Network mobility management optimization method and system

Abstract

The invention provides a network mobility management optimization method and system, and relates to the technical field of network optimization. The method comprises the following steps: firstly, acquiring first information of a target vehicle and second information of a deployed base station; secondly, generating a prediction track based on the real-time position, the real-time speed and the lane number of the target vehicle; matching the predicted trajectory with the coverage area of the base station, and screening out target base stations passing through the predicted trajectory to form a target base station set; then calculating an access pressure value for each target base station according to the real-time load data; then sorting the target base stations according to the numerical values of the access pressure values, and generating a switching priority list; and finally, when it is detected that the vehicle moves to the edge of the coverage area of any base station according to the predicted trajectory, controlling the vehicle to execute network switching operation according to the switching priority list. According to the technical scheme provided by the invention, prospective, accurate and smooth network switching management is realized.

Description

Network mobility management optimization methods and systems Technical Field

[0001] This application relates to the field of network optimization technology, and in particular to a method and system for optimizing network mobility management. Background Technology

[0002] With the development of vehicle-to-everything (V2X) and autonomous driving technologies, vehicles on highways have higher technical requirements for the continuity, stability and low latency of mobile communications. When driving at high speeds, vehicles need to frequently switch between different base stations to maintain network connectivity. Traditional passive switching strategies are prone to service interruption or service quality degradation due to decision lag.

[0003] Currently, one existing solution to this need is to match the vehicle's location information with digital maps to predict the vehicle's driving path, and then pre-select base stations that may enter its coverage area as candidates in preparation for handover. This solution shortens the handover decision time by predicting in advance and sorts the candidate base stations according to signal strength, aiming to achieve a smoother handover.

[0004] However, this existing solution has certain drawbacks. First, its path prediction mainly relies on general map matching, which does not fully consider the characteristics of vehicles being strictly confined to fixed lanes in highway environments. This can lead to discrepancies between the predicted trajectory and the actual driving path of the vehicle, resulting in an inaccurate set of candidate base stations locked in advance. Second, when screening and sorting candidate base stations, this solution usually uses the real-time signal strength as the main or sole criterion, failing to effectively incorporate the prediction of the base station load status at future moments. This may cause vehicles to switch to a base station with a strong signal at present but which is about to be overloaded, thus quickly facing the risk of service quality degradation or a second handover after the switch. Summary of the Invention

[0005] This application provides a network mobility management optimization method and system to solve the problem in the prior art that inaccurate network handover decisions and the impact on communication continuity and quality are caused by insufficient accuracy in vehicle trajectory prediction and inadequate consideration of future base station load.

[0006] In a first aspect, this application provides a network mobility management optimization method, comprising: acquiring first information of a target vehicle and second information of base stations deployed on a highway network, wherein the first information includes the real-time location, real-time speed, and lane number of the target vehicle, and the second information includes real-time load data and geographical location data corresponding to multiple base stations; generating a predicted trajectory of the target vehicle based on the real-time location, real-time speed, and lane number of the target vehicle; matching the predicted trajectory with the coverage area of ​​the base stations, and filtering out target base stations that pass through the predicted trajectory to form a target base station set; calculating an access pressure value for each target base station according to the real-time load data corresponding to each target base station in the target base station set; sorting the target base stations in the target base station set according to the magnitude of the access pressure value to generate a handover priority list; and controlling the vehicle to perform a network handover operation according to the handover priority list when it is detected that the vehicle moves to the edge of the coverage area of ​​any base station in the target base station set according to the predicted trajectory.

[0007] Optionally, generating a predicted trajectory for the target vehicle based on its real-time location, real-time speed, and lane number includes: retrieving corresponding lane centerline data from pre-stored lane information according to the lane number, wherein the lane centerline data is used to characterize the geometric orientation of the lane corresponding to the lane number on the highway; determining the initial heading of the target vehicle by matching the direction of the real-time speed with the tangent direction of the lane centerline data at the starting point, using the real-time location as the starting point; projecting the real-time location along the lane centerline data onto the initial heading to obtain a projection point, and using the projection point as the trajectory starting point of the predicted trajectory; recursively calculating a preset trajectory point sequence on the lane centerline data based on the trajectory starting point, according to the magnitude of the real-time speed and the geometry of the lane centerline data, wherein the positions of the trajectory points in the preset trajectory point sequence are constrained on the lane centerline data during the recursive calculation process; and connecting the trajectory starting point and the preset trajectory point sequence to generate the predicted trajectory of the target vehicle.

[0008] Optionally, taking the trajectory starting point as a reference, a preset trajectory point sequence is recursively calculated on the lane centerline data according to the magnitude of the real-time speed and the geometry of the lane centerline data. This includes: calculating the movement step size within a preset time interval based on the magnitude of the real-time speed; recursively calculating on the lane centerline data, starting from the trajectory starting point and following the geometric direction of the lane centerline data, according to the movement step size; in each recursive calculation, starting from the position of the previous trajectory point, moving the movement step size along the geometric direction of the lane centerline data to obtain the trajectory point corresponding to the current recursive calculation; repeating the recursive calculation process until the cumulative number of recursive calculations reaches a preset number, thereby obtaining a preset trajectory point sequence composed of the trajectory starting point and the trajectory points obtained in each recursive calculation in sequence.

[0009] Optionally, matching the predicted trajectory with the coverage area of ​​the base station and filtering out target base stations that pass through the predicted trajectory to form a target base station set includes: obtaining coverage shape information corresponding to each of the plurality of base stations, wherein the coverage shape information is used to define the geographical coverage area of ​​the base station; extracting multiple trajectory points from the predicted trajectory, taking each of the plurality of trajectory points as a point to be judged, and judging whether the point to be judged is located within the geographical coverage area of ​​the base station; if the point to be judged is located within the geographical coverage area of ​​the base station, then determining the base station as a target base station passed through by the predicted trajectory; and summarizing all target base stations determined to be passed through by the predicted trajectory to generate a target base station set.

[0010] Optionally, based on the real-time load data corresponding to each target base station in the target base station set, an access pressure value is calculated for each target base station, including: obtaining a preset capacity value for each target base station in the target base station set; extracting the current connection count of each target base station at the current moment from the real-time load data; calculating the load change rate of each target base station based on the change of the real-time load data within a preset time period; calculating the estimated travel time required for the target vehicle to travel from its current location into the geographical coverage area of ​​each target base station based on the predicted trajectory and the speed of the target vehicle; adjusting the current connection count based on the load change rate and the estimated travel time to obtain the predicted connection count of each target base station when the target vehicle is expected to arrive; and dividing the predicted connection count of each target base station by the corresponding preset capacity value to obtain the access pressure value.

[0011] Optionally, the target base stations in the target base station set are sorted according to the magnitude of the access pressure value to generate a handover priority list, including: comparing the access pressure values ​​corresponding to all target base stations in the target base station set; arranging the target base stations in the target base station set in order of the access pressure value from smallest to largest, wherein the target base station with the smallest access pressure value is arranged first; when at least two target base stations have the same access pressure value, obtaining the signal reception strength data of the at least two target base stations, and arranging the target base stations with the same access pressure value in a second order of the signal reception strength data from largest to smallest; and recording the identification information of each target base station in sequence according to the final arrangement order to generate a handover priority list.

[0012] Optionally, when it is detected that the vehicle has moved to the edge of the coverage area of ​​any base station in the target base station set according to the predicted trajectory, the vehicle is controlled to perform a network handover operation according to the handover priority list, including: acquiring signal strength information between the target vehicle and all target base stations in the target base station set in real time; comparing the signal strength between the target vehicle and the current base station and the signal strength between the target vehicle and the candidate target base station ranked first in the handover priority list, and calculating the signal strength difference; when the signal strength difference exceeds a preset signal strength threshold, determining that the vehicle has moved to the edge of the coverage area of ​​the current serving base station; in response to the determination, generating and sending a handover command according to the identification information of the candidate target base station ranked first in the handover priority list, the handover command being used to instruct the target vehicle to disconnect from the current base station and establish a connection with the candidate target base station; performing a network handover operation based on the handover command, and updating the handover priority list, wherein the candidate target base station ranked first in the handover priority list is removed.

[0013] The system comprises the following modules: an acquisition module for acquiring first information about the target vehicle and second information about the base stations deployed on the highway network; the first information includes the real-time location, real-time speed, and lane number of the target vehicle; and the second information includes real-time load data and geographical location data corresponding to multiple base stations. A generation module for generating a predicted trajectory for the target vehicle based on its real-time location, real-time speed, and lane number. A filtering module for matching the predicted trajectory with the coverage area of ​​the base stations to filter out target base stations that pass through the predicted trajectory, forming a target base station set. A calculation module for calculating the access pressure value for each target base station based on its real-time load data. A sorting module for sorting the target base stations in the target base station set according to the access pressure value to generate a handover priority list. An execution module for controlling the vehicle to perform a network handover operation according to the handover priority list when the vehicle is detected to have moved to the edge of the coverage area of ​​any base station in the target base station set along the predicted trajectory.

[0014] Thirdly, this application provides a computing device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement a network mobility management optimization method as described in the first aspect above.

[0015] Fourthly, this application provides a computer storage medium storing a computer program, which, when executed by a computer, implements a network mobility management optimization method as described in the first aspect.

[0016] This application obtains the real-time location, real-time speed, and lane number of the target vehicle, and combines this with pre-stored lane geometry information to generate a predicted vehicle trajectory strictly constrained within a specific lane. Based on this predicted trajectory, a target set of base stations is selected from those traversed by the trajectory. This method utilizes the scenario characteristics of fixed lane lines and limited lateral movement of vehicles on highways, transforming the physical boundaries of the lanes into hard constraints for trajectory generation, ensuring a high degree of consistency between the predicted trajectory and the actual driving path of the vehicle. Furthermore, the candidate base station set determined based on the high-precision trajectory can accurately reflect the wireless signal coverage area that the vehicle will enter in the future, thereby solving the problem of inaccurate candidate base station range caused by path prediction deviations and providing a precise spatial basis for subsequent handover decisions.

[0017] Furthermore, by comprehensively analyzing the real-time load data and trends of the target base stations, the load pressure on the base stations when the vehicle arrives is predicted, and the target base stations are prioritized accordingly to form a handover list. During the handover triggering phase, the optimal handover target is dynamically selected and the list is updated by combining real-time signal strength comparison with the priority list. This process introduces time-dimensional load estimation on the basis of accurate spatial prediction, enabling handover decisions to avoid base stations that are about to be overloaded. At the same time, real-time verification of signal strength ensures the timeliness of handover triggering, effectively avoiding secondary handovers or service quality degradation caused by switching to base stations with strong current signals but high future loads. Therefore, this application can achieve full-process decision optimization before, during, and after network handover in highway scenarios, improving handover accuracy and the overall stability and quality of communication connections.

[0018] These or other aspects of this application will become more apparent in the following description of the embodiments. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 shows a flowchart of a network mobility management optimization method provided in this application; Figure 2 shows a structural schematic diagram of a network mobility management optimization system provided in this application; Figure 3 shows a structural schematic diagram of a computing device provided in this application. Detailed Implementation

[0021] To enable those skilled in the art to better understand the present application, the technical solution of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0022] In some of the processes described in the specification, claims, and accompanying drawings of this application, multiple operations appearing in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or may be executed in parallel. The operation numbers, such as 101, 102, etc., are merely used to distinguish different operations and do not themselves represent any execution order. Furthermore, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel. It should be noted that the descriptions such as "first," "second," etc., in this document are used to distinguish different messages, devices, modules, etc., and do not represent a chronological order, nor do they limit "first" and "second" to different types.

[0023] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0024] Figure 1 is a flowchart of a network mobility management optimization method provided in this application. As shown in Figure 1, the method includes: step 101, obtaining first information of the target vehicle and second information of the base stations deployed on the highway network. The first information includes the real-time location, real-time speed and lane number of the target vehicle. The second information includes real-time load data and geographical location data corresponding to multiple base stations.

[0025] In this step, the first information refers to the dynamic data set related to the target vehicle's own state, which describes the vehicle's real-time motion attributes and location information. It is the basic input for generating the vehicle's predicted trajectory and is obtained through the positioning module, speed measurement module, and environmental perception module installed on the vehicle.

[0026] The second information refers to the set of static and dynamic data related to the communication infrastructure along the highway, which is used to describe the distribution status and current load of network resources. It serves as the basis for assessing the availability of potential switching targets and is obtained through the network management and geographic information system database on the access core network side.

[0027] Real-time location refers to the latitude and longitude coordinates of the target vehicle at a precise moment. It is used to determine the vehicle's absolute geographic coordinates and is obtained in real time through the vehicle's onboard global positioning module.

[0028] Real-time speed refers to the instantaneous speed of a target vehicle at a precise moment. It is used to calculate the vehicle's displacement capability over a short period of time and is measured in real time by an onboard speed sensor.

[0029] The lane number refers to the identifier of the specific lane in which the target vehicle is located on the current highway segment. It is used to associate the absolute position of the vehicle with a specific driving channel with a fixed geometry. The lane number is obtained by image recognition of the lane lines by the vehicle-mounted camera and then matching it with high-precision map data.

[0030] Real-time load data refers to the number of user terminal connections or resource utilization rate served by each base station in the highway network at the current moment. It is used to quantify the current busy level of the base station and is collected in real time through network management performance counters.

[0031] Geographic location data refers to the latitude and longitude coordinates of the locations where each base station is installed in the highway network. It is used to determine the fixed location of each base station in geographic space and is obtained from the geographic information database during the network planning and construction phase.

[0032] In this step, the vehicle first receives satellite signals through its built-in GPS to calculate its precise latitude and longitude coordinates, thus obtaining its real-time position. Simultaneously, an inertial measurement unit continuously measures wheel speed or vehicle acceleration, and calculates its real-time speed. Furthermore, the vehicle's forward-facing camera continuously captures images of the road ahead, using computer vision algorithms to identify lane markings in the images. The identified lane marking features are then matched with a high-precision map pre-installed in the vehicle's internal storage unit to determine the vehicle's precise lateral position relative to the lane model on the map. Finally, the vehicle outputs its lane number and integrates this information, transmitting it to the network-side control unit via the vehicle's wireless communication module. Secondly, the network... The network-side control unit, through a network management system connected to the core network, periodically subscribes to performance data related to all base stations along the highway and collects real-time load data, such as the current number of active user connections, from the performance counters of each base station. At the same time, the control unit queries and reads the latitude and longitude coordinates of all relevant base stations, i.e., their geographical location data, from a geographic information database that stores network infrastructure information. Finally, the network-side control unit integrates and correlates the first information received from the target vehicle, which includes real-time location, real-time speed, and lane number, with the second information obtained from the network side, which includes real-time load data and geographical location data of each base station, to prepare all the necessary data inputs for subsequent processing.

[0033] For example, a target vehicle is traveling on a highway. First, the vehicle calculates its real-time location using its onboard GPS module, such as 118.78 degrees east longitude and 31.99 degrees north latitude. Simultaneously, its onboard sensors measure its current real-time speed as 120 kilometers per hour. Second, the vehicle's front-facing camera captures the three lane markings. After matching the data with a high-precision map by the onboard processor, it is determined that the vehicle is traveling in the leftmost lane, corresponding to lane number 1. This information is then packaged into the first piece of information and sent to the backend server via the cellular network. At the same time, the server obtains the real-time load data of three nearby base stations A, B, and C from the network management platform. This includes the current number of user connections at base station A (45), 62 at base station B (62), and 38 at base station C (38), as well as their respective fixed geographical location data. This information is then integrated into the second piece of information. Thus, the collection of raw data on vehicle status and network environment is completed using these two types of information.

[0034] Step 102: Based on the real-time location, real-time speed and lane number of the target vehicle, generate the predicted trajectory of the target vehicle.

[0035] Optionally, step 101 may specifically include: step 1021, retrieving the corresponding lane centerline data from the pre-stored lane information according to the lane number, wherein the lane centerline data is used to characterize the geometric orientation of the lane corresponding to the lane number in the highway.

[0036] Step 1022: Using the real-time position as the starting point, match the direction of the real-time speed with the tangent direction of the lane centerline data at the starting point to determine the initial heading of the target vehicle.

[0037] Step 1023: Project the real-time position along the lane centerline data onto the initial heading to obtain a projection point, and use the projection point as the starting point of the predicted trajectory.

[0038] Step 1024: Using the trajectory starting point as a reference, a preset trajectory point sequence is recursively calculated on the lane centerline data according to the magnitude of the real-time speed and the geometry of the lane centerline data. During the recursive calculation, the positions of the trajectory points in the preset trajectory point sequence are constrained on the lane centerline data.

[0039] Optionally, step 1024 may include the following steps: calculating the movement step length within a preset time interval based on the magnitude of the real-time speed; performing recursive calculations on the lane centerline data, starting from the trajectory starting point and following the geometric direction of the lane centerline data according to the movement step length; in each recursive calculation, starting from the position of the previous trajectory point, moving the movement step length along the geometric direction of the lane centerline data to obtain the trajectory point corresponding to the current recursive calculation; repeating the recursive calculation process until the cumulative number of recursive calculations reaches a preset number, thereby obtaining a preset trajectory point sequence composed of the trajectory starting point and the trajectory points obtained in each recursive calculation in sequence.

[0040] Step 1025: Connect the trajectory starting point and the preset trajectory point sequence to generate the predicted trajectory of the target vehicle.

[0041] In this step, the predicted trajectory refers to the path that the target vehicle is expected to travel within a future time period. It is used to predict the vehicle's subsequent movement route and is generated through a series of calculations that combine the vehicle's real-time status and the inherent geometry of the road.

[0042] Lane centerline data refers to a series of ordered coordinate points used to accurately describe the shape of the centerline of a specific lane on a highway, defining the spatial orientation of that lane.

[0043] The geometric orientation of a lane refers to the direction of extension and curvature of a specific lane in space. It describes the physical path that a vehicle is allowed to travel within that lane, is characterized by lane centerline data, and is the basis for calculating the future position of a vehicle.

[0044] The tangent direction at the starting point refers to the instantaneous forward direction of the centerline at the nearest point corresponding to the real-time position of the vehicle on the lane centerline data. It is used to describe the local direction of the lane at that point and is obtained by performing geometric differential calculations on the lane centerline data.

[0045] The initial heading refers to the direction of travel of the target vehicle after it is aligned with the lane at the starting point. It is used to determine the forward direction of the vehicle traveling along the lane. It is determined by aligning and matching the direction vector of the vehicle's real-time speed with the tangent direction vector of the lane centerline at the starting point.

[0046] The projection point is a point obtained by vertically mapping the real-time position of the vehicle onto the nearest lane centerline. It is used to correct the actual position of the vehicle to the theoretical lane centerline and is obtained by calculating the shortest perpendicular from the real-time position to the lane centerline.

[0047] The real-time speed value refers to the scalar value of the target vehicle's current speed, without including directional information, and is used to calculate the distance the vehicle can travel per unit time in the future.

[0048] The geometry here specifically refers to the curve shape represented by the lane centerline data, including its curvature variation, which is used to determine the degree of path curvature in recursive calculations and is implicit in the coordinate sequence of the lane centerline data.

[0049] A preset trajectory point sequence refers to a set of future location points arranged in chronological order, used to jointly depict the predicted trajectory. It is obtained by recursively calculating multiple times along the lane centerline data, starting from the trajectory starting point.

[0050] The movement step length within a preset time interval refers to the path length that a vehicle is expected to move along the lane centerline within a preset future time segment, and is used to control the granularity of the recursive calculation.

[0051] A trajectory point refers to each individual future position coordinate point that constitutes a preset trajectory point sequence. It is the basic unit of the predicted trajectory and is obtained through recursive calculation each time.

[0052] In this step, firstly, a spatial index query algorithm is used to operate on a pre-stored map database containing all lane information. Using the lane number obtained from the target vehicle as the query key, the data record uniquely associated with that lane number is quickly located in the database's index structure, and the corresponding lane centerline data is read and retrieved. Secondly, a nearest neighbor search algorithm is used to find the point with the shortest Euclidean distance to the vehicle's real-time position in the coordinate sequence of the lane centerline data, and this point is defined as the matching point. Then, through vector calculation, a representation is calculated using the matching point and its next adjacent coordinate point. The tangent vector of the lane's local extension direction is calculated, and the velocity direction vector is simultaneously parsed from the vehicle's real-time velocity information. By comparing the dot product and direction of these two vectors, a vector alignment operation is performed to adjust the velocity direction to be consistent with the tangent direction, thereby determining the initial heading of the target vehicle along the lane. Next, a perpendicular calculation algorithm is used to find the line segment defined by the two closest consecutive coordinate points on the lane centerline data corresponding to the vehicle's real-time position. The perpendicular intersection point from the real-time position to the line segment is calculated, yielding the projection point. This projection point is assigned to a new variable, defining it as the starting point of the entire predicted trajectory. First, a scalar multiplication operation is performed, multiplying the real-time speed by a preset time interval constant to calculate the movement step size. The core of this recursive calculation is a numerical integration approach that accumulates step sizes along a curved path, with the constraint that the path must perfectly fit the lane centerline data. At the start of the calculation, the current point is set as the trajectory starting point. In each iteration, starting from the current point, the path length is accumulated forward along the coordinate point sequence of the lane centerline data. Then, the actual arc length is approximated by accumulating the chord lengths between adjacent coordinate points. When the accumulated length reaches the movement step size, linear interpolation is used to recalculate the path length in the previous iteration. A precise interpolation point is determined between the coordinate point and the next coordinate point. This interpolation point is the new trajectory point obtained in this iteration. Then, this new trajectory point is updated as the current point, and the above calculation process is repeated. Through a loop control structure, the loop terminates when the number of iterations reaches a preset number. Finally, an array of trajectory points arranged in the generation order is output, which is the preset trajectory point sequence. Finally, using the line segment connection method in computer graphics, adjacent points are connected with straight line segments in the order of the points in the preset trajectory point sequence to form a continuous polyline path. This path is the final predicted trajectory of the target vehicle.

[0053] For example, following the specific implementation of the previous step, firstly, the target vehicle is traveling at a speed of 120 km / h in lane number 1. Secondly, based on lane number 1, the lane centerline data of that lane is retrieved from the map database. Then, the lane centerline point near the real-time location is found, and the tangent direction of the lane centerline point is calculated to be 5 degrees due east of north. After matching, the vehicle's speed direction is also 5 degrees due east of north, so the initial heading is determined to be this direction. Then, the vehicle's current GPS position is vertically projected onto this lane centerline to obtain a slightly corrected point, and this point is set as the trajectory starting point. Subsequently, using a speed of 120 km / h, the future movement step size is calculated to be approximately 16.7 meters every 0.5 seconds. Then, starting from the trajectory starting point, along the lane centerline, a point is taken every 16.7 meters, and 20 points are taken consecutively to form a preset trajectory point sequence for the next 10 seconds. Finally, these points are connected sequentially to generate a predicted trajectory extending forward along the leftmost lane.

[0054] This step deeply integrates the real-time status of the vehicle with the fixed geometry of the highway lanes, and generates a deterministic future path that is strictly confined within the lane through constraint calculations. This fundamentally avoids the random bias of general trajectory prediction and provides a unique and reliable spatial reference for subsequent accurate locking of base stations along the route. It is the core foundation for achieving accurate decision-making in the entire mobility management process.

[0055] Step 103: Match the predicted trajectory with the coverage area of ​​the base station, and filter out the target base stations that pass through the predicted trajectory to form a target base station set.

[0056] Optionally, step 103 may specifically include: step 1031, obtaining coverage shape information corresponding to each of the plurality of base stations, wherein the coverage shape information is used to define the geographical coverage area of ​​the base station.

[0057] Step 1032: Extract multiple trajectory points from the predicted trajectory, and use each of the multiple trajectory points as a point to be judged to determine whether the point to be judged is located within the geographical coverage area of ​​the base station.

[0058] Step 1033: If the point to be determined is located within the geographical coverage area of ​​the base station, then the base station is determined as the target base station through which the predicted trajectory passes.

[0059] Step 1034: Summarize all target base stations identified as being traversed by the predicted trajectory to generate a target base station set.

[0060] In this step, the coverage area of ​​a base station refers to the spatial area that the signal emitted by the wireless communication base station can effectively serve. It is used to define the geographical range within which the base station can provide network connectivity, and is defined by both coverage shape information and geographic location data.

[0061] The target base station set refers to a set of selected base stations that have spatial intersection with the predicted vehicle trajectory, and is used as candidate targets for subsequent access pressure assessment and handover decisions.

[0062] Coverage shape information refers to geometric data used to describe the boundary shape of the geographical coverage area of ​​a single base station. It is used to accurately characterize the signal coverage model of the base station in spatial computing. It is obtained from the network planning and optimization platform and stored as coordinate parameters of polygons or multiple circles.

[0063] The geographic coverage area refers to a specific two-dimensional planar area defined by the coverage shape information of a base station. It is used to represent the precise service range of the base station in spatial relationship judgment and is obtained by rendering or calculating the geometry defined by the coverage shape information.

[0064] The point to be judged refers to a single coordinate point extracted from the predicted trajectory that needs to be judged in terms of its positional relationship. It is used to detect whether the predicted trajectory has entered the signal range of a certain base station.

[0065] The target base stations traversed by the predicted trajectory refer to those base stations whose geographical coverage area contains at least one point to be judged from the predicted trajectory. They are used to mark candidate base stations that have spatial intersection with the future path of the vehicle, and are obtained by performing location inclusion judgment of points and regions.

[0066] In this step, the coverage shape information of each base station is first obtained by querying the network infrastructure database. Using the base station's unique identifier as the query condition, pre-configured coverage model parameters, such as a list of polygon vertex coordinates, are retrieved from the infrastructure database. These coverage model parameters are read and parsed into geometric objects usable in the spatial computing engine, thus clearly defining the geographical coverage area of ​​each base station. Next, spatial matching and filtering operations are performed. From the generated predicted trajectory coordinate sequence, a series of discrete trajectory points are extracted at certain sampling intervals, and each extracted trajectory point is set as the current point to be judged. Then, for each base station, a spatial geometric calculation is performed. The core is an inclusion detection algorithm to determine whether a point is within a polygon, such as a ray... The method involves emitting a horizontal ray from the current point to be judged, calculating the number of intersections between the ray and the polygon boundary of the base station's geographical coverage area. If the number of intersections is odd, the point to be judged is determined to be within the geographical coverage area of ​​this base station. Based on this determination, a decision is made: if any point to be judged is determined to be within the geographical coverage area of ​​a base station, then that base station is marked as a target base station traversed by the predicted trajectory. Finally, the unique identifiers of all target base stations marked as traversed by the predicted trajectory are collected and placed into a new data list or set structure. This newly generated data set is the final target base station set, which includes all base stations that the vehicle is expected to enter its signal range during future travel.

[0067] For example, following the specific implementation of the previous step, firstly, a predicted trajectory of the vehicle along the leftmost lane for the next 10 seconds is generated. Secondly, the coverage shape information of base stations A, B, and C for this leftmost lane segment is queried from the network database. It is found that the coverage area of ​​base station A is a polygon with a radius of 800 meters, and the coverage areas of base stations B and C are also defined by their corresponding polygon coordinates. Next, from the predicted trajectory for 10 seconds, one point is taken every 0.5 seconds, and a total of 20 trajectory points are extracted as points to be judged. Then, for base station A, the ray casting method is used to determine whether these 20 points are within its coverage polygon. It is found that points 6 to 15 are all within the polygon. Therefore, base station A is determined as the target base station traversed by the predicted trajectory. Similarly, it is determined that base station B covers points 11 to 20, and base station C covers points 0 to 5. Finally, the identifiers of base stations A, B, and C are summarized to generate the target base station set {A, B, C}.

[0068] This step uses a high-precision spatial matching algorithm to compare the vehicle's predicted trajectory with the precise coverage area of ​​the base station, directly filtering out the base stations that the trajectory will inevitably pass through, forming a highly definite and non-redundant candidate set. This abandons the traditional method of fuzzy pre-selection based on coarse distance or signal strength thresholds, ensuring that all subsequent load assessments and handover decisions focus on the most relevant and inevitable base stations, thereby improving the computational efficiency and decision-making pertinence of subsequent processes.

[0069] Step 104: Calculate the access pressure value for each target base station based on the real-time load data corresponding to each target base station in the target base station set.

[0070] Optionally, step 104 may specifically include: step 1041, obtaining the preset capacity value of each target base station in the target base station set.

[0071] Step 1042: Extract the current number of connections of each target base station at the current moment from the real-time load data.

[0072] Step 1043: Calculate the load change rate of each target base station based on the changes in the real-time load data within a preset time period.

[0073] Step 1044: Based on the predicted trajectory and the speed of the target vehicle, calculate the estimated travel time required for the target vehicle to travel from its current location into the geographical coverage area of ​​each target base station.

[0074] Step 1045: Adjust the current number of connections based on the load change rate and the expected travel time to obtain the predicted number of connections for each target base station when the target vehicle is expected to arrive.

[0075] Step 1046: Divide the predicted number of connections for each target base station by the corresponding preset capacity value to obtain the access pressure value.

[0076] In this step, the access pressure value refers to a numerical indicator used to quantitatively assess the potential pressure faced by a target base station when accepting new user connections at a specific time in the future, and is used to compare the expected busy levels between different base stations.

[0077] The preset capacity value refers to the maximum number of user connections or resource limit that a single base station can support based on its hardware resources and network configuration. It is used to measure the absolute service capacity of the base station and is obtained from the base station's static configuration parameter database.

[0078] The current connection count refers to the total number of active user terminals or sessions being served by the target base station at the exact moment the calculation occurs, reflecting the base station's real-time load level.

[0079] The load change rate refers to the average change in the load of a target base station per unit time. It is used to describe whether the busy level is rising, falling, or stable. It is calculated by performing linear regression analysis on the real-time load data sequence within a preset time period.

[0080] The estimated journey time refers to the length of time required for a target vehicle to travel from its current location along a predicted trajectory until it first enters the geographical coverage area of ​​a target base station. It is used to estimate the time when the vehicle will arrive at the base station.

[0081] The predicted number of connections refers to the estimated total number of user connections that a base station may support at the time when the target vehicle is expected to arrive at the geographical coverage area of ​​the target base station, and is used to assess future load conditions.

[0082] In this step, firstly, a database query technique is used to send a query request to a relational database storing static configuration parameters of the base stations, using the unique identifier of each base station in the target base station set as the query key. The relational database performs an index lookup operation, returns the capacity limit field value in the record corresponding to the identifier, and reads this capacity limit field value as the preset capacity value. Secondly, data stream parsing technology is used to receive data streams containing performance indicators of each base station from the network management system in real time. For each base station in the target base station set, the data packets corresponding to the latest timestamp in the data stream are parsed, and the field representing the number of active connections is extracted, and the value of this field is used as the current number of connections. Next, time series analysis methods are applied to obtain the historical sequence of the number of connections of the target base station in the most recent preset time period from the local cache or historical database. Then, the least squares method is used to perform linear regression fitting on the historical sequence of the number of connections to calculate an optimal fitting line. The slope of this line, i.e., the value per unit time, is the maximum capacity value. The average change in the number of connections within a given time period is calculated and recorded as the load change rate of the base station. Then, spatial geometric calculations are performed using a ray-polygon intersection algorithm to find the first intersection point between the predicted trajectory and the geographical coverage area boundary of each target base station. Next, the path length is obtained by summing the lengths of all continuous line segments from the trajectory start point to the first intersection point. Finally, a division operation is performed, dividing the calculated path length by the real-time speed value obtained from the vehicle's first information to obtain the estimated travel time. Subsequently, a linear extrapolation prediction technique is used, performing a multiplication operation to multiply the load change rate by the estimated travel time to obtain a load change value. Then, an addition operation is performed to add this load change value to the current number of connections, and the result is the predicted number of connections. Finally, an arithmetic division operation is performed, using the predicted number of connections as the dividend and the obtained preset capacity value as the divisor, and the quotient is the access pressure value of the target base station.

[0083] For example, following the specific implementation of the previous step, firstly, the target vehicle is traveling along the highway towards the coverage area of ​​the selected base stations A, B, and C; secondly, the network configuration database is queried to find that the maximum designed connection count for base station A is 200, for base station B it is 180, and for base station C it is 220; these values ​​are their respective preset capacity values; next, the number of users currently being served by these three base stations is read from the real-time data stream, resulting in 120 current connections for base station A, 150 for base station B, and 80 for base station C; then, the historical load data of these three base stations over the past few minutes is further analyzed, and trend analysis reveals the load of base station A. The load on base station A is rising rapidly, while the load on base station B is decreasing slowly, and the load on base station C remains relatively stable, thus calculating the load change trends of each. Then, combining the predicted trajectory and current speed of the vehicle, the time required for the vehicle to enter the coverage area of ​​base stations A, B, and C is estimated. Based on the current number of connections of each base station, its own load change trend, and the time required for the vehicle to arrive, the estimated number of user connections for the three base stations at the expected arrival time of the vehicle is calculated. Finally, this estimated number of connections for each base station is divided by its own preset capacity value to obtain an access pressure value representing the future load pressure.

[0084] This step introduces a forward-looking time dimension assessment for handover decisions by dynamically predicting the future load pressure of each base station when the vehicle arrives. This changes the limitation of traditional solutions that make decisions based solely on the current instantaneous load of the base station, enabling the early identification and avoidance of base stations that appear idle at present but may become congested in the future, thereby improving the long-term stability and service quality of the connection after handover.

[0085] Step 105: Sort the target base stations in the target base station set according to the magnitude of the access pressure value, and generate a handover priority list.

[0086] Optionally, step 105 may specifically include: step 1051, comparing the access pressure values ​​corresponding to all target base stations in the target base station set.

[0087] Step 1052: Arrange the target base stations in the target base station set in order of the access pressure value from smallest to largest, wherein the target base station with the smallest access pressure value is arranged first.

[0088] Step 1053: When at least two target base stations have the same access pressure value, obtain the signal reception strength data of the at least two target base stations, and arrange the target base stations with the same access pressure value in a secondary order from the largest to the smallest value of the signal reception strength data.

[0089] Step 1054: Record the identification information of each target base station in sequence according to the final arrangement order to generate a handover priority list.

[0090] In this step, the handover priority list refers to an ordered list of target base station identifiers arranged according to a specific priority order, used to explicitly specify the order in which connections are attempted during network handover.

[0091] Signal reception strength data refers to the strength of the wireless signal received by the wireless communication module on the target vehicle from a specific base station. It is used to measure the instantaneous communication quality of the current link between the vehicle and the base station and is obtained by real-time measurement of the received signal through the vehicle terminal.

[0092] Identification information refers to a string of codes or names that can uniquely identify and distinguish different base stations. It is used to accurately locate a specific base station in network commands and lists, and is obtained from the base station's configuration information or network registration information.

[0093] In this step, firstly, the access pressure value calculated for each base station in the target base station set is read. By traversing and comparing these access pressure values, their relative magnitudes are determined. Secondly, a sorting algorithm, such as quicksort or bubble sort, is used to sort all target base stations according to the determined numerical relationships. The sorting criterion is the access pressure value, and the order is from the base station with the smallest value to the base station with the largest value; that is, the base station with the lightest load and the lowest access pressure will be ranked first. Next, after sorting, it is checked whether there are two or more base stations with identical access pressure values. If such a situation exists, a secondary sorting mechanism is initiated, that is, the signal data reported in real-time by the vehicle terminal is used to obtain the values ​​of these parallel base stations. The system first obtains the signal reception strength data at the vehicle's location. Then, for these parallel base stations, it sorts them again based on the magnitude of their signal reception strength data, this time according to signal strength, from the base station with the highest value to the base station with the lowest value. This way, under the premise of the same access pressure, the base station with the stronger signal will be given a higher priority. Finally, based on the final sorted order, a list generation operation is performed. Following the order from the first to the last character, the unique identification information of each target base station, such as the base station ID, is read sequentially and written into a new list data structure. This final generated list is the handover priority list, providing a clear action guide for subsequent handover operations.

[0094] For example, following the specific implementation of the previous step, firstly, the calculated access pressure values ​​of base stations A, B, and C are 0.63, 0.81, and 0.36, respectively. Then, these three values ​​are compared to confirm the order: Base station C - 0.36 < Base station A - 0.63 < Base station B - 0.81. Next, the access pressure values ​​are sorted in ascending order, resulting in a preliminary order of C, A, B. Since these three access pressure values ​​are different and not tied, a secondary sorting is unnecessary. Finally, according to this final order C->A->B, the identifiers of base stations C, A, and B are recorded sequentially to generate a handover priority list: Base station C_ID, Base station A_ID, Base station B_ID. This indicates that during handover, priority will be given to attempting to switch to base station C, which will have the lowest future load pressure.

[0095] This step transforms the predictive access pressure value into a clear sequence of action priorities. By prioritizing the minimum future load pressure and using real-time signal quality for fine-tuning, a priority list is generated that can directly guide handover operations. This ensures that handover decisions are always directed toward the most stable and least congested network resources, achieving proactive optimization of network traffic and connection quality.

[0096] Step 106: When it is detected that the vehicle moves to the edge of the coverage area of ​​any base station in the target base station set according to the predicted trajectory, the vehicle is controlled to perform a network handover operation according to the handover priority list.

[0097] Optionally, step 106 may specifically include: step 1061, acquiring in real time the signal strength information between the target vehicle and all target base stations in the target base station set.

[0098] Step 1062: Compare the signal strength between the target vehicle and the current base station with the signal strength between the target vehicle and the candidate target base station ranked first in the handover priority list, and calculate the signal strength difference.

[0099] Step 1063: When the signal strength difference exceeds a preset signal strength threshold, it is determined that the vehicle has moved to the edge of the coverage area of ​​the current serving base station.

[0100] Step 1064: In response to the determination, a handover instruction is generated and sent based on the identification information of the candidate target base station ranked first in the handover priority list. The handover instruction is used to instruct the target vehicle to disconnect from the current base station and establish a connection with the candidate target base station.

[0101] Step 1065: Execute a network handover operation based on the handover instruction and update the handover priority list, wherein the candidate target base station ranked first in the handover priority list is removed.

[0102] In this step, the network handover operation refers to the process by which the vehicle terminal disconnects its wireless connection with the current serving base station and establishes a connection with a new target base station. This is used to maintain the continuity of network services during movement. It is triggered by a handover command sent by the network control unit and is completed collaboratively by the vehicle terminal and the base station.

[0103] Signal strength information refers to the wireless signal power level values ​​measured by the target vehicle from its current serving base station and surrounding target base stations. It is used to evaluate the link quality between the vehicle and each base station in real time. It is obtained by measuring and reporting the received signals through the wireless receiving module of the vehicle terminal.

[0104] The signal strength difference refers to the arithmetic difference between the signal strength value received by the target vehicle from the candidate target base station and the signal strength value received from the current serving base station. It is used to quantify and compare the link quality of the two, and is obtained through subtraction.

[0105] The preset signal strength threshold refers to a pre-set standard value for the difference in signal strength, which is used as a judgment condition for triggering a handover decision. When the actual signal strength difference exceeds this threshold, it is considered that the handover trigger condition is met. This value is pre-configured in the device based on network optimization experience and measured data.

[0106] A handover command is a control signaling generated by the network control unit and sent to the vehicle terminal and the base station. It is used to precisely instruct the execution of a specific network handover action, and contains the identification information of the target base station. It is generated and transmitted through the network signaling protocol.

[0107] The candidate target base station specifically refers to the target base station that is currently ranked first in the handover priority list. It is the object recommended as the highest priority handover target at the current moment, and is obtained by the base station from the current handover priority list.

[0108] In this step, signal strength information is first acquired in real time through the measurement reporting mechanism in radio resource management. Simultaneously, the network sends a configuration to the vehicle terminal, instructing it to periodically measure the signal strength of the current serving base station and base stations in the target base station set. The vehicle terminal performs physical layer signal measurements and uploads the signal strength information, including the measurement results of each base station, to the network control unit via wireless signaling. Next, a difference comparison algorithm is used to extract the signal strength value between the vehicle and the current base station, and the signal strength value between the vehicle and the candidate target base station ranked first in the handover priority list, from the received signal strength information parsed by the network control unit. An arithmetic subtraction operation is then performed on the two strength values ​​to obtain the signal strength difference. Finally, a threshold judgment logic is used to make a handover trigger decision, and the calculated difference is used to determine the handover trigger value. The signal strength difference is compared with a pre-configured signal strength threshold. When the real-time difference exceeds the threshold, the trigger condition is deemed met, indicating that the vehicle has moved to the edge of the current base station's coverage area. Then, a handover command is generated and sent via the core network signaling protocol. After the trigger condition is met, the identification information of the candidate target base station at the top of the handover priority list is read, and a handover command containing this identification is constructed according to the mobility management protocol. This command is then sent to the relevant base station and vehicle terminal via the core network control plane interface. Finally, a network handover operation is performed and the data structure is updated. The vehicle terminal, the current base station, and the candidate target base station perform connection migration according to the standard procedure. After a successful handover, the handover priority list is updated, and the identification information of the candidate target base station that has been handed over is removed from the list.

[0109] For example, following the specific implementation of the previous step, firstly, the target vehicle is currently being served by base station D, and the generated handover priority list is in the order of base station C, base station A, and base station B; secondly, the vehicle terminal continuously measures and reports the signal strength information between itself and the currently serving base station D, as well as all base stations C, A, and B in the handover priority list; then, from the reported information, it is extracted that the signal strength received by the vehicle from base station D is relatively weak, while the signal strength received from the candidate target base station C, which is ranked first in the handover priority list, is significantly stronger, and there is a significant signal strength difference between the two; then, this real-time calculated signal strength difference is compared with a pre-set signal strength threshold. The system detects that the signal strength difference has exceeded a threshold, indicating that the vehicle has moved to the coverage edge of the current serving base station D, triggering a handover condition. In response to this determination, the system generates and sends a clear handover command based on the identifier of base station C, which is at the top of the handover priority list. This command then instructs the vehicle and base station to work together to perform a network handover operation from base station D to base station C. Finally, after a successful handover, the system updates the internal handover priority list, removing the identifier of base station C from the list. The updated handover priority list now lists base station A, base station B, and prepares for the vehicle's next possible handover.

[0110] This step combines the generated optimal handover priority list with real-time wireless link quality detection to achieve precise triggering and reliable execution. It ensures that handover actions only occur when signal conditions are truly met and strictly follows the predetermined priority to guide the best goal. Furthermore, the immediate update of the list after handover allows the entire management process to adapt to continuous mobile scenarios, thereby transforming the initial predictions and planning into a stable and smooth network handover experience.

[0111] Figure 2 is a schematic diagram of the network mobility management optimization system provided in this application. As shown in Figure 2, the system includes: an acquisition module 21, used to acquire first information of a target vehicle and second information of base stations deployed on the highway network. The first information includes the real-time location, real-time speed, and lane number of the target vehicle. The second information includes real-time load data and geographical location data corresponding to multiple base stations. A generation module 22 is used to generate a predicted trajectory of the target vehicle based on the real-time location, real-time speed, and lane number of the target vehicle. A filtering module 23 is used to match the predicted trajectory with the coverage area of ​​the base stations and filter out the target base stations that pass through the predicted trajectory to form a target base station set. A calculation module 24 is used to calculate the access pressure value for each target base station according to the real-time load data corresponding to each target base station in the target base station set. A sorting module 25 is used to sort the target base stations in the target base station set according to the magnitude of the access pressure value and generate a handover priority list. An execution module 26 is used to control the vehicle to perform a network handover operation according to the handover priority list when it is detected that the vehicle moves to the edge of the coverage area of ​​any base station in the target base station set according to the predicted trajectory.

[0112] The network mobility management optimization system shown in Figure 2 can execute the network mobility management optimization method shown in the embodiment of Figure 1. Its implementation principle and technical effects will not be elaborated further. The specific methods by which each module and unit of the network mobility management optimization system in the above embodiments perform operations have been described in detail in the embodiments related to this method, and will not be elaborated upon here.

[0113] In one possible design, the network mobility management optimization system of the embodiment shown in FIG2 can be implemented as a computing device, as shown in FIG3. The computing device may include a storage component 31 and a processing component 32; the storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are invoked and executed by the processing component 32.

[0114] The processing component 32 is used in a network mobility management optimization method according to the embodiment of FIG1 above.

[0115] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above-described method. Alternatively, the processing component may be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described method.

[0116] Storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0117] Of course, computing devices may also include other components, such as input / output interfaces, display components, communication components, etc.

[0118] Input / output interfaces provide interfaces between processing components and peripheral interface modules, which can be output devices, input devices, etc.

[0119] The communication components are configured to facilitate wired or wireless communication between computing devices and other devices.

[0120] The computing device can be a physical device or an elastic computing host provided by a cloud computing platform. In this case, the computing device can refer to a cloud server, and the aforementioned processing components, storage components, etc., can be basic server resources rented or purchased from the cloud computing platform.

[0121] This application also provides a computer storage medium storing a computer program, which, when executed by a computer, can implement a network mobility management optimization method according to the embodiment shown in FIG1 above.

[0122] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0123] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0124] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0125] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for optimizing network mobility management, characterized in that, include: The system acquires first information about the target vehicle and second information about the base stations deployed on the highway network. The first information includes the real-time location, real-time speed, and lane number of the target vehicle. The second information includes real-time load data and geographic location data corresponding to multiple base stations. Based on the real-time location, real-time speed, and lane number of the target vehicle, a predicted trajectory of the target vehicle is generated; the predicted trajectory is matched with the coverage area of ​​the base station, and target base stations that pass through the predicted trajectory are selected to form a target base station set; Based on the real-time load data corresponding to each target base station in the target base station set, an access pressure value is calculated for each target base station; based on the magnitude of the access pressure value, the target base stations in the target base station set are sorted to generate a handover priority list; when it is detected that the vehicle moves to the edge of the coverage area of ​​any base station in the target base station set according to the predicted trajectory, the vehicle is controlled to perform a network handover operation according to the handover priority list.

2. The method according to claim 1, characterized in that, Based on the real-time location, real-time speed, and lane number of the target vehicle, a predicted trajectory for the target vehicle is generated, including: retrieving corresponding lane centerline data from pre-stored lane information according to the lane number, wherein the lane centerline data is used to characterize the geometric orientation of the lane corresponding to the lane number on the highway; using the real-time location as the starting point, matching the direction of the real-time speed with the tangent direction of the lane centerline data at the starting point to determine the initial heading of the target vehicle; projecting the real-time location along the lane centerline data onto the initial heading to obtain a projection point, and using the projection point as the trajectory starting point of the predicted trajectory; using the trajectory starting point as a reference, recursively calculating a preset trajectory point sequence on the lane centerline data according to the magnitude of the real-time speed and the geometry of the lane centerline data, wherein during the recursive calculation process, the positions of the trajectory points in the preset trajectory point sequence are constrained on the lane centerline data; and connecting the trajectory starting point and the preset trajectory point sequence to generate the predicted trajectory of the target vehicle.

3. The method according to claim 2, characterized in that, Using the trajectory starting point as a reference, and based on the real-time speed and the geometry of the lane centerline data, a preset trajectory point sequence is recursively calculated on the lane centerline data. This includes: calculating the movement step size within a preset time interval based on the real-time speed; recursively calculating the trajectory point on the lane centerline data, starting from the trajectory starting point and following the geometric direction of the lane centerline data, according to the movement step size; in each recursive calculation, starting from the position of the previous trajectory point, moving the movement step size along the geometric direction of the lane centerline data to obtain the trajectory point corresponding to the current recursive calculation; repeating the recursive calculation process until the cumulative number of recursive calculations reaches a preset number, resulting in a preset trajectory point sequence composed of the trajectory starting point and the trajectory points obtained in each recursive calculation in sequence.

4. The method according to claim 1, characterized in that, Matching the predicted trajectory with the coverage area of ​​the base stations, and filtering out target base stations that pass through the predicted trajectory to form a target base station set, includes: obtaining coverage shape information corresponding to each of the plurality of base stations, the coverage shape information being used to define the geographical coverage area of ​​the base station; extracting multiple trajectory points from the predicted trajectory, taking each of the plurality of trajectory points as a point to be judged, and determining whether the point to be judged is located within the geographical coverage area of ​​the base station; if the point to be judged is located within the geographical coverage area of ​​the base station, then the base station is determined as a target base station passed through by the predicted trajectory; summarizing all target base stations determined to be passed through by the predicted trajectory to generate a target base station set.

5. The method according to claim 1, characterized in that, Based on the real-time load data corresponding to each target base station in the target base station set, the access pressure value is calculated for each target base station, including: obtaining the preset capacity value of each target base station in the target base station set; extracting the current connection number of each target base station at the current moment from the real-time load data; calculating the load change rate of each target base station based on the change of the real-time load data within a preset time period; calculating the estimated travel time required for the target vehicle to travel from its current location into the geographical coverage area of ​​each target base station based on the predicted trajectory and the speed of the target vehicle; adjusting the current connection number based on the load change rate and the estimated travel time to obtain the predicted connection number of each target base station when the target vehicle is expected to arrive; dividing the predicted connection number of each target base station by the corresponding preset capacity value to obtain the access pressure value.

6. The method according to claim 1, characterized in that, Based on the access pressure values, the target base stations in the target base station set are sorted to generate a handover priority list. This includes: comparing the access pressure values ​​of all target base stations in the target base station set; arranging the target base stations in the target base station set in ascending order of access pressure values, with the target base station with the smallest access pressure value placed first; when at least two target base stations have the same access pressure value, acquiring the signal reception strength data of the at least two target base stations, and then arranging the target base stations with the same access pressure value in descending order of the signal reception strength data; and recording the identification information of each target base station according to the final arrangement to generate the handover priority list.

7. The method according to claim 1, characterized in that, When it is detected that the vehicle has moved to the edge of the coverage area of ​​any base station in the target base station set according to the predicted trajectory, the system controls the vehicle to perform a network handover operation according to the handover priority list, including: acquiring signal strength information between the target vehicle and all target base stations in the target base station set in real time; comparing the signal strength between the target vehicle and the current base station and the signal strength between the target vehicle and the candidate target base station ranked first in the handover priority list, and calculating the signal strength difference; when the signal strength difference exceeds a preset signal strength threshold, determining that the vehicle has moved to the edge of the coverage area of ​​the current serving base station; in response to the determination, generating and sending a handover command according to the identification information of the candidate target base station ranked first in the handover priority list, the handover command instructing the target vehicle to disconnect from the current base station and establish a connection with the candidate target base station; performing a network handover operation based on the handover command, and updating the handover priority list, wherein the candidate target base station ranked first in the handover priority list is removed.

8. A network mobility management optimization system, characterized in that, include: The acquisition module is used to acquire first information of the target vehicle and second information of the base stations deployed on the highway network. The first information includes the real-time location, real-time speed and lane number of the target vehicle. The second information includes real-time load data and geographical location data corresponding to multiple base stations. The generation module is used to generate a predicted trajectory of the target vehicle based on the real-time location, real-time speed and lane number of the target vehicle; the filtering module is used to match the predicted trajectory with the coverage area of ​​the base station and filter out the target base stations that pass through the predicted trajectory to form a set of target base stations. The calculation module is used to calculate the access pressure value for each target base station based on the real-time load data corresponding to each target base station in the target base station set; the sorting module is used to sort the target base stations in the target base station set according to the magnitude of the access pressure value and generate a handover priority list. The execution module is used to control the vehicle to perform a network handover operation according to the handover priority list when it is detected that the vehicle has moved to the edge of the coverage area of ​​any base station in the target base station set according to the predicted trajectory.

9. A computing device, characterized in that, It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement a network mobility management optimization method as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that, The device contains a computer program that, when executed by a computer, implements a network mobility management optimization method as described in any one of claims 1 to 7.

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