EUHT base station optimization method based on autonomous driving vehicle network connection information

Abstract

The present invention provides an EUHT base station optimization method based on network connection information of an autonomous driving vehicle, comprising: obtaining network connection status data of the autonomous driving vehicle; wherein the network connection status data includes a network connection type; when the network connection type of the autonomous driving vehicle is different at two consecutive time points, determining that a network switching event occurs, and recording network switching information of the network switching event; combining the network switching information with the driving trajectory data of the autonomous driving vehicle to generate a network switching timing diagram; based on the network switching timing diagram, generating a network switching heat map, and identifying a network coverage optimization area in the network switching heat map; combining the driving frequency of the autonomous driving vehicle in the network coverage optimization area to generate an EUHT base station optimization deployment decision, which can dynamically identify network coverage problems and more accurately and quickly locate areas with weak EUHT network signals.

Description

Technical Field

[0001] The present invention relates to the field of autonomous driving wireless network communication technology, and in particular to an EUHT base station optimization method based on autonomous driving vehicle network connection information. Background Art

[0002] Autonomous vehicle communications primarily rely on cellular networks and Enhanced Ultra High Throughput (EUHT) technology to achieve high-frequency, high-density data transmission. EUHT, with its high bandwidth and low latency, effectively improves network transmission efficiency and data stability, while reducing the traffic costs associated with cellular network data transmission. Currently, EUHT technology has been officially put into use in autonomous driving demonstration zones.

[0003] However, in the actual scenario of autonomous driving demonstration areas, EUHT technology still has some defects. The first defect is that the uneven network coverage leads to poor communication stability: EUHT base station deployment is usually based on static models or preset rules (such as evenly spaced layout), which cannot adapt to the data transmission scenarios of autonomous driving vehicles in dynamic scenarios, such as terrain obstruction, resource preemption in high-density areas of autonomous driving vehicles, etc. When an autonomous driving vehicle enters an area without EUHT signal coverage, it needs to switch to the cellular network, which may cause a brief communication interruption, command delay or even control failure. The second defect is the lack of means to optimize deployment points: Traditional base station deployment and optimization solutions lack analysis of the actual operating trajectory and network switching behavior of autonomous driving vehicles. Existing technologies mostly guide EUHT base station deployment through simulated test signal strength or theoretical coverage models. For example, they rely on roadside signal detector equipment to collect data or locate signal blind spots through manual inspections. The above methods have the problems of being too inefficient and unable to respond to dynamic changes in the network connection status of autonomous driving vehicles in real time. Summary of the Invention

[0004] The present invention provides an EUHT base station optimization method based on the network connection information of an autonomous driving vehicle, which is used to solve the technical problem in the prior art that it is impossible to accurately and quickly locate areas with weak EUHT network signals.

[0005] In one aspect, the present invention provides a method for optimizing an EUHT base station based on network connection information of an autonomous driving vehicle, comprising:

[0006] Obtaining network connection status data of the autonomous driving vehicle; wherein the network connection status data includes a network connection type;

[0007] When the network connection types of the autonomous driving vehicle are different at two consecutive time points, determining that a network switching event occurs, and recording network switching information of the network switching event;

[0008] Combining the network switching information with the driving trajectory data of the autonomous driving vehicle to generate a network switching timing diagram;

[0009] generating a network switching heat map based on the network switching timing diagram, and identifying a network coverage optimization area in the network switching heat map;

[0010] Combined with the driving frequency of the autonomous driving vehicle in the network coverage optimization area, an EUHT base station optimization deployment decision is generated.

[0011] According to a EUHT base station optimization method based on network connection information of an autonomous driving vehicle provided by the present invention, the network switching information is combined with the driving trajectory data of the autonomous driving vehicle to generate a network switching timing diagram, including:

[0012] Combining network switching information and driving trajectory data, extract the vehicle's geographic location information when each network switching event occurs;

[0013] Record the occurrence time, geographic location information, and corresponding network connection type of each network switching event in a network switching event list;

[0014] Based on the network switching event list, a network switching timing diagram is generated in chronological order.

[0015] According to a EUHT base station optimization method based on autonomous driving vehicle network connection information provided by the present invention, generating a network switching heat map based on the network switching timing diagram includes:

[0016] The geographical area covered by the driving trajectory of the autonomous vehicle is evenly divided into multiple polygonal areas;

[0017] According to the network switching timing diagram, counting the number of occurrences of network switching events in each polygonal area;

[0018] Calculating the network switching density of each polygonal area based on the occurrence number;

[0019] generating a network switching heat map according to the network switching density;

[0020] The network switching heat map is superimposed on a map using a map visualization tool.

[0021] According to a EUHT base station optimization method based on autonomous driving vehicle network connection information provided by the present invention, identifying the network coverage optimization area in the network switching heat map includes:

[0022] Analyze the vehicle trajectory areas where EUHT-to-cellular network and cellular-to-EUHT network handovers occur during autonomous driving.

[0023] After switching the EUHT network to the cellular mobile network, the vehicle trajectory area that is passed by the EUHT network is switched back to the EUHT network again, and is regarded as the EUHT uncovered trajectory area;

[0024] Generate network coverage optimization area based on the superposition of EUHT uncovered trajectory areas of multiple autonomous vehicles;

[0025] The location of the network coverage optimization area in the network switching heat map of each polygonal area is identified.

[0026] According to a EUHT base station optimization method based on network connection information of an autonomous driving vehicle provided by the present invention, generating an EUHT base station optimization deployment decision in combination with the driving frequency of the autonomous driving vehicle in the network coverage optimization area includes:

[0027] Counting the driving frequency of the autonomous driving vehicle in each polygon in the network coverage optimization area;

[0028] According to the driving frequency, the base station construction priority of each polygonal area is sorted;

[0029] Select the target polygonal area according to the priority sorting;

[0030] An optimized deployment decision of EUHT base stations within the target polygonal area is generated.

[0031] According to the present invention, a method for optimizing EUHT base stations based on network connection information of autonomous vehicles is provided, wherein the method generates a network coverage optimization area based on the superposition of EUHT uncovered trajectory areas of multiple autonomous vehicles, including:

[0032] Extracting spatiotemporal features of the EUHT uncovered trajectory area of ​​each autonomous vehicle; wherein the spatiotemporal features include duration, trajectory length, and trajectory density;

[0033] Based on the spatiotemporal characteristics, cluster analysis is performed on the uncovered trajectory areas of each EUHT to identify network coverage optimization areas with similar spatiotemporal characteristics.

[0034] According to a EUHT base station optimization method based on autonomous driving vehicle network connection information provided by the present invention, cluster analysis is performed on each EUHT uncovered trajectory area based on the spatiotemporal characteristics to identify network coverage optimization areas with similar spatiotemporal characteristics, including:

[0035] The areas with similar spatiotemporal characteristics in the trajectory areas not covered by EUHT are divided into the same cluster;

[0036] For each cluster, calculate the average value of its spatiotemporal characteristics; wherein the average value includes the average uncovered trajectory duration, the average trajectory length, and the average trajectory density;

[0037] Evaluate the network coverage optimization priority of each cluster according to the average value of the spatiotemporal characteristics;

[0038] The area corresponding to the cluster with a priority higher than the preset level is selected as the network coverage optimization area.

[0039] According to the EUHT base station optimization method based on autonomous driving vehicle network connection information provided by the present invention, the identifying of the network coverage optimization area in the network switching heat map further includes:

[0040] Identifying a high-frequency switching area in the network switching heat map where the network switching density is higher than a preset density threshold;

[0041] The high-frequency switching area is used as a network coverage optimization area.

[0042] According to the EUHT base station optimization method based on the network connection information of the autonomous driving vehicle provided by the present invention, after identifying the high-frequency switching area in the network switching heat map where the network switching density is higher than a preset density threshold, the method further includes:

[0043] Acquiring driving speed data of the autonomous driving vehicle in the high-frequency switching area;

[0044] Screening a high-speed area in the high-frequency switching area where the driving speed data is greater than a preset speed threshold;

[0045] The taking the high-frequency handover area as the network coverage optimization area includes:

[0046] The high-speed area is used as a network coverage optimization area.

[0047] According to a EUHT base station optimization method based on autonomous driving vehicle network connection information provided by the present invention, identifying a high-frequency switching area in the network switching heat map where the network switching density is higher than a preset density threshold includes:

[0048] Based on the network switching heat map, determining a set of regions where the network switching density is higher than a preset density threshold;

[0049] For each area in the area set, calculating a fluctuation coefficient of its network switching density;

[0050] Filter out areas where the fluctuation coefficient is less than the preset fluctuation coefficient threshold.

[0051] The EUHT base station optimization method based on the network connection information of the autonomous driving vehicle provided by the present invention determines that a network switching event occurs and records the network switching information of the network switching event when the network connection type of the autonomous driving vehicle is different at two consecutive time points; combines the network switching information with the driving trajectory data of the autonomous driving vehicle to generate a network switching timing diagram; based on the network switching timing diagram, generates a network switching heat map, and identifies the network coverage optimization area in the network switching heat map; combines the driving frequency of the autonomous driving vehicle in the network coverage optimization area to generate the EUHT base station optimization deployment decision. This embodiment is based on the actual vehicle network connection status, is more in line with the actual road operation conditions, can dynamically identify network coverage problems, can more accurately and quickly locate EUHT network signal weak areas, and can save a lot of manual testing costs and optimization deployment costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0053] Figure 1 1 is a flow chart of a method for optimizing an EUHT base station based on network connection information of an autonomous driving vehicle provided by an embodiment of the present invention;

[0054] Figure 2 2. It is a schematic structural diagram of a EUHT base station optimization device based on autonomous driving vehicle network connection information provided by an embodiment of the present invention;

[0055] Figure 3 It is a structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0056] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0057] Figure 1 It is a flowchart of the EUHT base station optimization method based on the network connection information of the autonomous driving vehicle provided by an embodiment of the present invention.

[0058] See also Figure 1 , the EUHT base station optimization method based on the autonomous driving vehicle network connection information may include the following steps 101 to 105.

[0059] Step 101: Obtain network connection status data of the autonomous driving vehicle; wherein the network connection status data includes the network connection type.

[0060] In this step, the autonomous vehicle's network connection status data can be obtained through the onboard communication unit (OBU), vehicle monitoring system, or network infrastructure. Network connection types typically include cellular networks and ultra-high-speed wireless communications. In addition to the network connection type, network connection status data can also include data transmission rate, network latency, connection duration, geographic location information, timestamp, network switching frequency, vehicle speed, and vehicle identification information.

[0061] Step 102: When the network connection type of the autonomous driving vehicle is different at two consecutive time points, determine that a network switching event occurs, and record the network switching information of the network switching event.

[0062] In this step, the autonomous vehicle's network connection type can be detected at preset intervals. For example, if the network connection type detected at one point in time is ultra-high-speed wireless communication, and the network connection type detected at a second point in time is a cellular network, a network switching event is determined to have occurred. Network switching information can generally include the time of occurrence, the location (the latitude and longitude of the autonomous vehicle), and the network switching type (e.g., switching from EUHT to cellular network, or vice versa).

[0063] Step 103: Combine the network switching information with the driving trajectory data of the autonomous driving vehicle to generate a network switching timing diagram.

[0064] Step 103 may specifically include:

[0065] Combining network switching information and driving trajectory data, the geographic location information of the vehicle when each network switching event occurs is extracted;

[0066] Record the occurrence time, geographic location information, and corresponding network connection type (e.g., EUHT switching to cellular network, or cellular network switching to EUHT) of each network switching event in the network switching event list;

[0067] Based on the network switching event list, a network switching timing diagram is generated in chronological order.

[0068] In step 103, the driving trajectory data can be understood as the location of the autonomous vehicle at various points in time. Network switching information (such as the time of occurrence and type of network switch) can be correlated with the vehicle's driving trajectory data, extracting the vehicle's latitude and longitude coordinates at the time of each network switch event as geographic location information. For example, the occurrence time can be matched with the time point in the driving trajectory data to obtain geographic location information. A list of network switch events can be pre-created.

[0069] Step 104: Generate a network switching heat map based on the network switching timing diagram, and identify the network coverage optimization area in the network switching heat map.

[0070] Specifically, generating a network switching heat map based on the network switching timing diagram may include:

[0071] Step 1: Evenly divide the geographical area covered by the driving trajectory of the autonomous vehicle into multiple polygonal areas;

[0072] This step divides the geographic area into multiple smaller areas to more precisely count and analyze the distribution of network switching events. Polygons can generally be understood as regular polygons or centrally symmetrical figures, such as squares, regular triangles, and regular hexagons. Regular hexagons are generally preferred because they have higher space utilization and lower boundary errors when covering geographic areas. The default side length of a regular hexagon is 10 meters, but this can be adjusted based on actual needs.

[0073] Step 2: Count the number of network switching events in each polygonal area according to the network switching timing diagram;

[0074] This step can traverse each network switching event in the network switching time sequence diagram, determine the geographical location where it occurs, classify each network switching event into a corresponding polygonal area, and count the number of network switching events in each polygonal area to obtain the number of occurrences;

[0075] Step 3: Calculate the network switching density of each polygonal area based on the number of occurrences;

[0076] In this step, the number of network switching events in each polygonal area can be divided by the area of ​​the area to obtain the network switching density. The network switching density can reflect the stability of the network coverage in the area;

[0077] Step 4: Generate a network switching heat map based on the network switching density;

[0078] This step visualizes network handoff density in the form of a heat map, directly showing areas of weak network coverage. Color gradients are typically used to represent network handoff density, for example, red indicates high density and green indicates low density. Heat maps allow you to visually identify areas of weak network coverage, providing intuitive visualization support for optimizing base station deployment.

[0079] Step 5: Use a map visualization tool to overlay the network switching heat map on the map;

[0080] In this step, a map visualization tool (such as a GIS system) can be used to overlay the network switching heat map on the actual geographical map;

[0081] Step 105: Generate an EUHT base station optimization deployment decision based on the driving frequency of the autonomous driving vehicle in the network coverage optimization area.

[0082] In this embodiment, when the network connection types of the autonomous driving vehicle are different at two consecutive time points, a network switching event is determined to have occurred, and the network switching information of the network switching event is recorded; the network switching information is combined with the driving trajectory data of the autonomous driving vehicle to generate a network switching timing diagram; based on the network switching timing diagram, a network switching heat map is generated, and the network coverage optimization area in the network switching heat map is identified; combined with the driving frequency of the autonomous driving vehicle in the network coverage optimization area, the EUHT base station optimization deployment decision is generated. This embodiment is based on the actual vehicle network connection status, is more in line with actual road operation conditions, can dynamically identify network coverage problems, and can more accurately and quickly locate EUHT network signal weak areas, which can save a lot of manual testing costs and optimization deployment costs.

[0083] In one embodiment of the present specification, identifying a network coverage optimization area in a network handover heat map may include:

[0084] Analyze the vehicle trajectory areas where EUHT-to-cellular network and cellular-to-EUHT network handovers occur during autonomous driving.

[0085] After switching the EUHT network to the cellular mobile network, the vehicle trajectory area that is passed by the EUHT network is switched back to the EUHT network again, and is regarded as the EUHT uncovered trajectory area;

[0086] Generate network coverage optimization area based on the superposition of EUHT uncovered trajectory areas of multiple autonomous vehicles;

[0087] Identify the location of the network handoff heat map in each polygonal area where the network coverage is optimized.

[0088] In this embodiment, by analyzing the network switching trajectory of the autonomous driving vehicle, identifying the weak EUHT network signal area, and generating the network coverage optimization area, it is possible to accurately identify the weak network coverage area, provide intuitive visualization support, improve the reliability of optimization decisions, and improve the efficiency of network coverage optimization.

[0089] In one embodiment of this specification, the EUHT base station optimization deployment decision is generated based on the driving frequency of the autonomous driving vehicle in the network coverage optimization area, including:

[0090] Count the driving frequency of autonomous vehicles in each polygon within the network coverage optimization area;

[0091] According to the driving frequency, the base station construction priority of each polygon area is sorted;

[0092] Select the target polygonal area according to the priority sorting;

[0093] Generate optimal deployment decisions for EUHT base stations within the target polygon area.

[0094] In this embodiment, by counting the frequency of vehicle travel, it is possible to identify which areas are "hotspot" areas where vehicles frequently pass through, and these areas have a higher demand for network coverage. Areas with high frequencies have high priorities, indicating that these areas have a more urgent need for network coverage. Based on actual needs and resource conditions, multiple target areas can be selected for optimization. For example, the top 10% of polygonal areas are selected as target polygonal areas in order of arrangement. The optimization deployment decision can include parameters such as the specific location, number, and power of the base station. This embodiment can improve the scientificity and rationality of base station deployment, improve the efficiency of network coverage optimization, enhance the stability and reliability of network coverage, and save resources and costs.

[0095] In one embodiment of this specification, generating a network coverage optimization area based on superposition of EUHT uncovered trajectory areas of multiple autonomous driving vehicles includes:

[0096] Extracting spatiotemporal features of the EUHT uncovered trajectory area of ​​each autonomous vehicle; wherein the spatiotemporal features include duration, trajectory length, and trajectory density;

[0097] Based on the spatiotemporal characteristics, cluster analysis is performed on the uncovered trajectory areas of each EUHT to identify network coverage optimization areas with similar spatiotemporal characteristics.

[0098] In this embodiment, "similar spatiotemporal characteristics" refers to the combined temporal and spatial characteristics of vehicles in areas not covered by EUHT. Through cluster analysis, areas with similar spatiotemporal characteristics can be grouped together, thereby identifying network coverage areas that require optimization. Specifically, similar spatiotemporal characteristics include: similar durations: multiple vehicles spend similar amounts of time in an area; similar trajectory lengths: multiple vehicles travel similar distances in an area; similar trajectory densities: multiple vehicles have similar activity frequencies in an area; and similar trajectory distributions: multiple vehicles have similar trajectory distribution patterns in an area. This embodiment can more accurately locate areas requiring optimization.

[0099] Assume that in a certain autonomous driving demonstration area, there are multiple vehicles whose EUHT uncovered trajectory areas are as follows:

[0100] Vehicle A, duration: 10 minutes; track length: 10 kilometers; track density: 10 track points / minute;

[0101] Vehicle B, duration: 12 minutes; track length: 12 kilometers; track density: 11 track points / minute;

[0102] Vehicle C, duration: 2 minutes; track length: 2 kilometers; track density: 2 track points / minute.

[0103] Cluster analysis shows that the trajectory areas of vehicles A and B share similar spatiotemporal characteristics (long duration, long trajectory length, and high trajectory density), and can be grouped into the same cluster. However, the trajectory area of ​​vehicle C exhibits different spatiotemporal characteristics and can be grouped into a different cluster. Therefore, the trajectory areas of vehicles A and B can be identified as areas requiring priority network coverage optimization.

[0104] In one embodiment of this specification, based on spatiotemporal characteristics, cluster analysis is performed on each EUHT uncovered trajectory area to identify network coverage optimization areas with similar spatiotemporal characteristics, including:

[0105] Step 1: Divide the regions with similar spatiotemporal characteristics in the spatiotemporal characteristics of the trajectory areas not covered by EUHT into the same cluster;

[0106] In this step, clustering algorithms (such as K-means, DBSCAN, etc.) can be used to analyze the extracted spatiotemporal features and divide areas with similar spatiotemporal features into the same cluster;

[0107] Step 2: For each cluster, calculate the average value of its spatiotemporal characteristics; the average value includes the average uncovered trajectory duration, average trajectory length, and average trajectory density;

[0108] Step 3: Evaluate the network coverage optimization priority of each cluster based on the average value of spatiotemporal characteristics;

[0109] For example, clusters with long duration, long trajectory length, and high trajectory density have higher priority, indicating that these areas have a more urgent need for network coverage;

[0110] Step 4: Select the area corresponding to the cluster with a priority higher than the preset level as the network coverage optimization area.

[0111] In this example, it is assumed that in a certain autonomous driving demonstration area, two clusters are obtained through cluster analysis, and each cluster contains multiple EUHT uncovered trajectory areas. The representative spatiotemporal features calculated are as follows:

[0112] Cluster 1, average uncovered trajectory duration: 10 minutes; average trajectory length: 10 kilometers; average trajectory density: 10 trajectory points / minute;

[0113] Cluster 2, average uncovered trajectory duration: 2 minutes; average trajectory length: 2 kilometers; average trajectory density: 2 trajectory points / minute;

[0114] The average value of the spatiotemporal characteristics of cluster 1 shows that the vehicles in this area have a long stay time, a long travel distance, and frequent activities, so they have a higher priority and need to be optimized for base stations first.

[0115] In one embodiment of the present specification, identifying a network coverage optimization area in a network handover heat map further includes:

[0116] Identify high-frequency switching areas in the network switching heat map where the network switching density is higher than a preset density threshold;

[0117] High-frequency switching areas are used as network coverage optimization areas.

[0118] In this embodiment, high-frequency handover areas are identified, and resources are concentrated to optimize these areas, thereby improving the overall efficiency of network coverage optimization and reducing resource waste.

[0119] In one embodiment of the present specification, after identifying a high-frequency switching area in a network switching heat map where the network switching density is higher than a preset density threshold, the method further includes:

[0120] Obtain driving speed data of autonomous vehicles in high-frequency switching areas;

[0121] Screening high-speed areas where the driving speed data in the high-frequency switching area is greater than a preset speed threshold;

[0122] High-frequency handover areas are considered as network coverage optimization areas, including:

[0123] Use high-speed areas as network coverage optimization areas.

[0124] In this embodiment, not only can the areas with weak network coverage be accurately identified, but also the selection of optimized areas can be ensured to be more in line with the actual traffic operation conditions, especially the network coverage requirements in high-speed driving scenarios.

[0125] In one embodiment of the present specification, identifying a high-frequency switching area in a network switching heat map where the network switching density is higher than a preset density threshold includes:

[0126] Based on the network switching heat map, determine a set of areas where the network switching density is higher than a preset density threshold;

[0127] For each region in the region set, calculate the fluctuation coefficient of its network switching density;

[0128] Filter out areas where the fluctuation coefficient is less than the preset fluctuation coefficient threshold.

[0129] In this embodiment, by introducing the fluctuation coefficient indicator, areas with minimal fluctuations in network handover density are selected as optimization priorities. This not only accurately identifies areas with weak network coverage, but also ensures more stable and reliable selection of optimized areas, avoiding misjudgments caused by short-term fluctuations. The preset density threshold can be set and is not specifically defined here. The preset fluctuation coefficient threshold can be set and is not specifically defined here.

[0130] The coefficient of variation (CV) is a statistic used to measure the dispersion or variability of data. The mean and standard deviation of network handoff density can be calculated. The CV is the ratio of the standard deviation to the mean, typically expressed as a percentage.

[0131] Based on the same general inventive concept, the present invention also protects a EUHT base station optimization device based on the network connection information of the autonomous driving vehicle, such as Figure 2 As shown, Figure 2 This is a schematic diagram of the structure of a EUHT base station optimization device based on autonomous vehicle network connection information, provided in an embodiment of the present invention. The EUHT base station optimization device based on autonomous vehicle network connection information, provided in the present invention, is described below. The EUHT base station optimization device based on autonomous vehicle network connection information described below and the EUHT base station optimization method based on autonomous vehicle network connection information described above can be referenced in conjunction with each other.

[0132] The EUHT base station optimization device based on the network connection information of the autonomous driving vehicle includes an acquisition module 201, a network switching event module 202, a switching timing diagram module 203, a switching heat map module 204 and a base station optimization module 205.

[0133] The acquisition module 201 is used to obtain network connection status data of the autonomous driving vehicle; wherein the network connection status data includes the network connection type;

[0134] The network switching event module 202 is configured to determine that a network switching event occurs when the network connection type of the autonomous driving vehicle is different at two consecutive time points, and record network switching information of the network switching event;

[0135] The switching timing diagram module 203 is used to combine the network switching information with the driving trajectory data of the autonomous driving vehicle to generate a network switching timing diagram;

[0136] The switching heat map module 204 is used to generate a network switching heat map based on the network switching timing diagram, and identify the network coverage optimization area in the network switching heat map;

[0137] The base station optimization module 205 is used to generate EUHT base station optimization deployment decisions based on the driving frequency of the autonomous driving vehicle in the network coverage optimization area.

[0138] Figure 3 It is a structural diagram of an electronic device provided by an embodiment of the present invention.

[0139] like Figure 3 As shown, the electronic device may include: a processor 310, a communication interface 320, a memory 330, and a communication bus 340, wherein the processor 310, the communication interface 320, and the memory 330 communicate with each other via the communication bus 340. The processor 310 may call the logic instructions in the memory 330 to execute the EUHT base station optimization method based on the network connection information of the autonomous driving vehicle.

[0140] Furthermore, the logic instructions in the aforementioned memory 330 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product, stored in a storage medium, includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0141] On the other hand, the present invention also provides a computer program product, which includes a computer program, which can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the EUHT base station optimization method based on the autonomous driving vehicle network connection information provided by the above methods.

[0142] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the EUHT base station optimization method based on the autonomous driving vehicle network connection information provided by the above-mentioned methods.

[0143] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0144] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion 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, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0145] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A EUHT base station optimization method based on autonomous driving vehicle network connection information, characterized in that: include: Obtaining network connection status data of the autonomous driving vehicle; wherein the network connection status data includes a network connection type; When the network connection types of the autonomous driving vehicle are different at two consecutive time points, determining that a network switching event occurs, and recording network switching information of the network switching event; Combining the network switching information with the driving trajectory data of the autonomous driving vehicle to generate a network switching timing diagram; generating a network switching heat map based on the network switching timing diagram, and identifying a network coverage optimization area in the network switching heat map; Combined with the driving frequency of the autonomous driving vehicle in the network coverage optimization area, an EUHT base station optimization deployment decision is generated.

2. The EUHT base station optimization method based on autonomous driving vehicle network connection information according to claim 1, characterized in that: The combining of the network switching information and the driving trajectory data of the autonomous driving vehicle to generate a network switching timing diagram includes: Combining network switching information and driving trajectory data, extract the vehicle's geographic location information when each network switching event occurs; Record the occurrence time, geographic location information, and corresponding network connection type of each network switching event in a network switching event list; Based on the network switching event list, a network switching timing diagram is generated in chronological order.

3. The EUHT base station optimization method based on autonomous driving vehicle network connection information according to claim 1, characterized in that: Generating a network switching heat map based on the network switching timing diagram includes: The geographical area covered by the driving trajectory of the autonomous vehicle is evenly divided into multiple polygonal areas; According to the network switching timing diagram, counting the number of occurrences of network switching events in each polygonal area; Calculating the network switching density of each polygonal area based on the occurrence number; generating a network switching heat map according to the network switching density; The network switching heat map is superimposed on a map using a map visualization tool.

4. The EUHT base station optimization method based on autonomous driving vehicle network connection information according to claim 3, characterized in that: The identifying of the network coverage optimization area in the network switching heat map includes: Analyze the vehicle trajectory areas where EUHT-to-cellular network and cellular-to-EUHT network handovers occur during autonomous driving. After switching the EUHT network to the cellular mobile network, the vehicle trajectory area that is passed by the EUHT network is switched back to the EUHT network again, and is regarded as the EUHT uncovered trajectory area; Generate network coverage optimization area based on the superposition of EUHT uncovered trajectory areas of multiple autonomous vehicles; The location of the network coverage optimization area in the network switching heat map of each polygonal area is identified.

5. The EUHT base station optimization method based on autonomous driving vehicle network connection information according to claim 4, characterized in that: The generating of the EUHT base station optimization deployment decision based on the driving frequency of the autonomous driving vehicle in the network coverage optimization area includes: Counting the driving frequency of the autonomous driving vehicle in each polygon in the network coverage optimization area; According to the driving frequency, the base station construction priority of each polygonal area is sorted; Select the target polygonal area according to the priority sorting; An optimized deployment decision of EUHT base stations within the target polygonal area is generated.

6. The EUHT base station optimization method based on autonomous driving vehicle network connection information according to claim 4, characterized in that: The generation of a network coverage optimization area based on the superposition of EUHT uncovered trajectory areas of multiple autonomous driving vehicles includes: Extracting spatiotemporal features of the EUHT uncovered trajectory area of ​​each autonomous vehicle; wherein the spatiotemporal features include duration, trajectory length, and trajectory density; Based on the spatiotemporal characteristics, cluster analysis is performed on the uncovered trajectory areas of each EUHT to identify network coverage optimization areas with similar spatiotemporal characteristics.

7. The EUHT base station optimization method based on autonomous driving vehicle network connection information according to claim 6, characterized in that: Based on the spatiotemporal characteristics, cluster analysis is performed on each EUHT uncovered trajectory area to identify network coverage optimization areas with similar spatiotemporal characteristics, including: The areas with similar spatiotemporal characteristics in the trajectory areas not covered by EUHT are divided into the same cluster; For each cluster, calculate the average value of its spatiotemporal characteristics; wherein the average value includes the average uncovered trajectory duration, the average trajectory length, and the average trajectory density; Evaluate the network coverage optimization priority of each cluster according to the average value of the spatiotemporal characteristics; The area corresponding to the cluster with a priority higher than the preset level is selected as the network coverage optimization area.

8. The EUHT base station optimization method based on autonomous driving vehicle network connection information according to claim 4, characterized in that: The identifying of the network coverage optimization area in the network handover heat map further includes: Identifying a high-frequency switching area in the network switching heat map where the network switching density is higher than a preset density threshold; The high-frequency switching area is used as a network coverage optimization area.

9. The EUHT base station optimization method based on autonomous driving vehicle network connection information according to claim 8, characterized in that: After identifying a high-frequency switching area in the network switching heat map where the network switching density is higher than a preset density threshold, the method further includes: Acquiring driving speed data of the autonomous driving vehicle in the high-frequency switching area; Screening a high-speed area in the high-frequency switching area where the driving speed data is greater than a preset speed threshold; The taking the high-frequency handover area as the network coverage optimization area includes: The high-speed area is used as a network coverage optimization area.

10. The EUHT base station optimization method based on autonomous driving vehicle network connection information according to claim 8, characterized in that: The identifying a high-frequency switching area in the network switching heat map where the network switching density is higher than a preset density threshold includes: Based on the network switching heat map, determining a set of regions where the network switching density is higher than a preset density threshold; For each area in the area set, calculating a fluctuation coefficient of its network switching density; Filter out areas where the fluctuation coefficient is less than the preset fluctuation coefficient threshold.

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