Multi-link encryption transmission tunnel intelligent switching method

Abstract

The invention discloses an intelligent switching method for a multi-link encrypted transmission tunnel, and relates to the technical field of multi-link communication, the method constructs a multi-link communication system, combines real-time monitoring and intelligent analysis, can provide redundant and diversified link selection, and improves the communication efficiency on the basis of ensuring the communication stability and reliability. And the encryption strength and priority of the link are dynamically adjusted, and the security and efficiency of data transmission are optimized. The system intelligently judges link quality and reduces switching delay by monitoring factors such as electromagnetic interference, driving speed and building shielding in real time and combining an anti-interference capability index Kgr; and when the link quality is unstable, the encryption strength is dynamically adjusted and the link with better performance is switched to. In combination with vehicle load data and packet loss data, the system can further optimize link judgment and switch to a safer link in time to avoid communication interruption. The method is especially suitable for efficient and safe communication requirements of the unmanned vehicle in a complex and high-speed driving environment.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-link communication, and particularly to an intelligent switching method for multi-link encrypted transmission tunnels. Background Art

[0002] Driverless vehicles are a core component of modern intelligent transportation systems, and their operation highly depends on the reliability and security of network transmission. With the continuous development of vehicle-to-everything (V2X) technology, driverless vehicles not only need to communicate with cloud servers in real time but also interact with roadside units (RSUs), other vehicles (V2V), and pedestrian devices (V2P). These interaction data include high-precision map updates, traffic signal information, vehicle status data, and driving decision information, etc., which are characterized by high real-time, high bandwidth requirements, and high security requirements.

[0003] In actual application scenarios, the network transmission of driverless vehicles faces many challenges. First, the road environment is complex and changeable. For example, the signal coverage conditions in areas such as highways, urban streets, tunnels, and mountains vary significantly, which may lead to unstable links or signal interruptions. Second, the high-speed movement of vehicles causes frequent base station handovers, increasing data transmission latency and packet loss rate. Third, the communication environment of driverless vehicles is usually in a high-interference area, such as signal interference from other vehicle communication devices and physical obstacles blocking signals. In addition, potential network attack risks in the V2X system, such as man-in-the-middle attacks, data tampering, and information theft, etc., also pose higher requirements for the security of network transmission.

[0004] The traditional single-link network transmission method has been difficult to meet the communication requirements of driverless vehicles in complex environments. On the one hand, the single-link system cannot quickly switch links in case of network congestion or signal interruption, easily causing data transmission latency and affecting the real-time nature of driving decisions. On the other hand, a single encryption algorithm is vulnerable to specific attack methods and cannot cope with the ever-changing network security challenges. Therefore, to ensure the communication stability and security of driverless vehicles, there is an urgent need for a transmission method that supports multi-link dynamic switching and multiple encryptions, which can achieve efficient and low-latency network data transmission in complex communication environments. Summary of the Invention

[0005] Aiming at the deficiencies of the prior art, the present invention provides an intelligent switching method for multi-link encrypted transmission tunnels to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention is realized through the following technical solutions: An intelligent switching method for multi-link encrypted transmission tunnels, comprising the following steps:

[0007] S1. Pre - establish a multi - link communication system, which includes at least two communication links, including cellular networks, V2X communication, Wi - Fi, and satellite communication. Perform data encryption processing on each link based on different transmission standards to generate the first encryption instruction;

[0008] S2. Obtain the real - time location information of the driverless vehicle and monitor the influencing factors affecting the link performance in real - time; Analyze and calculate to obtain the electromagnetic interference factor E, driving speed factor V, building occlusion factor B, and weather condition factor W, and establish an interference data set; Construct the anti - interference ability index Kgr for each link;

[0009] S3. Use the neural network CNN technology to establish and train a link quality analysis model. Combine the real - time signal strength, delay, and bandwidth of each link, and associate with the anti - interference ability index Kgr to calculate and obtain the first communication quality index Q of each link i : And preliminarily evaluate the first communication quality index Q of each link i , to dynamically adjust the encryption intensity;

[0010] S4. After dynamically adjusting the encryption intensity in S3, collect the load data and packet loss data of each link connecting several vehicles, combine with the first communication quality index Q of each link i , associate to obtain the second communication quality index Q2, and perform a secondary determination on the second communication quality index Q2. If the second communication quality index Q2 is lower than the second communication quality threshold R2, it is determined as unsafe and automatically switched to a secure link; And perform a priority ranking on each available communication link, and preferentially select the first - priority link as the data transmission link of the current vehicle.

[0011] Preferably, S1 specifically includes:

[0012] S11. When the system starts, identify the type of each link; The link types include but are not limited to: cellular network 4G / 5G, V2X communication, Wi - Fi, and satellite communication;

[0013] S12. Perform data encryption processing on the transmission standard of each link to generate the first encryption instruction for encrypting data, specifically including:

[0014] For cellular network 4G / 5G and for satellite communication, select the symmetric encryption algorithm AES - 128 for encryption; For Wi - Fi and V2X communication, use the first public - key encryption algorithm for encryption.

[0015] Preferably, the first public - key encryption algorithm includes the following steps:

[0016] S121. Generate the first random prime number p and the second random prime number q for generating the key;

[0017] S122. Multiply the first random prime number p and the second random prime number q to obtain the common modulus n of the key pair.

[0018] S123. Calculate the Euler's totient function according to the first random prime number p, the second random prime number q, and the common modulus n of the key pair. The expression is:

[0019] φ(n) = (p - 1) × (q - 1);

[0020] In the formula, φ(n) represents the number of relatively prime integers of the common modulus n of the key pair.

[0021] S124. Randomly select the public key exponent e among the number of relatively prime integers φ(n) of the common modulus n of the key pair, satisfying the following conditions:

[0022] 1 < e < φ(n) and gcd(e, φ(n)) = 1;

[0023] In the formula, select e = 65537 and use the Euclidean algorithm to verify whether the public key exponent e is relatively prime to φ(n).

[0024] S125. Calculate the private key exponent d. The formula is:

[0025] d = e -1 (mod φ(n));

[0026] And use the extended Euclidean algorithm to find that the private key exponent d satisfies the following relationship:

[0027] e × mod φ(n) = 1;

[0028] In the formula, ensure that the private key exponent d is the unique solution and satisfies the condition 1 < d < φ(n).

[0029] S126. Based on S121 - S125, generate the public key (e, n) and the private key (d, n).

[0030] Preferably, S2 includes:

[0031] S211. Use the GPS positioning system to collect the real - time position information of the vehicle, including longitude, latitude, altitude, and driving speed data, establish an electronic map, and mark the position data on the electronic map as: (x, y, z, v); x represents longitude, y represents latitude, z represents altitude, and v represents the driving speed v.

[0032] S212. Install an electromagnetic wave intensity sensor on the vehicle to measure the power P of the interference signal in the environment in real - time i , and lock the real - time position of the interference source.

[0033] S213. By installing an electromagnetic wave intensity sensor on the vehicle, collect and obtain the total received power P of the link t ; and collect the transmission distance d between the vehicle and the link transmitter and the relative position D between the interference source and the vehicle;

[0034] S214. Extract the interference signal power P i , the total received power P of the link t , the transmission distance d between the vehicle and the link transmitter, and the relative position D between the interference source and the vehicle. After dimensionless processing, calculate and obtain the electromagnetic interference factor E through the following formula:

[0035]

[0036] S215. By using the in-vehicle speed sensor on the vehicle, measure the current driving speed v of the vehicle in real time, and obtain the signal carrier frequency f of the communication link through the frequency detection device in the communication module. Calculate and obtain the driving speed factor V through the following formula:

[0037]

[0038] In the formula, c represents the speed of light, which is set to 3×10 8 m / s.

[0039] Preferably, S2 further includes:

[0040] S216. By using lidar, collect the height h of the vehicle passing by the building, the signal path, and the angle θ between the signal propagation path and the normal of the building in real time;

[0041] S217. Collect the signal wavelength of the vehicle driving around the building, and calculate and obtain the diffraction coefficient β through the following formula:

[0042]

[0043] In the formula, λ represents the signal wavelength, where c represents the speed of light, f is the signal carrier frequency of the communication link; α is the width of the building, θ is the angle between the propagation path and the normal of the building; log 10 represents the logarithm operation with base 10;

[0044] S218. Extract the height h of the building, the diffraction coefficient β, and the transmission distance d between the vehicle and the link transmitter. After dimensionless processing, calculate and obtain the building occlusion factor B through the following formula:

[0045]

[0046] Where ρ represents the building material coefficient, including: when the building material is glass, ρ = 0.1; when the building material is concrete, ρ = 0.8; when the building material is steel, ρ = 1.2.

[0047] Preferably, S2 further includes:

[0048] S219. By installing a meteorological sensor on the vehicle, the air humidity sd, air temperature wd, and real-time air pressure qy of the external environment of the vehicle are collected in real time. After dimensionless processing, the atmospheric absorption coefficient A is calculated and obtained through the following formula:

[0049]

[0050] Where C1 represents the absorption coefficient constant, which is related to the signal frequency and air composition. For a 2.4 GHz signal, the typical value C1 = 0.01; represents the standard temperature threshold, represents the standard atmospheric pressure threshold;

[0051] S220. The rainfall intensity, fog density, and snowfall intensity are collected in real time, and the rain attenuation index K rain , and fog attenuation index K fog and snow attenuation index K snow are calculated and obtained through the following formula:

[0052] K rain = R n × f δ ;

[0053] K fog = η × M × f 2 ;

[0054] K snow = S × μ;

[0055] Where R represents the rainfall intensity, n represents the first empirical parameter, δ represents the second empirical parameter. When f = 5 GHz, n = 1.2; δ = 0.8; η represents the ratio constant, η = 0.2; M represents the fog density, which is obtained through the meteorological sensor; f represents the signal carrier frequency f of the communication link; S represents the snow intensity, which is obtained through the snow amount sensor, and μ represents the refractive coefficient of snow for the signal, μ = 0.15;

[0056] S221. Combining the atmospheric absorption coefficient A, rain attenuation index K rain , and fog attenuation index K fog and snow attenuation index K snow : The weather factor W is calculated and obtained through the following formula:

[0057] Kz = K rain + K fog + Ksnow

[0058] W = A×e -Kz×d ;

[0059] In the formula, Kz represents the comprehensive attenuation factor, d represents the transmission distance between the vehicle and the link transmitter; e -Kz×d represents the exponential attenuation term, indicating that the signal attenuation increases with the increase of the transmission distance d, and e is the base of the natural logarithm;

[0060] S222. Combine the electromagnetic interference factor E, the driving speed factor V, the building shielding factor B, and the weather condition factor W to establish an interference data set; and after dimensionless processing of the electromagnetic interference factor E, the driving speed factor V, the building shielding factor B, and the weather condition factor W, obtain the anti-interference ability index Kgr through the following associated formula:

[0061]

[0062] In the formula, a1, a2, a3, and a4 respectively represent the weight values of the electromagnetic interference factor E, the driving speed factor V, the building shielding factor B, and the weather condition factor W, and a1 + a2 + a3 + a4 ≥ 1.

[0063] Preferably, S3 includes:

[0064] S31. Match the electromagnetic interference factor E, the driving speed factor V, the building shielding factor B, the weather condition factor W, and the actual signal strength, delay, and bandwidth data of each link to construct a training data set, and use a neural network model to establish a link quality analysis model, and divide it into a 70% training set, a 15% validation set, and a 15% test set through the training data set for training and validation;

[0065] S32. Through the trained link quality analysis model, combine the real-time signal strength, delay, and bandwidth of each link, and associate with the anti-interference ability index Kgr, and calculate and obtain the first communication quality index Q of each link through the following formula i :

[0066]

[0067] In the formula, Q i represents the first communication quality index of the i-th link, Sx i represents the real-time signal strength of the i-th link, Yc i represents the real-time delay of the i-th link, Dk i represents the real-time bandwidth of the i-th link, Kgr i,k represents the anti-interference ability index of the j-th vehicle of the i-th link, and a5, a6, a7, and a8 represent weight values, and a5 + a6 + a7 + a8 ≥ 1;

[0068] S33. Preset the first communication quality threshold R1, and evaluate the first communication quality index Q of the i-th link i against the first communication quality threshold R1 to preliminarily determine whether the link communication quality is qualified, including:

[0069] When the first communication quality index Q of the i-th link i ≥ the first communication quality threshold R1, it indicates that the communication quality of the current i-th link is qualified, and there is no need to change the encryption. Continue to communicate with the first encryption instruction;

[0070] When the first communication quality index Q of the i-th link i < the first communication quality threshold R1, it indicates that the communication quality of the current i-th link is unqualified, and a second encryption instruction is generated, specifically including:

[0071] For cellular networks 4G / 5G and for satellite communications, change the encryption from selecting the symmetric encryption algorithm AES-128 to selecting the symmetric encryption algorithm AES-256; for Wi-Fi and V2X communications, use encryption based on the second public key encryption algorithm.

[0072] Preferably, the second public key encryption algorithm is an extension of the first public key encryption algorithm, specifically including the following steps:

[0073] S321. Generate the first random prime number p, the second random prime number q, and the third random prime number r for generating the key;

[0074] S322. Multiply the first random prime number p, the second random prime number q, and the third random prime number r to obtain the common modulus n of the key pair;

[0075] S323. Calculate the Euler's totient function according to the first random prime number p, the second random prime number q, the third random prime number r, and the common modulus n of the key pair. The expression is:

[0076] φ(n) = (p - 1)×(q - 1)×(r - 1);

[0077] In the formula, φ(n) represents the number of relatively prime integers of the common modulus n of the key pair;

[0078] S324. Randomly select the public key exponent e among the relatively prime integers φ(n) of the common modulus n of the key pair, satisfying the following conditions:

[0079] 1 < e < φ(n) and gcd(e, φ(n)) = 1;

[0080] Wherein, the range of the dynamically adjusted public key exponent e is e ∈ {3, 5, 17, 65537}, and the Euclidean algorithm is used for verification to check whether the public key exponent e is relatively prime to φ(n);

[0081] S325. Calculate the private key exponent d using the formula:

[0082] d = e -1 (mod φ(n));

[0083] And through the extended Euclidean algorithm, find the private key exponent d that satisfies the following relationship:

[0084] e × mod φ(n) = 1;

[0085] Wherein, ensure that the private key exponent d is the unique solution and satisfies the condition 1 < d < φ(n);

[0086] S326. Based on S321 - S325, generate the public key (e, n) and the private key (d, n);

[0087] The difference between the second public key encryption algorithm and the first public key encryption algorithm lies in the different number of random prime numbers and the different public key exponent e, which increases the difficulty of key cracking.

[0088] Preferably, S4 includes:

[0089] S41. Collect the number of currently connected vehicles N c on each link and the data load L of each vehicle, and calculate the total link load L through the following formula t :

[0090]

[0091] Wherein, L i represents the data load of the i-th vehicle;

[0092] S42. Use a network monitoring tool to record the total number of packets P l sent on the link and the number of lost packets P t , and calculate the packet loss rate P through the following formula loss :

[0093]

[0094] S43. According to the total link load L t , the packet loss rate P loss and the first communication quality index Q i , after dimensionless processing, obtain the second communication quality index Q2 through the following formula:

[0095] Q2 = Q i × exp(-ω1 × Lt )×(1 - ω2×P loss );

[0096] Wherein, ω1 is the influence coefficient of the load on the communication quality, and its value is set between 0.01 - 0.1; ω2 is the influence coefficient of the packet loss rate on the communication quality, and its value is set between 0.1 - 1; when the link load L t is relatively high, Q2 will decrease with the exponential function exp. Similarly, the increase of the packet loss rate P loss will also reduce Q2.

[0097] Preferably, S4 further includes:

[0098] S44. Preset a second communication quality threshold R2, and evaluate the second communication quality index Q2 with the second communication quality threshold R2 to secondarily judge whether the link communication security is abnormal, including:

[0099] When the second communication quality index Q2 ≥ the second communication quality threshold R2, it indicates that the current link communication is secure, and a first security flag is generated;

[0100] When the second communication quality index Q2 < the second communication quality threshold R2, it indicates that the current link communication is insecure, and automatically switch to a secure link where the second communication quality index Q2 ≥ the second communication quality threshold R2;

[0101] And sort the second communication quality indices Q2 of the multiple links generating the first security flag in descending order of priority to generate a switching queue, and preferentially select the link with the maximum value of the second communication quality index Q2 in the switching queue as the first - priority link, as the data transmission link of the current vehicle.

[0102] The present invention provides a multi - link encrypted transmission tunnel intelligent switching method. It has the following beneficial effects:

[0103] (1) By constructing a communication system including multiple communication links (such as cellular networks, V2X communications, Wi - Fi, and satellite communications), the present invention provides redundancy and diverse options in wireless communication links. Even if a certain link fails or its quality deteriorates, the system can intelligently switch to an alternative link, thereby improving the stability and reliability of the communication system, and is particularly suitable for the communication requirements of driverless vehicles in complex environments.

[0104] (2) By real - time monitoring the electromagnetic interference factor E, driving speed factor V, building occlusion factor B, and weather condition factor W that affect the performance of communication links, and calculating the anti - interference ability index Kgr, the system can intelligently judge the quality of each link. This method optimizes the link selection strategy by combining the establishment of an interference data set and real - time data, thereby reducing the delay during link switching and ensuring the efficient communication of driverless vehicles in complex environments.

[0105] (3) Based on the link quality analysis, the present invention can dynamically adjust the encryption intensity according to the first communication quality index Q i and can switch from the first encryption instruction to the second encryption instruction to ensure the security of data during transmission. When the communication link quality is unstable or in an insecure state, the system can ensure the confidentiality and integrity of data transmission by adjusting the encryption intensity and selecting high-quality links, meeting the strict requirements of driverless vehicles for secure communication.

[0106] (4) The present invention further optimizes the link quality judgment by combining the vehicle's load data and packet loss data. By calculating the second communication quality index Q2 and performing a secondary determination, the system can timely determine whether there are performance problems with the link and automatically switch to a more secure and efficient link when an insecure situation occurs. Prioritizing the selection of the link with the best performance effectively avoids communication interruptions and improves the efficiency and quality of data transmission, especially providing significant advantages in dynamic and high-speed driving scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0107] Figure 1 It is a schematic diagram of the steps of an intelligent switching method for a multi-link encrypted transmission tunnel according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0108] The technical solutions in the embodiments of the present invention will be clearly and completely described below 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 of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0109] Embodiment 1

[0110] Please refer to Figure 1 , the present invention provides an intelligent switching method for a multi-link encrypted transmission tunnel, including the following steps:

[0111] S1. A multi-link communication system is established in advance, which includes at least two or more communication links, including cellular networks, V2X communication, Wi-Fi, and satellite communication. Data encryption processing is performed on each link based on different transmission standards to generate the first encryption instruction;

[0112] S2. Obtain the real-time position information of the driverless vehicle and monitor the influencing factors affecting the link performance in real time; analyze and calculate to obtain the electromagnetic interference factor E, the driving speed factor V, the building occlusion factor B, and the weather condition factor W, and establish an interference data set; construct the anti-interference ability index Kgr of each link;

[0113] S3. Use the neural network CNN technology to establish and train a link quality analysis model, combine the real-time signal strength, delay, and bandwidth of each link, and associate with the anti-interference ability index Kgr to calculate and obtain the first communication quality index Q of each link i : And preliminarily evaluate the first communication quality index Q of each link i , so as to dynamically adjust the encryption strength;

[0114] S4. After dynamically adjusting the encryption strength in S3, collect the load data and packet loss data of each link connecting several vehicles, and combine with the first communication quality index Q of each link i , associate to obtain the second communication quality index Q2, and perform a secondary determination on the second communication quality index Q2. If the second communication quality index Q2 is lower than the second communication quality threshold R2, it is determined to be unsafe and automatically switch to a secure link; and perform a priority ranking on each available communication link, and preferentially select the first priority link as the data transmission link of the current vehicle.

[0115] In this embodiment, by introducing a multi-link communication system such as a cellular network, V2X communication, Wi-Fi, and satellite communication, the vehicle can select the link with the best signal for data transmission at any time, thereby reducing the risk brought by single-link failure or poor signal. When the link is unstable, interfered greatly, or the link is determined to be unsafe, the system can automatically switch to a link with a higher priority to ensure the continuity and stability of communication, which is especially suitable for complex and changeable road environments.

[0116] By introducing the electromagnetic interference factor E, driving speed factor V, building occlusion factor B, and weather condition factor W, establish the anti-interference ability index Kgr to quantitatively evaluate the link performance, which helps to quickly screen out communication links with stronger anti-interference ability. Build a link quality analysis model based on CNN technology, combine interference factors with link performance data, and dynamically evaluate the communication quality of the link, effectively improving the communication stability of the vehicle in high-interference areas.

[0117] Combine the real-time signal strength, delay, and bandwidth of each link, and associate with the anti-interference ability index Kgr to calculate and obtain the first communication quality index Q of each link i : And preliminarily evaluate the first communication quality index Q of each link i , so as to dynamically adjust the encryption strength; Through multi-link and multi-layer encryption technologies, significantly enhance the security of data during transmission, and resist network attacks such as man-in-the-middle attacks and data tampering. According to the link communication quality, dynamically adjust the complexity and strength of the encryption algorithm, balance the resource overhead of encryption calculation and communication security, and ensure the communication reliability in scenarios with high security requirements.

[0118] After dynamically adjusting the encryption strength in S3, collect the load data and packet loss data of each link connecting several vehicles, and combine the first communication quality index Q of each link i , associate to obtain the second communication quality index Q2, dynamically optimize the communication link selection, and reduce the transmission delay and packet loss rate. Sort the links by priority, and preferentially select the link with the highest transmission efficiency as the data transmission channel of the current vehicle to ensure that the driving decision information of the driverless vehicle can be quickly transmitted.

[0119] Embodiment 2

[0120] This embodiment is an explanatory description carried out in Embodiment 1. Specifically, S1 specifically includes:

[0121] S11. When the system starts, identify the type of each link; the link types include but are not limited to: cellular network 4G / 5G, V2X communication, Wi-Fi, and satellite communication;

[0122] S12. Perform data encryption processing on the transmission standard of each link to generate a first encryption instruction for encrypting data, specifically including:

[0123] For cellular network 4G / 5G and for satellite communication, select the symmetric encryption algorithm AES-128 for encryption; for Wi-Fi and V2X communication, use the first public key encryption algorithm for encryption. Select a suitable encryption algorithm according to the link characteristics to improve the security and efficiency of data transmission. Use the symmetric encryption algorithm (AES-128) to encrypt high-speed transmission links (such as 4G / 5G and satellite communication) to balance efficiency and security. Use the public key encryption algorithm for links with low latency requirements (such as Wi-Fi and V2X communication) to improve the anti-attack ability.

[0124] The first public key encryption algorithm includes the following steps:

[0125] S121. Generate a first random prime number p and a second random prime number q for generating the key; use random prime numbers to increase the complexity and anti-cracking ability of the key and enhance the security of the algorithm.

[0126] S122. Multiply the first random prime number p and the second random prime number q to obtain the common modulus n of the key pair; its value is composed of the product of two random prime numbers and is difficult to crack by factoring. The large number property of n increases the computational complexity of the key and improves the anti-attack ability of the encryption system.

[0127] S123. According to the first random prime number p, the second random prime number q, and the common modulus n of the key pair, calculate the Euler's totient function, and the expression is:

[0128] φ(n) = (p - 1) × (q - 1);

[0129] In the formula, φ(n) represents the number of relatively prime integers of the public modulus n of the key pair;

[0130] S124. Randomly select the public key exponent e from the number of relatively prime integers φ(n) of the public modulus n of the key pair, satisfying the following conditions:

[0131] 1 < e < φ(n) and gcd(e, φ(n)) = 1;

[0132] In the formula, select e = 65537, and use the Euclidean algorithm to verify and check whether the public key exponent e is relatively prime to φ(n);

[0133] S125. Calculate the private key exponent d, and the formula is:

[0134] d = e -1 (mod φ(n));

[0135] And use the extended Euclidean algorithm to find that the private key exponent d satisfies the following relationship:

[0136] e × mod φ(n) = 1;

[0137] In the formula, ensure that the private key exponent d is the unique solution and satisfies the condition 1 < d < φ(n);

[0138] S126. Based on S121 - S125, generate the public key (e, n) and the private key (d, n).

[0139] In this embodiment, S11 realizes the comprehensive identification of the multi - link communication system, which helps to formulate optimization strategies according to the characteristics of different links. Provide link type information, which provides a basis for the subsequent selection of encryption algorithms and dynamic switching strategies. Improve the automation level in the system startup phase and reduce manual intervention.

[0140] Based on S121 - S125, generate the public key and the private key, complete the key generation process. The generated public key and private key are used for encryption and decryption respectively to ensure the availability of the encryption system. The public key can be made public for data encryption, while the private key is kept secret for decryption to improve the security of communication. Through the above steps, this encryption algorithm has significant beneficial effects in terms of randomness, security, efficiency, and compatibility, providing a strong security guarantee for data transmission in a multi - link communication environment.

[0141] Embodiment 3

[0142] This embodiment is an explanatory description based on Embodiment 1. Specifically, S2 includes:

[0143] S211. Use the GPS positioning system to collect the real-time position information of the vehicle, including longitude, latitude, altitude, and driving speed data, establish an electronic map, and mark the position data on the electronic map as: (x, y, z, v); x represents longitude, y represents latitude, z represents altitude, and v represents the driving speed v.

[0144] S212. By installing an electromagnetic wave intensity sensor on the vehicle, measure the power P of the interference signal in the environment in real time i , and lock the real-time position of the interference source; obtain the intensity and position of the interference signal in real time, and improve the accurate identification ability of the interference source.

[0145] S213. By installing an electromagnetic wave intensity sensor on the vehicle, collect and obtain the total received power P of the link t ; and collect the transmission distance d between the vehicle and the link transmitter and the relative position D between the interference source and the vehicle.

[0146] S214. Extract the interference signal power P i , the total received power P of the link t , the transmission distance d between the vehicle and the link transmitter, and the relative position D between the interference source and the vehicle. After dimensionless processing, calculate and obtain the electromagnetic interference factor E through the following formula:

[0147]

[0148] S215. Use the vehicle speed sensor on the vehicle to measure the current driving speed v of the vehicle in real time, and obtain the signal carrier frequency f of the communication link through the frequency detection device in the communication module. Calculate and obtain the driving speed factor V through the following formula:

[0149]

[0150] In the formula, c represents the speed of light, which is set to 3×10 8 m / s. Considering the relationship between the relative motion speed and the carrier frequency, it can more accurately reflect the influence of the Doppler effect on the signal.

[0151] In this embodiment, the introduction of the electromagnetic interference factor E quantifies the comprehensive influence of interference on the communication link, and helps the link quality analysis model to more comprehensively reflect the complexity of the communication environment. The driving speed factor V quantifies the influence of the driving speed on the performance of the communication link, especially the dynamic influence of high-speed driving on the signal quality.

[0152] Embodiment 4

[0153] This embodiment is an explanatory description based on Embodiment 3. Specifically, S2 further includes:

[0154] S216. Real - time collect the height h of the vehicle passing by a building, the signal path, and the angle θ between the signal propagation path and the normal of the building through lidar;

[0155] S217. Collect the signal wavelength when the vehicle drives around the building, and calculate the diffraction coefficient β through the following formula:

[0156]

[0157] In the formula, λ represents the signal wavelength, where c represents the speed of light, f is the signal carrier frequency of the communication link; α is the width of the building, θ is the angle between the propagation path and the normal of the building; log 10 represents the logarithm operation with base 10; by introducing the diffraction coefficient β, quantitatively evaluate the influence of the building on signal diffraction, especially the signal loss in complex paths. Combine lidar and signal sensors to quickly and efficiently obtain building - related data;

[0158] S218. Extract the height h of the building, the diffraction coefficient β, and the transmission distance d between the vehicle and the link transmitter. After dimensionless processing, calculate the building occlusion factor B through the following formula:

[0159]

[0160] In the formula, ρ represents the building material coefficient, including: when the building material is glass, ρ = 0.1; when the building material is concrete, ρ = 0.8; when the building material is steel, ρ = 1.2.

[0161] In this embodiment, dynamically calculate the building occlusion factor B, which provides an important input for the construction of the link anti - interference ability index Kgr.

[0162] Example 5

[0163] This embodiment is an explanatory description based on Example 4. Specifically, S2 further includes:

[0164] S219. By installing a meteorological sensor on the vehicle, real - time collect the air humidity sd, air temperature wd, and real - time air pressure qy of the vehicle's external environment. After dimensionless processing, calculate the atmospheric absorption coefficient A through the following formula:

[0165]

[0166] In the formula, C1 represents the absorption coefficient constant, which is related to the signal frequency and air composition. For a 2.4GHz signal, the typical value C1 = 0.01; represents the standard temperature threshold, Represents the standard atmospheric pressure threshold; considering the influence of key meteorological factors such as air humidity, temperature, and air pressure on signal propagation, accurately calculate the atmospheric absorption coefficient A; through real-time data collection, adapt to signal propagation conditions in different environments, and support all-weather link optimization.

[0167] S220. Real-time collect the rainfall intensity, fog density, and snowfall intensity, and calculate the rain attenuation index K through the following formula rain , and the fog attenuation index K fog and the snow attenuation index K snow :

[0168] K rain =R n ×f δ ;

[0169] K fog =η×M×f 2 ;

[0170] K snow =S×μ;

[0171] In the formula, R represents the rainfall intensity, n represents the first empirical parameter, δ represents the second empirical parameter. When f = 5 GHz, n = 1.2; δ = 0.8; η represents the ratio constant, η = 0.2; M represents the fog density, obtained through a meteorological sensor; f represents the signal carrier frequency f of the communication link; S represents the snow intensity, obtained through a snow amount sensor, and μ represents the refractive coefficient of snow for the signal, μ = 0.15; considering the signal attenuation characteristics of different weather conditions of rain, fog, and snow, improve the accuracy of link anti-interference ability analysis under complex meteorological conditions. Distinguish the different attenuation effects of rain, fog, and snow on the signal, and support more accurate network planning and optimization strategies

[0172] S221. Combine the atmospheric absorption coefficient A, the rain attenuation index K rain , and the fog attenuation index K fog and the snow attenuation index K snow : Calculate the weather factor W through the following formula:

[0173] Kz = K rain +K fog +K snow

[0174] W = A×e -Kz×d ;

[0175] In the formula, Kz represents the comprehensive attenuation factor, d represents the transmission distance between the vehicle and the link transmitter; e -Kz×d represents the exponential attenuation term, indicating that the signal attenuation increases with the increase of the transmission distance d, and e is the base of the natural logarithm;

[0176] S222. Combine the electromagnetic interference factor E, the driving speed factor V, the building shielding factor B, and the weather condition factor W to establish an interference data set; after dimensionless processing of the electromagnetic interference factor E, the driving speed factor V, the building shielding factor B, and the weather condition factor W, obtain the anti-interference ability index Kgr through the following associated formula:

[0177]

[0178] In the formula, a1, a2, a3, and a4 respectively represent the weight values of the electromagnetic interference factor E, the driving speed factor V, the building shielding factor B, and the weather condition factor W, and a1 + a2 + a3 + a4 ≥ 1.

[0179] In this embodiment, by comprehensively considering atmospheric absorption, rain, fog, snow attenuation, and transmission distance, the weather factor W is calculated to comprehensively evaluate the impact of weather on the communication link. Through dynamic calculation of different environmental parameters, it supports real-time link status update and optimization. Provide accurate interference assessment in extreme weather to ensure communication stability. The anti-interference ability index Kgr comprehensively models multiple key factors such as electromagnetic interference, vehicle driving speed, building shielding, and weather conditions, providing a comprehensive anti-interference ability assessment.

[0180] Embodiment 6

[0181] This embodiment is an explanatory description based on Embodiment 5. Specifically, S3 includes:

[0182] S31. Match the electromagnetic interference factor E, the driving speed factor V, the building shielding factor B, the weather condition factor W, and the actual signal strength, delay, and bandwidth data of each link to construct a training data set, and use a neural network model to establish a link quality analysis model, and divide the training data set into 70% training set, 15% validation set, and 15% test set for training and validation;

[0183] S32. Through the trained link quality analysis model, combine the real-time signal strength, delay, and bandwidth of each link, and associate with the anti-interference ability index Kgr, and calculate and obtain the first communication quality index Q of each link through the following formula i :

[0184]

[0185] In the formula, Q i represents the first communication quality index of the i-th link, Sx i represents the real-time signal strength of the i-th link, Yc i represents the real-time delay of the i-th link, Dk i represents the real-time bandwidth of the i-th link, Kgr i,kDenote the anti-interference ability index of the j-th vehicle on the i-th link. a5, a6, a7, and a8 represent weight values, and a5 + a6 + a7 + a8 ≥ 1;

[0186] S33. Preset the first communication quality threshold R1, and evaluate the first communication quality index Q of the i-th link to preliminarily determine whether the link communication quality is qualified, including: i Compare with the first communication quality threshold R1 to initially determine whether the link communication quality is qualified, including:

[0187] When the first communication quality index Q of the i-th link i ≥ the first communication quality threshold R1, it indicates that the communication quality of the current i-th link is qualified, and there is no need to change the encryption. Still continue the communication with the first encryption instruction;

[0188] When the first communication quality index Q of the i-th link i < the first communication quality threshold R1, it indicates that the communication quality of the current i-th link is unqualified, and generate a second encryption instruction, specifically including:

[0189] For cellular networks 4G / 5G and for satellite communications, change the encryption from selecting the symmetric encryption algorithm AES - 128 to selecting the symmetric encryption algorithm AES - 256; for Wi-Fi and V2X communications, use encryption based on the second public key encryption algorithm.

[0190] In this embodiment, by comprehensively considering multiple factors such as signal strength, delay, bandwidth, and anti-interference ability index, multi-dimensional analysis of the link quality can be carried out, thereby ensuring the accurate evaluation of communication quality. Through the comparison between the first communication quality index and the preset first communication quality threshold R1, dynamic monitoring and evaluation of the link communication quality are realized, providing a basis for the optimization of the communication link and encryption adjustment. When the link quality is unqualified, the encryption algorithm is automatically upgraded (such as from AES - 128 to AES - 256), effectively improving the security of communication and preventing data leakage and attacks.

[0191] Embodiment 7

[0192] This embodiment is an explanatory description based on Embodiment 6. Specifically, the second public key encryption algorithm is an extension of the first public key encryption algorithm, specifically including the following steps:

[0193] S321. Generate the first random prime number p, the second random prime number q, and the third random prime number r for generating the key;

[0194] S322. Multiply the first random prime number p, the second random prime number q, and the third random prime number r to obtain the common modulus n of the key pair;

[0195] S323. Calculate the Euler's totient function based on the first random prime number p, the second random prime number q, the third random prime number r, and the common modulus n of the key pair. The expression is:

[0196] φ(n) = (p - 1) × (q - 1) × (r - 1);

[0197] In the formula, φ(n) represents the number of relatively prime integers to the common modulus n of the key pair;

[0198] S324. Randomly select a public key exponent e from the number of relatively prime integers φ(n) of the common modulus n of the key pair, satisfying the following conditions:

[0199] 1 < e < φ(n) and gcd(e, φ(n)) = 1;

[0200] In the formula, dynamically adjust the range of the public key exponent e, e ∈ {3, 5, 17, 65537}, and use the Euclidean algorithm to verify and check whether the public key exponent e is relatively prime to φ(n);

[0201] S325. Calculate the private key exponent d. The formula is:

[0202] d = e -1 (mod φ(n));

[0203] And through the extended Euclidean algorithm, find that the private key exponent d satisfies the following relationship:

[0204] e × mod φ(n) = 1;

[0205] In the formula, ensure that the private key exponent d is the unique solution and satisfies the condition 1 < d < φ(n);

[0206] S326. Based on S321 - S325, generate the public key (e, n) and the private key (d, n);

[0207] The difference between the second public key encryption algorithm and the first public key encryption algorithm lies in the different number of random prime numbers and the different public key exponent e, which increases the difficulty of key cracking.

[0208] In this embodiment, the second public-key encryption algorithm uses three random prime numbers p, q, and r, while the first public-key encryption algorithm usually only uses two random prime numbers p and q. This increases the complexity of key generation and enhances the security of the encryption algorithm. In the second public-key encryption algorithm, the selection range of the public-key exponent e is dynamically adjusted, and it is verified whether e is relatively prime to the Euler's totient function φ(n) through the Euclidean algorithm. This makes the process of selecting the public-key exponent more flexible and secure, reducing the risk of being cracked. Since it involves three random prime numbers instead of two, and the selection and verification process of the public-key exponent e is more complex, this greatly increases the difficulty of key cracking. The cracker needs to solve more mathematical problems, enhancing the security of the encryption system.

[0209] Example 8

[0210] This embodiment is an explanatory illustration based on Embodiment 1. Specifically, S4 includes:

[0211] S41. Collect the number of vehicles N currently connected to each link c and the data load L of each vehicle, and calculate the total link load L through the following formula t :

[0212]

[0213] In the formula, L i represents the data load of the i-th vehicle;

[0214] S42. Use a network monitoring tool to record the total number of packets P sent by the link l and the number of lost packets P t , and calculate the packet loss rate P through the following formula loss :

[0215]

[0216] S43. Based on the total link load L t , the packet loss rate P loss and the first communication quality index Q i , after dimensionless processing, the second communication quality index Q2 is obtained through the following formula:

[0217] Q2 = Q i × exp(-ω1 × L t ) × (1 - ω2 × P loss );

[0218] In the formula, ω1 is the influence coefficient of the load on the communication quality, and its value is set between 0.01 - 0.1; ω2 is the influence coefficient of the packet loss rate on the communication quality, and its value is set between 0.1 - 1; when the link load L tWhen it is relatively high, Q2 will decrease exponentially with the exponential function exp. Similarly, the increase in the packet loss rate P loss will also reduce Q2.

[0219] S44. Preset the second communication quality threshold R2, and evaluate the second communication quality index Q2 with the second communication quality threshold R2 to secondarily determine whether the link communication security is abnormal, including:

[0220] When the second communication quality index Q2 ≥ the second communication quality threshold R2, it indicates that the current link communication is secure, and a first security mark is generated;

[0221] When the second communication quality index Q2 < the second communication quality threshold R2, it indicates that the current link communication is insecure, and automatically switch to a secure link where the second communication quality index Q2 ≥ the second communication quality threshold R2;

[0222] And sort the second communication quality indexes Q2 of multiple links that generate the first security mark from largest to smallest to generate a switching queue, and preferentially select the link with the largest value of the second communication quality index Q2 in the switching queue as the first-priority link to be the data transmission link of the current vehicle.

[0223] When a link interruption occurs, reconnect and repeat steps S1 - S4 until the second communication quality index Q2 is qualified.

[0224] In this embodiment, by combining the link load, the packet loss rate, and the first communication quality index, the system can evaluate the quality of the link in real time and dynamically. This method of comprehensively considering multiple factors can more accurately reflect the actual situation of the link. According to the comparison between the second communication quality index Q2 and the second communication quality threshold R2, it automatically determines whether the link is secure. If the communication quality does not meet the requirements, the system can quickly switch to a more secure link, thereby avoiding communication interruptions or data loss and improving the stability of the communication system. By sorting the links according to the second communication quality index and selecting the link with the best quality as the preferred transmission channel, the communication path is optimized, the use of unstable links is reduced, and the reliability and efficiency of data transmission are improved. By effectively managing factors such as link load and packet loss rate, the system can avoid the decline in communication quality caused by excessive load or high packet loss rate, ensuring the reliability and timeliness of data. This method dynamically adjusts the selection of the link according to real-time data and link conditions, ensuring the best communication quality in different traffic and network environments and enhancing the adaptive ability of the system.

[0225] The setting of the size of the threshold is for the convenience of comparison. Regarding the size of the threshold, it depends on the amount of sample data and the number of base numbers set by those skilled in the art for each group of sample data; as long as it does not affect the proportional relationship between the parameters and the quantified values.

[0226] The above formulas are all obtained by collecting a large amount of data for software simulation and selecting a formula close to the true value. The coefficients in the formula are set by those skilled in the art according to the actual situation. As mentioned above, the above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, makes equivalent substitutions or changes, and should be covered by the protection scope of the present invention.

Claims

1. A method for intelligent switching of multi-link encrypted transmission tunnels, characterized in that, It includes the following steps: S1. Pre - establish a multi - link communication system, which includes at least two communication links, including cellular network, V2X communication, Wi - Fi and satellite communication. Perform data encryption processing on each link based on different transmission standards to generate the first encryption instruction; S2. Obtain the real - time position information of the driverless vehicle and monitor the influencing factors affecting the link performance in real - time; Analyze and calculate to obtain the electromagnetic interference factor E, driving speed factor V, building occlusion factor B and weather condition factor W, and establish an interference data set; Construct the anti - interference ability index Kgr of each link; S3. Use the neural network CNN technology to establish and train a link quality analysis model. Combine the real-time signal strength, delay, and bandwidth of each link, and associate the anti-interference ability index Kgr to calculate and obtain the first communication quality index Q of each link. i : And preliminarily evaluate the first communication quality index Q of each link. i to dynamically adjust the encryption strength; S4. After dynamically adjusting the encryption strength in S3, collect the load data and packet loss data of each link connecting several vehicles, and combine with the first communication quality index Q of each link i , associate to obtain the second communication quality index Q2, and perform a secondary determination on the second communication quality index Q2. If the second communication quality index Q2 is lower than the second communication quality threshold R2, it is determined to be insecure and automatically switched to a secure link; and perform a priority ranking on each available communication link, and preferentially select the first priority link as the data transmission link of the current vehicle.

2. The intelligent switching method for a multi-link encrypted transmission tunnel according to claim 1, wherein, S1 specifically includes: S11. When the system starts, identify the type of each link; The link types include but are not limited to: cellular network 4G / 5G, V2X communication, Wi - Fi and satellite communication; S12. Perform data encryption processing on the transmission standards of each link to generate the first encryption instruction for encrypting data, specifically including: For cellular network 4G / 5G and for satellite communication, select the symmetric encryption algorithm AES - 128 for encryption; For Wi - Fi and V2X communication, use the first public - key encryption algorithm for encryption.

3. The intelligent switching method for a multi-link encrypted transmission tunnel according to claim 2, characterized in that, The first public - key encryption algorithm includes the following steps: S121. Generate the first random prime number p and the second random prime number q for generating the key; S122. Multiply the first random prime number p and the second random prime number q to obtain the public modulus n of the key pair; S123. According to the first random prime number p, the second random prime number q and the public modulus n of the key pair, calculate the Euler's totient function, and the expression is: φ(n)=(p - 1)×(q - 1); In the formula, φ(n) represents the number of relatively prime integers of the public modulus n of the key pair; S124. Randomly select the public - key exponent e from the number of relatively prime integers φ(n) of the public modulus n of the key pair, satisfying the following conditions: 1 < e < φ(n) and gcd(e,φ(n)) = 1; In the formula, select e = 65537 and use the Euclidean algorithm to verify and check whether the public - key exponent e is relatively prime to φ(n); S125. Calculate the private - key exponent d, and the formula is: d = e -1 (mod φ(n)); And through the extended Euclidean algorithm, find that the private - key exponent d satisfies the following relationship: e×modφ(n)=1; In the formula, ensure that the private - key exponent d is the unique solution and satisfies the condition 1 < d < φ(n); S126. Based on S121 - S125, generate the public key (e,n) and the private key (d,n).

4. A multi-link encrypted transmission tunnel intelligent switching method according to claim 1, characterized in that S2 It includes: S211. Use the GPS positioning system to collect the real - time position information of the vehicle, including longitude, latitude, altitude and driving speed data, establish an electronic map, and mark the position data on the electronic map as: (x,y,z,v); x represents longitude, y represents latitude, z represents altitude, and v represents the driving speed v; S212. By installing an electromagnetic wave intensity sensor on the vehicle, the power P of the interference signal in the environment is measured in real time i , and the real-time position of the interference source is locked; S213. By installing an electromagnetic wave intensity sensor on the vehicle, collect and obtain the total received power P of the link t ; and collect the transmission distance d between the vehicle and the link transmitter and the relative position D between the interference source and the vehicle; S214. Extract the interference signal power P i , the total received power of the link P t , the transmission distance d between the vehicle and the link transmitter, and the relative position D between the interference source and the vehicle. After dimensionless processing, the electromagnetic interference factor E is calculated through the following formula: S215. Measure the current driving speed v of the vehicle in real - time through the on - vehicle speed sensor on the vehicle, and obtain the signal carrier frequency f of the communication link through the frequency detection device in the communication module. Calculate and obtain the driving speed factor V through the following formula: where c represents the speed of light, which is set to 3×10 8 m / s.

5. A multi-link encrypted transmission tunnel intelligent switching method according to claim 4, characterized in that S2 also includes: S216. Real - time collect the height h of the vehicle passing by a building, the signal path, and the angle θ between the signal propagation path and the normal of the building through lidar; S217. Collect the signal wavelength when the vehicle drives around the building, and calculate the diffraction coefficient β through the following formula: where λ represents the signal wavelength, where c represents the speed of light, f is the signal carrier frequency of the communication link; α is the width of the building, and θ is the angle between the propagation path and the normal of the building; log 10 denotes the logarithm operation with base 10; S218. Extract the height h of the building, the diffraction coefficient β, the transmission distance d between the vehicle and the link transmitter. After dimensionless processing, calculate the building occlusion factor B through the following formula: In the formula, ρ represents the building material coefficient, including: when the building material is glass, ρ = 0.1; when the building material is concrete, ρ = 0.8; when the building material is steel, ρ = 1.

2.

6. The intelligent switching method for a multi-link encrypted transmission tunnel according to claim 5, wherein S2 also includes: S219. By installing a meteorological sensor on the vehicle, real - time collect the air humidity sd, air temperature wd and real - time air pressure qy of the vehicle's external environment. After dimensionless processing, calculate the atmospheric absorption coefficient A through the following formula: Wherein, C1 represents the absorption coefficient constant, which is related to the signal frequency and air composition. For a 2.4 GHz signal, the typical value of C1 = 0.01; represents the standard temperature threshold, represents the standard atmospheric pressure threshold; S220. Real-time collect rainfall intensity, fog density, and snowfall intensity, and calculate and obtain the rain attenuation index K, the fog attenuation index K, and the snow attenuation index K through the following formula: rain and the fog attenuation index K fog and the snow attenuation index K snow : K rain = R n × f δ ; K fog = η × M × f 2 ; K snow = S × μ; In the formula, R represents the rainfall intensity, n represents the first empirical parameter, δ represents the second empirical parameter. When f = 5GHz, n = 1.2; δ = 0.8; η represents the ratio constant, η = 0.2; M represents the fog density, obtained through the meteorological sensor; f represents the signal carrier frequency f of the communication link; S represents the snow intensity, obtained through the snow - amount sensor, and μ represents the refractive coefficient of snow to the signal, μ = 0.15; S221. Combine the atmospheric absorption coefficient A, the rain attenuation exponent K rain , and the fog attenuation exponent K fog and the snow attenuation exponent K snow : Obtain the weather factor W by calculating through the following formula: Kz = K rain + K fog + K snow W = A × e -Kz×d ; Wherein, Kz represents the comprehensive attenuation factor, and d represents the transmission distance between the vehicle and the link transmitter; e -Kz×d represents the exponential attenuation term, indicating that the signal attenuation increases with the increase of the transmission distance d, where e is the base of the natural logarithm; S222. Combine the electromagnetic interference factor E, the driving speed factor V, the building occlusion factor B and the weather condition factor W to establish an interference data set; and after dimensionless processing of the electromagnetic interference factor E, the driving speed factor V, the building occlusion factor B and the weather condition factor W, obtain the anti - interference ability index Kgr through the following related formula: In the formula, a1, a2, a3 and a4 respectively represent the weight values of the electromagnetic interference factor E, the driving speed factor V, the building occlusion factor B and the weather condition factor W, and a1 + a2 + a3 + a4≥1.

7. A multi-link encrypted transmission tunnel intelligent switching method according to claim 1, wherein S3 Include: S31. Match the electromagnetic interference factor E, the driving speed factor V, the building occlusion factor B, the weather condition factor W and the actual signal strength, delay and bandwidth data of each link to construct a training data set, and use a neural network model to establish a link quality analysis model, and perform training and verification by dividing the training data set into 70% training set, 15% validation set and 15% test set; S32. By using the trained link quality analysis model, combining the real-time signal strength, latency, and bandwidth of each link, and associating with the anti-interference ability index Kgr, the first communication quality index Q of each link is calculated and obtained through the following formula i :[[]]END]] where Q i represents the first communication quality index of the i-th link, Sx i represents the real-time signal strength of the i-th link, Yc i represents the real-time delay of the i-th link, Dk i represents the real-time bandwidth of the i-th link, Kgr i,k represents the anti-interference ability index of the j-th vehicle on the i-th link, and a5, a6, a7, and a8 represent weight values, and a5 + a6 + a7 + a8 ≥ 1; S33. Preset a first communication quality threshold R1, and evaluate the first communication quality index Q of the i-th link against the first communication quality threshold R1 to preliminarily determine whether the link communication quality is qualified, including: i And evaluate the first communication quality index Q of the i-th link against the first communication quality threshold R1 to preliminarily determine whether the link communication quality is qualified, including: When the first communication quality index Q of the i-th link i ≥ the first communication quality threshold R1, it indicates that the communication quality of the current i-th link is qualified, there is no need to replace the encryption, and communication continues with the first encryption instruction; When the first communication quality index Q of the i-th link i < the first communication quality threshold R1, indicating that the communication quality of the current i-th link is unqualified, generate a second encryption instruction, specifically including: For cellular networks 4G / 5G and for satellite communications, change the encryption from selecting the symmetric encryption algorithm AES - 128 to selecting the symmetric encryption algorithm AES - 256; for Wi - Fi and V2X communications, use encryption based on the second public - key encryption algorithm.

8. A multi-link encrypted transmission tunnel intelligent switching method according to claim 7, characterized in that The second public - key encryption algorithm is an extension of the first public - key encryption algorithm, specifically including the following steps: S321. Generate the first random prime number p, the second random prime number q and the third random prime number r for generating keys; S322. Multiply the first random prime number p, the second random prime number q and the third random prime number r to obtain the common modulus n of the key pair; S323. Calculate the Euler's totient function according to the first random prime number p, the second random prime number q, the third random prime number r, and the common modulus n of the key pair. The expression is: φ(n) = (p - 1)×(q - 1)×(r - 1); Where φ(n) represents the number of relatively prime integers of the common modulus n of the key pair; S324. Randomly select the public key exponent e from the number of relatively prime integers φ(n) of the common modulus n of the key pair, satisfying the following conditions: 1 < e < φ(n) and gcd(e, φ(n)) = 1; Where the range of the public key exponent e is dynamically adjusted, e ∈ {3, 5, 17, 65537}, and the Euclidean algorithm is used to verify whether the public key exponent e is relatively prime to φ(n); S325. Calculate the private key exponent d, and the formula is: d = e -1 (mod φ(n)); And through the extended Euclidean algorithm, find that the private key exponent d satisfies the following relationship: e × mod φ(n) = 1; Where it is ensured that the private key exponent d is the unique solution and satisfies the condition 1 < d < φ(n); S326. Based on S321 - S325, generate the public key (e, n) and the private key (d, n); The difference between the second public key encryption algorithm and the first public key encryption algorithm lies in the different number of random prime numbers and the different public key exponent e, which increases the difficulty of key cracking.

9. A multi-link encrypted transmission tunnel intelligent switching method according to claim 1, characterized in that S4 Including: S41. Collect the number of vehicles N currently connected to each link c and the data load L of each vehicle, and calculate the total link load L through the following formula t : where L i represents the data load of the i-th vehicle; S42. Use a network monitoring tool to record the total number of packets sent P l and the number of lost packets P t , and calculate the packet loss rate P through the following formula loss : S43. According to the total link load L t , packet loss rate P loss and the first communication quality index Q i , after dimensionless processing, the second communication quality index Q2 is obtained by being associated through the following formula: Q2 = Q i × exp(-ω1 × L t ) × (1 - ω2 × P loss ); Where ω1 is the influence coefficient of the load on the communication quality, and its value is set to be between 0.01 and 0.1; ω2 is the influence coefficient of the packet loss rate on the communication quality, and its value is set to be between 0.1 and 1; when the link load L t is relatively high, Q2 will decrease exponentially with the exponential function exp. Similarly, the increase in the packet loss rate P loss will also reduce Q2.

10. A multi-link encrypted transmission tunnel intelligent switching method according to claim 9, characterized in that S4 also includes: S44. Preset the second communication quality threshold R2, and evaluate the second communication quality index Q2 with the second communication quality threshold R2 to secondarily judge whether the link communication security is abnormal, including: When the second communication quality index Q2 ≥ the second communication quality threshold R2, it indicates that the current link communication is secure, and generate the first security mark; When the second communication quality index Q2 < the second communication quality threshold R2, it indicates that the current link communication is insecure, and automatically switch to a secure link where the second communication quality index Q2 ≥ the second communication quality threshold R2; And sort the second communication quality indices Q2 of the multiple links generating the first security mark in descending order of priority to generate a switching queue, and preferentially select the link with the maximum value of the second communication quality index Q2 in the switching queue as the first - priority link as the data transmission link of the current vehicle.

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