A method and system for optimizing wireless communication access in multi-node collision

Abstract

The application belongs to the technical field of wireless communication, and provides a wireless communication access optimization method and system for multi-node conflict, comprising the following steps: obtaining historical access data of a wireless communication device, wherein the historical access data comprises historical traffic state data and historical access effect data; performing quantitative analysis on traffic density characteristics based on the historical traffic state data; and combining the historical access effect data to filter out events with high density and high access performance as high-quality access events. The application can meet the core demand for strong inhibition of channel conflict in a high-density scenario of Internet of Vehicles, reduce the number of node transmissions per unit time through dynamic adjustment of the broadcast cycle, stagger the beacon transmission time window, avoid multiple nodes occupying limited wireless channels at the same time from the root cause, eliminate signal collision and data loss, and meet the safety demand for real-time updating of beacon data for key collaborative functions such as collision warning and lane cooperation of Internet of Vehicles.

Description

Technical Field

[0001] This invention belongs to the field of wireless communication technology, specifically a method and system for optimizing wireless communication access with multiple node conflicts. Background Technology

[0002] Wireless communication access refers to a technology that allows devices to access communication networks without physical cables, using wireless transmission media such as radio waves, microwaves, and infrared rays. Its core is to enable wireless signal interaction between terminals (such as mobile phones, IoT devices, vehicle terminals, etc.) and base stations, access points, or other nodes to complete data transmission and reception and network connection. It features mobility, flexibility, and wide coverage.

[0003] In high-speed mobile networks such as the Internet of Vehicles (IoV), the core essence of multi-node conflicts is that a large number of vehicle nodes (dynamically moving, with transient topology changes) compete for resources on limited wireless channels, leading to signal collisions, data loss, or excessive latency during simultaneous transmission. Currently, in IoV, vehicles need to achieve coordination (such as collision avoidance and lane keeping) by periodically broadcasting beacons (containing key information such as location, speed, and vehicle status). However, most IoV networks currently use fixed-period broadcasting, which leads to all vehicles sending beacons at a fixed period (such as 100ms) in high-density scenarios (such as when dozens of vehicles are less than 10 meters apart in traffic jams). This results in multiple nodes transmitting simultaneously, leading to intense channel contention and a sharp increase in the conflict rate.

[0004] Therefore, the present invention provides a method and system for optimizing wireless communication access in the event of multi-node conflicts. Summary of the Invention

[0005] In order to overcome the shortcomings of the prior art, at least one technical problem raised in the background art is solved.

[0006] The technical solution adopted by this invention to solve its technical problem is: a method for optimizing wireless communication access with multiple node conflicts, comprising the following steps:

[0007] Acquire historical access data of wireless communication devices. The historical access data includes historical traffic status data and historical access performance data. Quantitatively analyze traffic density characteristics based on historical traffic status data. Combine historical access performance data to select high-density events with high access performance as high-quality access events.

[0008] Based on high-quality access events, the historical traffic state data of high-quality access events are clustered according to the similarity of traffic state characteristics to generate multiple sets of similar access events with consistent scenario attributes.

[0009] For each set of similar access events, a stability analysis is performed on the broadcast period sequence corresponding to the events in the set to determine the optimal broadcast period for each set.

[0010] Acquire real-time traffic status data, match similar access event sets, and output the corresponding optimal broadcast period.

[0011] As a further aspect of the present invention: the historical traffic status data includes: vehicle density per unit time and per unit road segment, vehicle spacing, vehicle speed, and vehicle topology change frequency;

[0012] Historical access performance data includes: data transmission success rate, signal conflict rate, and data transmission delay duration extracted from communication device logs.

[0013] As a further aspect of the present invention: the process of acquiring the high-quality access event is as follows:

[0014] Preset high-density scene thresholds and preset access performance compliance thresholds;

[0015] Quantitatively determine historical traffic status data on a time-by-time and road-by-road basis, and mark historical time periods and road segments belonging to high-density scenarios;

[0016] Events that meet both of the following conditions are defined as high-quality access events;

[0017] Condition 1: The time period and road segment where the event occurred have been identified as a high-density scene;

[0018] Condition 2: All access effect data corresponding to the event reaches the preset threshold.

[0019] As a further aspect of the present invention: the process of obtaining the similar access event set is as follows:

[0020] Acquire high-quality access events and classify them using the K-Means algorithm, which is tailored to the specific scenario.

[0021] First, determine the optimal number of clusters K using the elbow rule;

[0022] Using standardized traffic state core feature parameters as clustering samples, the algorithm is iteratively calculated until the cluster center is stable, and high-quality access events with similar features are grouped into the same group.

[0023] Cluster center stability refers to simultaneously satisfying the conditions that the change in cluster centers is ≤0.01 and the change rate in sample allocation is ≤5%.

[0024] After the similar access event set is generated and labeled for clustering, multiple sets of similar access events are generated.

[0025] As a further aspect of the present invention: the process of performing stability analysis is as follows:

[0026] Based on any set of similar access events, the corresponding broadcast period sequence is obtained;

[0027] Calculate the standard deviation of the broadcast period sequence as a stable performance value, and compare the stable performance value with a preset standard deviation threshold.

[0028] If the stable performance value is less than or equal to the preset standard deviation threshold, it indicates that the corresponding broadcast period sequence is stable.

[0029] If the stable performance value is greater than the preset standard deviation threshold, it indicates that the corresponding broadcast period sequence is unstable.

[0030] As a further aspect of the present invention: the process of determining the optimal broadcast period corresponding to each set is as follows:

[0031] If the broadcast period sequence is stable, the mean of the broadcast period sequence is calculated as the optimal broadcast period for the set of similar access events.

[0032] As a further aspect of the present invention: the process of determining the optimal broadcast period corresponding to each set is as follows:

[0033] If the broadcast period sequence is unstable, rearrange the broadcast period sequence in ascending order and select the broadcast period corresponding to three-quarters of the position as the optimal broadcast period.

[0034] As a further aspect of the present invention: the process of matching similar access event sets and outputting the corresponding optimal broadcast period is as follows:

[0035] Acquire real-time traffic status data and perform matching analysis with similar access event sets to match access event sets;

[0036] Retrieve the optimal broadcast period corresponding to the set of matching access events and use it as the current broadcast period.

[0037] As a further aspect of the present invention: the process of obtaining the matching access event set is as follows:

[0038] Calculate the Euclidean distance between real-time traffic status data and the cluster centers corresponding to each set of similar access events;

[0039] Extract the set of similar access events with the smallest Euclidean distance value as the matching access event set.

[0040] A wireless communication access optimization system for multiple node conflicts, the system comprising:

[0041] Event filtering module: acquires historical access data of wireless communication devices, including historical traffic status data and historical access performance data. Based on the historical traffic status data, it performs quantitative analysis on traffic density characteristics and combines the historical access performance data to filter out high-density events with high access performance as high-quality access events.

[0042] Event classification module: Based on high-quality access events, the historical traffic status data of high-quality access events are clustered according to the similarity of traffic status characteristics to generate multiple sets of similar access events with consistent scenario attributes;

[0043] Broadcast period analysis module: For each set of similar access events, perform stability analysis on the broadcast period sequence corresponding to the events in the set of similar access events, and determine the optimal broadcast period for each set;

[0044] Broadcast cycle adaptation module: acquires real-time traffic status data, matches similar access event sets, and outputs the corresponding optimal broadcast cycle, thereby optimizing the dynamic adaptation of the broadcast cycle to the real-time scenario.

[0045] The beneficial effects of this invention are as follows:

[0046] This invention not only meets the core requirement of effectively suppressing channel conflicts in high-density vehicle-to-everything (V2X) scenarios by dynamically adjusting the broadcast period to reduce the number of node transmissions per unit time and staggering the beacon transmission time window, thus fundamentally preventing multiple nodes from simultaneously occupying limited wireless channels and eliminating signal collisions and data loss, but also meets the security requirements of real-time beacon data updates for key collaborative functions such as V2X collision avoidance warning and lane coordination. Attached Figure Description

[0047] The invention will now be further described with reference to the accompanying drawings.

[0048] Figure 1 This is a flowchart of the steps of a wireless communication access optimization method for multi-node conflicts according to the present invention.

[0049] Figure 2 This is an architecture diagram of a wireless communication access optimization system for multi-node conflicts according to the present invention. Detailed Implementation

[0050] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments. Example

[0051] Please see Figure 1 As shown in the figure, a wireless communication access optimization method for multi-node conflicts according to an embodiment of the present invention includes the following steps:

[0052] Step S10: Obtain historical access data of wireless communication devices. The historical access data includes historical traffic status data and historical access effect data. Based on the historical traffic status data, perform quantitative analysis on traffic density characteristics. Combine the historical access effect data to select high-density events with high access performance as high-quality access events.

[0053] In S10, historical access data is collected for wireless communication devices in the vehicle network, including historical traffic status data and historical access effect data.

[0054] Specifically, historical traffic data includes, but is not limited to: vehicle density (vehicles / km), vehicle spacing (meters), vehicle speed (km / h), and vehicle topology change frequency (number of times vehicles enter and exit the road segment per minute) within a unit time (e.g., 1-minute interval) and a unit road segment (e.g., 1-kilometer range) through vehicle sensors, roadside monitoring equipment, and traffic management systems.

[0055] Historical access performance data includes, but is not limited to: data transmission success rate extracted from communication device logs, i.e., the proportion of successfully transmitted beacons to the total number of transmitted beacons; signal collision rate, i.e., the proportion of transmissions with signal collisions to the total number of transmissions; and data transmission delay, i.e., the time difference between the transmission of a beacon and the receipt of an acknowledgment.

[0056] In S10, a high-density scene threshold is first preset, which is set by those skilled in the art based on historical experience. For example, the characteristic of dozens of vehicles with a distance of less than 10 meters during traffic jams is used to define a high-density scene with a vehicle density of ≥30 vehicles / km and an average vehicle distance of ≤10 meters.

[0057] Quantitatively determine historical traffic status data on a time-by-time and road-by-road basis, and mark historical time periods and road segments belonging to high-density scenarios;

[0058] The preset access performance thresholds are set by those skilled in the art based on historical experience, for example, data transmission success rate ≥90%, signal conflict rate ≤5%, and data transmission delay ≤50ms;

[0059] Events that meet both of the following conditions are defined as high-quality access events;

[0060] Condition 1: The time period and road segment where the event occurred have been identified as a high-density scene;

[0061] Condition 2: All access performance data (data transmission success rate, signal conflict rate, and data transmission delay) corresponding to the event reach the preset threshold.

[0062] Step S10, which filters high-density events with satisfactory access performance, essentially identifies the core scenarios where multi-node conflicts frequently occur. The root cause of multi-node conflicts is the contention for resources by high-density nodes within a limited channel. Filtering events with satisfactory access performance further eliminates failed cases that still occur in high-density scenarios, retaining only successful cases with no conflicts and high performance in high-density scenarios. This reduces the possibility of subsequent analysis being interfered with by invalid samples such as low-density conflict-free cases and high-density conflict failure cases, and reduces the likelihood of the optimization strategy deviating from the core issue.

[0063] Furthermore, subsequent clustering can be grouped based on detailed differences in traffic conditions to generate event sets with highly consistent scene attributes. This can match high-density sub-scenes with different conflict intensities. Through scene-consistent clustering, customized optimization solutions can be provided for conflict scenarios of different intensities, thereby improving the accuracy of conflict resolution.

[0064] Step S20: Based on high-quality access events, perform cluster analysis on the historical traffic state data of high-quality access events according to the similarity of traffic state characteristics to generate multiple sets of similar access events with consistent scenario attributes;

[0065] In S20, high-quality access events are acquired, and the K-Means algorithm for scenario classification is selected to classify the high-quality access events.

[0066] First, determine the optimal number of clusters K using the elbow rule;

[0067] For example, based on historical data distribution, K=3 is determined, corresponding to typical scenarios of "low speed high density", "medium speed medium-high density", and "extremely high congestion density".

[0068] Using standardized traffic state core feature parameters as clustering samples, the algorithm is iteratively calculated until the cluster center is stable, and high-quality access events with similar features are grouped into the same group.

[0069] Cluster center stability refers to simultaneously satisfying the conditions that the change in cluster centers is ≤0.01 and the change rate in sample allocation is ≤5%.

[0070] After the similar access event set is generated and labeled and clustered, multiple sets of similar access events are generated. The events in each set have consistent scene attributes, and scene labeling is performed on each set.

[0071] Example: Set 1: Vehicle density 30-40 vehicles / km, average vehicle spacing 8-10 meters, driving speed 10-20 km / h;

[0072] Set 2: Vehicle density 40-50 vehicles / km, average vehicle spacing 5-8 meters, driving speed 0-10 km / h;

[0073] Calculate the variance of traffic state characteristic parameters of events within a set of similar access events;

[0074] If the variance is less than or equal to the corresponding preset threshold (e.g., 5%), then the set is considered valid.

[0075] If any variance exceeds the limit, readjust the clustering parameters (such as K value, algorithm radius) until the set meets the scenario consistency requirements.

[0076] Step S20 uses cluster analysis to group high-quality access events into similar groups, which allows for the analysis of the relationship between broadcast cycles and traffic conditions in the following analysis, avoiding cross-scenario data mixing. If no grouping is used, all high-density events will be mixed together for analysis, resulting in situations where there are both low-speed congestion and medium-speed slow traffic under the same density, affecting the analysis results.

[0077] Step S30: For each set of similar access events, perform stability analysis on the broadcast period sequence corresponding to the events in the set of similar access events, and determine the optimal broadcast period for each set;

[0078] In S30, a set of similar access events is obtained, and the broadcast period corresponding to each access event in the set is obtained;

[0079] Based on any set of similar access events, the corresponding broadcast period sequence is obtained;

[0080] Calculate the standard deviation of the broadcast period sequence as a stable performance value, and compare the stable performance value with a preset standard deviation threshold.

[0081] If the stable performance value is less than or equal to the preset standard deviation threshold, it indicates that the corresponding broadcast cycle sequence is stable. Then, the mean of the broadcast cycle sequence is calculated as the optimal broadcast cycle for the set of similar access events.

[0082] If the stable performance value is greater than the preset standard deviation threshold, it indicates that the corresponding broadcast cycle sequence is unstable. The broadcast cycle sequence is rearranged in ascending order, and the broadcast cycle corresponding to the three-quarters position is selected as the optimal broadcast cycle.

[0083] It should be noted that the total number of values ​​in the broadcast period sequence is A. If the product of three-quarters and A is not an integer, it will be rounded up.

[0084] The broadcast period refers to the time interval during which nodes (such as vehicle terminals) send beacons (containing key data such as location and speed). It is directly related to channel conflict. The larger the period, the fewer transmissions a node makes per unit time, and the time windows for nodes to send beacons are staggered. Even in high-density scenarios, this can reduce the probability of multiple nodes occupying the channel at the same time, thereby reducing conflicts at the source. Therefore, the value corresponding to three-quarters of the position is selected as the optimal broadcast period. This can not only meet the need for strong suppression of channel conflicts in high-density scenarios, but also take into account the real-time requirements of beacon data for the core collaborative functions of the Internet of Vehicles, while also ensuring the scenario adaptability and operational stability of the period adjustment.

[0085] Step S40: Obtain real-time traffic status data, match similar access event sets, and output the corresponding optimal broadcast period to optimize the dynamic adaptation of the broadcast period to the real-time scenario;

[0086] In S40, real-time traffic status data is acquired, and the Euclidean distance value between the cluster center corresponding to each set of similar access events is calculated.

[0087] Extract the set of similar access events with the smallest Euclidean distance value as the matching access event set;

[0088] Retrieve the optimal broadcast period corresponding to the set of matching access events and use it as the current broadcast period.

[0089] This embodiment focuses on accurately adapting the broadcast cycle to reduce channel contention. It adopts a closed-loop design for the entire process, which includes screening historical high-quality access events, grouping based on traffic state similarity, optimizing broadcast cycle stability, and matching the output cycle in real time. First, based on historical traffic state data and access effect data, high-quality access events in high-density scenarios are screened out.

[0090] Then, the K-Means clustering algorithm is used to group high-quality events according to the similarity of traffic state characteristics, generating event sets with consistent scene attributes. Next, broadcast cycle stability analysis is carried out for each set to determine the optimal broadcast cycle that is suitable for the scene.

[0091] Finally, by calculating the Euclidean distance between the real-time traffic status and the cluster centers of each set, the corresponding set is matched and the optimal period is output, thereby optimizing the access performance of multi-node wireless communication in the Internet of Vehicles. This effectively reduces the signal collision rate in high-density scenarios from a "sudden increase" in a fixed period to ≤5%, stabilizes the beacon data transmission success rate to ≥90%, controls the data transmission latency to ≤50ms, and optimizes the problem of multi-node clustering transmission caused by a fixed period.

[0092] It can meet the core requirement of effectively suppressing channel conflicts in high-density vehicle-to-everything (V2X) scenarios by dynamically adjusting the broadcast period to reduce the number of node transmissions per unit time and stagger the beacon transmission time window, thereby preventing multiple nodes from occupying the limited wireless channel at the same time and eliminating signal collisions and data loss. It can also meet the security requirements of real-time beacon data updates for key collaborative functions such as V2X collision avoidance warning and lane coordination. Neither the mean period of the stable sequence nor the 75th percentile period of the unstable sequence exceeds the data real-time threshold, ensuring that the update frequency of key beacon data such as position and speed matches the perception and decision-making requirements of vehicle dynamic movement, and avoiding data lag due to excessively long periods, which would affect the accuracy and security of collaborative functions. Example

[0093] Based on the same inventive concept as the multi-node conflict wireless communication access optimization method in the foregoing embodiments, such as Figure 2 As shown, this application provides a wireless communication access optimization system for multi-node conflicts, wherein the system specifically includes:

[0094] Event filtering module: acquires historical access data of wireless communication devices, including historical traffic status data and historical access performance data. Based on the historical traffic status data, it performs quantitative analysis on traffic density characteristics and combines the historical access performance data to filter out high-density events with high access performance as high-quality access events.

[0095] Event classification module: Based on high-quality access events, the historical traffic status data of high-quality access events are clustered according to the similarity of traffic status characteristics to generate multiple sets of similar access events with consistent scenario attributes;

[0096] Broadcast period analysis module: For each set of similar access events, perform stability analysis on the broadcast period sequence corresponding to the events in the set of similar access events, and determine the optimal broadcast period for each set;

[0097] Broadcast cycle adaptation module: acquires real-time traffic status data, matches similar access event sets, and outputs the corresponding optimal broadcast cycle, thereby optimizing the dynamic adaptation of the broadcast cycle to the real-time scenario.

[0098] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for optimizing wireless communication access in the event of multi-node conflicts, characterized in that: Includes the following steps: Acquire historical access data of wireless communication devices. The historical access data includes historical traffic status data and historical access performance data. Quantitatively analyze traffic density characteristics based on historical traffic status data. Combine historical access performance data to select high-density events with high access performance as high-quality access events. Based on high-quality access events, the historical traffic state data of high-quality access events are clustered according to the similarity of traffic state characteristics to generate multiple sets of similar access events with consistent scenario attributes. For each set of similar access events, a stability analysis is performed on the broadcast period sequence corresponding to the events in the set to determine the optimal broadcast period for each set. Acquire real-time traffic status data, match similar access event sets, and output the corresponding optimal broadcast period.

2. The wireless communication access optimization method for multi-node conflicts according to claim 1, characterized in that: The historical traffic data includes: vehicle density per unit time and per unit road segment, vehicle spacing, vehicle speed, and vehicle topology change frequency; Historical access performance data includes: data transmission success rate, signal conflict rate, and data transmission delay duration extracted from communication device logs.

3. The wireless communication access optimization method for multi-node conflicts according to claim 1, characterized in that: The process of obtaining the high-quality access event is as follows: Preset high-density scene thresholds and preset access performance compliance thresholds; Quantitatively determine historical traffic status data on a time-by-time and road-by-road basis, and mark historical time periods and road segments belonging to high-density scenarios; Events that meet both of the following conditions are defined as high-quality access events; Condition 1: The time period and road segment where the event occurred have been identified as a high-density scene; Condition 2: All access effect data corresponding to the event reaches the preset threshold.

4. The wireless communication access optimization method for multi-node conflicts according to claim 1, characterized in that: The process of obtaining the set of similar access events is as follows: Acquire high-quality access events and classify them using the K-Means algorithm, which is tailored to the specific scenario. First, determine the optimal number of clusters K using the elbow rule; Using standardized traffic state core feature parameters as clustering samples, the algorithm is iteratively calculated until the cluster center is stable, and high-quality access events with similar features are grouped into the same group. Cluster center stability refers to simultaneously satisfying the conditions that the change in cluster centers is ≤0.01 and the change rate in sample allocation is ≤5%. After the similar access event set is generated and labeled for clustering, multiple sets of similar access events are generated.

5. The wireless communication access optimization method for multi-node conflicts according to claim 1, characterized in that: The process of performing stability analysis is as follows: Based on any set of similar access events, the corresponding broadcast period sequence is obtained; Calculate the standard deviation of the broadcast period sequence as a stable performance value, and compare the stable performance value with a preset standard deviation threshold. If the stable performance value is less than or equal to the preset standard deviation threshold, it indicates that the corresponding broadcast period sequence is stable. If the stable performance value is greater than the preset standard deviation threshold, it indicates that the corresponding broadcast period sequence is unstable.

6. The wireless communication access optimization method for multi-node conflicts according to claim 5, characterized in that: The process of determining the optimal broadcast period for each set is as follows: If the broadcast period sequence is stable, the mean of the broadcast period sequence is calculated as the optimal broadcast period for the set of similar access events.

7. The wireless communication access optimization method for multi-node conflicts according to claim 5, characterized in that: The process of determining the optimal broadcast period for each set is as follows: If the broadcast period sequence is unstable, rearrange the broadcast period sequence in ascending order and select the broadcast period corresponding to three-quarters of the position as the optimal broadcast period.

8. The wireless communication access optimization method for multi-node conflicts according to claim 1, characterized in that: The process of matching similar access event sets and outputting the corresponding optimal broadcast period is as follows: Acquire real-time traffic status data and perform matching analysis with similar access event sets to match access event sets; Retrieve the optimal broadcast period corresponding to the set of matching access events and use it as the current broadcast period.

9. The wireless communication access optimization method for multi-node conflicts according to claim 8, characterized in that: The process of obtaining the matching access event set is as follows: Calculate the Euclidean distance between real-time traffic status data and the cluster centers corresponding to each set of similar access events; Extract the set of similar access events with the smallest Euclidean distance value as the matching access event set.

10. A wireless communication access optimization system for multiple node conflicts, characterized in that, The system is used to perform the method according to any one of claims 1-9, the system comprising: Event filtering module: acquires historical access data of wireless communication devices, including historical traffic status data and historical access performance data. Based on the historical traffic status data, it performs quantitative analysis on traffic density characteristics and filters out high-density events with high access performance as high-quality access events. Event classification module: Based on high-quality access events, the historical traffic status data of high-quality access events are clustered according to the similarity of traffic status characteristics to generate multiple sets of similar access events with consistent scenario attributes; Broadcast period analysis module: For each set of similar access events, perform stability analysis on the broadcast period sequence corresponding to the events in the set of similar access events, and determine the optimal broadcast period for each set; Broadcast cycle adaptation module: acquires real-time traffic status data, matches similar access event sets, and outputs the corresponding optimal broadcast cycle, thereby optimizing the dynamic adaptation of the broadcast cycle to the real-time scenario.

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