, it is judged 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] Also, for example, if the first instruction information is not provided in the terminal positioning data, the environment judgment is based on the current geographical position, rate and direction, that is, if the user's direction has not changed for a long time and the rate is maintained at 20-40km / 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 rate is about 80km / h, the terminal is judged to be riding on a subway with a high probability. The final output of the traffic model selectively provides the predicted instruction information of the traffic model.

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

[0059] Here, models that are relatively complex and difficult to distinguish may be distinguished by pre-set model parameters. For example, a bus model and a car model 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 at 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 difference is too large, the first network element modifies the traffic model based on the terminal positioning data at the time. In this process, it may modify or confirm the indoor / outdoor indication and send it to the NWDAF user as the final output information.

[0063] Step 8: The traffic model outputs data such as transportation modes, position prediction, rate prediction, orientation prediction, indoor / outdoors instructions, etc. required for NWDAF users at a certain time in the future. Up to this point, the environment judgment and model optimization are constantly improved, and the terminal positioning data used are all the data obtained up to this point.

[0064] In this example, we propose a method in which NWDAF predicts terminal position changes based on terminal positioning data provided by LCS, and determines the terminal's commuting environment in collaboration with the terminal's current rate information, combined with orientation and reported indoor / outdoor indication 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, thereby providing a reasonable terminal trajectory prediction map.

[0065] Example 2 is as 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 aging, which is the terminal positioning data in the previous second or that meets the aging 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 for step 3 to provide a more accurate prediction for a certain time 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 hotspot map of the area at a certain time in the future. The output may be in the form of the probability that the terminal is in a certain area at a certain time in the future, the change trend of the terminal rate 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 on the collected positioning data, including geographic location information, accuracy information, rate, orientation, indoors / outdoors indication and other information (including whether the UE is on the ground or underground, indoors or outdoors, inside or outside the vehicle, etc.).

[0069] To sum up, in the embodiment of the present application, the NWDAF determines the traffic environment (including but not limited to walking, cycling, bus, subway, car, etc.) in which the terminal rides according to the geographical location, rate, and orientation information, establishes a traffic model, and predicts the terminal's position and state at a certain time in the future. Alternatively, the terminal positioning data may be used as a data set to provide a prediction service in the supervised learning model of the NWDAF to predict the terminal's position and state 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 executes the terminal information prediction method as an example to describe the terminal information prediction device according to the embodiment of the present application.

[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 in a first time period into a traffic model or a supervised learning model, and obtaining terminal prediction information in a second time period output by the traffic model or the supervised learning model.

[0072] In one alternative embodiment, the terminal prediction information in the second time period is: Position prediction information of the terminal within a second time period; and rate prediction information for the terminal within a second time period; and Direction prediction information of the terminal within a second time period; and 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 a second time period; and A total amount of terminals located in the second area during the second time period; and a number of terminals located in the second area during the second time period and in the first traffic environment; a ratio of a number of terminals located within the second region and in the first traffic environment during the second time period; an average moving speed of a terminal located in the second region during a second time period and in the first traffic environment; a heading of a first vehicle located within a second region during a second time period; and A number of terminals having the same orientation in the first transportation means located in the second area during the second time period; and the number of terminals located in the second area during the second time period and having a moving speed exceeding 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; and a number of terminals located in the second region during the second time period and having the first orientation; a ratio of a 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 within 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 A total number of people located within the second area during the first time period; and A total amount of terminals located in the second area during the first time period; and A number of terminals located in the second area during the first time period and in the first traffic environment; A ratio of a number of terminals located in a second region and in a first traffic environment during a first time period; an average moving speed of a terminal located in a second region and in a first traffic environment during a first time period; a heading of a first vehicle located within a second region during a first time period; A number of terminals having the same orientation in the first transportation means located in the second area during the first time period; the number of terminals located in the second area during the first time period and having a moving speed exceeding 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; and A number of terminals located in the second region during the first time period and having the first orientation; a ratio of a 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 within a second region during 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 prediction information within the second time period or statistical information within the first time period to a user of the NWDAF.

[0075] As an 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 optional embodiment, the terminal prediction information in the second time period output by the traffic model is: Further included is a predicted transportation mode of 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: a 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; and the number of terminals located 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 obtain positioning data that satisfies a preset condition; A first determination module for performing an environment determination for a terminal based on positioning data and a terminal history usage model that satisfy a preset condition, and determining a traffic environment in which the terminal is currently located, the traffic environment including at least one of walking, bicycle, bus, subway, and car; and a first model determination module for 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 prediction module is The first prediction sub-module is 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 optional embodiment, the training modification module comprises: a filtering submodule for performing data filtering on terminal positioning data within a first time period to obtain positioning data that satisfies a preset condition; a judgment submodule for making an environment judgment for the terminal based on the positioning data and the terminal history usage model that satisfy a preset condition, and determining a traffic environment in which the terminal is currently located, the traffic environment including at least one of walking, bicycle, bus, subway, and car; an acquisition submodule for inputting the positioning data that satisfies a preset condition into a traffic model corresponding to a 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 in a third time period with the positioning data of a third time period and making a training correction to a traffic model based on a comparison result, the third time period being before the second time period.

[0081] In one alternative embodiment, the prediction module comprises: A second prediction sub-module is included for inputting terminal positioning data that satisfies an aging 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 a terminal; The method further includes a verification module for verifying the output accuracy of the supervised learning model based on the terminal positioning data that meets the aging requirement.

[0083] As one optional embodiment, the terminal positioning data that satisfies the aging requirement includes terminal positioning data prior to the second time period.

[0084] As one optional embodiment, the terminal positioning data within the first time period is Terminal identifier information; A group identifier of the group to which the terminal belongs; and 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 time stamp and The life 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 exterior region includes at least one of a ground level, an exterior, and an exterior of a vehicle, and the interior 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 terminal is in the room; Indicating that the terminal is outdoors; indicating that the terminal 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 the drone, vehicle-to-everything architecture, and thereby providing smart transportation planning for the urban bus system.

[0087] It should be clarified that the terminal information prediction device according to the embodiments of the present application is an apparatus for predicting the terminal information, and all the embodiments of the 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 embodiment of the present application may be an electronic device, for example, an electronic device having an operating system, or may be a component in an electronic device, for example, an integrated circuit or a chip. The electronic device may be a terminal, or may be other devices other than a terminal. Exemplarily, the terminal may include, but is not limited to, the types of terminal 11 listed above, and the other devices may be servers, network attached storage (NAS), etc., and the embodiment of the present application is not specifically limited.

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

[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, each step of the embodiment of the terminal information prediction method can be realized and the same technical effect can be achieved, and in order to avoid repetition of description, no further description will be given here.

[0091] The embodiment of the present application further provides a first network element, including a processor and a communication interface, where the communication interface is used for obtaining terminal positioning data in a first time period through a location service LCS architecture, and the processor is used for inputting the terminal positioning data in the first time period into a traffic model or a supervised learning model, and obtaining terminal prediction information in a second time period output by the traffic model or the supervised learning model. The first network element can be a network side device, and the embodiment of the network side device corresponds to the embodiment of the above-mentioned network side device method, and each implementation process and realization manner of the above-mentioned method embodiment 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, and the radio frequency device 72 transmits the received information through the antenna 71 after processing it.

[0093] The methods performed by the network side equipment in the above embodiments may be implemented in a 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 to call the program in the memory 75 and perform 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 capable of running on the processor 74, and the processor 74 can call the instructions or programs in the memory 75 to execute the method performed by each module shown in FIG. 5 and achieve the same technical effect, which will not be described further here in order to avoid repetition.

[0097] An embodiment of the present application further provides a readable storage medium, in which a program or instruction is stored, and when the program or instruction is executed by a processor, each process of the embodiment of the terminal information prediction method can be realized and the same technical effect can be achieved, and in order to avoid repetition of description, no further description will be given here.

[0098] Wherein, the processor is the processor in the terminal according to 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 being coupled to the processor, the processor being used to run a program or instruction to realize each process of the embodiment of the above-mentioned terminal information prediction method, and the same technical effect can be achieved, 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 the present application may be referred to as system level chips, system chips, chip systems, or systems on chips.

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

[0102] It should be explained that in this specification, the terms "comprise", "include", or any other variants thereof are intended to cover the non-exclusive "comprise", whereby a process, method, article, or apparatus that includes a set of elements includes not only those elements, but also other elements not expressly listed or inherent to such process, method, article, or apparatus. In the absence of further limitations, an element limited by the phrase "comprises a" does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes this element. It should be pointed out that the scope of the method and apparatus 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 reverse order based on the functions involved, for example, the described method can be performed in a different order than described, and various steps can be added, omitted, or combined. Also, features described with reference to some examples can be combined in other examples.

[0103] From the above description of the embodiments, it is clear to those skilled in the art that 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. In light of this understanding, the technical proposal of the present application may be substantially or the part that contributes 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 some instructions for causing a terminal (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0104] The above describes the embodiments of the present application in conjunction with the drawings, but 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 suggestions of this application and make many forms without departing from the spirit and scope of the claims of this application, all of which belong to the protection scope of this application.

Claims

1. A method for predicting terminal information, comprising: A first network element obtains terminal positioning data within a first time period via a location service LCS architecture; A method for predicting terminal information, comprising: the first network element 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.

2. The terminal prediction information within the second time period is Position prediction information of the terminal within a second time period; and rate prediction information for the terminal within a second time period; and Direction prediction information of the terminal within a second time period; and 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 a second time period; and A total amount of terminals located in the second area during the second time period; and a number of terminals located in the second area during the second time period and in the first traffic environment; a ratio of a number of terminals located within the second region and in the first traffic environment during the second time period; an average moving speed of a terminal located in the second region during a second time period and in the first traffic environment; a heading of a first vehicle located within a second region during a second time period; and A number of terminals having the same orientation in the first transportation means located in the second area during the second time period; and the number of terminals located in the second area during the second time period and having a moving speed exceeding 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; and a number of terminals located in the second region during the second time period and having the first orientation; a ratio of a number of terminals located in the second region and having the first orientation within the second 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 second time period.

3. When the terminal positioning data in the first time period includes first instruction information indicating that the terminal is in an external area or an internal area, the prediction instruction information included in the terminal prediction information in the second time period is different from the first instruction information, Or, The method of claim 2, wherein 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.

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

5. 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: a 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; and the number of terminals located within the first area during the second time period; and and a trend in the number of terminals in the first area during a second time period.

6. Before the first network element inputs the 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 preset 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 preset condition, and determines a traffic environment in which the terminal is currently located, the traffic environment including at least one of walking, cycling, bus, subway, and car; The first network element further includes determining a traffic model corresponding to a traffic environment in which the terminal is currently located based on a traffic environment in which the terminal is currently located; Here, the first network element inputting terminal positioning data within a first time period into a traffic model includes: The method according to claim 1 , further comprising: 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.

7. Before the first network element inputs the 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.

8. 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 preset 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 preset condition, and determines a traffic environment in which the terminal is currently located, the traffic environment including at least one of walking, cycling, bus, subway, and car; The first network element inputs the positioning data of the terminal that satisfies a preset condition into a traffic model corresponding to a traffic environment in which the terminal is currently located, and obtains terminal prediction information within a third time period output by the traffic model; 8. The method of claim 7, further comprising: comparing terminal prediction information in a third time period with positioning data of 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.

9. The first network element inputs terminal positioning data within a first time period into a supervised learning model, The method of claim 1 , comprising the first network element inputting terminal positioning data that satisfies an aging requirement into the supervised learning model.

10. Before the first network element inputs terminal positioning data that satisfies 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 method of claim 9 , further comprising: the first network element being used to verify output accuracy of the supervised learning model based on terminal positioning data that satisfies the aging requirement.

11. The method of claim 9 or 10, wherein the terminal positioning data satisfying the aging requirement comprises terminal positioning data prior to the second time period.

12. The terminal positioning data within the first time period includes Terminal identifier information; A group identifier of the group to which the terminal belongs; and 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 time stamp and The life cycle of positioning data; and a first indication information indicating that the terminal is in an external area or an internal area.

13. The external region includes at least one of a ground, an exterior, and an exterior of a vehicle, and the internal region includes at least one of a basement, an interior, 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 terminal is in the room; Indicating that the terminal is outdoors; indicating that the terminal is in the vehicle; 13. The method according to claim 2 or 12, wherein the method is used to indicate at least one of: indicating that the terminal is outside the vehicle;

14. The method comprises: The first network element further includes collecting 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 is: A total number of people located within the second area during the first time period; and A total amount of terminals located in the second area during the first time period; and A number of terminals located in the second area during the first time period and in the first traffic environment; A ratio of a number of terminals located in a second region and in a first traffic environment during a first time period; an average moving speed of a terminal located in a second region and in a first traffic environment during a first time period; a heading of a first vehicle located within a second region during a first time period; A number of terminals having the same orientation in the first transportation means located in the second area during the first time period; the number of terminals located in the second area during the first time period and having a moving speed exceeding 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; and A number of terminals located in the second region during the first time period and having the first orientation; a ratio of a number of terminals located in the second region and having the first orientation within the first time period; The method according to any one of claims 1 to 11, wherein the first transportation means is one of a total number of transportation means located in a second region during a first time period.

15. said first network element being a network element having a network data analysis function NWDAF; The method comprises: The method of claim 1, 2 or 14, further comprising the first network element transmitting terminal prediction information in the second time period or statistical information in the first time period to a user of the NWDAF.

16. A terminal information prediction device, an acquisition module for acquiring terminal positioning data within a first time period via a location service LCS architecture; A device for predicting terminal information, comprising: 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.

17. The terminal prediction information within the second time period is Position prediction information of the terminal within a second time period; and rate prediction information for the terminal within a second time period; and Direction prediction information of the terminal within a second time period; and 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 a second time period; and A total amount of terminals located in the second area during the second time period; and a number of terminals located in the second area during the second time period and in the first traffic environment; a ratio of a number of terminals located within the second region and in the first traffic environment during the second time period; an average moving speed of a terminal located in the second region during a second time period and in the first traffic environment; a heading of a first vehicle located within a second region during a second time period; and A number of terminals having the same orientation in the first transportation means located in the second area during the second time period; and the number of terminals located in the second area during the second time period and having a moving speed exceeding 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; and a number of terminals located in the second region during the second time period and having the first orientation; a ratio of a number of terminals located in the second region and having the first orientation within the second time period; 17. The apparatus of claim 16, wherein the first transportation mode is one of a total number of transportation modes located within a second region during a second time period.

18. 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, The device of claim 17, wherein 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.

19. The terminal prediction information in the second time zone output by the traffic model is:

20. The apparatus of claim 17, further comprising a predicted traffic behavior of the terminal within a second time period.

20. 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: a 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; and the number of terminals located 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.

21. The apparatus comprises: a first filtering module for performing data filtering on terminal positioning data within a first time period to obtain positioning data that satisfies a preset condition; A first determination module for performing an environment determination for a terminal based on positioning data and a terminal history usage model that satisfy a preset condition, and determining a traffic environment in which the terminal is currently located, the traffic environment including at least one of walking, bicycle, bus, subway, and car; and a first model determination module for 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 prediction module is The apparatus according to claim 16, further comprising a first prediction submodule 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.

22. The apparatus comprises: The apparatus of claim 16 , further comprising a training modification module for performing a training modification to the traffic model based on the positioning data within the first time period.

23. The training modification module includes: a filtering submodule for performing data filtering on terminal positioning data within a first time period to obtain positioning data that satisfies a preset condition; a judgment submodule for making an environment judgment for the terminal based on the positioning data and the terminal history usage model that satisfy a preset condition, and determining a traffic environment in which the terminal is currently located, the traffic environment including at least one of walking, bicycle, bus, subway, and car; an acquisition submodule for inputting the positioning data that satisfies a preset condition into a traffic model corresponding to a traffic environment in which the terminal is currently located, and acquiring terminal prediction information within a third time period output by the traffic model; 23. The apparatus of claim 22, further comprising: a correction sub-module for comparing terminal prediction information in a third time period with positioning data of a third time period and making training corrections to a traffic model based on a comparison result, the third time period being prior to the second time period.

24. The prediction module includes: The apparatus of claim 16 , further comprising a second prediction sub-module for inputting terminal positioning data that satisfies an aging requirement into the supervised learning model.

25. The apparatus comprises: a training module for training the supervised learning model using historical positioning data of a terminal; 25. The apparatus of claim 24, further comprising: a validation module for use in validating an output accuracy of the supervised learning model based on terminal positioning data that satisfies the aging requirement.

26. 26. Apparatus according to claim 24 or 25, wherein the terminal positioning data satisfying the aging requirement comprises terminal positioning data prior to the second time period.

27. The terminal positioning data within the first time period includes Terminal identifier information; A group identifier of the group to which the terminal belongs; and 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 time stamp and The life cycle of positioning data, and a first indication indicating that the terminal is in an external area or an internal area.

28. The external region includes at least one of a ground, an exterior, and an exterior of a vehicle, and the internal region includes at least one of a basement, an interior, 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 terminal is in the room; Indicating that the terminal is outdoors; indicating that the terminal is in the vehicle; and indicating that the terminal is outside the vehicle.

29. The apparatus comprises: Further comprising a statistics module for collecting statistical information within the first time period; Here, the statistical information within the first time period is A total number of people located within the second area during the first time period; and A total amount of terminals located in the second area during the first time period; and A number of terminals located in the second area during the first time period and in the first traffic environment; A ratio of a number of terminals located in a second region and in a first traffic environment during a first time period; an average moving speed of a terminal located in a second region and in a first traffic environment during a first time period; a heading of a first vehicle located within a second region during a first time period; A number of terminals having the same orientation in the first transportation means located in the second area during the first time period; the number of terminals located in the second area during the first time period and having a moving speed exceeding 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; and A number of terminals located in the second region during the first time period and having the first orientation; a ratio of a number of terminals located in the second region and having the first orientation within the first time period; 26. The apparatus according to any one of claims 16 to 25, wherein the first transportation means is one of a total number of transportation means located in a second region during a first time period.

30. said device being a network element having a network data analysis function NWDAF, The apparatus comprises:

30. The apparatus of claim 16, 17 or 29, further comprising a transmission module for transmitting terminal forecast information in the second time zone or statistical information in the first time zone to a user of the NWDAF.

31. 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 15.

32. A readable storage medium having a program or instructions stored therein, the program or instructions implementing the steps of the terminal information prediction method according to any one of claims 1 to 15 when executed by a processor.

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