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

Abstract

The invention discloses a vehicle adaptive network switching and traffic sharing method based on multi-source cooperation, and relates to the field of vehicle network management, and the main scheme is as follows: obtaining a signal quality index by analyzing signal quality data, and further calculating to obtain a signal quality comprehensive index; analyzing and calculating the network stability data to obtain a network stability parameter and a network stability index; the method comprises the following steps of: obtaining traffic package data, and performing comprehensive analysis in combination with a signal quality index and a network stability index to obtain a traffic cost efficiency index; threshold values in the judgment threshold value set are compared with corresponding indexes respectively; judging whether to execute a network switching early warning instruction; the method comprises the following steps: acquiring switching priority indexes of different operators, and selecting a switching network; the problem that in the prior art, the network quality is judged only according to limited parameters such as simple signal strength, the actual performance of the network cannot be evaluated comprehensively and accurately, and consequently network switching is not timely or the network is switched to an improper network is solved.

Description

Technical Field

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

[0002] With the rapid development of vehicle networking technology, vehicles are increasingly dependent on the network during driving, involving not only basic functions such as navigation and entertainment, but also key applications such as autonomous driving and real-time traffic information interaction. The vehicle adaptive network switching and traffic sharing method has broad application prospects. On the one hand, it can provide a more stable and efficient network connection for vehicles, ensuring driving safety and riding experience. For example, in the autonomous driving scenario, the vehicle needs to obtain high-precision map data and information of surrounding vehicles and road facilities in real time. Stable network switching can avoid driving risks caused by network interruption or lag. On the other hand, with the intelligence and networking of vehicles, the amount of data generated by vehicles has increased explosively. The traffic sharing function can effectively utilize the network resources between vehicles and reduce operating costs.

[0003] The vehicle networking environment is complex, with multiple network systems and operator networks. The signal quality, bandwidth, stability, etc. of different networks vary greatly. During the movement of the vehicle, it needs to continuously adapt to the changing network environment. Adaptive network switching can ensure that the vehicle is always connected to the most suitable network to meet the different requirements of various applications for the network. Timely network switching can avoid problems such as data transmission interruption and application lag caused by network instability, and ensure the continuous communication between the vehicle and the outside world.

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

[0005] (I) Technical Problems to be Solved Aiming at the deficiencies of the prior art, the present invention provides an eSIM automatic network switching strategy management system and method based on multi-source data fusion, which at least solves the problem in the prior art that only limited parameters such as simple signal strength are used to judge the network quality, and the actual performance of the network cannot be comprehensively and accurately evaluated, resulting in untimely network switching or switching to an inappropriate network.

[0006] (II) Technical Solutions To achieve the above objectives, the present invention is realized through the following technical solutions: The vehicle adaptive network switching and traffic sharing method based on multi-source collaboration includes: The vehicle adaptive network switching and traffic sharing method based on multi-source collaboration includes: Step 1: Obtain signal quality data through the vehicle's wireless communication module, the vehicle's global positioning system, and digital map data, analyze the data to obtain signal quality parameters, and analyze and obtain the signal quality index; Step 2: Calculate the comprehensive signal quality index by analyzing the signal quality index; Step 3: Obtain network stability data through the operator's package contract and the communication module, and through analysis and calculation, obtain the network stability parameters; Step 4: Calculate the network stability index by analyzing the network stability parameters; Step 5: Obtain the traffic package data through the operator's package contract, and conduct a comprehensive analysis in combination with the signal quality index and the network stability index to obtain the traffic cost efficiency index; Step 6: Set a set of judgment thresholds, and compare the thresholds in the set of judgment thresholds with the signal quality index, the network stability index, and the traffic cost efficiency index respectively; 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, calculate the switching priority index of the corresponding operator according to the network switching data, and select to switch the network; Step 8: During the normal network usage process, if a traffic sharing request from another vehicle is received, comprehensively evaluate the network usage requirements of the vehicle itself and the requesting vehicle, and determine whether to execute the traffic sharing instruction.

[0007] In the preferred solution of the above vehicle adaptive network switching and traffic sharing method based on multi-source collaboration, the signal quality parameters include signal strength value, signal-to-noise ratio, and path loss value; specifically: Obtain the received signal strength indication value within a unit period through the vehicle's built-in wireless communication module, calculate the average value of the received signal strength indication value, and calculate the signal strength value RS through the signal strength value calculation model; Obtain the measured useful signal power and noise power received through the vehicle's wireless communication module, calculate the signal-to-noise ratio based on the useful signal power and the noise power, and calculate the signal-to-noise ratio SN according to the signal-to-noise ratio calculation model; Determine the distance between the vehicle and the base station and the propagation environment through the vehicle's global positioning system and digital map data, and then use the Okumura-Hata model to calculate the path loss value PL.

[0008] In the preferred solution of the above vehicle adaptive network switching and traffic sharing method based on multi-source collaboration, by further analyzing the signal quality index, calculate the comprehensive signal quality index SQI, and the calculation formula is as follows: ; 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.

[0009] In the preferred solution of the above vehicle adaptive network handover and traffic sharing method based on multi-source collaboration, the network stability parameters include the bandwidth effectiveness factor, the packet loss tolerance value, and the channel utilization tolerance value; specifically: Obtain the maximum bandwidth nominal by the network through the operator's package contract, measure the current available bandwidth of the vehicle in real time, and input the maximum bandwidth and the 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 tolerance PLR according to the packet loss rate tolerance calculation model; Obtain the channel utilization rate through the communication module, and calculate the channel utilization tolerance value CU according to the channel utilization rate calculation model.

[0010] In the preferred solution of the above vehicle adaptive network handover and traffic sharing method based on multi-source collaboration, by further analyzing the network stability parameters, the network stability index is calculated, and the calculation formula is as follows: ; Among them, NSI represents the network stability index.

[0011] In the preferred solution of the above vehicle adaptive network handover and traffic sharing method based on multi-source collaboration, obtain the current operator traffic unit price and the remaining traffic of the package through the operator's package contract; predict the traffic demand value in the next half hour through the LSTM model, and conduct a comprehensive analysis in combination with the signal quality index and the network stability index to obtain the traffic cost efficiency index; the formula is as follows: ; Among them, CEI represents the traffic cost efficiency index; CM represents the current operator traffic unit price; PU represents the traffic prediction value in 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.

[0012] In the preferred solution of the above vehicle adaptive network handover and traffic sharing method based on multi-source collaboration, the method for judging whether to execute the network handover warning instruction is: Set the signal quality index threshold, the network stability index threshold, and the 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 < 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 < 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 < the traffic cost efficiency index threshold, it indicates that the traffic cost is unqualified; When the evaluation result of any index is unqualified, execute the network switching warning instruction.

[0013] In the preferred scheme of the vehicle adaptive network switching and traffic sharing method based on multi-source collaboration described above, the formula for calculating the switching priority index of different operators is as follows: ; Among them, DSPk represents the switching priority index of the kth operator; RSS k represents the signal strength value of the base station of the kth operator; R k represents the coverage radius of the base station of the kth operator; Dk represents the predicted distance from the base station of the kth operator to the vehicle; Ck represents the unit traffic cost value of the kth operator; Pk represents the network load index of the base station of the kth operator; k represents the serial number of the operator; φ1 represents the weight coefficient of the signal strength value; φ2 represents the weight coefficient of the ratio of the coverage radius to the predicted distance; φ3 represents the weight coefficient of; Traverse the switching priority indices of different operators, and select the operator network corresponding to the maximum value of the switching priority index for network switching.

[0014] In the preferred scheme of the vehicle adaptive network switching and traffic sharing method based on multi-source collaboration described above, comprehensively evaluate the network usage requirements of the own vehicle and the requesting vehicle, and calculate the traffic sharing ability index. The formula is as follows: ; 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, with a value range of [0, 1].

[0015] In the preferred scheme of the vehicle adaptive network switching and traffic sharing method based on multi-source collaboration described above, the method for determining whether to execute the traffic sharing instruction is: Set a threshold for the traffic sharing ability index, compare the traffic sharing ability index with the threshold. When the traffic sharing ability index > the traffic sharing ability index threshold, allow the execution of the traffic sharing instruction; when the traffic sharing ability index ≤ the traffic sharing ability index threshold, reject the execution of the traffic sharing instruction.

[0016] (3) Beneficial effects The present invention provides an eSIM automated network switching strategy management system and method based on multi-source data fusion, having the following beneficial effects: (1) The solution starts from two key dimensions of signal quality and network stability respectively. First, obtain signal quality data and analyze and extract signal quality parameters, and further deeply analyze to obtain accurate signal quality indexes. Similarly, process and calculate the network stability data to obtain network stability parameters and indexes. Through a progressive and detailed analysis method, compared with the prior art that only relies 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 a possibly deviated choice, but based on the accurate grasp of the real state of the network to judge whether to execute the network switching warning instruction, effectively avoiding problems such as frequent switching or untimely switching caused by inaccurate network evaluation.

[0017] (2) By combining the traffic package data in the operator's package contract and comprehensively analyzing it with the signal quality index and the network stability index, the traffic cost efficiency index is obtained. This realizes the dual consideration of performance and cost. In practical applications, vehicle users no longer need to bear unnecessary high traffic costs for pursuing high-quality networks, nor need to endure network lags, interruptions, etc. due to simply controlling costs. Based on the accurate traffic cost efficiency index, the vehicle can intelligently switch between different networks and select the network that best meets the current network usage requirements under the premise of cost-effectiveness, realizing the refined management and optimized allocation of traffic resources, greatly reducing the network usage cost of the vehicle, and enhancing the user's cost-effective experience in network consumption.

[0018] (3) By constructing a network switching warning and traffic sharing mechanism, after setting the 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 to execute the network switching warning instruction, greatly shortening the decision-making delay time, which conforms to the characteristics of the rapidly changing network environment in the vehicle high-speed movement scenario, ensuring that the vehicle can switch to a better network in the shortest time, and minimizing problems such as data transmission interruption or application lag caused by network switching lag. After executing the network switching warning instruction, 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 the switching. When the vehicle is using the network normally, it can receive traffic sharing requests from other vehicles, and scientifically judge whether to execute the traffic sharing instruction through comprehensive evaluation of the network usage requirements of both parties. This not only expands the acquisition channels of vehicle network resources, realizes traffic mutual assistance and sharing among vehicles, alleviates the traffic pressure of a single vehicle to a certain extent, improves the utilization rate of network resources in the entire vehicle networking ecosystem, but also avoids the negative impact of unreasonable sharing on its own network usage experience, optimizes the collaborative configuration efficiency of network resources among vehicles, and lays a solid foundation for building an efficient, intelligent, and collaborative vehicle networking network management environment. Description of the Drawings

[0019] Figure 1 It 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 Embodiments

[0020] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0021] Embodiment 1

[0022] Please refer to Figure 1 , the present invention provides a vehicle adaptive network switching and traffic sharing method based on multi-source collaboration, including: Step 1: Obtain signal quality data through the wireless communication module of the vehicle, the global positioning system of the vehicle, and digital map data, analyze it to obtain signal quality parameters, and analyze and obtain a signal quality index.

[0023] Step 101: Obtain the received signal strength indication (RSSI) value within a unit period through the in-vehicle wireless communication module (such as an LTE or 5G module), calculate the average value of the RSSI value, and calculate the signal strength value using the signal strength value calculation model. The formula on which the signal strength value calculation model is based is as follows: 。

[0024] Where RS represents the signal strength value, and its value range is [0, 1]; RSSI avg represents the average value of the received signal strength indication value; RSSI min and RSSI max represent the maximum and minimum values of the received signal strength indication value respectively, which are determined according to specific communication standards and devices. For example, in the LTE network, the typical range of RSSI may be from -110 dBm (weak signal) to -50 dBm (strong signal).

[0025] In this solution, the wireless communication module is used to obtain the received signal strength indication value and calculate its average value, and then through the signal strength value calculation model formula, it is converted into the signal strength value RS with a value range of [0, 1]. This technology effectively solves the problem that in the vehicle network connection environment, the signal strength value units and dimensions are not unified under different operator networks and different communication systems (such as LTE and 5G), making it difficult to directly compare and comprehensively evaluate. It realizes the accurate quantization and standardization processing of the signal strength, making the signal strength presented in a unified dimensionless numerical form, which is convenient for subsequent comprehensive analysis and comparison with other network performance indicators, laying a foundation for accurately judging the network signal quality. Thus, in the process of vehicle adaptive network switching, it improves the accuracy and rationality of network selection, ensures that the vehicle preferentially connects to the network with better signal strength, and enhances the network communication quality.

[0026] Step 102: Obtain the measured useful signal power and noise power received through the vehicle's wireless communication module, and input the useful signal power and noise power into the signal-to-noise ratio calculation model to calculate the signal-to-noise ratio. The formula on which it is based is as follows: ; Where SN represents the signal-to-noise ratio; P signal represents the useful signal power; P noise represents the noise power.

[0027] The solution obtains the useful signal power and noise power through the wireless communication module, which can solve the problem of inaccurate signal-to-noise ratio in the traditional vehicle network signal evaluation. The previous evaluation methods may only rely on simple indicators such as signal strength and cannot comprehensively reflect the signal quality. As a key indicator for measuring signal quality, the formula converts the ratio of useful signal power to noise power into an easily comparable numerical form through precise mathematical calculations, thereby providing a more accurate and intuitive quantitative basis for the vehicle network signal quality evaluation. During the vehicle's driving process, the signal interference degree varies in different environments. Through this formula, the signal-to-noise ratio can be accurately obtained, providing reliable data support for subsequent comprehensive analysis of signal quality parameters.

[0028] Step 103: Determine the distance and propagation environment between the vehicle and the base station through the vehicle's global positioning system and digital map data, and 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: ; where PL represents the path loss value; f is the operating frequency, h b is the antenna height of the base station, h m is the vehicle antenna height, d represents the distance between the base station and the vehicle, and α(h m ) is the height loss factor of the vehicle antenna, which can be calculated according to 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), C is a correction factor adjusted according to the propagation environment, generally between -2 and +4, depending on the propagation environment. In an urban environment, where there are dense buildings and many obstacles, the path loss is large, and the value of C is usually positive; in a suburban or open area, the path loss is relatively small, and the value of C may be negative or zero.

[0029] It should be noted that before calculating the path loss value, several corresponding parameters need to be normalized and preprocessed to eliminate the dimensions of different parameters, facilitating subsequent formula calculations.

[0030] The solution utilizes the global positioning system and digital map data of the vehicle, combines with the Okumura-Hata model to calculate the path loss value. This technology focuses on solving the problems of inaccurate path loss assessment and difficulty in adapting to complex and changeable propagation environments in traditional vehicle network communication. Previous vehicle network systems may estimate path loss only based on single factors such as simple distance or signal strength, unable to comprehensively consider the combined effects of multiple factors such as operating frequency, base station and vehicle antenna heights, and propagation environment, resulting in large errors in path loss assessment in different environments. Through the Okumura-Hata model, multiple key factors such as operating frequency, base station antenna height, vehicle antenna height, distance between the vehicle and the base station, and propagation environment correction factor are comprehensively considered, and the path loss value can be accurately calculated. This accurate path loss calculation enables the vehicle network system to better adapt to various complex communication environments. Whether in the urban scene with dense buildings and numerous obstacles or in the open area of the suburbs, it can accurately evaluate the network connection status based on the actual path loss situation.

[0031] Step 2: Through further analysis of the signal quality index, the comprehensive signal quality index is calculated, and the calculation formula is as follows: ; Among them, SQI represents the comprehensive signal quality index; 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; and β1 + β2 + β3 = 1, which can be adjusted according to the actual situation. Here, the values can be taken as: β1 = 0.4, β2 = 0.3, β3 = 0.3.

[0032] It should be noted that before calculating the signal quality index, several corresponding parameters need to be preprocessed by normalization to eliminate the dimensions of different parameters, facilitating subsequent formula calculations.

[0033] The solution comprehensively considers the signal strength value, signal-to-noise ratio, and path loss value, sets the weight coefficients according to the importance of each factor and the actual situation, and calculates the signal quality index. This technology focuses on solving the problem that traditional signal quality assessment only relies on a single index and cannot comprehensively reflect the network communication quality. In the past, relying solely on single indicators such as signal strength or signal-to-noise ratio for assessment could not accurately reflect the true situation of the network. During the vehicle's driving process, the signal strength, signal-to-noise ratio, and path loss of different networks may change with the environment, and a single index may fluctuate greatly, while the comprehensive index SQI is more stable and reliable. It can truly reflect the changing trend of network communication quality, enabling the vehicle to detect the deterioration of network quality in advance, make timely warnings and switch to a better-quality network, reduce communication interruptions or data transmission delays caused by network quality problems, improve the success rate and timeliness of network switching, ensure the continuity and stability of vehicle communication, and enhance the user experience.

[0034] Step 3: Obtain network stability data and calculate network stability parameters through analysis and calculation.

[0035] Step 301: Obtain the maximum nominal bandwidth of the network through the operator's package contract, and measure the current available bandwidth of the vehicle in real time using the iperf3 tool; input the maximum bandwidth and the available bandwidth into the bandwidth effectiveness factor calculation model to calculate the bandwidth effectiveness factor. The calculation formula on which the bandwidth effectiveness factor calculation model is based is as follows: ; where BE represents the bandwidth effectiveness factor; B ant represents the current available bandwidth; B anmax represents the maximum bandwidth; e represents the base of the natural logarithm, with a value of 2.71828; λ represents the bandwidth utilization sensitivity coefficient, with a value range of [0, 1]. When the available bandwidth approaches the nominal value, the attenuation effect weakens, and the value taken is smaller.

[0036] The solution combines the current available bandwidth and the maximum nominal bandwidth of the network, and introduces the bandwidth utilization sensitivity coefficient, which can accurately evaluate the actual utilization efficiency of the vehicle network bandwidth, and solves the problem that traditional bandwidth evaluation methods only focus on the size of the bandwidth value while ignoring the bandwidth utilization efficiency. Traditional bandwidth evaluation often only looks at the absolute value of the current available bandwidth and cannot accurately reflect whether the bandwidth is effectively utilized. The formula introduces the maximum bandwidth as a benchmark to calculate the relative utilization rate of the current bandwidth, and considers the change in marginal benefit brought about by the increase in the degree of bandwidth utilization through an exponential decay function. The bandwidth effectiveness factor calculated by the formula can comprehensively consider the actual utilization efficiency of the current bandwidth, enabling the vehicle to more comprehensively evaluate the bandwidth performance of the target network when performing network switching. When the BE value is low, even if the signal quality is acceptable, the system will give priority to switching to a network with higher bandwidth utilization efficiency, thereby ensuring that the vehicle is always connected to a network that can provide the best bandwidth performance, improving the stability and fluency of network usage, and optimizing the user experience.

[0037] Step 302: Obtain the packet loss rate through the communication module and calculate the packet loss tolerance value based on the packet loss tolerance calculation model; the calculation formula on which the calculation model is based is as follows: ; where PLR represents the packet loss tolerance value, DB represents the packet loss rate, and ε represents the smoothing factor to avoid a denominator of 0.

[0038] The solution combines the square root function and the hyperbolic tangent function to convert the packet loss rate into a tolerance metric that better meets the actual communication requirements. When the packet loss rate is low, the PLR value is high, indicating that the network has a high tolerance for packet loss; as the packet loss rate increases, the PLR value decreases non-linearly, reflecting an increased sensitivity of the network to packet loss. This precise quantification method makes the network quality assessment more meticulous and can better reflect the actual transmission capacity of the network; in the scenario of traffic sharing between vehicles, the accurate calculation of the packet loss tolerance value PLR provides a key network reliability reference for traffic sharing decisions, solving the problem of inaccurate assessment of packet loss in traditional traffic sharing decisions. In the past, when vehicles decided whether to share traffic, they might lack an in-depth assessment of the network packet loss situation, resulting in a higher packet loss risk in the network where traffic is shared. The PLR value calculated by this formula enables vehicles to comprehensively evaluate the packet loss tolerance of their own and the requesting vehicle's networks when receiving a traffic sharing request.

[0039] Step 303: Obtain the channel utilization rate through the communication module and calculate the channel utilization rate tolerance value according to the channel utilization rate calculation model. The formula is as follows: ; where CU represents the channel utilization rate tolerance value and XD represents the channel utilization rate.

[0040] The solution calculates the channel utilization rate tolerance value CU, where XD represents the channel utilization rate, which can effectively solve the problem of lack of precise quantification of the impact of channel utilization rate in traditional network quality assessment. Traditional assessment methods may only focus on the absolute value of the channel utilization rate and cannot accurately reflect the actual impact of the channel utilization rate on network communication quality. This formula combines the logarithmic function and linear transformation to convert the channel utilization rate into a tolerance value, which can more intuitively reflect the performance of the network under different channel utilization rates. When the channel utilization rate is low, the CU value is high, indicating that the network has a high tolerance for the channel utilization rate; as the channel utilization rate increases, the CU value decreases non-linearly, reflecting an increased sensitivity of the network to high channel utilization rates.

[0041] Step Four: Further analyze the network stability parameters to calculate the network stability index. The calculation formula is as follows: .

[0042] where NSI represents the network stability index.

[0043] It should be noted that before calculating the network stability index, several corresponding parameters need to be normalized and preprocessed to eliminate the dimensions of different parameters for subsequent formula calculations.

[0044] The solution realizes the accurate quantification of network stability by comprehensively considering three key factors: bandwidth, packet loss, and channel utilization rate, and introducing a smoothing factor to avoid extreme cases where the denominator is zero. Specifically, the bandwidth effectiveness factor BE reflects the actual utilization efficiency of the bandwidth, the packet loss tolerance value PLR reflects the network's tolerance to packet loss, and the channel utilization tolerance value CU reflects the impact of channel utilization rate on network performance. Multiplying these three factors gives the numerator part, which reflects the comprehensive performance of the network in terms of bandwidth, packet loss, and channel utilization rate. The denominator part reflects the deficiencies of the network in these aspects through (1 - BE)(1 - PLR)(1 - CU), and adding the smoothing factor ε ensures the stability of the calculation. This comprehensive quantification method makes the network stability evaluation more comprehensive and accurate, and can better reflect the actual operating conditions of the network.

[0045] Step Five: Obtain the traffic package data through the operator's package contract, and conduct a comprehensive analysis in combination with the signal quality index and the network stability index to obtain the traffic cost efficiency index. Step 501: Obtain the current operator's traffic unit price and the remaining traffic of the package through the operator's package contract.

[0046] Step 502: Predict the traffic demand value for the next half hour through the LSTM model, and conduct a comprehensive analysis in combination with the signal quality index and the network stability index to obtain the traffic cost efficiency index; the formula is as follows: ; where CEI represents the traffic cost efficiency index; CM represents the current operator's traffic unit price; PU represents the traffic prediction 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 (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 the actual situation and can take values: ω1 = 0.6, ω2 = 0.4.

[0047] It should be noted that before calculating the traffic cost efficiency index, several corresponding parameters need to be preprocessed by normalization to eliminate the dimensions of different parameters and facilitate subsequent formula calculations.

[0048] The formula comprehensively considers multi-dimensional factors such as signal quality, network stability, traffic cost, and traffic demand. It combines the signal quality index and the network stability index through weighted averaging, and considers the relationship between the traffic unit price and the ratio of future traffic demand to remaining traffic, thereby quantifying the cost efficiency of traffic usage. This solution uses the LSTM model to predict the future half-hour traffic demand value PU, combines it with the remaining traffic RD, enabling the CEI to dynamically reflect the traffic cost efficiency, helping users plan traffic usage in advance, avoiding arrears or waste, and helping users make better traffic usage decisions between different operator networks. When the CEI is high, it indicates that the traffic cost efficiency of the current network is high, and users can give priority to using this network; otherwise, they can consider switching to other networks or adjusting traffic usage strategies to reduce costs while ensuring network quality and improving the user experience.

[0049] Step 6: Set a set of judgment thresholds, and compare the thresholds in the set of judgment thresholds with the signal quality index, network stability index, and traffic cost efficiency index respectively; determine whether to execute the network switching warning instruction.

[0050] Step 601: Set the signal quality index threshold, network stability index threshold, and traffic cost efficiency index threshold to form a set of judgment thresholds.

[0051] Step 602: Compare the signal quality index with the signal quality index threshold. When the signal quality index < the signal quality index threshold, it means that the signal quality is unqualified; compare the network stability index with the network stability index threshold. When the network stability index < the network stability index threshold, it means 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 < the traffic cost efficiency index threshold, it means that the traffic cost is unqualified.

[0052] It should be noted that the signal quality index threshold, network stability index threshold, and traffic cost efficiency index threshold can be set according to historical empirical data. For example, the network data under normal network conditions at different time periods in historical data can be statistically analyzed, and the corresponding signal quality index and network stability index can be calculated respectively. Then, the average value and variance value can be calculated respectively. The signal quality index threshold can be set as 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 as 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 determine the threshold range of the traffic cost efficiency index according to the sensitivity of vehicle users to traffic costs and the requirements for network transmission efficiency. It can also be referred to by statistically analyzing the questionnaires of different customers and adjusted according to the real-time price. For example, for users sensitive to costs, when the network cost-benefit ratio is higher than 0.3 yuan / Mbps, it can be considered that the traffic cost efficiency is low, and the corresponding traffic cost efficiency index threshold is set to 0 - 0.3; when it is 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.

[0053] Step 603: When the evaluation result of any index is unqualified, execute the network switching warning instruction.

[0054] By comprehensively considering multi-dimensional factors such as signal quality, network stability, and traffic cost, the solution makes the network switching decision more scientific and reasonable, and can provide users with a more stable and smoother network experience.

[0055] Step Seven: 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 to switch the network.

[0056] Step 701: Obtain the unit traffic cost value through the operator tariff table; obtain the load data of the base station through the active broadcast of the base station, use the vehicle-mounted map module to obtain the current position of the vehicle and the distribution information of surrounding base stations, and combine the set driving route to calculate the predicted distance of the nearest base station of different operators through the vehicle-mounted computing unit or ECU; obtain the coverage radius data nominal for different base stations from the operator network server or the base station information database pre-stored locally in the vehicle by the vehicle-mounted communication module.

[0057] Step 702: Through comprehensive analysis of the network switching data, obtain the switching priority index of different operators, and the formula is as follows: ; Among them, DSPk represents the switching priority index of k operators; RSS kRepresents the signal strength value of the base station of the k-th operator; R k Represents the coverage radius of the base station of the k-th operator; Dk represents the predicted distance from the base station of the k-th operator to the vehicle; Ck represents the unit traffic cost value of the k-th operator; Pk represents the network load index of the base stations of k operators; k represents the serial number of the operator, taking positive integer values; φ1 represents the weight coefficient of the signal strength value; φ2 represents the weight coefficient of the ratio of the coverage radius to the predicted distance; φ3 represents The weight coefficient of, which can be adjusted according to the actual situation. Here, it can take the value: φ1 = 0.4, φ2 = 0.2, φ3 = 0.4.

[0058] The formula synthesizes multiple key factors such as signal strength, base station coverage range and vehicle distance, traffic cost, and network load, and calculates the handover priority index of each operator's network through weighted summation, so as to provide a quantitative basis for network handover decisions; it provides a comprehensive and quantitative evaluation basis for network handover, enabling the vehicle to accurately select the optimal network for handover among multiple operator networks, reducing misjudgment caused by single-index evaluation, and improving the accuracy and rationality of network handover.

[0059] It should be noted that before calculating the handover priority index, several corresponding parameters need to be normalized and preprocessed to eliminate the dimensions of different parameters for subsequent formula calculations.

[0060] It should be noted that the calculation method of Pk is: ; where δ represents the load sensitivity coefficient, taking values in [0, 5]. Here, it can take the value of 0.2.

[0061] Step 703: Traverse the handover priority indices of different operators, select the operator network corresponding to the maximum handover priority index, and perform network handover.

[0062] Step Eight: During the normal network usage process, if a traffic sharing request from another vehicle is received, comprehensively evaluate the network usage requirements of the vehicle itself and the requesting vehicle, and determine whether to execute the traffic sharing instruction.

[0063] Step 801: Through comprehensive analysis of the network usage data of the vehicle itself and the request data of the requesting vehicle, calculate the traffic sharing ability index; the formula is as follows: ; Where Ban Q Represents the required bandwidth of the requesting vehicle; BC represents the battery consumption percentage, taking values in [0, 100]; TL represents the trust level of the requesting vehicle, which can be obtained from the trust library of the blockchain, taking values in [0, 1].

[0064] It should be noted that before calculating the traffic sharing capacity index, several corresponding parameters need to be preprocessed by normalization to eliminate the dimensions of different parameters, which is convenient for subsequent formula calculations.

[0065] Traditional methods only focus on the remaining traffic volume and do not comprehensively consider factors such as bandwidth supply and demand, battery status, and trust level, which easily lead to unreasonable sharing decisions. By comprehensively considering multiple factors, when a vehicle receives a sharing request, it can comprehensively evaluate its own sharing capacity and the credibility of the requester.

[0066] Step 802: Set the traffic sharing capacity index threshold, and compare the traffic sharing capacity index with the traffic sharing capacity index threshold. When the traffic sharing capacity index > the traffic sharing capacity index threshold, allow the execution of the traffic sharing instruction; when the traffic sharing capacity index ≤ the traffic sharing capacity index threshold, reject the execution of the traffic sharing instruction.

[0067] When the 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 the user experience, comprehensively evaluates the bandwidth supply and demand situation, and the vehicle can reasonably share the excess bandwidth with other vehicles while meeting its own needs, avoiding traffic waste, and improving the traffic resource utilization rate of the entire vehicle networking system. It should be noted that by analyzing historical traffic sharing requests and execution data, the typical distribution of the traffic sharing capacity index is determined. For example, when calculating the signal strength value for different traffic sharing capacity indexes during the execution of the traffic sharing instruction, the distribution of the signal strength values is statistically analyzed, and the traffic sharing capacity index corresponding to no impact on the signal strength value of one's own vehicle is selected as the traffic sharing capacity index threshold.

[0068] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware or a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution.

[0069] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0070] As described above, it is only the specific implementation manner of this application. However, the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed by this application can easily think of changes or substitutions, which should all be covered within the protection scope of this application.

Claims

1. A vehicle adaptive network switching and traffic sharing method based on multi-source collaboration, characterized in that Including: Step 1: Obtain signal quality data through the vehicle's wireless communication module, the vehicle's Global Positioning System, and digital map data, analyze it to obtain signal quality parameters, and analyze to obtain a signal quality index; Step 2: Calculate a comprehensive signal quality index by analyzing the signal quality index; Step 3: Obtain network stability data through the operator's package contract and the communication module, and through analysis and calculation, obtain network stability parameters; Step 4: Calculate a network stability index by analyzing the network stability parameters; Step 5: Obtain traffic package data through the operator's package contract, and conduct a comprehensive analysis in combination with the signal quality index and the network stability index to obtain a traffic cost efficiency index; Step 6: Set a set of judgment thresholds, and compare the thresholds in the set of judgment thresholds with the signal quality index, the network stability index, and the traffic cost efficiency index respectively; Determine whether to execute a network switching warning instruction; Step 7: After executing the network switching warning instruction, obtain network switching data of different operators, calculate the switching priority index of the corresponding operator according to the network switching data, and select to switch the network; Step 8: During normal network usage, if a traffic sharing request from another vehicle is received, comprehensively evaluate the network usage requirements of one's own vehicle and the requesting vehicle, and determine whether to execute a traffic sharing instruction.

2. The vehicle adaptive network switching and traffic sharing method based on multi-source collaboration according to claim 1, characterized in that The signal quality parameters include signal strength value, signal-to-noise ratio, and path loss value; Specifically: Obtain the Received Signal Strength Indicator (RSSI) value within a unit period through the vehicle's built-in wireless communication module, calculate the average value of the RSSI value, and calculate the signal strength value RS through the signal strength value calculation model; Obtain the measured useful signal power and noise power received through the vehicle's wireless communication module, calculate the signal-to-noise ratio based on the useful signal power and noise power, and calculate the signal-to-noise ratio SN through the signal-to-noise ratio calculation model; Determine the distance between the vehicle and the base station and the propagation environment through the vehicle's Global Positioning System and digital map data, and then use the Okumura-Hata model to calculate the path loss value PL.

3. The method for vehicle adaptive network switching and traffic sharing based on multi-source collaboration according to claim 2, wherein By further analyzing the signal quality index, calculate the comprehensive signal quality index SQI, and the calculation formula is as follows: ； 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.

4. The method for vehicle adaptive network switching and traffic sharing based on multi-source collaboration according to claim 3, wherein The network stability parameters include bandwidth effectiveness factor, packet loss tolerance value, and channel utilization tolerance value; Specifically: Obtain the nominal maximum bandwidth of the network through the operator's package contract, measure the current available bandwidth of the vehicle in real time, and input the maximum bandwidth and the 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 tolerance PLR according to the packet loss rate tolerance calculation model; Obtain the channel utilization rate through the communication module, and calculate the channel utilization tolerance value CU according to the channel utilization rate calculation model.

5. The method for vehicle adaptive network switching and traffic sharing based on multi-source collaboration according to claim 4, wherein By further analyzing the network stability parameters, calculate the network stability index, and the calculation formula is as follows: ； Among them, NSI represents the network stability index.

6. The vehicle adaptive network switching and traffic sharing method based on multi-source collaboration according to claim 5, characterized in that Obtain the current operator's traffic unit price and the remaining traffic of the package through the operator's package contract; predict the traffic demand value in the next half hour through the LSTM model, and conduct comprehensive analysis in combination with the signal quality index and the network stability index to obtain the traffic cost efficiency index; the formula 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 prediction value in 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.

7. The method for vehicle adaptive network switching and traffic sharing based on multi-source collaboration according to claim 6, characterized in that The method for judging whether to execute the network switching warning instruction is as follows: Set the signal quality index threshold, the network stability index threshold, and the 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 < the signal quality index threshold, it means that the signal quality is unqualified; compare the network stability index with the network stability index threshold. When the network stability index < the network stability index threshold, it means 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 < the traffic cost efficiency index threshold, it means that the traffic cost is unqualified; When the evaluation result of any one of the indicators is unqualified, execute the network switching warning instruction.

8. The method for vehicle adaptive network switching and traffic sharing based on multi-source collaboration according to claim 7, wherein Calculate the switching priority index of different operators. The formula is as follows: ； Among them, DSPk represents the handover 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 base station of the kth operator; Dk represents the predicted distance of the base station of the kth operator from the vehicle; Ck represents the unit traffic cost value of the kth operator; Pk represents the network load index of the base stations of k operators; k represents the serial number of the operator; φ1 represents the weight coefficient of the signal strength value; φ2 represents the weight coefficient of the ratio of the coverage radius to the predicted distance; φ3 represents the weight coefficient of; Traverse the switching priority index of different operators, and select the operator network corresponding to the maximum value of the switching priority index for network switching.

9. The method for vehicle adaptive network switching and traffic sharing based on multi-source collaboration according to claim 7, wherein Comprehensively evaluate the network usage requirements of the vehicle itself and the requesting vehicle, and calculate the traffic sharing ability index. The formula is as follows: ； 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, with a value range of [0, 1].

10. The method for vehicle adaptive network switching and traffic sharing based on multi-source collaboration according to claim 9, wherein, The method for judging whether to execute the traffic sharing instruction is as follows: Set the traffic sharing ability index threshold, compare the traffic sharing ability index with the traffic sharing ability index threshold. When the traffic sharing ability index > the traffic sharing ability index threshold, allow the execution of the traffic sharing instruction; when the traffic sharing ability index ≤ the traffic sharing ability index threshold, reject the execution of the traffic sharing instruction.

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