A communication load detection system and method for a vehicle networking environment

Abstract

The application discloses a communication load detection system and method for a vehicle networking environment, relates to the technical field of data analysis, and evaluates signal quality attenuation degrees of each frequency band based on signal transmission condition data, distance and path loss data of each frequency band when a vehicle is driving; evaluates vehicle driving risk based on driving state data of the vehicle when the vehicle is driving on a road; obtains the most suitable signal transmission frequency band when the current vehicle is driving according to the evaluation results of the signal quality attenuation degrees of each frequency band and the evaluation results of the vehicle driving risk; and matches the vehicle driving risk with the communication strategy by associating the communication strategy with real-time driving risk of the vehicle, so that the transmission safety of key information is ensured, resource utilization is optimized, and communication conflicts are reduced.

Description

Technical Field

[0001] This application belongs to the field of data analysis, specifically a communication load detection system and method for the Internet of Vehicles environment. Background Technology

[0002] Currently, vehicle communication widely employs various wireless communication technologies, such as Dedicated Short Range Communication (DSRC), cellular networks (4G LTE, 5G NR-V2X), and Wi-Fi. These technologies operate in different frequency bands (e.g., 5.9 GHz, 3.5 GHz, 700 MHz), each with unique advantages and disadvantages in different scenarios. For example, high-frequency communication typically offers greater bandwidth and higher data transmission rates, but its signals are more susceptible to obstruction and weather conditions, resulting in higher path loss and relatively shorter transmission distances. Low-frequency communication, while having lower speeds, boasts stronger diffraction capabilities, more stable transmission, and wider coverage. However, significant limitations still exist in existing technologies.

[0003] 1. Failure to fully consider dynamically changing channel characteristics: High-speed vehicle movement leads to drastic changes in the communication environment, and signal transmission conditions (such as multipath fading, Doppler effect, and interference) and path loss are dynamic and complex. Existing strategies often make judgments based on instantaneous or average signal strength, lacking a forward-looking assessment of the signal quality attenuation trend and degree in specific frequency bands, and are unable to predict and switch before substantial signal degradation occurs.

[0004] 2. Failure to correlate communication strategies with real-time vehicle driving risks: The degree of danger in vehicle driving is dynamic; for example, in high-risk scenarios such as high-speed following, sudden braking, vehicles merging into blind spots, and slippery roads in rainy or snowy weather, the requirements for the reliability and low latency of safety messages (such as collision warnings and emergency braking commands) are extremely high; existing technologies generally separate the selection of communication networks from the safety level of the vehicle's own driving status, which may lead to safety-critical messages being transmitted through a frequency band with rapidly degrading signal quality in high-risk situations, causing communication failures or delays, thereby triggering safety accidents; in order to solve the problems raised in the background technology, this application designs a communication load detection system and method for the vehicle network environment. Summary of the Invention

[0005] To address the aforementioned technical shortcomings, this application proposes a communication load detection system and method for the Internet of Vehicles (IoV) environment.

[0006] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: This application provides a communication load detection method for the Internet of Vehicles environment, which includes the following specific steps:

[0007] S1. Acquire data on signal transmission in each frequency band, distance and path loss data, and vehicle driving status data on the road when the vehicle is in motion.

[0008] S2. Evaluate the degree of signal quality attenuation in each frequency band based on data on signal transmission in each frequency band during vehicle operation and data on distance and path loss.

[0009] S3. Assess vehicle driving hazard based on vehicle driving status data while driving on the road;

[0010] S4. Based on the evaluation results of signal quality attenuation in each frequency band and the evaluation results of vehicle driving hazard, the most suitable signal transmission frequency band for the current vehicle driving is obtained.

[0011] It should be noted that, as a preferred technical solution for communication load detection in a vehicle-to-everything (V2X) environment, the specific steps of S1 are as follows:

[0012] S11. By comparing the attenuation values ​​of typical obstacles and the propagation characteristics of frequency bands, and by using a spectrum analyzer and a communication tester, data on the signal transmission of each frequency band when the vehicle is in motion are obtained. Among them, the data on the signal transmission of each frequency band when the vehicle is in motion includes data on the obstruction of each frequency band by obstacles, data on the diffraction loss of each frequency band, data on the channel occupancy rate of each frequency band when the vehicle is in motion, and data on the signal-to-interference-plus-noise ratio of each frequency band.

[0013] S12. Obtain distance and path loss data through vehicle terminal logs, including data on the change in relative distance between the vehicle and the currently linked base station when the vehicle is driving.

[0014] S13. Obtain vehicle status data while driving on the road via the vehicle CAN bus. The vehicle status data while driving on the road includes data on the yaw rate change, speed change, and driving status acquisition cycle.

[0015] It should be noted that, as a preferred technical solution for communication load detection in a vehicle-to-everything (V2X) environment, step S2 includes the following specific steps:

[0016] S21. Obtain the obstacle loss assessment results for each frequency band from the data on the obstruction of obstacles to each frequency band and the diffraction loss data of each frequency band when the vehicle is moving.

[0017] S22. The transmission hazard assessment results for each frequency band are obtained from the channel occupancy rate data and the signal-to-interference-plus-noise ratio data of each frequency band when the vehicle is in motion.

[0018] S23. The motion distance loss assessment result is obtained from the data on the change of the relative motion distance between the vehicle and the currently linked base station when the vehicle is moving.

[0019] S24. Obtain the motion distance loss assessment results, the transmission hazard assessment results for each frequency band, and the obstacle loss assessment results for each frequency band. Weight these assessment results and sum them to obtain the signal quality attenuation assessment results for each frequency band. It should be noted that the transmission hazard assessment results for each frequency band are used to evaluate the susceptibility of signals in different frequency bands to interference during transmission. The motion distance loss assessment results are based on the basic path loss, with the effects of obstacle obstruction and susceptibility to interference superimposed on the basic path loss. This is because obstacles and interference around the vehicle will attenuate the signal during travel. The weights assigned to the motion distance loss assessment results, the transmission hazard assessment results for each frequency band, and the obstacle loss assessment results are because: in environments such as tunnels, the weight of obstacle loss may be the highest; while in open areas with high-speed movement, the weight of motion distance loss may be the greatest.

[0020] It should be noted that, as a preferred technical solution for a communication load detection method for a vehicle-to-everything (V2X) environment, the specific steps of S21 are as follows: Based on the data on the obstruction of obstacles to each frequency band and the diffraction loss data of each frequency band signal during vehicle operation, an obstacle loss assessment for each frequency band is performed. The obstacle loss assessment process for each frequency band is as follows: The data on the obstruction of obstacles to each frequency band during vehicle operation is divided by a reference obstruction data for each frequency band to quantify the impact of obstacle obstruction on signal attenuation in each frequency band; the diffraction loss data for each frequency band signal is divided by a reference diffraction loss to quantify the diffraction loss in each frequency band signal; the impact of obstacle obstruction on signal attenuation in each frequency band and the diffraction loss in each frequency band are weighted and summed to obtain the results for each frequency band signal attenuation. The results of the frequency band obstacle loss assessment are as follows: It should be noted that the data on the obstruction of obstacles in each frequency band during vehicle movement reflects the degree to which direct signals are blocked, while the diffraction loss data for each frequency band measures the inherent loss of the signal when it goes around a specific obstacle. Dividing the obstruction data by a reference obstruction data aims to eliminate the influence of absolute values ​​and obtain a relative proportion. Similarly, dividing the diffraction loss by a reference diffraction loss compares an absolute loss value with a reference loss value. The results of the comparison are weighted and summed because if there is almost no line-of-sight path between the vehicle and the obstacle, the weight of diffraction loss should be higher; if there is only partial obstruction, the weight of obstruction may be even greater.

[0021] It should be noted that, as a preferred technical solution for communication load detection in a vehicle-to-everything (V2X) environment, the specific steps of S22 are as follows: Based on the channel occupancy rate data and signal-to-interference-plus-noise ratio (SNR) data of each frequency band during vehicle operation, an assessment of the transmission risk of each frequency band is performed. The assessment process for the transmission risk of each frequency band is as follows: the difference between the channel occupancy rate data of each frequency band and the minimum channel occupancy rate of each frequency band during vehicle operation is divided by the difference between the maximum channel occupancy rate of each frequency band and the minimum channel occupancy rate of each frequency band, quantifying the channel occupancy status of each frequency band; the difference between the signal-to-interference-plus-noise ratio (SNR) data of each frequency band and a reference is divided by the reference... The signal-to-interference-plus-noise ratio quantifies the interference situation of channels in each frequency band. The channel occupancy and interference situations of each frequency band are weighted and summed to obtain the transmission risk assessment results for each frequency band. It should be noted that the channel occupancy situation of each frequency band is quantified first, and a normalization method is used to eliminate dimensions. This is because the absolute values ​​of channel occupancy rates can vary greatly across different frequency bands and regions, and directly using the raw values ​​cannot fairly compare the busy levels of different frequency bands. Channel risks come from two aspects: lack of resources and poor environment. Weighted summation can combine the risks of these two dimensions into a single indicator.

[0022] It should be noted that, as a preferred technical solution for communication load detection in a vehicle-to-everything (V2X) environment, the specific steps of S23 are as follows:

[0023] S231. Based on the data on the change in the relative distance between the vehicle and the current linked base station during vehicle movement, the signal attenuation strength when the relative distance between the vehicle and the current linked base station is d is obtained. The formula for the signal attenuation strength when the relative distance between the vehicle and the current linked base station is d is: ,in, For the reference distance between the vehicle and the currently linked base station Path loss at time, Here, n is the path loss exponent, and d is the relative distance between the vehicle and the currently linked base station when the vehicle is traveling. It should be noted that by estimating the signal strength at a certain location when the vehicle is traveling, the communication quality at that location can be judged, and the signal attenuation trend when the vehicle is traveling can be predicted in advance.

[0024] S232. Obtain the relative motion distances between the vehicle and the current linked base station from the data on the changes in the relative motion distance between the vehicle and the current linked base station during vehicle travel. Substitute each relative motion distance between the vehicle and the current linked base station into the formula for calculating the signal attenuation intensity when the relative motion distance between the vehicle and the current linked base station is d, and obtain the signal attenuation intensity corresponding to each relative motion distance between the vehicle and the current linked base station during vehicle travel. Sum the signal attenuation intensities of each relative motion distance between the vehicle and the current linked base station during vehicle travel and calculate the average value to quantify the signal attenuation during vehicle travel. Divide the signal attenuation during vehicle travel by the reference signal attenuation value to obtain the assessment result of the motion distance loss degree.

[0025] It should be noted that, as a preferred technical solution for communication load detection in a vehicle-to-everything (V2X) environment, the specific steps of S3 are as follows: Vehicle driving hazard assessment is performed based on data of yaw rate changes, speed changes, and driving state acquisition cycle data while the vehicle is driving on the road. The process for obtaining the vehicle driving hazard assessment result is as follows: The yaw rate changes while the vehicle is driving on the road are integrated over the driving state acquisition cycle data; the integrated result is divided by the product of the driving state acquisition cycle and the reference yaw rate to quantify the lateral driving hazard; the speed changes are integrated over the driving state acquisition cycle data; the integrated result is divided by the product of the driving state acquisition cycle and the reference acceleration to quantify the longitudinal driving hazard; the lateral and longitudinal driving hazard are weighted and summed to obtain the vehicle driving hazard assessment result.

[0026] It should be noted that, as a preferred technical solution for a communication load detection method for the Internet of Vehicles environment, the specific steps of S4 are as follows: divide the reciprocal of the signal quality attenuation assessment result of each frequency band by the vehicle driving hazard assessment result to obtain the driving hazard matching assessment result of each frequency band; compare the driving hazard matching assessment result of each frequency band with the set driving hazard matching assessment result threshold; and take the frequency band that is closest to the driving hazard matching assessment result threshold as the most suitable signal transmission frequency band for the current vehicle driving.

[0027] A communication load detection system for a vehicle-to-everything (V2X) environment is provided, which is based on the aforementioned communication load detection method for a V2X environment. Specifically, it includes a vehicle communication data acquisition module, a signal quality attenuation assessment module, a vehicle driving hazard assessment module, and a signal transmission frequency band analysis module. The vehicle communication data acquisition module is used to acquire data on signal transmission status of each frequency band, distance and path loss data, and driving status data of the vehicle on the road when it is driving.

[0028] The signal quality attenuation assessment module is used to assess the signal quality attenuation of each frequency band based on data on signal transmission status of each frequency band during vehicle operation and distance and path loss data.

[0029] The vehicle driving hazard assessment module is used to assess vehicle driving hazards based on the vehicle's driving status data while driving on the road.

[0030] The signal transmission frequency band analysis module is used to determine the most suitable signal transmission frequency band for the current vehicle driving based on the evaluation results of the signal quality attenuation degree of each frequency band and the evaluation results of the vehicle driving hazard.

[0031] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention acquires data on signal transmission status of each frequency band, distance and path loss data, and driving status data of the vehicle on the road when it is in motion; it evaluates the signal quality attenuation degree of each frequency band based on the signal transmission status data and distance and path loss data of each frequency band when the vehicle is in motion; it evaluates the driving hazard of the vehicle based on the driving status data of the vehicle on the road; and it obtains the most suitable signal transmission frequency band for the current vehicle driving based on the evaluation results of the signal quality attenuation degree of each frequency band and the evaluation results of the driving hazard of the vehicle, thereby ensuring the security of critical information transmission, optimizing resource utilization, and reducing communication conflicts. Attached Figure Description

[0032] Figure 1 This is a schematic diagram of the overall process of a communication load detection method for a vehicle-to-everything (V2X) environment according to this application.

[0033] Figure 2 This is a schematic diagram of step S2 of the communication load detection method for the Internet of Vehicles environment proposed in this application.

[0034] Figure 3 This is a schematic diagram of the overall framework of a communication load detection system for a vehicle networking environment according to this application.

[0035] Figure 4 This is a schematic diagram illustrating the process of obtaining the driving hazard matching assessment results of the communication load detection method for the Internet of Vehicles environment according to this application. Detailed Implementation

[0036] To better understand this application, various aspects of this application will be described in more detail with reference to the accompanying drawings.

[0037] To address the technical problems raised in the background art, this application provides a preferred embodiment:

[0038] The specific content of this embodiment is as follows:

[0039] like Figure 1As shown, a communication load detection method for a vehicle-to-everything (V2X) environment includes the following specific steps:

[0040] S1. Acquire data on signal transmission in each frequency band, distance and path loss data, and vehicle driving status data on the road when the vehicle is in motion.

[0041] In this embodiment, the specific steps of S1 are as follows:

[0042] S11. Data on signal transmission in each frequency band during vehicle operation includes data on obstruction of each frequency band by obstacles, diffraction loss data for each frequency band, channel occupancy data for each frequency band during vehicle operation, and signal-to-interference-plus-noise ratio data for each frequency band. The acquisition process is as follows: data on obstruction of each frequency band by obstacles during vehicle operation is obtained through attenuation values ​​of typical obstacles; diffraction loss data for each frequency band is obtained through comparative studies of frequency band propagation characteristics; channel occupancy data for each frequency band during vehicle operation is obtained through a spectrum analyzer; and signal-to-interference-plus-noise ratio data for each frequency band is obtained through a communication tester.

[0043] S12. Distance and path loss data includes data on the change in the relative distance between the vehicle and the currently linked base station when the vehicle is moving. The acquisition process is as follows: obtain the vehicle position through the vehicle terminal log, and obtain the data on the change in the relative distance between the vehicle and the currently linked base station when the vehicle is moving by using the vehicle position and the base station position.

[0044] S13. The driving status data of the vehicle when it is driving on the road includes the yaw rate change data, the driving speed change data and the driving status acquisition cycle data. The acquisition process is as follows: the yaw rate change data, the driving speed change data and the driving status acquisition cycle data of the vehicle when it is driving on the road are acquired through the vehicle CAN bus.

[0045] S2, such as Figure 2 As shown, the signal quality attenuation of each frequency band is evaluated based on data on signal transmission in each frequency band during vehicle operation, as well as distance and path loss data.

[0046] S21. Obtain the obstacle loss assessment results for each frequency band from the data on the obstruction of obstacles to each frequency band and the diffraction loss data of each frequency band when the vehicle is moving.

[0047] In this embodiment, the specific steps of S21 are as follows: Based on the data on the obstruction of obstacles to each frequency band and the diffraction loss data of each frequency band signal during vehicle travel, an obstacle loss assessment is performed for each frequency band. The obstacle loss assessment process for each frequency band is as follows: The data on the obstruction of obstacles to each frequency band during vehicle travel is divided by a reference obstruction data for each frequency band to quantify the impact of obstacle obstruction on signal attenuation in each frequency band; the diffraction loss data for each frequency band signal is divided by a reference diffraction loss to quantify the diffraction loss in each frequency band; the impact of obstacle obstruction on signal attenuation in each frequency band and the diffraction loss in each frequency band are weighted and summed to obtain the obstacle loss assessment result for each frequency band. It should be noted that large static objects (such as buildings, tunnels, etc.) can block the direct path of the signal, causing significant signal attenuation. During vehicle travel, the vehicle will continuously enter and leave the shadow areas of these objects. The higher the obstacle, the larger the area of ​​obstruction, and the closer the vehicle is to the obstacle, the greater the impact of the obstacle on signal transmission, and the more severe the signal attenuation. The higher the frequency, the greater the attenuation and signal loss when passing through the same obstacle; the higher the frequency, the worse the diffraction ability and the greater the diffraction loss; the physical scale of diffraction is related to the wavelength; the longer the wavelength (i.e., the lower the frequency), the easier it is for the wave to bypass obstacles of similar size; the shorter the wavelength (i.e., the higher the frequency), the more the wave behaves like light, tending to propagate in straight lines, making it difficult to bypass obstacles, and resulting in greater loss; when there is a line-of-sight path between the vehicle and the obstacle: the signal mainly reaches the receiver from the transmitter through direct light. Obstacles may cause some obstruction, but the core propagation channel is still the straight path. In this case, the focus of the evaluation is the extent of obstruction (i.e., the degree of obstruction), while diffraction is only a secondary, supplementary path; when there is no line-of-sight path: the direct path is completely blocked, and the signal cannot arrive directly; in this case, the signal must pass through by other means, the most important mechanism being diffraction. In this situation, the level of diffraction ability (i.e., the magnitude of diffraction loss) becomes the decisive factor for whether the signal can exist.

[0048] S22. The transmission hazard assessment results for each frequency band are obtained from the channel occupancy rate data and the signal-to-interference-plus-noise ratio data of each frequency band when the vehicle is in motion.

[0049] In this embodiment, the specific steps of S22 are as follows: Based on the channel occupancy rate data and signal-to-interference-plus-noise ratio data of each frequency band during vehicle operation, a transmission risk assessment for each frequency band is performed. The transmission risk assessment process for each frequency band is as follows: The difference between the channel occupancy rate data and the minimum channel occupancy rate of each frequency band during vehicle operation is divided by the difference between the maximum and minimum channel occupancy rates of each frequency band, quantifying the channel occupancy status of each frequency band; the difference between the signal-to-interference-plus-noise ratio data and a reference value is divided by the reference signal-to-interference-plus-noise ratio, quantifying the interference status of each frequency band; the weighted sum of the channel occupancy status and the interference status of each frequency band is then added to obtain the transmission risk assessment result for each frequency band. It should be noted that... This method first quantifies the channel occupancy status of each frequency band and then uses normalization to eliminate dimensions. This is because the absolute values ​​of channel occupancy rates can vary greatly across different frequency bands and regions, making it impossible to fairly compare the busyness of different frequency bands using raw values ​​directly. The closer the result is to 1, the worse the current channel availability and the higher the risk; the closer it is to 0, the more idle the channel and the better its availability. The signal-to-interference-plus-noise ratio is the gold standard for measuring communication quality, directly determining the data transmission rate and bit error rate. In densely populated urban areas, channel congestion is likely the main problem, so occupancy can be assigned a higher weight. In suburban areas, where there are fewer vehicles but there may be unknown strong interference sources (such as illegal radio stations), interference can be assigned a higher weight.

[0050] S23. The motion distance loss assessment result is obtained from the data on the change of the relative motion distance between the vehicle and the currently linked base station when the vehicle is moving.

[0051] In this embodiment, the specific steps of S23 are as follows:

[0052] S231. Based on the data on the change in the relative distance between the vehicle and the current linked base station during vehicle movement, the signal attenuation strength when the relative distance between the vehicle and the current linked base station is d is obtained. The formula for the signal attenuation strength when the relative distance between the vehicle and the current linked base station is d is: ,in, For the reference distance between the vehicle and the currently linked base station Path loss at time, For reference distance, n is the path loss exponent, and d is the relative distance between the vehicle and the currently linked base station when the vehicle is traveling. It should be noted that path loss refers to the reduction in signal strength caused by the influence of the propagation medium (such as air, atmosphere, electromagnetic wave absorption, etc.) during the process of the signal from the transmitter to the receiver. In wireless communication, the greater the path loss, the weaker the received signal and the worse the communication quality. It is a known reference value used to set the starting point for path loss, representing the strength of signal attenuation under the reference condition. Typically, it's 1 meter or 10 meters; n is the path loss exponent, which describes the relationship between signal strength attenuation and distance change. The value of n usually ranges from 2 to 4, depending on the propagation environment (e.g., city, suburbs, or open area). In free space (ideal conditions with no obstacles), n is 2, where signal attenuation with distance follows the inverse square law. In urban environments, due to obstruction and reflection from buildings, roads, and other objects, n may be greater than 2, usually between 3 and 4. In multipath propagation environments (e.g., urban canyons or indoor environments), n may be even higher because the signal is affected by more reflection and refraction, leading to additional attenuation. The reason for multiplying n by 10 is that signal strength (i.e., power) is usually expressed in decibels (dB), and decibels are a logarithmic unit. Each logarithmic unit change results in a tenfold increase or decrease. Therefore, to ensure that the change in signal strength attenuation conforms to the calculation rules of decibels, n needs to be multiplied by 10. Distance and reference distance The logarithm of the ratio represents the effect of distance variation on signal attenuation. This represents the ratio of the current distance to the reference distance. As the distance d increases, the signal strength weakens, and this weakening is logarithmic. The logarithmic operation means that the signal attenuation does not increase linearly with distance, but rather decreases according to a certain power. This also means that the signal strength gradually weakens with increasing distance, but the attenuation rate slows down (for example, the attenuation is greater when the distance increases from 10 meters to 100 meters, but the attenuation is slower when increasing from 100 meters to 1000 meters). This indicates the signal attenuation at a distance d relative to the reference distance. The larger d is, the greater the signal attenuation will be; by estimating the signal strength at a certain location when the vehicle is moving, the communication quality at that location can be judged, and the signal attenuation trend when the vehicle is moving can be predicted in advance.

[0053] S232. Obtain the relative motion distances between the vehicle and the current linked base station from the data on the changes in the relative motion distance between the vehicle and the current linked base station during vehicle movement. Substitute each relative motion distance between the vehicle and the current linked base station into the signal attenuation strength calculation formula when the relative motion distance between the vehicle and the current linked base station is d, to obtain the signal attenuation strength corresponding to each relative motion distance between the vehicle and the current linked base station. Sum the signal attenuation strengths of each relative motion distance between the vehicle and the current linked base station and calculate the average value to quantify the signal attenuation during vehicle movement. Divide the signal attenuation during vehicle movement by the reference signal attenuation value to obtain the assessment result of the motion distance loss. It should be noted that the relative motion between the vehicle and the base station causes the distance to change continuously, and the signal strength attenuates as the transmission distance increases.

[0054] S24. Obtain the motion distance loss assessment results, the transmission hazard assessment results for each frequency band, and the obstacle loss assessment results for each frequency band. Weight the motion distance loss assessment results, the transmission hazard assessment results for each frequency band, and the obstacle loss assessment results for each frequency band, and then sum them to obtain the signal quality attenuation assessment results for each frequency band. It should be noted that the motion distance loss assessment results are the basic path loss. The obstruction effect of obstacles is superimposed on the basic path loss. This is because obstacles around the vehicle will attenuate the signal during the vehicle's movement.

[0055] S3. Assess vehicle driving hazard based on vehicle driving status data while driving on the road;

[0056] In this embodiment, S3 includes the following specific steps: Assessing vehicle driving hazard based on data of yaw rate changes, speed changes, and driving state acquisition cycle data while the vehicle is driving on the road. The process for obtaining the vehicle driving hazard assessment result is as follows: integrating the yaw rate changes data of the vehicle while driving on the road onto the driving state acquisition cycle data, dividing the integral result by the product of the driving state acquisition cycle and the reference yaw rate, quantifying the lateral driving hazard of the vehicle; integrating the speed changes data of the vehicle onto the driving state acquisition cycle data, dividing the integral result by the product of the driving state acquisition cycle and the reference driving acceleration, quantifying the longitudinal driving hazard of the vehicle; and quantifying the lateral driving hazard of the vehicle... The vehicle driving hazard assessment result is obtained by weighting the driving hazard situation and the longitudinal driving hazard situation of the vehicle and then adding them together. It should be noted that yaw rate refers to the angular velocity of the vehicle around its vertical axis (the unit is usually degrees / second or radians / second), which can be understood as the speed of turning. When the vehicle is driving stably (e.g., smoothly cornering), this value is stable and predictable. The reason for analyzing the data on the changes in yaw rate of the vehicle while driving on the road is that yaw rate is a key parameter that can directly and in real time reflect the lateral motion stability of the vehicle. Its abnormal changes (especially sudden increases) are strong evidence to determine whether the vehicle is in a high-risk driving state (e.g., emergency avoidance, loss of control). When driving a car in daily life, when the car is moving at a constant angle When smoothly turning a corner, a vehicle will have a stable and moderate yaw rate; when a car is in an emergency lane change or hazard avoidance situation, it will generate a momentary high yaw rate; the purpose of using integration in dangerous lateral driving situations is to restore the total change in yaw rate by integrating the rate of change of yaw rate, that is, to accumulate the total deviation of yaw rate from the stable state over a period of time. The larger this accumulated value, the more severe the lateral instability; period multiplied by reference value: represents the total normal yaw rate change that can be generated by smoothly turning at a safe reference speed within one period; integration result divided by period multiplied by reference value: compares the accumulated abnormal change to a normal and safe benchmark, and the result is a dimensionless ratio; driving Speed ​​changes refer to acceleration or deceleration. Sudden acceleration (e.g., slamming on the gas) or sudden braking (e.g., slamming on the brakes) are both danger signals, easily causing rear-end collisions. Sudden braking can also lead to vehicle instability (e.g., wheel lock-up and skidding). The purpose of integration: Integrating acceleration yields the change in speed, i.e., the total deviation of speed from a constant speed over a period of time. The larger this cumulative value, the more severe the longitudinal acceleration or deceleration, and the higher the risk. Dividing by the product of the period and the reference acceleration is also for normalization. The significance of division: Comparing the abnormal speed change with a normal cruising baseline distance yields a dimensionless ratio used to quantify the degree of longitudinal danger. Sudden acceleration and sudden braking will significantly increase this ratio.

[0057] S4. Based on the evaluation results of signal quality attenuation in each frequency band and the evaluation results of vehicle driving hazards, determine the most suitable signal transmission frequency band for the current vehicle driving.

[0058] like Figure 4 As shown, in this embodiment, the specific steps of S4 are as follows: divide the reciprocal of the signal quality attenuation assessment result of each frequency band by the vehicle driving hazard assessment result to obtain the driving hazard matching assessment result of each frequency band; compare the driving hazard matching assessment result of each frequency band with the set driving hazard matching assessment result threshold; and select the frequency band that is closest to the driving hazard matching assessment result threshold as the most suitable signal transmission frequency band for the current vehicle driving. It should be noted that the meaning of the frequency band that is closest to the driving hazard matching assessment result threshold is: the frequency band with the smallest absolute value among the differences between the driving hazard matching assessment result of each frequency band and the set driving hazard matching assessment result threshold. By matching the signal transmission quality of each frequency band with the hazard of vehicle driving, the more dangerous vehicle driving data is transmitted through the safer information transmission channel, thereby improving the utilization rate of channel transmission.

[0059] Based on the above implementation, this embodiment has the following advantages over the prior art: This embodiment acquires data on signal transmission status of each frequency band, distance and path loss data, and driving status data of the vehicle on the road when it is driving; it evaluates the signal quality attenuation degree of each frequency band based on the signal transmission status data and distance and path loss data of each frequency band when the vehicle is driving; it evaluates the driving hazard of the vehicle based on the driving status data of the vehicle on the road; and it obtains the most suitable signal transmission frequency band for the current vehicle driving based on the evaluation results of the signal quality attenuation degree of each frequency band and the evaluation results of the driving hazard of the vehicle, thus ensuring the transmission security of key information, optimizing resource utilization, and reducing communication conflicts.

[0060] like Figure 3As shown, this embodiment also provides a communication load detection system for a vehicle-to-everything (V2X) environment, which is implemented based on the aforementioned communication load detection method for a V2X environment. Specifically, it includes a vehicle communication data acquisition module, a signal quality attenuation assessment module, a vehicle driving hazard assessment module, and a signal transmission frequency band analysis module. The vehicle communication data acquisition module acquires data on signal transmission status at various frequency bands, distance and path loss data, and vehicle driving status data on the road. The signal quality attenuation assessment module assesses the signal quality attenuation at various frequency bands based on the signal transmission status data and distance and path loss data. The vehicle driving hazard assessment module assesses vehicle driving hazard based on the vehicle driving status data on the road. The signal transmission frequency band analysis module determines the most suitable signal transmission frequency band for the current vehicle driving based on the signal quality attenuation assessment results and the vehicle driving hazard assessment results.

[0061] The specific steps for implementing the corresponding functions of each unit module in the communication load detection system for the Internet of Vehicles environment described above can be found in the steps of the embodiment of the communication load detection method for the Internet of Vehicles environment described above, and will not be repeated here.

[0062] 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 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 foregoing application concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions claimed in this application.

Claims

1. A communication load detection method for a vehicle-to-everything (V2X) environment, characterized in that, include: S1. Acquire data on signal transmission in each frequency band, distance and path loss data, and vehicle driving status data on the road when the vehicle is in motion. S2. Based on the signal transmission data of each frequency band during vehicle operation and the distance and path loss data, assess the degree of signal quality attenuation in each frequency band. S2 includes the following specific steps: S21. Obtain the obstacle loss assessment results for each frequency band from the data on the obstruction of obstacles to each frequency band and the diffraction loss data of each frequency band when the vehicle is moving. S22. The transmission hazard assessment results for each frequency band are obtained from the channel occupancy rate data and the signal-to-interference-plus-noise ratio data of each frequency band when the vehicle is in motion. S23. The motion distance loss assessment result is obtained from the data on the change of the relative motion distance between the vehicle and the currently linked base station when the vehicle is moving. S24. Obtain the motion distance loss assessment results, the transmission hazard assessment results of each frequency band, and the obstacle loss assessment results of each frequency band. Weight the motion distance loss assessment results, the transmission hazard assessment results of each frequency band, and the obstacle loss assessment results of each frequency band and add them together to obtain the signal quality attenuation assessment results of each frequency band. S3. Assess vehicle driving hazard based on vehicle driving status data while on the road. The specific steps of S3 are as follows: Assess vehicle driving hazard based on data regarding yaw rate changes, speed changes, and driving status acquisition period data while on the road. The process for obtaining the vehicle driving hazard assessment result is as follows: Integrate the yaw rate changes on the driving status acquisition period data, divide the integral result by the product of the driving status acquisition period and the reference yaw rate to quantify the lateral driving hazard; Integrate the speed changes on the driving status acquisition period data, divide the integral result by the product of the driving status acquisition period and the reference acceleration to quantify the longitudinal driving hazard; Weight the lateral and longitudinal driving hazard results and sum them to obtain the vehicle driving hazard assessment result. S4. Based on the evaluation results of signal quality attenuation in each frequency band and the evaluation results of vehicle driving hazard, the most suitable signal transmission frequency band for the current vehicle driving is obtained. The specific steps of S4 are as follows: divide the reciprocal of the evaluation results of signal quality attenuation in each frequency band by the evaluation results of vehicle driving hazard to obtain the driving hazard matching evaluation results of each frequency band; compare the driving hazard matching evaluation results of each frequency band with the set driving hazard matching evaluation result threshold; and take the frequency band that is closest to the driving hazard matching evaluation result threshold as the most suitable signal transmission frequency band for the current vehicle driving.

2. The communication load detection method for a vehicle-to-everything (V2X) environment as described in claim 1, characterized in that, The specific steps of S21 are as follows: Based on the data on the obstruction of obstacles to each frequency band and the diffraction loss data of each frequency band signal during vehicle travel, an obstacle loss assessment is performed for each frequency band. The obstacle loss assessment process for each frequency band is as follows: The data on the obstruction of obstacles to each frequency band during vehicle travel is divided by the reference obstruction data for each frequency band to quantify the impact of obstacle obstruction on signal attenuation in each frequency band; the diffraction loss data for each frequency band signal is divided by the reference diffraction loss to quantify the diffraction loss in each frequency band; the impact of obstacle obstruction on signal attenuation in each frequency band and the diffraction loss in each frequency band are weighted and summed to obtain the obstacle loss assessment result for each frequency band.

3. The communication load detection method for a vehicle-to-everything (V2X) environment as described in claim 2, characterized in that, The specific steps of S22 are as follows: Based on the channel occupancy rate data and the signal-to-interference-plus-noise ratio data of each frequency band during vehicle operation, an assessment of the transmission risk of each frequency band is performed. The assessment process for each frequency band transmission risk is as follows: The difference between the channel occupancy rate data and the minimum channel occupancy rate of each frequency band during vehicle operation is divided by the difference between the maximum and minimum channel occupancy rates of each frequency band, quantifying the channel occupancy status of each frequency band; the difference between the signal-to-interference-plus-noise ratio data and the reference value of each frequency band is divided by the reference signal-to-interference-plus-noise ratio, quantifying the interference status of each frequency band channel; the weighted sum of the channel occupancy status and the interference status of each frequency band is then used to obtain the transmission risk assessment result for each frequency band.

4. The communication load detection method for a vehicle-to-everything (V2X) environment as described in claim 3, characterized in that, The specific steps of S23 are as follows: S231. Based on the data on the change in the relative distance between the vehicle and the current linked base station during vehicle movement, the signal attenuation strength when the relative distance between the vehicle and the current linked base station is d is obtained. The formula for the signal attenuation strength when the relative distance between the vehicle and the current linked base station is d is: ,in, For the reference distance between the vehicle and the currently linked base station Path loss at time, For reference distance, n is the path loss exponent, and d is the relative distance between the vehicle and the currently linked base station when the vehicle is traveling; S232. Obtain the relative motion distances between the vehicle and the current linked base station from the data on the changes in the relative motion distance between the vehicle and the current linked base station during vehicle travel. Substitute each relative motion distance between the vehicle and the current linked base station into the formula for calculating the signal attenuation intensity when the relative motion distance between the vehicle and the current linked base station is d, and obtain the signal attenuation intensity corresponding to each relative motion distance between the vehicle and the current linked base station during vehicle travel. Sum the signal attenuation intensities of each relative motion distance between the vehicle and the current linked base station during vehicle travel and calculate the average value to quantify the signal attenuation during vehicle travel. Divide the signal attenuation during vehicle travel by the reference signal attenuation value to obtain the assessment result of the motion distance loss degree.

5. A communication load detection system for a vehicle-to-everything (V2X) environment, implemented based on the communication load detection method for a V2X environment according to any one of claims 1-4, characterized in that, Specifically, it includes a vehicle communication data acquisition module, a signal quality attenuation assessment module, a vehicle driving hazard assessment module, and a signal transmission frequency band analysis module. The vehicle communication data acquisition module is used to acquire data on the signal transmission status of each frequency band, distance and path loss data, and driving status data of the vehicle on the road when it is driving. The signal quality attenuation assessment module is used to assess the signal quality attenuation of each frequency band based on data on signal transmission status of each frequency band during vehicle operation and distance and path loss data. The vehicle driving hazard assessment module is used to assess vehicle driving hazards based on the vehicle's driving status data while driving on the road. The signal transmission frequency band analysis module is used to determine the most suitable signal transmission frequency band for the current vehicle driving based on the evaluation results of the signal quality attenuation degree of each frequency band and the evaluation results of the vehicle driving hazard.

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