A vehicle networking edge cache allocation method based on vehicle movement prediction

Abstract

The application provides a vehicle networking edge cache allocation method based on vehicle movement prediction, comprising the following steps: initializing road network data, grid dividing the road network and marking the hot area of vehicles on the road network, and initializing the cache space of the road side node and the vehicle; under the condition that the road side node or the vehicle responds to the request content sent by the user, updating the content popularity list maintained by itself according to the request content, and updating the cache of itself according to the content popularity list; predicting the moving direction of the vehicle close to the hot area, if the driving direction of the vehicle is to the hot area, the cache of the vehicle is updated in advance; and in response to determining that the driving direction of the vehicle is not to the hot area, returning to step S2 and continuing to wait for the user to send the request content. The method can effectively solve the cache allocation problem of the current vehicle networking cache vehicle and road side node, reduce the network delay, and improve the cache efficiency.

Description

Technical Field

[0001] This application relates to the field of vehicle network caching technology, specifically to a vehicle network edge cache allocation method based on vehicle movement prediction. Background Technology

[0002] Vehicle-to-everything (V2X) systems leverage the perception and communication capabilities of vehicles to monitor and scientifically manage traffic conditions in real time, providing users with a safe, comfortable, and intelligent driving experience and transportation services, thereby improving the intelligence level of social transportation services. Currently, the amount of data in V2X systems is increasing dramatically. Although mobile cloud computing can greatly enhance the data processing capabilities of V2X systems, when vehicles are far from cloud servers, network link traffic limitations and transmission latency become significant factors affecting the quality of V2X data services. Traditional V2X systems urgently need transformation to meet stringent transmission latency requirements.

[0003] Mobile edge network architecture extends cloud computing services to the network edge using mobile base stations. It leverages network virtualization technology to push network services and resources, such as computing, caching, and communication, closer to end users at the network edge. Dense networks facilitate the implementation of mobile edge networks. Mobile edge network environments typically deploy a large number of edge fog nodes, which provide communication, computing, and caching services to mobile intelligent vehicles. In vehicle-to-everything (V2X) networks, fog nodes providing caching capabilities mainly include Roadside Units (RSUs) and Intelligent and Connected Vehicles (ICVs). RSUs are intelligent transportation infrastructure deployed on both sides of the road, providing communication, computing, and caching services to vehicles. RSUs can also communicate with each other; through collaborative caching, cache hit rates can be effectively improved, redundant cache space reduced, and backhaul link pressure on the backbone network alleviated during peak hours. ICVs possess certain storage and computing capabilities and use their caching capabilities to provide edge caching services for V2X networks. When ICVs approach other vehicles on the road, they can provide content delivery services to the best of their ability during the brief contact time. However, the design of caching solutions in the vehicular network (V2V) edge network environment is more challenging due to factors such as vehicle mobility, uncertainty of user needs, a large number of edge fog nodes, small coverage areas of edge fog nodes, and limited storage capacity of edge fog nodes. In particular, it is crucial to fully and rationally utilize the limited storage capacity of edge fog nodes to allocate cache and reduce network transmission latency for end-user resource requests. A key characteristic distinguishing V2V from general mobile networks is the high-speed mobility of vehicles. Because vehicles are traveling on roads, the network topology of V2V is unstable, and vehicles switch edge fog nodes with which they communicate during travel. Understanding vehicle movement patterns and hotspot areas on the road is essential for assisting caching decisions.

[0004] Therefore, the lag in cross-regional cache adjustment for vehicles is a problem that urgently needs to be solved in this field. Summary of the Invention

[0005] This application proposes a vehicle network edge cache allocation method based on vehicle movement prediction to solve the technical problems mentioned in the background section above.

[0006] In a first aspect, embodiments of this application provide a method for allocating edge caches in a vehicle-to-everything (V2X) network based on vehicle mobility prediction, comprising the following steps:

[0007] S110. Initialize road network data, divide the road network into grids and mark the vehicle hotspot areas on the road network, and initialize the cache space for roadside nodes and vehicles.

[0008] S120. Under the condition that the roadside node or vehicle responds to the request content sent by the user, update the content popularity list maintained by itself according to the request content, and update its own cache according to the content popularity list.

[0009] S130. Predict the movement direction of vehicles approaching the hotspot area. If the vehicle's direction of travel is towards the hotspot area, update the vehicle's cache in advance; and

[0010] S140. In response to determining that the vehicle's direction of travel is not toward a hotspot area, return to step S120 and continue waiting for the user to send the request content.

[0011] This method enables the rational allocation of edge cache and the rapid adjustment of vehicle cache content, thereby improving cache efficiency.

[0012] In some embodiments, in step S120, the popularity of resource c in the content popularity list is updated using the following formula:

[0013] ;

[0014] in, The decay coefficient of resource popularity; The popularity of resource c; For old The value; For the new The value; The decay period of resource popularity and The larger the value, the slower the resource's popularity declines; The cache node since the last update The time interval; The popularity increment is a fixed constant.

[0015] This formula sets a halving decay period for the popularity of resources. Every time Over time, the popularity of a resource is halved, while a popularity increment is also set. The popularity of a resource increases each time a resource request is received. This resource popularity formula takes into account both the frequency and freshness of resource requests.

[0016] In some embodiments, the following formula is used in step S130, which predicts the direction of movement of vehicles near the hotspot area:

[0017] ;

[0018] in, The grid number to which the vehicle is most likely to go; For vehicle historical trajectory from the region After exiting, enter the area. The number of times; For vehicles to adjacent grids The straight-line distance from the geographic center; For vehicles to adjacent grids The angle difference between the direction of the geographic center and the direction of vehicle travel; R is the side length of the grid. The length of the diagonal of the rectangle formed by two adjacent grids; For a fixed angle of 180 degrees, The range of values ​​is .

[0019] This formula can accurately predict the grid number that a vehicle is most likely to go to, so that the next step can determine whether the vehicle has entered a hotspot area and whether the vehicle's cache should be updated in advance.

[0020] In some embodiments, the formula used to pre-update the vehicle's cache in step S130 is:

[0021] ;

[0022] in, For deployment at roadside nodes in hotspot areas that vehicles are about to approach; To adjust the parameters, Popularity of resource c; For the old The value; For the new The value of .

[0023] This formula integrates the resource popularity records of the vehicle itself with the resource popularity records of RSU, enabling the resource popularity of the vehicle to change significantly towards the resource popularity of RSU. This eliminates the time required for the vehicle to slowly adjust its resource popularity and avoids a large number of cache misses.

[0024] In some embodiments, the sub-step of marking vehicle hotspot areas on the road network in step S110 specifically includes calculating the area heat using area trajectory density, and the formula used is:

[0025] ;

[0026] in, Indicates the region exist The density of trajectory points within the time interval Indicates the region exist The number of trajectory points within the time interval Indicates the region The area.

[0027] In some embodiments, the sub-step of marking vehicle hotspot areas on the road network in step S110 further includes the following steps:

[0028] Select the grid with the highest trajectory point density from the set of road network grids to be divided. ,

[0029] Detect grid Is the trajectory point density greater than or equal to the trajectory point density threshold of the pre-selected hotspot area? ;

[0030] Response to determination The trajectory point density is greater than or equal to the trajectory point density threshold of the pre-selected hotspot area. Then, based on the above Expand the hotspot area around the center.

[0031] In some embodiments, expanding the hotspot area specifically includes the following steps:

[0032] S111, Set the pre-selected hotspot areas and grid Add to collection middle;

[0033] S112, Judgment and Sets Are the trajectory point densities of adjacent grid cells greater than or equal to the trajectory point density threshold of the pre-selected hotspot region? ;

[0034] S113. In response to determining that it is greater than or equal to, add adjacent mesh cells to the set. middle;

[0035] S114. Return to step S112 and continue to determine the set. Are the trajectory point densities of adjacent grid cells greater than or equal to the trajectory point density threshold of the pre-selected hotspot region? until all of the set The trajectory point density of the connected grid cells is all less than the trajectory point density threshold of the pre-selected hotspot region. ;

[0036] S115, Include in the set The grid in the set is from the entire grid set. Remove from the set and Add elements from the hotspot area set middle.

[0037] This method allows for real-time updates of hotspot areas and continuous selection of the entire grid. The hotspot region is expanded using the grid with the highest trajectory density until the trajectory point density of the remaining regions is less than the pre-selected hotspot region trajectory point density threshold. Or the entire grid for .

[0038] In some embodiments, in step S120, the requested content includes: the time when the content request was generated, the expiration time of the content request, and the requested resource.

[0039] Secondly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs any of the methods described above.

[0040] The vehicle network edge cache allocation method based on vehicle movement prediction provided in this application embodiment can effectively solve the current cache allocation problem of vehicle and roadside nodes in the vehicle network, reduce network latency, and improve cache efficiency. Attached Figure Description

[0041] Figure 1 This is a flowchart of an embodiment of the vehicle-to-everything (V2X) edge cache allocation method according to this application;

[0042] Figure 2 This is a schematic diagram of an embodiment of hotspot area expansion according to this application;

[0043] Figure 3 This is a diagram showing the distance and angle between a moving vehicle and the center of an adjacent grid.

[0044] Figure 4 This is a comparative diagram of the average cache hit rates of four methods: VMPCCA, PCA, LRUCA, and NCLCA.

[0045] Figure 5 This is a comparative diagram showing the average cache space utilization of four methods: VMPCCA, PCA, LRUCA, and NCLCA.

[0046] Figure 6 This is a comparative diagram showing the cache hit rates of VMPCCA, PCA, LRUCA, and NCLCA under different resource heat distributions;

[0047] Figure 7 This is a schematic diagram of the structure of a computer system suitable for implementing the electronic devices of the present application embodiments. Detailed Implementation

[0048] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0049] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0050] Figure 1 A flowchart illustrating an embodiment of the vehicle-to-everything (V2X) edge cache allocation method of this application is shown. The method 100 includes the following steps:

[0051] S110. Initialize road network data, divide the road network into grids and mark the vehicle hotspot areas on the road network, and initialize the cache space for roadside nodes and vehicles.

[0052] In this embodiment, the most basic data for the road network is a map data file exported from OpenStreet. First, non-road elements are filtered out from the map file, and then the file is imported into a database (such as Neo4j) or memory. Simultaneously, taxi location and driving records are read, and the speed network is calculated statistically. The taxi location and driving records can be derived from all valid data from the taxi company within a specific time period, in the following format:

[0053] ;

[0054] The data, from left to right, consists of longitude (lon), latitude (lat), direction (dir), speed (speed), date (day, e.g., month and day), hour (hour), minute (minute), and date (date). Each road segment in the speed network has 144 values, representing the travel speed of that segment within every 10 minutes of the day. The method for calculating each speed is to calculate the weighted average of the speeds of the taxis recorded above in the corresponding time period and on the corresponding road segment.

[0055] The map is divided into grids, specifically by dividing the map into k independent, non-overlapping small grids with side length R. After the division, the set of all grids is obtained. .

[0056] In this embodiment, vehicle hotspot areas on the road network are marked based on historical data. Specifically, the trajectory point density of each grid can be calculated using trajectory data, and the trajectory point density of each grid area can be obtained by the following formula:

[0057] ;

[0058] in, Indicates the region exist The density of trajectory points within the time interval Indicates the region exist The number of trajectory points within the time interval Indicates the region The area.

[0059] Figure 2 A schematic diagram of an embodiment of hotspot region extension according to this application is shown, such as... Figure 2 As shown, the sub-step of marking vehicle hotspot areas on the road network also includes the following steps:

[0060] Select the grid with the highest trajectory point density from the set of road network grids to be divided. , determine grid Is the trajectory point density greater than or equal to the trajectory point density threshold of the pre-selected hotspot area? ;

[0061] Response to determination The trajectory point density is greater than or equal to the trajectory point density threshold of the pre-selected hotspot area. Then, according to the above Expand the hotspot area around the center.

[0062] Specifically, expanding the hotspot area includes the following steps:

[0063] S111, Set the pre-selected hotspot areas And grid Add to collection middle;

[0064] S112, Judgment and Sets Are the trajectory point densities of adjacent grid cells greater than or equal to the trajectory point density threshold of the pre-selected hotspot region? ;

[0065] S113. In response to determining that it is greater than or equal to, add the adjacent mesh cell to the set. middle;

[0066] S114. Return to step S12 and continue to determine the set. Are the trajectory point densities of adjacent grid cells greater than or equal to the trajectory point density threshold of the pre-selected hotspot region? until all of the set The trajectory point density of adjacent grid cells is less than the trajectory point density threshold of the pre-selected hotspot region. ;

[0067] S115, Include in the set The grid in the set is from the entire grid set. Remove from the set and Add elements from the hotspot area set middle.

[0068] This method allows for real-time updates of hotspot areas and continuous selection of the entire grid. The hotspot region is expanded using the grid with the highest trajectory density until the trajectory point density of the remaining regions is less than the pre-selected hotspot region trajectory point density threshold. Or the entire grid for .

[0069] Continue to refer to Figure 1 Step S120: Under the condition that the roadside node or vehicle responds to the request content sent by the user, update the content popularity list maintained by itself according to the request content, and update its own cache according to the content popularity list.

[0070] In this embodiment, the data format of the request content includes the following: ;

[0071] Where t0 represents the time when the content request was generated, t1 represents the expiration time of the content request, and c represents the requested resource.

[0072] In this embodiment, in step S120, both roadside nodes and vehicles act as cache nodes. Each cache node can independently accept and respond to requests, and each cache node maintains its own resource popularity array based on the requests it receives. Specifically, resources popularity This means that in the initial stage of caching, the popularity of any resource on a cache node is 0. When a cache node receives a request for a resource... When requesting resources The popularity can be updated using the following formula:

[0073] ;

[0074] in, This is the decay coefficient of resource popularity; The popularity of resource c; For the old The value; For the new The value; The decay period of resource popularity and The larger the value, the slower the resource's popularity declines; The cache node since the last update The time interval; The popularity increment is a fixed constant.

[0075] This formula sets a halving decay period for the popularity of resources. Every time Over time, the popularity of a resource is halved, while a popularity increment is also set. The popularity of a resource increases each time a resource request is received. This resource popularity formula takes into account both the frequency and freshness of resource requests.

[0076] Continue to refer to Figure 1 S130. Predict the movement direction of vehicles approaching the hotspot area. If the vehicle's direction of travel is towards the hotspot area, update the vehicle's cache in advance. S140. In response to determining that the vehicle's direction of travel is not towards the hotspot area, return to step S120 and continue waiting for the user to send the request content.

[0077] In this embodiment, resource requests are often highly concentrated and frequent within hotspot areas, resulting in caching and access pressure on cache nodes within these areas. If the cache can be adjusted in advance based on the demand in the hotspot area before vehicles enter, caching efficiency can be significantly improved, caching pressure in the hotspot area can be reduced, and transmission latency can be lowered.

[0078] This application analyzes vehicle movement from two aspects when vehicle navigation information is unknown: first, the vehicle's historical trajectory data; and second, the vehicle's current driving status. To record the number of times a vehicle enters or exits adjacent grids, i.e., the number of times a vehicle enters or exits from a grid. After exiting, enter The number of times. For example, the existing trajectory. ,So , By counting the number of trajectory point pairs in the trajectory data, let N be... The set of adjacent grid indices is used to calculate the vehicle's position within the grid using the following formula. Drive to the adjacent grid The probability of:

[0079] ;

[0080] Since the vehicle's current driving conditions also affect the prediction of vehicle mobility, the most relevant factors are the distance of the vehicle from the target area and the direction of the vehicle's travel. Figure 3 This diagram illustrates the distances and angles between a moving vehicle and the centers of adjacent grid cells, in conjunction with a reference. Figure 3 Assuming the vehicle is currently in the grid When driving within a grid, the vehicle moves to its adjacent grid. The straight-line distance from the geographic center is Vehicles move to adjacent grids The angle difference between the direction of the geographic center and the direction of vehicle travel is _____. Then, a score can be calculated using the following formula to measure the vehicle's next move to the adjacent grid. Probability level:

[0081] ;

[0082] Where R is the side length of the grid. It is the length of the diagonal of the rectangle formed by two adjacent grids. For a fixed angle of 180 degrees, The range of values ​​is . The smaller, The larger the value, the better.

[0083] By combining the above two formulas, the following formula can be used to calculate the vehicle's movement from the grid. Drive to the adjacent grid Total probability score:

[0084] ;

[0085] Therefore, when the vehicle is When traveling, the grid number that it is most likely to visit next can be calculated using the following formula:

[0086] ;

[0087] In this embodiment, if the grid number calculated by the above formula belongs to a hotspot area, the popularity of the resource c stored in the vehicle is updated in advance by the following formula when the vehicle is about to enter the hotspot area.

[0088] ;

[0089] in, For RSUs deployed in hotspot areas that vehicles are about to reach, To adjust the parameters, This is used to adjust the degree to which the resource popularity of the original vehicle changes to the resource popularity of the RSU area; Popularity of resource c; For the old The value; For the new The value of .

[0090] This formula integrates the resource popularity records of the vehicle itself with the resource popularity records of RSU, enabling the resource popularity of the vehicle to change significantly towards the resource popularity of RSU. This eliminates the time required for the vehicle to slowly adjust its resource popularity and avoids a large number of cache misses.

[0091] The following experiments were conducted to verify the proposed vehicle-to-everything (V2X) edge cache allocation method based on vehicle movement prediction.

[0092] Actual road network data and vehicle trajectory data were used. The road network data came from the open-source map OpenStreetMap (OSM), with a map range of [118.070E, 118.118E] × [24.469N, 24.490N]. The vehicle trajectory data used was from Xiamen City in July 2014. This trajectory data was mainly used for two purposes: firstly, for data positioning, statistical analysis, and calculation to establish a speed network; and secondly, for vehicle direction prediction.

[0093] The experiment was written in Java. The specific process is as follows: Taxi trajectory data was read, located, and statistically analyzed beforehand. A speed network was established and stored in the road network. For each road segment, one speed was recorded every 10 minutes, resulting in 144 speeds per segment. Furthermore, the Xiamen map is gridded. The simulator reads the information files of ICV, RSU, and content requests, generates ICV, RSU, and requests, and simulates content delivery. First, the caches of ICV and RSU are initialized. Then, the caches are updated according to the content requests that appear, and content delivery services are provided. The simulation terminates when the simulation time expires.

[0094] The experiment simulated 25 taxis, generating passenger requests at a frequency of one every 5 seconds, with the requests distributed according to a Zipf distribution. The experiment first tested the Vehicle Mobility Prediction Collaborative Cache Algorithm (VMPCCA), obtaining results for average cache hit rate and average space utilization. We will use VMPCCA to refer to this method below. In addition, the experiment set up three comparison algorithms. One comparison algorithm used a popularity-based caching algorithm (PCA), prioritizing the caching of content with the highest popularity; another comparison algorithm used an LRU-based caching algorithm (LRUCA), applying the LRU eviction principle to caches on cache nodes; and the last comparison algorithm used a network-central locations-based caching algorithm (NCLCA), which caches resources in a selected set of network-central locations cache nodes, storing popular resources in these nodes.

[0095] Figure 4 The comparison of the average cache hit rates of four methods—VMPCCA, PCA, LRUCA, and NCLCA—is shown. Figure 4 As shown, the average cache hit rate of these four algorithms increases with the number of vehicles. Among them, the VMPCCA curve remains above the LRUCA curve. The NCLCA curve is below the LRUCA curve when the number of CCVs is less than 15, but gradually surpasses it as the number of CCVs increases, eventually exceeding the LRUCA curve. This is because the increase in the number of vehicles leads to an increase in cache nodes. In the NCLCA algorithm, the collaborative caching effect between RSU and CCV is enhanced, resulting in a greater increase in its cache hit rate.

[0096] Figure 5 The comparison of average cache space utilization for four methods—VMPCCA, PCA, LRUCA, and NCLCA—is shown. Figure 5As shown, the average cache space utilization of each algorithm decreases with the increase of the number of CCVs. Among them, the curves of VMPCCA, PCA, and LRUCA are relatively close, because their strategies are relatively similar, and therefore their storage space utilization is also relatively similar. However, NCLCA takes a completely different approach, considering cache allocation from the perspective of network topology, which reduces some redundant storage space. Therefore, the NCLCA curve is always on the lower side.

[0097] Figure 6 The comparison of cache hit rates for VMPCCA, PCA, LRUCA, and NCLCA under different resource heat distributions is shown, such as... Figure 6 As shown, the average cache hit rate of all algorithms decreases under a uniform resource popularity distribution environment compared to a Zipf distribution environment. Under a uniform resource popularity distribution environment, the optimization effects of each algorithm on resource popularity are not significant, so the average cache hit rates of each algorithm are not significantly different. However, under a Zipf distribution, different resources have different levels of popularity; resources with higher popularity appear more frequently, and each algorithm tends to cache resources with higher popularity. Therefore, under a Zipf distribution, the average cache hit rate of each algorithm is higher, and the differences in cache hit rates between different algorithms are more pronounced.

[0098] Simulation experiments show that the vehicle movement prediction-based cache allocation proposed in this invention can reasonably allocate cache space, improve cache hit rate, and increase cache efficiency.

[0099] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs any of the methods described above.

[0100] like Figure 7 As shown, the computer system 700 includes a central processing unit (CPU) 701, which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) 702 or programs loaded from storage section 708 into random access memory (RAM) 703. The RAM 703 also stores various programs and data required for the operation of the system 700. The CPU 701, ROM 702, and RAM 703 are interconnected via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0101] The following components are connected to the I / O interface 705: an input section 706 including a keyboard, mouse, etc.; an output section 707 including a liquid crystal display (LCD) and speakers, etc.; a storage section 708 including a hard disk, etc.; and a communication section 709 including a network interface card such as a LAN card and a modem, etc. The communication section 709 performs communication processing via a network such as the Internet. A drive 710 is also connected to the I / O interface 705 as needed. A removable medium 711, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 710 as needed so that computer programs read from it can be installed into the storage section 708 as needed.

[0102] Specifically, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 709, and / or installed from removable medium 711. When the computer program is executed by central processing unit (CPU) 701, it performs the functions defined in the methods of this application. It should be noted that the computer-readable medium described in this application can be a computer-readable signal medium or a computer-readable medium or any combination thereof. The computer-readable medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this application, a computer-readable medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable medium that can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. Program code contained on a computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0103] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0104] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0105] The modules described in the embodiments of this application can be implemented in software or hardware. The described modules can also be housed in a processor; for example, a processor may be described as including an acquisition module, an analysis module, and an output module. The names of these modules do not necessarily limit the functionality of the module itself.

[0106] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described inventive concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. A method for allocating edge cache in a vehicle-to-everything (V2X) network based on vehicle movement prediction, characterized in that, Includes the following steps: S110. Initialize road network data, divide the road network into grids and mark the vehicle hotspot areas on the road network, and initialize the cache space for roadside nodes and vehicles. S120, under the condition that the roadside node or vehicle responds to the request content sent by the user, update the content popularity list maintained by itself according to the request content, and update its own cache according to the content popularity list; S130. Predict the movement direction of vehicles approaching the hotspot area. If the vehicle's driving direction is towards the hotspot area, update the vehicle's cache in advance. as well as S140. In response to determining that the vehicle's driving direction is not towards the hotspot area, return to step S120 and continue waiting for the user to send request content. In step S120, the popularity of resource c in the content popularity list is updated using the following formula: ； in, This is the decay coefficient of resource popularity; The popularity of resource c; For the old The value; For the new The value; The decay period of resource popularity and , The larger the value, the slower the resource's popularity declines; The cache node since the last update The time interval; The increment of popularity is a fixed constant; In step S130, the following formula is used to predict the direction of movement of vehicles approaching the hotspot area: ； in, The grid number to which the vehicle is most likely to go; For vehicle historical trajectory from the region After exiting, enter the area. The number of times; For vehicles to adjacent grids The straight-line distance from the geographic center; For vehicles to adjacent grids The angle difference between the direction of the geographic center and the direction of vehicle travel; The side length of the grid; The length of the diagonal of the rectangle formed by two adjacent grids; For a fixed angle of 180 degrees, The range of values ​​is ; In step S130, the formula used to update the vehicle's cache in advance is: ； in, For deployment at roadside nodes in hotspot areas that vehicles are about to approach; To adjust the parameters, The popularity of resource c; For the old The value; For the new The value of .

2. The vehicle-to-everything (V2X) edge cache allocation method based on vehicle mobility prediction according to claim 1, characterized in that, In step S110, the sub-step of marking vehicle hotspot areas on the road network specifically includes calculating the area heat using area trajectory density, and the formula used is: ； in, Indicates the region exist The density of trajectory points within the time interval Indicates the region exist The number of trajectory points within the time interval Indicates the region The area.

3. The vehicle-to-everything (V2X) edge cache allocation method based on vehicle movement prediction according to claim 2, characterized in that, In step S110, the sub-step of marking vehicle hotspot areas on the road network further includes the following steps: Select the grid with the highest trajectory point density from the set of road network grids to be divided. Determine the grid Is the trajectory point density greater than or equal to the trajectory point density threshold of the pre-selected hotspot area? ; In response to determining the The trajectory point density is greater than or equal to the trajectory point density threshold of the pre-selected hotspot region. Then, according to the above Expand the hotspot area around the center.

4. The vehicle-to-everything (V2X) edge cache allocation method based on vehicle movement prediction according to claim 3, characterized in that, Expanding the hotspot area specifically includes the following steps: S111, let the set of pre-selected hotspot areas and the grid Join the set middle; S112, Determine the relationship between the set Whether the trajectory point density of adjacent grid cells is greater than or equal to the trajectory point density threshold of the pre-selected hotspot region. ; S113. In response to determining that it is greater than or equal to, add the adjacent grid cell to the set. middle; S114. Return to step S112 and continue to determine the relationship with the set. Whether the trajectory point density of adjacent grid cells is greater than or equal to the trajectory point density threshold of the pre-selected hotspot region. until all of the set The trajectory point density of adjacent grid cells is less than the trajectory point density threshold of the pre-selected hotspot region. ; S115, Included in the set The grid in the set is from the entire grid set. Remove from the set, and Add elements from the hotspot area set middle.

5. The vehicle-to-everything (V2X) edge cache allocation method based on vehicle mobility prediction according to claim 1, characterized in that, In step S120, the request content includes: the time when the content request was generated, the expiration time of the content request, and the requested resource.

6. A computer-readable storage medium having a computer program stored thereon, the computer program, when executed by a processor, implementing the method as described in any one of claims 1-5.

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