Network seamless connection method for vehicle communication

Abstract

The invention relates to the technical field of vehicle communication networks, and discloses a network seamless connection method for vehicle communication, and the method comprises the steps: determining a signal switching time sequence regulation and control parameter according to the moving speed of a vehicle, planning a signal transmission path in advance, predicting and adjusting an optimal access point, and precisely selecting a switching time point when the vehicle passes through different networks at a high speed. If a part of flow is transferred in advance close to the junction of the cellular base station, data breakpoints are effectively avoided, and smooth and uninterrupted communication is ensured. Meanwhile, communication link quality prediction model parameters are updated according to real-time distance changes between a vehicle and other nodes, when the vehicle gets close to or away from a base station, the model automatically adjusts the parameters, the connection stability is guaranteed, a bandwidth allocation scheme is formulated by estimating the vehicle density, counting the total request number and combining the load balance degree, and the bandwidth is redistributed according to requirements. And maximum utilization of resources is realized.

Description

Technical Field

[0001] The present application relates to the technical field of vehicle communication networks, and in particular to a network seamless connection method for vehicle communication. Background Art

[0002] In today's complex and ever-changing traffic environment, ensuring smooth and stable communication between vehicles in different network coverage areas is the key to achieving efficient operation of intelligent transportation systems. To this end, we have developed a set of advanced vehicle communication network seamless connection solutions, dedicated to improving the reliability and efficiency of vehicle communications in all aspects.

[0003] During vehicle driving, precise control of signal switching is crucial. This solution uses a precise algorithm to dynamically determine the best time to switch signals based on the real-time speed of the vehicle. For example, when the vehicle is driving at high speed, it can predict and switch signals in time to avoid data transmission interruption caused by delayed or premature switching. This is like paving a smooth highway for vehicle communications to ensure uninterrupted information flow.

[0004] The distance between a vehicle and surrounding nodes (such as base stations and other vehicles) changes all the time, which has a significant impact on the quality of the communication link. Our solution continuously monitors the distance dynamics and intelligently optimizes the parameters of the communication link quality prediction model. When the vehicle approaches the base station quickly, the model automatically adjusts the parameters to enhance the signal reception strength. Conversely, when moving away from the base station, the signal transmission strategy is optimized in advance to ensure the stability of the connection and keep the vehicle communication in the best state at all times.

[0005] Faced with the problem of network congestion that is prone to occur in high-density communication scenarios, this solution demonstrates strong adaptability. It can perceive the network load in real time, flexibly adjust the resource allocation strategy between vehicles, give priority to communication resources for key businesses during busy network hours, reasonably allocate bandwidth, effectively improve resource utilization efficiency, and reduce the probability of congestion, just like a traffic command system to divert congested sections of road and ensure smooth network traffic.

[0006] Different in-vehicle applications have different requirements for data transmission priorities. Our solution fully takes this feature into account and reasonably adjusts the information queuing order according to the application type. For example, vehicle safety warning information has the highest priority and can be transmitted quickly to ensure driving safety; while entertainment application data is transmitted in an orderly manner under the premise of ensuring key business, realizing efficient collaboration of various businesses.

[0007] In certain specific environments, low signal-to-noise ratio seriously affects communication quality. Our solution deeply analyzes the characteristics of surrounding interference sources, finely configures the frequency and strength of wireless signals, cleverly avoids interference frequency bands, accurately adjusts signal strength, and effectively improves communication quality. Even in complex electromagnetic environments, vehicle communications can be clear and stable.

[0008] In summary, this vehicle communication network seamless connection solution comprehensively improves the vehicle's adaptability in complex network environments through multi-dimensional optimization measures, providing strong technical support for the vigorous development of intelligent transportation. Summary of the Invention

[0009] To solve the problems raised in the above background technology, this application provides a network seamless connection method for vehicle communication.

[0010] This application provides a network seamless connection method for vehicle communication, adopting the following technical solutions: A network seamless connection method for vehicle communication includes: S101. Determine the signal switching timing control parameter based on the moving speed of the vehicle in different network coverage areas; S102. Predict and adjust the optimal access point of the upcoming new network section according to the signal switching timing control parameter; S103. Dynamically update the communication link quality prediction model parameters based on the real-time distance change between the vehicle and other nodes; S104. Regulate the information queuing order according to the data priorities of different types of in-vehicle applications to ensure the response of critical services.

[0011] Preferably, further regulating the signal switching timing based on the moving speed of the vehicle in different network coverage areas further includes: Obtain the speed v of the current vehicle; Predict the vehicle distance change Δd in the next cycle based on Δd = vt; When the vehicle speed satisfies the following condition, adjust the network access point A: \[ v > \frac{Th}{\Delta t},\] where Th is the switching critical speed threshold, and \(\Deltat\) represents the minimum time interval; Start the target cell search in advance to ensure continuous data flow.

[0012] Preferably, further improving the steps to adapt to the changing actual scenario further includes: Obtain the distance d_i from adjacent communication nodes; Use the formula to predict the link quality Q = w_r / r^4, where w is the weight coefficient and r refers to the relative distance between nodes; When the predicted communication interruption risk P_break > P_th (\(P_{break} = exp^{\lambda d^{n}}\)), switch the connection in advance; Optimize the link path configuration to maintain stable transmission.

[0013] Preferably, the improvement of the dynamic resource allocation strategy further includes: Estimate the vehicle density \(\rho_v\) and count the total number of requests \(N_{tot}\); Set the bandwidth allocation ratio \(B_{Ratio}\) and formulate a plan according to the load balance degree \(L_{bal}(L_{bal}=\log((1 / N_{tot})\sum_i(N_i / B_R)_i))\); Determine the data volume \(D_{car}\) obtained by a single vehicle according to the formula: \(D_{car}=D_{max}*e^{\alpha\cdot\rho}\); Redistribute the bandwidth to each active session according to the demand to improve the performance.

[0014] Preferably, adding new rules at the level of different types of in-vehicle applications further includes: Analyze the task level \(Level_{Task}\) of each service type; Allocate the queue position \(Index_P=(N_H + w_{Level})\) according to the real-time task level, where \(N_H\) is the default queuing height and \(w_{Level}\) assigns a higher value to high-priority tasks; Use the formula \(Delay_i = k_{delay}*(p(i) / P)\) to judge the information processing delay; here \(p(i)\) is the service ratio and \(P\) is the maximum possibility per unit cycle.

[0015] Implement the plug-and-play protocol for low-latency demand applications.

[0016] Preferably, optimizing the wireless spectrum and power allocation method in the presence of interference includes the following improvement measures: Identify the main interference type \(T_{dist}\); Determine the channel availability through frequency scanning and match the optimal transmit power \(P_{tx}\); Adopt \(S = C / (I + N)\), where \(C\) is the carrier carrier intensity, \(I\) is the interference degree, and \(N\) is the background noise, as a measurement parameter for adjustment; If \(S > S_{min}\), reduce the interference effect and increase the effective transmission range.

[0017] Preferably, increasing the service enhancement function based on geographic information further involves the following actions: Locate the vehicle GPS coordinates; Generate a regional map \(M_{map}\) in combination with the geographic information system; According to the formula \(f(x)= \eta * x + bias_g\), where \(x\) is the position coordinate on the map, and \(\eta\) and \(bias_g\) define the mapping relationship; Apply the location-related optimization parameters to the above algorithm.

[0018] Preferably, a more accurate time synchronization correction step is added, specifically manifested as: Monitor the internal clock deviation \(\delta_c\) of the system; Calculate the delay compensation \(Tc = \tau+\gamma\cdot|\delta_t - \delta_m|\), where \(\tau\) is the baseline delay and \(\gamma\) is the compensation factor; Introduce multi-source references to verify the accuracy: \(T_{corr}=(T1 + T.. + T_N) / N\), where other \(T\) represents other reference stations; After performing the correction, confirm the time scale consistency again.

[0019] Preferably, the supplementary measures for strengthening the connection reliability in special environments such as tunnels cover the following links: Record the recent historical average propagation loss \(L_{avg}\); Select the backup channel \(C_{alt}\), and at the same time adjust the working state \(F_{mod}=F_{init}(F_{sen} / F_{ref})\); Based on the formula \[SINR = SINR * \log_2(1+\gamma / L_{avg}),\) where \(\gamma\) represents the signal quality gain parameter; Actively reduce the influence factors of frequency deviation when in a restricted area.

[0020] Preferably, introduce an intelligent algorithm to predict the future network conditions and make a forward-looking plan, including these detailed actions: Collect a large sample of historical traffic \(H_{ts}\) in an approximately similar time period; Predict the possible events \(E_{pre}=P_{Event}(H_{ts})\); Based on logistic regression, establish an evaluation function \(F(x)= 1 / (1 + exp(\beta*(tt_0)))\), where \(t\) is the current moment, and \(\beta\), \(t_0\) are the adaptive parameter groups; Make a pre-allocation strategy in advance to deal with the probability of emergencies.

[0021] In summary, the present application includes at least one of the following beneficial technical effects: The present invention determines the signal switching timing control parameters according to the vehicle moving speed, plans the signal transmission path in advance, predicts and adjusts the optimal access point. When the vehicle travels at high speed across different networks, it can accurately select the switching time point. For example, it transfers part of the traffic in advance near the cellular base station junction, effectively avoiding data breakpoints and ensuring smooth and uninterrupted communication. At the same time, the communication link quality prediction model parameters are updated according to the real-time distance change between the vehicle and other nodes. When the vehicle approaches or moves away from the base station, the model automatically adjusts the parameters to ensure connection stability.

[0022] By estimating the vehicle density, counting the total number of requests, and formulating a bandwidth allocation plan in combination with the load balancing degree, the bandwidth is redistributed according to the demand to achieve the maximum utilization of resources. Inclined resource injection is given to local hot spots, and the priority is reduced in areas with few users. A dedicated channel is reserved for emergency service vehicles, improving the resource utilization efficiency and maintaining the system throughput performance.

[0023] By regulating the information queuing order according to the data priorities of different types of in-vehicle applications, the highest permissions are given to commands related to safety monitoring, such as functions like automatic braking and obstacle warning, to ensure that the vehicle can make decisive decisions in critical situations. A weight scoring mechanism is introduced to quantify the importance of applications, enabling the system to quickly make correct choices. For example, during the driving process of a driverless bus, a speed reduction warning notice due to too many pedestrians in the upcoming turning section will take precedence over a navigation software request for navigation update. Description of the Drawings

[0024] Figure 1 is a flowchart of a method for seamless network connection for vehicle communication. Detailed Embodiments

[0025] The following details the embodiments of the present application, and the examples of the embodiments are shown in the drawings.

[0026] In the description of this specification, the reference to terms "certain embodiments", "one embodiment", "some embodiments", "illustrative embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiments or examples are included in at least one embodiment or example of the present application. In this specification, the schematic descriptions of the above terms do not necessarily refer to the same embodiments or examples. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0027] The embodiments of the present application disclose a method for seamless network connection for vehicle communication. Referring to Figure 1 , when the vehicle moves at the network edge, the system needs to obtain real-time information about the vehicle's speed and its location.

[0028] The system determines the next best network coverage area that the vehicle may enter based on this, that is, predicts the network environment. This step aims to plan the signal transmission path in advance and avoid unnecessary interruptions. When the vehicle travels at high speed across different networks, data breakpoints may occur due to large speed changes. At this time, the most suitable time point is selected through the analysis of the moving trajectory and speed, and the handover operation is triggered.

[0029] Specifically, the system uses the data collected by the sensors pre-installed in the vehicle, combines the GPS coordinates and the map information system, and infers the best timing to accurately cut into the target area in a mathematical modeling way.

[0030] For example, before approaching the boundary of a cellular base station, some traffic is transferred in advance according to the prediction, so as to achieve a smooth transition and ensure smooth and uninterrupted communication. Considering that vehicles on urban roads may interact with roadside infrastructure or other vehicle networking nodes frequently, and the distances between them are constantly changing, in order to keep the communication link in a good state all the time, it is necessary to make dynamic corrections to the parameters of the link quality prediction model. Based on the position information and signal strength data obtained from real-time monitoring, an adaptive algorithm is used to update the parameter values in real time to ensure that the prediction results are close to the real situation. Once a significant change in the relative position between two nodes is detected, such as a sudden increase or decrease, the current environment is immediately re-evaluated to see if it is suitable to continue maintaining the established connection. If the prediction stability is poor, it is recommended to execute the reconnection process to ensure that data packets are normally transmitted without loss.

[0031] Specifically, in an intersection environment controlled by an intelligent traffic light, vehicle A is slowly approaching the traffic light, while vehicle B is driving quickly in the same direction. When the distance between them becomes unstable due to significant changes in their respective driving patterns, the system can immediately modify the corresponding variables of the model based on the latest obtained data to ensure that the V2X (vehicle-to-everything) message exchange between the two is not affected.

[0032] High-density communication scenarios are the main factors leading to communication congestion. Therefore, it is necessary to flexibly allocate the limited available spectrum according to the working status and service requirements of each sub-network to achieve the maximum utilization of resources. Specifically, the coordination unit is responsible for collecting the global load information first, and then the optimization configuration suggestions are calculated through deep learning algorithms. Inclined resource injection is given to local hot spots to improve the processing efficiency, while a lower priority is maintained in areas with fewer users to reduce energy consumption. The ultimate goal is to keep the throughput performance of the overall system unchanged.

[0033] In addition, by dividing different levels of QoS (Quality of Service), exclusive channels can be reserved for emergency service vehicles such as ambulances and fire trucks to ensure that they can enjoy priority access rights at any time. Different types of in-vehicle application programs have different time requirements for real-time response. Therefore, it is very important to queue and process instructions according to importance and timeliness. For commands related to safety monitoring, such as functions like automatic braking and obstacle warning, the highest priority is given and they are responded to immediately. The remaining less important content is arranged in sequence according to the set thresholds. Finally, general messages wait to be sent during idle periods to ensure decisive and timely decision-making in critical situations. In addition, a weight scoring mechanism can be introduced to quantify the importance scores of various applications to help the system make correct choices more quickly.

[0034] For example, during the driving process of a driverless bus, it receives a warning notice that there are too many pedestrians in the upcoming turning section ahead and it needs to slow down. This command is relatively urgent and it should be ranked above the request of its navigation software for navigation update because it is related to safety issues. The last part focuses on improving the negative effects brought by external adverse factors. For example, problems such as electromagnetic interference increase the internal communication noise level and reduce the clarity of effective signals. Therefore, an environmental perception layer is introduced to specifically capture the characteristics of potential threat sources, and then active avoidance or shielding technologies are implemented. From a hardware perspective, the anti-disturbance performance of the device is enhanced. At the software level, with the help of adaptive modulation and demodulation technologies and coding gain schemes, the transmission parameters are flexibly changed to move away from the interference band, thereby restoring the ideal SNR (signal-to-noise ratio). Specifically, assume that the vehicle drives through a certain area where there is a large radio tower station emitting strong power broadcast radio waves, which will affect the in-vehicle short-wave radio device. At this time, it is necessary to comprehensively adjust through the methods mentioned above to restore the originally weakened call quality to the normal level.

[0035] Through such a series of coherent, orderly and complementary technical means, not only the efficient and stable vehicle-to-vehicle and vehicle-to-infrastructure networking functions are realized, but also various challenges faced in the actual application scenarios are solved, which promotes the development process of the construction of a smart city in a broader sense.

[0036] Next, the signal switching timing control parameters based on the moving speed of the vehicle in different network coverage areas of the present invention are described. First, in the first step of this method, the moving trajectory of the vehicle and the network area are modeled. This step is to obtain the speed of the vehicle and the information of each network coverage area where the vehicle is located. For example, the geographical location and moving speed information can be obtained in real time through the GPS module installed on the vehicle, and at the same time, the radio signal strength provided by the roadside unit (RSU) is collected to clarify different network coverage situations.

[0037] Subsequently, determine the key metrics for signal handover. During this process, identify key performance indicators (KPIs) to quantify network performance. For example, metrics such as packet loss rate and latency can be selected as one of the reference benchmark points, and ensure that those specific metrics that can reflect the seamless communication requirements are chosen. Specifically, set the maximum allowable packet loss rate within a reasonable range to 3%, and try to keep the average end-to-end delay below 100 milliseconds. This is because exceeding this limit will significantly affect the quality of service experience in the actual scenario.

[0038] Next is to establish a mathematical model to determine the speed function and threshold setting formula under handover conditions. For the speed variable v, its value range can vary according to the actual road conditions and is generally set to (0, 120] km / h; the threshold thd selects a suitable initial value as the comparison standard based on the pre-determined critical point. The meaning of the formula in this step is to define when to trigger the channel conversion operation. When the vehicle speed exceeds a certain preset rate and approaches the boundary position, start calculating the time required to enter the next service area, so as to determine when to execute the handover command, making the transition smooth and natural without interruption. After that, in one embodiment, apply this algorithm to optimize the communication stability in the vehicle networking environment. For example, on urban expressways, there are a large number of high-speed cars frequently entering and leaving the WiFi hotspot coverage area and alternately connecting to the cellular data network. In this case, the above-mentioned method plays a crucial role. Whenever it is detected that the vehicle reaches the predetermined condition, the corresponding processing program will be started to adjust the configuration parameters to ensure that users can always obtain high-quality and smooth data transmission services without interference. At the same time, dynamically monitor the status of surrounding base stations to promptly respond to possible problems and prevent errors in advance.

[0039] After the entire process is completed, the system also needs to continuously carry out a self-learning improvement mechanism, accumulate more experience and data, and continuously iterate and improve the existing parameter configuration to make the whole process more accurate, efficient and reliable. For example, train a prediction model through machine learning, and correct the decision-making logic under new situations that may be encountered in the future according to historical cases and real-time feedback results to achieve intelligent management and resource allocation.

[0040] The process of predicting parameters according to the signal switching timing control of the present invention includes: the first step is to collect basic data such as signaling parameters and vehicle location information in the current network segment; the second step is to analyze and process these signaling parameters, and derive a prediction algorithm through historical data analysis and machine learning models; the third step is to use the prediction algorithm to infer the location of the best access point and determine the precise range of the signal switching time point; the fourth step is to lock the access device to the optimal target node in advance in the short time before the signal switching is about to occur, and finally perform the switching action to achieve a smooth transition to ensure the stability and efficiency of the communication connection. On this basis, further fine-tune the specific setting parameters within the new network segment to ensure the optimal overall communication quality.

[0041] In the first step, it is necessary to obtain a series of prior information sources that are crucial to the subsequent steps. For example, signal strength (in dBm) is one of the important bases for judging whether the communication link is good or not. The value is usually between -50 and -120 (the limit value in different environments will vary). The closer to the zero point of the negative axis, the better the reception condition. Therefore, when the parameter is greater than or equal to -80 dBm, it is regarded as an excellent horizontal line under normal operation and can be the preferred judgment condition. In addition to the above-mentioned information, it also includes the spatial geographic coordinates and movement speed V (V>=0 km / h) given by the GPS positioning module. These data can reflect the vehicle's location and movement trajectory characteristics, which are helpful for a more comprehensive assessment of the future action plan direction. In order to accurately capture any changes that may exist during movement, continuous and uninterrupted real-time update monitoring is required.

[0042] For the processing method in the second step, it mainly relies on powerful computing power and advanced technical means to carry out complex calculation processes. For example, in one embodiment, a special software toolkit optimized for this scenario is established based on a big data analysis platform combined with a variety of mainstream machine learning methods such as random forests or support vector machines. This solution can not only quickly mine potential pattern rules, but also overcome the inherent shortcomings of a single algorithm in the past and provide more accurate and stable prediction performance results. Specifically, it finds successful cases that are highly similar to the current situation from a large number of past records, and then extracts their common characteristics to guide the decision-making process as a template to help determine the set of options and then select the optimal option to lay a solid foundation for subsequent dynamic adaptation and pave the way. The formula set here: P(t|X)=P(X|t)*P(t) / Σ[P(X|i)*P(i)] (where X refers to the overall variable space composed of all influencing factors, t is the target time node; i belongs to any other feasible selection branch) is intended to express the selection strategy problem corresponding to the time point with the maximum probability of an event under a given specific time and space background.

[0043] In the third stage, based on the previously formed conclusions, clear instructions are given for the next step of action, that is, selecting the most appropriate geographical site for docking preparation. Since the vehicle is on the move, factors such as the immediately obtained relative distance DeltaD (meter-level accuracy), the estimated time to arrival ETT (in seconds), and the coverage ranges R of multiple surrounding candidate cells need to be comprehensively considered. Finally, one or several eligible standard candidate lists are determined and waiting for the final review and calibration before official activation.

[0044] The last part, namely the fourth and fifth steps of the operation, is closely linked and inseparable. Once the timing is judged to be mature, the transfer procedure is immediately initiated to seamlessly connect the original line to the new address. At the same time, the parameters are immediately adjusted to meet the local environmental requirements to achieve the ideal effect, such as adjusting the transmission power Level (expressed in percentage), reselection frequency Band, etc., to ensure that the overall transmission efficiency will not be affected and continue to maintain a high level of performance.

[0045] Next, a communication link quality prediction model parameter is described for dynamically updating based on the real-time distance changes between the vehicle and other nodes. In this process, first, the real-time distance information between the vehicle and each other node is obtained. These distance data are used to characterize the relative position relationship of each node in the vehicle networking. For example, the distance information collected by in-vehicle sensors or GPS devices can reflect the distances between vehicles and between vehicles and roadside infrastructure.

[0046] Then, according to the changes in the real-time distance, the key parameters that may affect the communication quality between nodes are calculated. This calculation involves a series of factor analyses related to physical propagation characteristics, such as signal attenuation, multipath effect, etc. For each key parameter in the formula, there are specific meanings and setting ranges to ensure accuracy. For example, for the signal attenuation parameter (α), 0 < α ≤ 2. Under the direct vision condition without obstacles, it is close to the optimal value of 1, which reflects the ideal state propagation loss degree in free space.

[0047] Next is to construct an initial prediction model for the communication link quality. In one embodiment, a machine learning algorithm such as support vector machine (SVM) is used. Using the above-obtained key parameters as feature inputs, the estimated value is output to characterize the link quality level. After the initial construction, it needs to be continuously optimized to improve the accuracy.

[0048] To maintain the effectiveness of the prediction model, it is necessary to adjust the corresponding internal trade-off factors according to real-time changes. As the vehicle moves, the distance between it and other nodes changes, and the originally determined optimal configuration may deviate from the current environment. At this time, dynamic adjustment is crucial. Specifically, the weighted average update rule is adopted: a certain weight is assigned to the new observation (the weight is usually set as w, and 0 < w ≤ 1). When frequent observations show a trend, the weight is increased to ensure that the most recent data has a more prominent impact on the result, so as to quickly adapt to the optimal parameter adjustment in the changing environment.

[0049] In a specific example scenario, suppose a vehicle is driving in a network environment and the aforementioned method is applied to ensure stable and smooth V2V / V2I (Vehicle to Vehicle / Vehicle to Infrastructure) communication connections. When vehicle B approaches the fixed communication station C on the roadside, the system can immediately update the connection situation between the two to maintain efficient and reliable data exchange, and then support functions such as more intelligent transportation scheduling decisions, achieving the optimization and improvement effect of the seamless docking operation process within the vehicle network.

[0050] Next, to describe the regulation of the information queuing order according to the data priorities of different types of in-vehicle applications in the present invention to ensure key service responses. First, it is necessary to analyze the characteristics of different in-vehicle applications, define the data priority levels of each application, and formulate a set of criteria to measure the importance of information for different types of in-vehicle applications. In this step, it means classifying different applications such as the autonomous driving system (AS), vehicle health monitoring (VHM), and entertainment information system according to the requirements for real-time performance and reliability, and respectively marking them as high, medium, and low three priority levels. For example, the communication requirements of safety-related application programs have the highest level of urgency and accuracy and should be given the highest priority for processing.

[0051] After receiving the determined priorities above, it is a necessary step to construct a multi-queue data transmission model. Each queue is dedicated to the collection and distribution of one or more specific data streams. In one embodiment, there is a queue reserved specifically for the road condition perception data of AS, and another is used for non-delay-sensitive broadcast audio file sharing, etc. This design can ensure that urgent or high-priority information can be processed faster and more effectively.

[0052] Then, an algorithm is established to implement the adjustment of the order of all messages waiting to be sent or in the process of transmission according to the weights based on the above level division. There is a key calculation formula for dynamically allocating the weight W: W = α * D + β * I - γ * H. The parameter D represents the importance factor of the delay requirement, taking a real number between 0 and 1. For applications with strict delay restrictions, this coefficient should be close to the maximum value of 1. And I represents the influence factor of the amount of information, also in the range of [0, 1], aiming to evaluate the relative complexity brought by the content carried in a single communication. Finally, H in front of the minus sign, that is, the error rate penalty coefficient, is also defined as a real number field (0, 1) according to a similar standard. In the optimal case when no errors occur, the minimum limit of 0 is taken; on the contrary, if the transmission quality is extremely unstable and prone to errors, a relatively high positive value should be given as much as possible to weaken the ranking of the corresponding data packets. Such a setting can promote the natural tendency to select more important message transmissions during resource allocation, ensuring the stability of the overall network performance.

[0053] Specifically, when the vehicle encounters a warning of road construction ahead, this mechanism can immediately interrupt the ordinary music stream playing service through this mechanism, so that the warning notification from the environmental monitoring system can be preferentially pushed. Similarly, in the vehicle networking environment, assuming that a certain node temporarily acts as a temporary transfer station to forward data to multi-destination devices, the channel bandwidth and sorting rules are reasonably allocated according to the priorities of the application scenarios behind these final receivers.

[0054] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A method for seamless connection of a network for vehicle communication, characterized in that, Including: S101. Determine signal handover timing control parameters based on the moving speed of the vehicle in different network coverage areas; S102. Predict and adjust the optimal access point of the upcoming new network section according to the signal handover timing control parameters; S103. Dynamically update the parameters of the communication link quality prediction model based on the real-time distance change between the vehicle and other nodes; S104. Regulate the information queuing order according to the data priorities of different types of in-vehicle applications to ensure the response of critical services.

2. The network seamless connection method for vehicle communication according to claim 1, characterized in that The regulation of the signal handover timing based on the moving speed of the vehicle in different network coverage areas further includes: Obtain the speed v of the current vehicle; Predict the vehicle distance change Δd in the next period based on Δd = vt; When the vehicle speed meets the following conditions, adjust the network access point A: \[ v > \frac{Th}{\Delta t},\] where Th is the handover critical speed threshold, and \(\Delta t\) represents the minimum time interval; Start the target cell search in advance to ensure continuous data flow.

3. A network seamless connection method for vehicle communication according to claim 2, characterized in that, The further improvement of the steps to adapt to the changing actual scenario further includes: Obtain the distance d_i from the adjacent communication node; Use the formula to predict the link quality Q = w_r / r^4, where w is the weight coefficient and r refers to the relative distance between nodes; When the predicted communication interruption risk P_break > P_th (\(P_{break} = exp^{\lambda d^{n}}\)), switch the connection in advance; Optimize the link path configuration to maintain stable transmission.

4. A network seamless connection method for vehicle communication according to claim 3, characterized in that, The improvement of the dynamic resource allocation strategy further includes: Estimate the vehicle density \(\rho_v\) and count the total number of requests N_tot; Set the bandwidth allocation ratio B_Ratio and formulate a plan according to the load balance degree L_bal (L_bal = log((1 / Ntot)∑_i(N_i / BR)_i)); Determine the data volume D_car obtained by a single vehicle according to the formula: \(D_{car} = D_max * e^{\alpha \cdot\rho}\); Redistribute the bandwidth to each active session according to the demand to improve the performance.

5. A network seamless connection method for vehicle communication according to claim 4, characterized in that Adding new rules at the level of different types of in-vehicle applications further includes: Analyze the task level Level_Task of each service type; Allocate the queue position Index_P = (N_H + w_Level) according to the real-time task level, where N_H is the default queuing height and wLevel assigns a higher value to high-priority tasks; Use the formula \(Delay_i = k_{delay}*(p(i) / P)\) to judge the information processing delay; here p(i) is the service ratio and P is the maximum possibility in a unit cycle; Implement the plug-and-play protocol for low-latency demand applications.

6. A network seamless connection method for vehicle communication according to claim 5, characterized in that, Optimizing the wireless spectrum and power allocation method in the presence of interference includes the following improvement measures: Identify the main interference type T_dist; Determine channel availability through frequency scanning and match it with the optimal transmit power Ptx; Adopt S = C / (I+N), where C is the carrier carrier intensity, I is the interference level, and N is the background noise, as a measurement parameter for adjustment; If \(S > S_min\), reduce the interference effect and increase the effective transmission range.

7. A method for seamless network connection for vehicle communication according to claim 6, characterized in that Increasing the service enhancement function based on geographical information further involves the following actions: Locate the vehicle's GPS coordinates; Combine with the geographical information system to generate the regional map M_map; According to the formula \(f(x)= \eta * x bias_g\), where x is the position coordinate on the map, and \(\eta\) and bias_g define the mapping relationship; Apply the location-related optimization parameters to the above algorithms.

8. A network seamless connection method for vehicle communication according to claim 7, characterized in that, A more accurate time synchronization correction step is added, specifically manifested as: Monitor the internal clock deviation \(\delta_c\) of the system; Calculate the delay compensation Tc = τ + γ·|\(\delta_t\delta_m\)|, where τ is the baseline delay and γ is the compensation factor; Introduce multi-source references to verify the accuracy: T_corr=(T1+ T.. + T_N ) / N, where other Ts represent other reference stations; After performing the correction, confirm the timestamp consistency again.

9. A network seamless connection method for vehicle communication according to claim 1, characterized in that Supplementary measures to enhance connection reliability in special environments such as tunnels cover the following aspects: Record the recent historical average propagation loss L_avg; Select the alternative channel C_alt and at the same time adjust the working state F_mod = F_init(F_sen / F_ref); Based on the formula \[SINR = SINR * \log_2(1 + \gamma / L_{avg}),\) where \(\gamma\) represents the signal quality gain parameter; Actively reduce the influencing factors of frequency deviation when in a restricted area.

10. A network seamless connection method for vehicle communication according to claim 1, characterized in that, Introduce intelligent algorithms to predict future network conditions and make forward-looking plans, including these detailed actions: Collect a large sample of historical traffic H_ts in approximately similar time periods; Predict possible events E_pre = P_Event(H_ts); Based on logistic regression, establish an evaluation function \(F(x)= 1 / (1 + exp(\beta *(tt_0))),\) where t is the current moment, and \(\beta\), t_0 are the adaptive parameter groups; Make pre-allocation policies in advance to deal with the probability of emergencies.

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