Vehicle adaptive network switching and traffic sharing method based on multi-source collaboration

Abstract

The present invention discloses a vehicle adaptive network switching and traffic sharing method based on multi-source collaboration, which relates to the field of vehicle network management. The main scheme is: obtaining a signal quality index by analyzing signal quality data, and further calculating a comprehensive signal quality index; obtaining a network stability parameter and a network stability index by analyzing and calculating network stability data; obtaining traffic package data and performing a comprehensive analysis in combination with the signal quality index and the network stability index to obtain a traffic cost efficiency index; comparing the thresholds in a judgment threshold set with corresponding indicators respectively; judging whether to execute a network switching early warning instruction; obtaining the switching priority indexes of different operators and selecting a switching network; solving the problem in the prior art that network quality is judged only based on limited parameters such as simple signal strength, and the actual performance of the network cannot be comprehensively and accurately evaluated, resulting in untimely network switching or switching to an inappropriate network.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle network management, and in particular to a vehicle adaptive network switching and traffic sharing method based on multi-source collaboration. Background Art

[0002] With the rapid development of Internet of Vehicles (IoV) technology, vehicles are becoming increasingly dependent on the network while driving. This not only affects basic functions such as navigation and entertainment, but also critical applications such as autonomous driving and real-time traffic information exchange. Vehicle adaptive network switching and traffic sharing methods have broad application prospects. On the one hand, they can provide vehicles with more stable and efficient network connections, ensuring driving safety and a better driving experience. For example, in autonomous driving scenarios, vehicles need to obtain high-precision map data and information about surrounding vehicles and road facilities in real time. Stable network switching can avoid driving risks caused by network interruptions or freezes. On the other hand, with the increasing intelligence and networking of vehicles, the amount of data generated by vehicles is exploding. Traffic sharing can effectively utilize network resources between vehicles and reduce operating costs.

[0003] The Internet of Vehicles (IoV) environment is complex, with a variety of network standards and carrier networks. Signal quality, bandwidth, and stability vary significantly across these networks. Vehicles must constantly adapt to changing network environments while on the move. Adaptive network switching ensures that vehicles are always connected to the most appropriate network, meeting the diverse network requirements of various applications. Timely network switching prevents data transmission interruptions and application lag caused by network instability, ensuring continuous communication between the vehicle and the outside world.

[0004] Some existing network switching methods often judge network quality based on limited parameters such as simple signal strength, which cannot comprehensively and accurately evaluate the actual performance of the network, resulting in untimely network switching or switching to an inappropriate network, affecting user experience. Summary of the Invention

[0005] (1) Technical problems solved

[0006] To address the shortcomings of the existing technology, the present invention provides an eSIM automated network switching policy management system and method based on multi-source data fusion, which at least solves the problem that the existing technology only judges network quality based on limited parameters such as simple signal strength, cannot comprehensively and accurately evaluate the actual performance of the network, and leads to untimely network switching or switching to an inappropriate network.

[0007] (2) Technical solution

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: A vehicle adaptive network switching and traffic sharing method based on multi-source collaboration includes: A vehicle adaptive network switching and traffic sharing method based on multi-source collaboration includes:

[0009] Step 1: Obtain signal quality data through the vehicle's wireless communication module, the vehicle's global positioning system, and digital map data, analyze and obtain signal quality parameters, and analyze and obtain a signal quality index;

[0010] Step 2: Analyze the signal quality index and calculate the comprehensive signal quality index;

[0011] Step 3: Obtain network stability data through the operator's package contract and communication module, and obtain network stability parameters through analysis and calculation;

[0012] Step 4: Calculate the network stability index by analyzing the network stability parameters;

[0013] Step 5: Obtain data on data packages from the operator's package contracts and perform a comprehensive analysis based on the signal quality index and network stability index to obtain the data cost efficiency index.

[0014] Step 6: Set a judgment threshold set and compare the thresholds in the judgment threshold set with the signal quality index, the network stability index, and the traffic cost efficiency index respectively; and determine whether to execute the network switching warning instruction;

[0015] Step 7: After executing the network switching warning instruction, obtain the network switching data of different operators, calculate the switching priority index of the corresponding operator based on the network switching data, and select the switching network;

[0016] Step 8: During normal network use, if a traffic sharing request is received from another vehicle, a comprehensive assessment of the network needs of the vehicle and the requesting vehicle is conducted to determine whether to execute the traffic sharing instruction.

[0017] In the preferred embodiment of the vehicle adaptive network switching and traffic sharing method based on multi-source collaboration, the signal quality index includes the signal strength value RS, the signal-to-noise ratio SN, and the path loss value PL; specifically:

[0018] Obtaining received signal strength indication values within a unit period through a built-in wireless communication module in the vehicle, calculating an average of the received signal strength indication values, and calculating a signal strength value RS through a signal strength value calculation model;

[0019] The useful signal power and noise power received by the vehicle are obtained through the wireless communication module, and the signal-to-noise ratio is calculated based on the useful signal power and the noise power, and the signal-to-noise ratio SN is calculated according to the signal-to-noise ratio calculation model;

[0020] The distance and propagation environment between the vehicle and the base station are determined through the vehicle's global positioning system and digital map data, and then the path loss value PL is calculated using the Okumura-Hata model.

[0021] In the preferred embodiment of the vehicle adaptive network switching and traffic sharing method based on multi-source collaboration, the signal quality index (SQI) is calculated by further analyzing the signal quality index. The calculation formula is as follows:

[0022] ;

[0023] Among them, β1 represents the weight coefficient of the signal strength value; β2 represents the weight coefficient of the signal-to-noise ratio; and β3 represents the weight coefficient of the path loss value.

[0024] In the preferred embodiment of the vehicle adaptive network switching and traffic sharing method based on multi-source collaboration, the network stability parameters include a bandwidth effectiveness factor, a packet loss tolerance value, and a channel utilization tolerance value; specifically,

[0025] Obtain the network's nominal maximum bandwidth through the operator's package contract, measure the vehicle's current available bandwidth in real time, and input the maximum bandwidth and current available bandwidth into the bandwidth effectiveness factor calculation model to obtain the bandwidth effectiveness factor BE;

[0026] Obtain the packet loss rate through the communication module, and calculate the packet loss rate tolerance PLR based on the packet loss rate tolerance calculation model;

[0027] The channel utilization is obtained through the communication module, and the channel utilization tolerance value CU is calculated according to the channel utilization calculation model.

[0028] In the preferred embodiment of the vehicle adaptive network switching and traffic sharing method based on multi-source collaboration, the network stability parameter is further analyzed to calculate the network stability index, and the calculation formula is as follows:

[0029] ;

[0030] Among them, NSI stands for Network Stability Index.

[0031] In the preferred solution of the multi-source collaborative vehicle adaptive network switching and traffic sharing method, the current operator's traffic unit price and the remaining traffic of the package are obtained through the operator's package contract. The traffic demand value for the next half hour is predicted using the LSTM model, and a comprehensive analysis is performed in combination with the signal quality index and the network stability index to obtain the traffic cost efficiency index. The formula is as follows:

[0032] ;

[0033] Among them, CEI represents the traffic cost efficiency index; CM represents the current operator's traffic unit price; PU represents the traffic forecast value for the next half hour based on historical data; RD represents the remaining traffic of the current package; ω1 represents the weight coefficient of the signal quality index; ω2 represents the weight coefficient of the network stability index.

[0034] In the preferred embodiment of the vehicle adaptive network switching and traffic sharing method based on multi-source collaboration, the method for determining whether to execute the network switching warning instruction is:

[0035] Set the signal quality index threshold, network stability index threshold, and traffic cost efficiency index threshold to form a judgment threshold set;

[0036] Compare the signal quality index with the signal quality index threshold. When the signal quality index is less than the signal quality index threshold, it indicates that the signal quality is unqualified. Compare the network stability index with the network stability index threshold. When the network stability index is less than the network stability index threshold, it indicates that the signal stability is unqualified. Compare the traffic cost efficiency index with the traffic cost efficiency index threshold. When the traffic cost efficiency index is less than the traffic cost efficiency index threshold, it indicates that the traffic cost is unqualified.

[0037] When the evaluation result of any indicator is unqualified, the network switching warning instruction is executed.

[0038] In the preferred solution of the vehicle adaptive network switching and traffic sharing method based on multi-source collaboration, the switching priority index of different operators is calculated based on the following formula:

[0039] ;

[0040] Where DSPk represents the switching priority index of k operators; RSS k represents the signal strength value of the base station of the kth operator; R krepresents the coverage radius of the k-th operator's base station; Dk represents the predicted distance between the k-th operator's base station and the vehicle; Ck represents the unit traffic cost value of the k-th operator; Pk represents the network load index of the k-th operator's base station; k represents the operator's serial number; φ1 represents the weight coefficient of the signal strength value; φ2 represents the weight coefficient of the ratio of coverage radius to predicted distance; φ3 represents The weight coefficient of

[0041] Traverse the switching priority indexes of different operators, select the operator network corresponding to the maximum switching priority index, and perform network switching.

[0042] In the preferred embodiment of the vehicle adaptive network switching and traffic sharing method based on multi-source collaboration, a comprehensive evaluation is performed on the network requirements of the own vehicle and the requesting vehicle to calculate the traffic sharing capability index. The formula is as follows:

[0043] ;

[0044] Among them, Ban Q represents the required bandwidth of the requesting vehicle; BC represents the battery consumption percentage; TL represents the trust level of the requesting vehicle, which is in the range of [0, 1].

[0045] In the preferred embodiment of the vehicle adaptive network switching and traffic sharing method based on multi-source collaboration, the method for determining whether to execute the traffic sharing instruction is:

[0046] Set a traffic sharing capability index threshold and compare the traffic sharing capability index with the traffic sharing capability index threshold. When the traffic sharing capability index is greater than the traffic sharing capability index threshold, the traffic sharing instruction is allowed to be executed; when the traffic sharing capability index is less than or equal to the traffic sharing capability index threshold, the traffic sharing instruction is rejected.

[0047] (3) Beneficial effects

[0048] The present invention provides an eSIM automated network switching strategy management system and method based on multi-source data fusion, which has the following beneficial effects:

[0049] (1) The solution starts from the two key dimensions of signal quality and network stability. It first obtains signal quality data and analyzes and extracts signal quality parameters. Further in-depth analysis is performed to obtain an accurate signal quality index. Similarly, the network stability data is processed and calculated to obtain network stability parameters and indexes. Through a step-by-step, detailed and in-depth analysis method, compared with existing technologies that only rely on simple indicators such as single signal strength, it can more comprehensively and accurately reflect the actual performance status of the network. In this way, in the subsequent network switching decision-making process, it is no longer based on one-sided information to make choices that may cause deviations. Instead, it is based on an accurate grasp of the actual state of the network to determine whether to execute the network switching warning instruction, effectively avoiding problems such as frequent switching or untimely switching due to inaccurate network assessment.

[0050] (2) By combining the traffic package data in the operator package contract with the signal quality index and network stability index for comprehensive analysis, the traffic cost efficiency index is derived. This achieves a dual consideration of performance and cost. In actual applications, vehicle users no longer need to bear unnecessary high traffic fees in pursuit of high-quality networks, nor do they need to endure network freezes and interruptions simply for cost control. Based on the accurate traffic cost efficiency index, vehicles can intelligently switch between different networks and select the network that best meets the current network needs under the premise of cost-effectiveness, thus achieving refined management and optimized configuration of traffic resources, greatly reducing the network usage cost of vehicles and improving the cost-effectiveness experience of users in network consumption.

[0051] (3) By building a network switching warning and traffic sharing mechanism, after setting a judgment threshold set, by comparing the signal quality, network stability, and traffic cost efficiency index with the threshold, it can quickly and accurately determine whether it is necessary to execute the network switching warning instruction, greatly shortening the decision delay time, in line with the characteristics of the rapid change of the network environment in the high-speed movement scenario of vehicles, ensuring that the vehicle can switch to a better network in the shortest time, and minimizing the problems of data transmission interruption or application freeze caused by network switching lag. After the network switching warning instruction is executed, step seven calculates the switching priority index based on the network switching data of different operators and selects the switching network, avoiding blind switching and further improving the pertinence and effectiveness of switching. When a vehicle is using the network normally, it can receive traffic sharing requests from other vehicles, and scientifically determine whether to execute traffic sharing instructions through a comprehensive assessment of the network needs of both parties. This not only expands the channels for obtaining vehicle network resources, but also realizes mutual sharing of traffic between vehicles, alleviates the traffic pressure of individual vehicles to a certain extent, and improves the utilization rate of network resources in the entire Internet of Vehicles ecosystem. It also avoids the negative impact of unreasonable sharing on its own network experience, optimizes the collaborative configuration efficiency of network resources between vehicles, and lays a solid foundation for building an efficient, intelligent, and collaborative Internet of Vehicles network management environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 The figure is a schematic diagram of the steps of the vehicle adaptive network switching and traffic sharing method based on multi-source collaboration of the present invention. DETAILED DESCRIPTION

[0053] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0054] Example 1

[0055] See also Figure 1 The present invention provides a vehicle adaptive network switching and traffic sharing method based on multi-source collaboration, including:

[0056] Step 1: Obtain signal quality data through the vehicle's wireless communication module, the vehicle's global positioning system, and digital map data and analyze them to obtain signal quality parameters, and then analyze and obtain a signal quality index.

[0057] Step 101: Obtain received signal strength indicators (RSSIs) within a unit period through a vehicle's built-in wireless communication module (e.g., an LTE or 5G module), calculate an average of the RSSIs, and calculate the signal strength using a signal strength calculation model. The signal strength calculation model is based on the following formula:

[0058] .

[0059] RS represents the signal strength value, which ranges from [0, 1]; RSSI avg Indicates the average value of the received signal strength indicator; RSSI min and RSSI max They represent the maximum and minimum received signal strength indicator values, respectively, and are determined by the specific communication standard and device. For example, in an LTE network, the typical RSSI range may be -110dBm (weak signal) to -50dBm (strong signal).

[0060] The solution uses a wireless communication module to obtain the received signal strength indicator value and calculate its average value, which is then converted into a signal strength value RS in the range of [0, 1] through the signal strength value calculation model formula. This technology effectively solves the problem of inconsistent signal strength value units and dimensions under different operator networks and different communication standards (such as LTE and 5G) in the vehicle network connection environment, making direct comparison and comprehensive evaluation difficult. It also achieves precise quantification and standardization of signal strength, so that the signal strength is presented in a unified dimensionless numerical form, which facilitates subsequent comprehensive analysis and comparison with other network performance indicators, laying the foundation for accurately judging network signal quality. Therefore, during the vehicle's adaptive network switching process, the accuracy and rationality of network selection are improved, ensuring that the vehicle preferentially connects to the network with better signal strength, thereby enhancing network communication quality.

[0061] Step 102: The useful signal power and noise power received by the vehicle's wireless communication module are measured and input into a signal-to-noise ratio calculation model to calculate the signal-to-noise ratio. The formula is as follows:

[0062] ;

[0063] Where SN represents the signal-to-noise ratio; P signal Indicates the useful signal power; P noise Represents the noise power.

[0064] This solution uses wireless communication modules to obtain useful signal power and noise power, addressing the inaccurate signal-to-noise ratio (SNR) assessment in traditional vehicle network signal evaluations. Previous evaluation methods may rely solely on simple metrics such as signal strength, failing to fully reflect signal quality. However, the SNR is a key indicator of signal quality. This formula uses precise mathematical calculations to convert the ratio of useful signal power to noise power into an easily comparable numerical value, providing a more accurate and intuitive quantitative basis for vehicle network signal quality assessment. As a vehicle moves, the degree of signal interference varies in different environments. This formula accurately determines the SNR, providing reliable data support for subsequent comprehensive analysis of signal quality parameters.

[0065] Step 103: Determine the distance between the vehicle and the base station and the propagation environment using the vehicle's global positioning system and digital map data. Then, use the Okumura-Hata model to calculate the path loss value. The formula for calculating the path loss value using the Okumura-Hata model is as follows:

[0066] ;

[0067] Where PL represents the path loss value; f is the operating frequency, h b is the antenna height of the base station, hm The car gives you the antenna height, d represents the distance between the base station and the vehicle, α (h m ) is the height loss factor of the vehicle antenna, which can be calculated based on the empirical formula of the Okumura-Hata model. The formula of the Okumura-Hata model is: a(h m )=(1.1*log10(f)−0.7)*h m −(1.56*log10(f)−0.8), where C is a correction factor adjusted for the propagation environment, typically ranging from -2 to +4. In urban environments, where dense buildings and numerous obstacles result in high path loss, C is typically positive. In suburban or open areas, where path loss is relatively low, C may be negative or zero.

[0068] It should be noted that before calculating the path loss value, the corresponding parameters need to be normalized and preprocessed to eliminate the dimensions of different parameters to facilitate subsequent formula calculations.

[0069] This solution utilizes the vehicle's global positioning system and digital map data, combined with the Okumura-Hata model, to calculate path loss. This technology addresses the inaccurate path loss assessments in traditional vehicular network communications, which struggle to adapt to complex and changing propagation environments. Previous vehicular network systems may estimate path loss based solely on simple factors such as distance or signal strength, failing to fully consider the combined effects of multiple factors, including operating frequency, base station and vehicle antenna heights, and propagation environment. This results in significant errors in path loss estimation across diverse environments. The Okumura-Hata model, however, comprehensively considers multiple key factors, including operating frequency, base station antenna height, vehicle antenna height, distance between vehicle and base station, and propagation environment correction factors, enabling precise path loss calculation. This precise path loss calculation enables vehicular network systems to better adapt to diverse communication environments, enabling accurate assessment of network connectivity based on actual path loss conditions, whether in densely populated urban environments with numerous buildings and obstacles or in open rural areas.

[0070] Step 2: Further analyze the signal quality index and calculate the comprehensive signal quality index. The calculation formula is as follows:

[0071] ;

[0072] Where SQI represents the signal quality index; β1 represents the weight coefficient of the signal strength value; β2 represents the weight coefficient of the signal-to-noise ratio; and β3 represents the weight coefficient of the path loss value. β1+β2+β3=1, which can be adjusted according to actual conditions. Here, the values can be: β1=0.4, β2=0.3, and β3=0.3.

[0073] It should be noted that before calculating the signal quality index, it is necessary to normalize the corresponding parameters to eliminate the dimensions of different parameters to facilitate subsequent formula calculations.

[0074] The solution comprehensively considers signal strength, signal-to-noise ratio, and path loss, assigning weights based on the importance of each factor and the actual situation, and calculating a signal quality index (SQI). This technology addresses the problem that traditional signal quality assessment relies solely on a single metric, failing to fully reflect network communication quality. Previously, assessments based solely on single metrics, such as signal strength or signal-to-noise ratio, failed to accurately reflect the true network condition. While a vehicle is in motion, the signal strength, signal-to-noise ratio, and path loss of different networks may fluctuate significantly due to environmental factors. A single metric, however, can fluctuate significantly, while the comprehensive SQI is more stable and reliable. It accurately reflects changing trends in network communication quality, enabling vehicles to proactively detect network degradation, initiate a timely warning, and switch to a higher-quality network. This reduces communication interruptions or data transmission delays caused by network quality issues, improves the success rate and timeliness of network handovers, ensures continuous and stable vehicle communication, and enhances the user experience.

[0075] Step 3: Obtain network stability data and obtain network stability parameters through analysis and calculation.

[0076] Step 301: Obtain the network's nominal maximum bandwidth from the operator's package contract, and use the iperf3 tool to measure the vehicle's current available bandwidth in real time. Input the maximum bandwidth and available bandwidth into a bandwidth effectiveness factor calculation model to calculate the bandwidth effectiveness factor. The bandwidth effectiveness factor calculation model is based on the following formula:

[0077] ;

[0078] Where BE represents the bandwidth effectiveness factor; B ant Indicates the current available bandwidth; B anmax represents the maximum bandwidth; e represents the base of the natural logarithm, which is 2.71828; λ represents the bandwidth utilization sensitivity coefficient, which is in the range of [0, 1]. When the available bandwidth approaches the nominal value, the attenuation effect weakens, and the smaller the value, the weaker the attenuation effect.

[0079] This solution combines the current available bandwidth with the network's nominal maximum bandwidth and introduces a bandwidth utilization sensitivity factor to accurately assess the actual utilization efficiency of a vehicle's network bandwidth. This addresses the problem of traditional bandwidth assessment methods that focus solely on the numerical value of bandwidth while ignoring bandwidth utilization efficiency. Traditional bandwidth assessment methods often only consider the absolute value of the current available bandwidth, which cannot accurately reflect whether the bandwidth is being effectively utilized. The formula uses the maximum bandwidth as a benchmark to calculate the relative utilization of the current bandwidth and uses an exponential decay function to account for the change in marginal benefits as bandwidth utilization increases. The bandwidth effectiveness factor calculated by this formula comprehensively considers the actual utilization efficiency of the current bandwidth, allowing vehicles to more comprehensively evaluate the bandwidth performance of the target network when switching networks. When the BE value is low, even if signal quality is acceptable, the system will prioritize switching to networks with higher bandwidth utilization, ensuring that the vehicle is always connected to the network that offers the best bandwidth performance, improving network stability and smoothness, and optimizing the user experience.

[0080] Step 302: Obtain the packet loss rate through the communication module, and calculate the packet loss tolerance value based on the packet loss rate tolerance calculation model; the calculation formula based on the calculation model is as follows:

[0081] ;

[0082] PLR represents the packet loss tolerance value, DB represents the packet loss rate, and ε represents the smoothing factor to avoid the denominator being zero.

[0083] This solution uses a combination of square root and hyperbolic tangent functions to convert packet loss rate into a tolerance metric that better reflects actual communication needs. When the packet loss rate is low, the PLR value is high, indicating a high network tolerance to packet loss. As the packet loss rate increases, the PLR value decreases nonlinearly, reflecting the network's increased sensitivity to packet loss. This precise quantification method enables more nuanced network quality assessment and better reflects the network's actual transmission capacity. In inter-vehicle traffic sharing scenarios, the accurate calculation of the packet loss tolerance value (PLR) provides a critical reference for network reliability in traffic sharing decisions, resolving the issue of inaccurate packet loss assessment in traditional traffic sharing decisions. Previously, vehicles may have lacked a thorough assessment of network packet loss conditions when deciding whether to share traffic, resulting in a higher risk of packet loss on the shared network. The PLR value calculated using this formula enables a vehicle to comprehensively assess the packet loss tolerance of both its own network and the requesting vehicle's network upon receiving a traffic sharing request.

[0084] Step 303: Obtain channel utilization through the communication module, and calculate the channel utilization tolerance value according to the channel utilization calculation model. The formula is as follows:

[0085] ;

[0086] CU represents the channel utilization tolerance value, and XD represents the channel utilization.

[0087] This solution calculates a channel utilization tolerance value (CU), where XD represents channel utilization. This effectively addresses the lack of precise quantification of the impact of channel utilization in traditional network quality assessments. Traditional assessment methods may focus solely on the absolute value of channel utilization, failing to accurately reflect its actual impact on network communication quality. This formula, combining a logarithmic function with a linear transformation, converts channel utilization into a tolerance value, providing a more intuitive reflection of network performance under varying channel utilization. When channel utilization is low, the CU value is high, indicating a high tolerance for channel utilization. As channel utilization increases, the CU value decreases nonlinearly, reflecting the network's increased sensitivity to high channel utilization.

[0088] Step 4: By further analyzing the network stability parameters, the network stability index is calculated based on the following formula:

[0089] .

[0090] Among them, NSI stands for Network Stability Index.

[0091] It should be noted that before calculating the network stability index, it is necessary to normalize the corresponding parameters to eliminate the dimensions of different parameters to facilitate subsequent formula calculations.

[0092] The solution achieves precise quantification of network stability by integrating three key factors: bandwidth, packet loss, and channel utilization, and introducing a smoothing factor to avoid the extreme case of a zero denominator. Specifically, the bandwidth effectiveness factor BE reflects the actual bandwidth utilization efficiency, the packet loss tolerance value PLR reflects the network's tolerance for packet loss, and the channel utilization tolerance value CU reflects the impact of channel utilization on network performance. Multiplying these three factors together, the numerator reflects the network's overall performance in terms of bandwidth, packet loss, and channel utilization; the denominator (1-BE)(1-PLR)(1-CU) reflects the network's shortcomings in these areas, and the smoothing factor ε ensures the stability of the calculation. This comprehensive quantification method makes network stability assessment more comprehensive and accurate, and can better reflect the actual operating status of the network.

[0093] Step 5: Obtain data on traffic packages through the operator's package contract, and conduct a comprehensive analysis based on the signal quality index and network stability index to obtain the traffic cost efficiency index.

[0094] Step 501: Obtain the current operator's traffic unit price and the remaining traffic of the package through the operator's package contract.

[0095] Step 502: Use the LSTM model to predict the traffic demand value for the next half hour, and perform a comprehensive analysis based on the signal quality index and the network stability index to obtain the traffic cost efficiency index. The formula used is as follows:

[0096] ;

[0097] Among them, CEI represents the traffic cost efficiency index; CM represents the current operator's traffic unit price; PU represents the traffic forecast value for the next half hour based on historical data, which can be obtained by inputting historical traffic usage data into the long short-term memory network (LSTM) model; RD represents the remaining traffic of the current package; ω1 represents the weight coefficient of the signal quality index; ω2 represents the weight coefficient of the network stability index, and ω1+ω2=1, which can be adjusted according to actual conditions, and can take the following values: ω1=0.6, ω2=0.4.

[0098] It should be noted that before calculating the traffic cost efficiency index, the corresponding parameters need to be normalized and preprocessed to eliminate the dimensions of different parameters to facilitate subsequent formula calculations.

[0099] The formula comprehensively considers multiple factors, including signal quality, network stability, traffic cost, and traffic demand. It combines the signal quality index and network stability index through a weighted average, and takes into account the unit price of traffic and the ratio of future traffic demand to remaining traffic, thereby quantifying the cost-effectiveness of traffic usage. This solution uses an LSTM model to predict the traffic demand (PU) value for the next half hour. Combined with the remaining traffic (RD), the CEI dynamically reflects traffic cost efficiency, helping users plan their traffic usage in advance and avoid arrears or waste. It also helps users make better traffic usage decisions across different carriers' networks. A high CEI indicates that the current network has a high traffic cost efficiency, and users should prioritize using that network. Conversely, if the CEI is low, users can consider switching to other networks or adjusting their traffic usage strategies to reduce costs and improve user experience while ensuring network quality.

[0100] Step 6: Set a judgment threshold set, and compare the thresholds in the judgment threshold set with the signal quality index, the network stability index, and the traffic cost efficiency index respectively; and determine whether to execute the network switching warning instruction.

[0101] Step 601: Set a signal quality index threshold, a network stability index threshold, and a traffic cost efficiency index threshold to form a judgment threshold set.

[0102] Step 602: Compare the signal quality index with the signal quality index threshold. When the signal quality index is less than the signal quality index threshold, it indicates that the signal quality is unqualified; compare the network stability index with the network stability index threshold. When the network stability index is less than the network stability index threshold, it indicates that the signal stability is unqualified; compare the traffic cost efficiency index with the traffic cost efficiency index threshold. When the traffic cost efficiency index is less than the traffic cost efficiency index threshold, it indicates that the traffic cost is unqualified.

[0103] It should be noted that the signal quality index threshold, network stability index threshold and traffic cost efficiency index threshold can be set based on historical experience data; for example, the network data under normal network conditions in different time periods in the historical data can be counted, and the corresponding signal quality index and network stability index can be calculated respectively, and then the average and variance values can be calculated respectively, and the signal quality index threshold can be set to the average value of the signal quality index or the signal quality index + n times the variance value; the network stability index threshold can be set to the average value of the network stability index or the signal quality index + n times the variance value; the traffic cost efficiency index threshold can be determined based on the vehicle users' sensitivity to traffic costs and their requirements for network transmission efficiency. The threshold range of the traffic cost efficiency index can also be determined by statistically analyzing questionnaires from different customers and adjusting it according to real-time prices. For example, for cost-sensitive users, when the network cost-effectiveness ratio is higher than 0.3 yuan / Mbps, the traffic cost efficiency can be considered low, and the corresponding traffic cost efficiency index threshold is set to 0-0.3; between 0.1-0.3 yuan / Mbps, the traffic cost efficiency is average, and the corresponding threshold is 0.3-0.6; when it is lower than 0.1 yuan / Mbps, the traffic cost efficiency is high, and the corresponding threshold is 0.6-1.

[0104] Step 603: When the evaluation result of any indicator is unqualified, execute the network switching warning instruction.

[0105] The solution makes network switching decisions more scientific and reasonable by comprehensively considering multi-dimensional factors such as signal quality, network stability, and traffic costs, and can provide users with a more stable and smoother network experience.

[0106] Step 7: After executing the network switching warning instruction, obtain the network switching data of different operators, calculate the switching priority index of the corresponding operator according to the network switching data, and select the switching network.

[0107] Step 701: Obtain the unit traffic cost value through the operator's tariff table; obtain the base station load data through the base station's active broadcast, use the on-board map module to obtain the vehicle's current location and surrounding base station distribution information, and combine the set driving path to calculate the predicted distance of the nearest base station of different operators through the on-board computing unit or ECU; the on-board communication module obtains the nominal coverage radius data of different base stations from the operator's network server or the base station information database pre-stored in the vehicle.

[0108] Step 702: By comprehensively analyzing the network handover data, the handover priority indexes of different operators are obtained, based on the following formula:

[0109] ;

[0110] Where DSPk represents the switching priority index of k operators; RSS k represents the signal strength value of the base station of the kth operator; R k represents the coverage radius of the k-th operator's base station; Dk represents the predicted distance between the k-th operator's base station and the vehicle; Ck represents the unit traffic cost value of the k-th operator; Pk represents the network load index of the k-th operator's base station; k represents the operator's serial number, which is a positive integer; φ1 represents the weight coefficient of the signal strength value; φ2 represents the weight coefficient of the ratio of coverage radius to predicted distance; φ3 represents The weight coefficients can be adjusted according to the actual situation. Here, the values can be: φ1=0.4, φ2=0.2, φ3=0.4.

[0111] The formula integrates multiple key factors such as signal strength, base station coverage and vehicle distance, traffic cost, and network load, and calculates the switching priority index of each operator's network through weighted summation, thus providing a quantitative basis for network switching decisions; it provides a comprehensive and quantitative evaluation basis for network switching, enabling vehicles to accurately select the optimal network for switching among multiple operator networks, reducing misjudgments caused by single indicator evaluation, and improving the accuracy and rationality of network switching.

[0112] It should be noted that before calculating the handover priority index, it is necessary to perform normalization preprocessing on the corresponding parameters to eliminate the dimensions of different parameters to facilitate subsequent formula calculations.

[0113] It should be noted that the calculation method of Pk is: ; Among them, δ represents the load sensitivity coefficient, which is in the range of [0, 5] and can be taken as 0.2 here.

[0114] Step 703: traverse the switching priority indexes of different operators, select the operator network corresponding to the maximum switching priority index, and perform network switching.

[0115] Step 8: During normal network use, if a traffic sharing request is received from another vehicle, a comprehensive assessment of the network needs of the vehicle and the requesting vehicle is conducted to determine whether to execute the traffic sharing instruction.

[0116] Step 801: Calculate the traffic sharing capability index by comprehensively analyzing the network usage data of the own vehicle and the request data of the requesting vehicle. The formula is as follows:

[0117] ;

[0118] Among them, Ban Q represents the required bandwidth of the requesting vehicle; BC represents the battery consumption percentage, which takes a value of [0, 100]; TL represents the trust level of the requesting vehicle, which can be obtained from the trust library of the blockchain, and takes a value of [0, 1].

[0119] It should be noted that before calculating the traffic sharing capability index, the corresponding parameters need to be normalized and preprocessed to eliminate the dimensions of different parameters to facilitate subsequent formula calculations.

[0120] Traditional approaches focus solely on remaining traffic without comprehensively considering factors such as bandwidth supply and demand, battery status, and trustworthiness, which can easily lead to irrational sharing decisions. By comprehensively considering these multiple factors, a vehicle can fully assess its own sharing capabilities and the requester's trustworthiness when receiving a sharing request.

[0121] Step 802: Set the traffic sharing capability index threshold, compare the traffic sharing capability index with the traffic sharing capability index threshold, and when the traffic sharing capability index is greater than the traffic sharing capability index threshold, allow the traffic sharing instruction to be executed; when the traffic sharing capability index is less than or equal to the traffic sharing capability index threshold, refuse to execute the traffic sharing instruction.

[0122] When VSC is higher than the threshold, sharing is allowed to improve resource utilization; otherwise, sharing is rejected to avoid risks. This mechanism improves the accuracy and rationality of sharing decisions, makes sharing more in line with actual conditions, enhances user experience, and comprehensively evaluates bandwidth supply and demand. Vehicles can meet their own needs while reasonably sharing excess bandwidth with other vehicles, avoiding traffic waste and improving traffic resource utilization of the entire Internet of Vehicles system.

[0123] It should be noted that the typical distribution of traffic sharing capability indices is determined by analyzing historical traffic sharing request and execution data. For example, the signal strength values are calculated when executing traffic sharing instructions for different traffic sharing capability indices, and the distribution of signal strength values is statistically analyzed. The traffic sharing capability index corresponding to no impact on the signal strength value of the own vehicle is selected as the traffic sharing capability index threshold.

[0124] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those skilled in the art will appreciate that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution.

[0125] 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, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0126] The above is only a specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in this application, which should be covered by the scope of protection of the present application.

Claims

1. A vehicle adaptive network switching and traffic sharing method based on multi-source collaboration is characterized by: include: Step 1: Obtain signal quality parameters and analyze them to obtain the signal quality index. The signal quality index includes the signal strength value RS, the signal-to-noise ratio SN, and the path loss value PL. The specific acquisition method is as follows: Obtaining received signal strength indication values within a unit period through a built-in wireless communication module in the vehicle, calculating an average of the received signal strength indication values, and calculating a signal strength value RS through a signal strength value calculation model; The useful signal power and noise power received by the vehicle are obtained through the wireless communication module, and the signal-to-noise ratio is calculated based on the useful signal power and the noise power, and the signal-to-noise ratio SN is calculated according to the signal-to-noise ratio calculation model; The distance between the vehicle and the base station and the propagation environment are determined using the vehicle's global positioning system and digital map data, and the path loss value PL is calculated using the Okumura-Hata model. Step 2: By further analyzing the signal quality index, the signal quality comprehensive index SQI is calculated based on the following calculation formula: ； Among them, β1 represents the weight coefficient of the signal strength value; β2 represents the weight coefficient of the signal-to-noise ratio; β3 represents the weight coefficient of the path loss value; Step 3: Obtain network stability data through the operator's package contract and communication module, and obtain network stability parameters through analysis and calculation; Step 4: Calculate the network stability index by analyzing the network stability parameters; Step 5: Obtain data on data packages from the operator's package contracts and perform a comprehensive analysis based on the signal quality index and network stability index to obtain the data cost efficiency index. Step 6: Set a judgment threshold set and compare the thresholds in the judgment threshold set with the signal quality index, the network stability index, and the traffic cost efficiency index respectively; and determine whether to execute the network switching warning instruction; Step 7: After executing the network switching warning instruction, obtain the network switching data of different operators and calculate the switching priority index of the corresponding operator based on the network switching data. The formula is as follows: ； Where DSPk represents the switching priority index of k operators; RSS k represents the signal strength value of the base station of the kth operator; R k represents the coverage radius of the k-th operator's base station; Dk represents the predicted distance between the k-th operator's base station and the vehicle; Ck represents the unit traffic cost value of the k-th operator; Pk represents the network load index of the k-th operator's base station; k represents the operator's serial number; φ1 represents the weight coefficient of the signal strength value; φ2 represents the weight coefficient of the ratio of coverage radius to predicted distance; φ3 represents The weight coefficient of , and select the switching network; Step 8: During normal network use, if a traffic sharing request is received from another vehicle, a comprehensive assessment of the network needs of the vehicle and the requesting vehicle is conducted to determine whether to execute the traffic sharing instruction.

2. The vehicle adaptive network switching and traffic sharing method based on multi-source collaboration according to claim 1 is characterized in that: Network stability parameters include bandwidth effectiveness factor, packet loss tolerance value, and channel utilization tolerance value; specifically: Obtain the network's nominal maximum bandwidth through the operator's package contract, measure the vehicle's current available bandwidth in real time, and input the maximum bandwidth and current available bandwidth into the bandwidth effectiveness factor calculation model to obtain the bandwidth effectiveness factor BE; Obtain the packet loss rate through the communication module, and calculate the packet loss rate tolerance PLR based on the packet loss rate tolerance calculation model; The channel utilization is obtained through the communication module, and the channel utilization tolerance value CU is calculated according to the channel utilization calculation model.

3. The vehicle adaptive network switching and traffic sharing method based on multi-source collaboration according to claim 2 is characterized in that: By further analyzing the network stability parameters, the network stability index is calculated based on the following calculation formula: ； Among them, NSI stands for Network Stability Index.

4. The vehicle adaptive network switching and traffic sharing method based on multi-source collaboration according to claim 3 is characterized in that: The current carrier's data unit price and the remaining data in the package are obtained from the carrier's package contract. The data demand value for the next half hour is predicted using the LSTM model. Combined with the signal quality index and network stability index, a comprehensive analysis is performed to obtain the data cost efficiency index. The formula used is as follows: ； Among them, CEI represents the traffic cost efficiency index; CM represents the current operator's traffic unit price; PU represents the traffic forecast value for the next half hour based on historical data; RD represents the remaining traffic of the current package; ω1 represents the weight coefficient of the signal quality index; ω2 represents the weight coefficient of the network stability index.

5. The vehicle adaptive network switching and traffic sharing method based on multi-source collaboration according to claim 4 is characterized in that: The method for determining whether to execute the network switching warning instruction is: Set the signal quality index threshold, network stability index threshold, and traffic cost efficiency index threshold to form a judgment threshold set; Compare the signal quality index with the signal quality index threshold. When the signal quality index is less than the signal quality index threshold, it indicates that the signal quality is unqualified. Compare the network stability index with the network stability index threshold. When the network stability index is less than the network stability index threshold, it indicates that the signal stability is unqualified. Compare the traffic cost efficiency index with the traffic cost efficiency index threshold. When the traffic cost efficiency index is less than the traffic cost efficiency index threshold, it indicates that the traffic cost is unqualified. When the evaluation result of any indicator is unqualified, the network switching warning instruction is executed.

6. The vehicle adaptive network switching and traffic sharing method based on multi-source collaboration according to claim 5 is characterized in that: The method for selecting switching network is: Traverse the switching priority indexes of different operators, select the operator network corresponding to the maximum switching priority index, and perform network switching.

7. The vehicle adaptive network switching and traffic sharing method based on multi-source collaboration according to claim 6 is characterized in that: Comprehensively evaluate the network needs of your own vehicles and the requesting party's vehicles and calculate the traffic sharing capability index based on the following formula: ； Among them, Ban Q represents the required bandwidth of the requesting vehicle; BC represents the battery consumption percentage; TL represents the trust level of the requesting vehicle, which is in the range of [0, 1].

8. The vehicle adaptive network switching and traffic sharing method based on multi-source collaboration according to claim 7 is characterized in that: The method to determine whether to execute the traffic sharing instruction is: Set a traffic sharing capability index threshold and compare the traffic sharing capability index with the traffic sharing capability index threshold. When the traffic sharing capability index is greater than the traffic sharing capability index threshold, the traffic sharing instruction is allowed to be executed; when the traffic sharing capability index is less than or equal to the traffic sharing capability index threshold, the traffic sharing instruction is rejected.

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