Intelligent switching and cooperative communication method of satellite and ground network

Abstract

The application discloses a kind of intelligent switching cooperative communication methods of satellite and ground network, the method includes: multi-dimensional parameter real-time acquisition;With vehicle terminal, multi-dimensional parameter is decided, and decision result is output;According to decision result, control ground communication module and satellite communication module in vehicle terminal switch, and ground satellite network and / or satellite network are connected and data is sent;The result of each transmission and the input parameter when decision are recorded and uploaded to cloud policy management platform.The intelligent switching cooperative communication method of satellite and ground network provided by the application can dynamically select or cooperatively use optimal link for each communication based on multi-dimensional parameter fusion calculation, and construct an iterative optimization technology closed loop.

Description

Technical Field

[0001] This invention relates to the field of vehicle communication technology, and in particular to a method for intelligent switching and cooperative communication between satellite and terrestrial networks. Background Technology

[0002] With the development of intelligent and connected vehicles, eCall (automatic emergency call) systems have become a key technology for improving road traffic safety. Existing technical solutions are mainly divided into two categories: systems based on terrestrial cellular networks (such as 4G / 5G), and eCall systems that integrate satellite communication modules as backup.

[0003] To address the issue of blind spots in terrestrial network coverage, the integration of satellite and terrestrial network communication has become an important direction, and some advanced solutions have emerged aimed at optimizing the handover and coordination between the two networks.

[0004] For example, patent CN121151982A proposes a system and method for collaborative communication and predictive seamless handover between satellite network and cellular network. This solution predicts future link quality using a machine learning model and performs predictive handover and intelligent routing based on data priorities (such as critical control data and collaborative sensing data). In scenarios such as vehicle platooning control, it effectively improves the smoothness of handover and the reliability of critical data transmission, representing an advanced technological approach in this field. Its core lies in a predictive handover method based on historical data and future environmental predictions. It uses machine learning models (such as LSTM) to predict the link quality curves of satellite network and cellular network in the future and initiates handover in advance based on the prediction results. The decision-making basis is mainly the predicted network quality and preset, static data priorities (such as critical control data and collaborative sensing data).

[0005] However, the current switching methods still have the following obvious shortcomings:

[0006] (1) Insufficient economic optimization: The decision-making model of the existing solution (such as CN121151982A) does not take communication cost as the core quantitative decision parameter. In emergency call scenarios, this may result in the system being unable to finely control the use of high-cost satellite links while ensuring communication reliability, resulting in high operating costs.

[0007] (2) Weak adaptability of business models: There are huge differences between the business models of vehicle platooning communication and vehicle-mounted emergency calls. The latter has the characteristics of suddenness, one-time nature and extreme end-to-end success rate requirements. The existing solutions are based on the priority division and optimization goals of platooning control design, and have not been deeply adapted to the core requirement of emergency calls to "establish a connection with the highest success rate, the fastest speed and cost consideration at the moment of the accident".

[0008] (3) Limited real-time response capability in complex weak signal environments: Emergency calls often occur in complex areas with rapid signal fluctuations (such as the edge of cities). Existing predictive handover models may fail to predict accurately or respond late due to the irregularity of the scenario, causing call attempts to repeatedly fail on the ground network and delaying rescue opportunities.

[0009] (4) Lack of adaptive optimization capability: The performance of emergency call systems is strongly correlated with the regional network environment and pricing strategies. Existing solutions are mostly static or semi-static strategies, which make it difficult to continuously optimize the decision-making logic through feedback from actual operational data, and long-term performance may degrade. Summary of the Invention

[0010] To address the shortcomings of existing technologies, this invention provides an intelligent switching and collaborative communication method between satellite and terrestrial networks. The method uses a vehicle-mounted terminal to dynamically select or collaboratively use the optimal link for each communication based on multi-dimensional parameter fusion calculation.

[0011] This invention provides the following solutions:

[0012] This invention provides a method for intelligent handover and cooperative communication between satellite and terrestrial networks, the method comprising:

[0013] S1. Real-time acquisition of multi-dimensional parameters;

[0014] S2. Use the vehicle-mounted terminal to make decisions based on multi-dimensional parameters and output the decision results;

[0015] S3. Based on the decision results, control the switching between the ground communication module and the satellite communication module in the vehicle terminal, establish a connection with the ground satellite network and / or satellite network and send data;

[0016] S4. Record the results of each transmission and the input parameters during decision-making, and upload them to the cloud-based policy management platform.

[0017] Furthermore, the multi-dimensional parameters mentioned in step S1 include network state parameters, service characteristic parameters, and cost strategy parameters.

[0018] Furthermore, the network status parameters include the signal quality of satellite and terrestrial networks and the latency of satellite or terrestrial networks; the cost strategy parameters include network tariff unit price and cost control strategy type; and the service characteristic parameters include data type, urgency, and data volume.

[0019] Furthermore, step S2 employs a weighted scoring method for decision-making, including the following process:

[0020] S2.1 Parameter Normalization: The multi-dimensional parameters are mapped to the range of 0 to 100 according to their values ​​or importance, respectively, to obtain the terrestrial network quality index Q_g, satellite network quality index Q_s, cost index C and service urgency index U;

[0021] S2.2 Determine the current weight coefficient W=[W_q, W_c, W_u] based on the current service type, where W_q, W_c and W_u are the network quality weight, cost weight and service urgency weight under the current service type, respectively, and W_q+W_c+W_u=1;

[0022] S2.3 Calculate the terrestrial network comprehensive index Score_g and the satellite network comprehensive index Score_s using the following formulas:

[0023] Score_g=W_q*Q_g+W_c*C+W_u*U,

[0024] Score_s=W_q*Q_s+W_c*C+W_u*U;

[0025] S2.4. Make a decision by combining the terrestrial network comprehensive index Score_g and the satellite network comprehensive index Score_s.

[0026] Furthermore, in step S2.1, the cost index C is calculated using the following mapping function:

[0027] When Price_current <Price_threshold:

[0028] C=C_max*(1-Price_current / Price_threshold),

[0029] When Price_current ≥ Price_threshold:

[0030] C=0;

[0031] Where C_max is the maximum value of 100, Price_current is the estimated unit cost of transmitting current service data on this link, which is calculated by combining the network tariff unit price and the cost control strategy type, and Price_threshold is the highest cost threshold.

[0032] Furthermore, step S2.4 employs a hierarchical sequence to execute the decision-making process, including the following steps:

[0033] a. First-level adjudication, ultimate reliability guarantee adjudication: This level of adjudication is triggered if and only if both of the following conditions are met simultaneously:

[0034] Business urgency condition: The current business urgency index U is higher than the preset value;

[0035] Network availability conditions: Both the terrestrial network quality index Q_g and the satellite network quality index Q_s are higher than the preset values;

[0036] The ruling is as follows: A dual-network parallel transmission strategy will be adopted, establishing two transmission channels simultaneously on the ground and satellite for the current business data, and sending identical copies of the data.

[0037] Degradation process: If only the business urgency condition is met, but the network availability condition is not met, then the process proceeds to the second-level decision-making stage.

[0038] b. Second-level decision-making, single-link intelligent selection decision:

[0039] Let the network comprehensive index of the currently used link be Score_x, and the network comprehensive index of another link be Score_y. When Score_y - Score_x > the switching trigger threshold, the decision is to select the other link.

[0040] When Score_x < the lower limit of link quality, a handover assessment is forcibly triggered, that is, to assess whether Score_y - Score_x > the handover trigger threshold. If it is true, another link is selected.

[0041] Furthermore, in the second-level adjudication, if the service data to be transmitted consists of multiple logically separable data packets with different service characteristics, the service data is decomposed into independent sub-data packets, and each sub-data packet is independently adjudicated and executed using the single-link intelligent selection adjudication logic.

[0042] Furthermore, in step S4, the cloud-based strategy management platform analyzes the results and input parameters during decision-making, optimizes the input parameters during decision-making, and sends updates to the vehicle terminal via OTA.

[0043] The beneficial effects of this invention based on its technical solution are as follows:

[0044] (1) Because the quantifiable cost index C and dynamic weight W_c are innovatively introduced into the decision-making model, the system can jointly optimize the technical performance and economic cost of the same service on different links at the protocol stack and link control level, and realize the precise technical scheduling of resources in all dimensions, including economic resources, thereby achieving the unity of communication reliability and operational economy at the technical level.

[0045] (2) This invention enables the decision-making model to be deeply bound to the characteristics of emergency call services from the underlying logic by pre-setting differentiated weight coefficient sets (W_q, W_c, W_u) for different service types. Combined with real-time (non-predictive) parameter acquisition and index calculation, the system can make millisecond-level technical responses to complex non-steady-state links such as "weak and unstable", overcoming the delay or misjudgment problems that may exist in traditional prediction models in emergency scenarios.

[0046] (3) This invention records and uploads decision metadata (input parameters, results) to the cloud for regression analysis, and iteratively optimizes the weight coefficients and scoring mapping function to construct a complete technical adaptive closed loop. This enables the system's decision logic to dynamically evolve with real-world data, maintaining technical optimality in the long term and solving the technical problem of poor environmental adaptability of static strategies.

[0047] (4) Due to the adoption of real-time acquisition of the multi-dimensional parameters and intelligent link decision based on weighted scoring or fuzzy logic, the present invention can quantitatively evaluate complex network conditions such as weak signals, thereby triggering the switch to satellite network or dual-network parallel operation in advance or intelligently under the condition that the traditional solution will still be stuck or fail on the ground network, avoiding communication failure caused by the quality fluctuation of a single network.

[0048] (5) By introducing a data packet coordination strategy, this invention can control the transmission of latency-sensitive data streams at the protocol stack level, prioritizing the transmission of the most critical alarm information via the current optimal link (such as satellite). This enables the most critical alarm information to be sent out via the fastest path, which can shorten the alarm call establishment time by an average of several seconds to tens of seconds compared to traditional solutions, thus gaining golden time for life rescue.

[0049] (6) This invention incorporates cost as a core decision variable into emergency call link decision-making, and can flexibly select the more economical link according to the business value. For example, while ensuring emergency calls, non-emergency services such as status reporting are routed to the low-cost network to achieve effective control of operating costs.

[0050] (7) Smooth intelligent switching reduces the feeling of communication interruption, and multi-layer decision protection (real-time scoring, dual-network parallelism, feedback optimization) greatly improves the overall robustness of the system and user experience in complex and ever-changing environments.

[0051] (8) The dynamic weighting mechanism designed for the emergency call service model in this invention ensures strict alignment between the strategy and the business objectives. The introduced cloud-based feedback learning loop enables the system to adapt to different regional networks and tariff policies, maintain optimal performance in the long term, and solve the problem of poor environmental adaptability of static strategies. Attached Figure Description

[0052] To more clearly illustrate the technical solutions in the embodiments of this specification or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0053] Figure 1 This is a flowchart illustrating a method for intelligent handover and collaborative communication between satellite and terrestrial networks.

[0054] Figure 2 This is a schematic diagram of the hardware system.

[0055] Among them: 101-Vehicle dual-mode communication T-Box, 102-Cloud policy management platform, 103-Emergency call center, 104-Ground base station, 105-Satellite. Detailed Implementation

[0056] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention are within the protection scope of the embodiments of the present invention.

[0057] This embodiment provides a method for intelligent handover and cooperative communication between satellite and terrestrial networks, referring to... Figure 1 The method includes:

[0058] S1. Real-time collection of multi-dimensional parameters, including network status parameters, service characteristic parameters, and cost strategy parameters. Network status parameters include signal quality of satellite and terrestrial networks and latency of satellite or terrestrial networks; cost strategy parameters include network tariff unit price and cost control strategy type; service characteristic parameters include data type, urgency, and data volume.

[0059] S2. Utilize the in-vehicle terminal to make decisions based on multi-dimensional parameters and output the decision results. Specifically, lightweight machine learning models or other decision-making schemes can be employed. This embodiment uses a weighted scoring method for decision-making, including the following process:

[0060] S2.1 Parameter Normalization: The multi-dimensional parameters are mapped to the range of 0 to 100 according to their values ​​or importance, respectively, to obtain the terrestrial network quality index Q_g, satellite network quality index Q_s, cost index C, and service urgency index U.

[0061] Network quality index Q_g: A lookup table or function mapping based on numerical values. For example, when the terrestrial network RSRP (Reference Signal Receiving Power) > -90dBm, the terrestrial network quality index Q_g = 90; when RSRP < -120dBm, Q_g = 10; a linear mapping exists between these values.

[0062] Cost Index C: This index estimates and maps transmission costs based on the current service's preset cost strategy, real-time network tariff unit price, and data volume. A specific mapping function is: C = C_max * (1 - Price_current / Price_threshold), where Price_current is the estimated unit cost of transmitting current service data on this link, Price_threshold is the maximum allowed cost threshold for this service type, and C_max is the maximum cost score (e.g., 100). When Price_current ≥ Price_threshold, C = 0. Price_current can be dynamically calculated based on (network tariff unit price × data volume) / package capacity factor. The data volume can be calculated based on the current cost control strategy.

[0063] Business urgency index U: Determined by business type. For example, U=100 for "emergency voice call"; U=90 for "collision MSD (Minimum Set of Data) data"; U=20 for "vehicle status reporting".

[0064] S2.2 Determine the current weight coefficient W=[W_q, W_c, W_u] based on the current service type, where W_q, W_c and W_u are the network quality weight, cost weight and service urgency weight under the current service type, respectively, and W_q+W_c+W_u=1.

[0065] The following are two common weight allocation schemes for different business scenarios:

[0066] Emergency voice call: W_q=0.7 (quality priority), W_c=0.1 (cost insensitive), W_u=0.2.

[0067] Vehicle status reporting: W_q=0.3, W_c=0.6 (cost sensitive), W_u=0.1.

[0068] S2.3 Calculate the terrestrial network comprehensive index Score_g and the satellite network comprehensive index Score_s using the following formulas:

[0069] Score_g=W_q*Q_g+W_c*C+W_u*U,

[0070] Score_s=W_q*Q_s+W_c*C+W_u*U;

[0071] S2.4. Make a decision by combining the comprehensive terrestrial network index Score_g and the comprehensive satellite network index Score_s:

[0072] a. First-level adjudication, ultimate reliability guarantee adjudication: This level of adjudication is triggered if and only if both of the following conditions are met simultaneously:

[0073] Business urgency condition: The current business urgency index U is higher than the preset value (e.g., U downgrade processing ≥ downgrade processing 95).

[0074] Network availability conditions: Both the terrestrial network quality index Q_g and the satellite network quality index Q_s are higher than the preset values ​​(e.g., downgrade processing Q_g or Q_s > downgrade processing 30).

[0075] The ruling is as follows: A dual-network parallel transmission strategy will be adopted, establishing two transmission channels simultaneously on the ground and satellite for the current business data, and sending identical copies of the data.

[0076] Degradation process: If only the business urgency condition is met, but the network availability condition is not met, then the process proceeds to the second-level decision-making stage.

[0077] b. Second-level decision-making, single-link intelligent selection decision (introducing a hysteresis mechanism to prevent ping-pong handover):

[0078] Let the network comprehensive index of the currently used link be Score_x, and the network comprehensive index of another link be Score_y. When Score_y - Score_x > the switching trigger threshold, the decision is to select the other link.

[0079] When Score_x < the lower limit of link quality, a handover assessment is forcibly triggered, that is, to assess whether Score_y - Score_x > the handover trigger threshold. If it is true, another link is selected.

[0080] If the service data to be transmitted consists of multiple logically separable sub-data packets with different service characteristics (such as different types and different urgency levels U) (for example, an emergency call event generates both "voice packets" and "MSD packets"), then each sub-data packet is independently adjudicated and executed using single-link intelligent selection adjudication logic.

[0081] Each sub-data packet will obtain an independent link selection result (which may differ) based on its own weight and real-time network score, thereby achieving a coordinated transmission effect where high-urgency sub-packets (such as voice) are sent through high-quality links (such as satellite), while low-urgency sub-packets (such as status reports) are sent through low-cost links (such as ground).

[0082] S3. Based on the decision results, control the ground communication module and satellite communication module in the vehicle-mounted dual-mode communication T-Box to perform corresponding connection establishment, data routing, transmission and seamless switching actions;

[0083] S4. Record the results of each transmission and the input parameters during decision-making, and upload them to the cloud-based policy management platform. The cloud-based policy management platform analyzes the results and the input parameters during decision-making, optimizes the input parameters during decision-making, and sends updates to the vehicle terminal via OTA. The updated parameters will participate in subsequent decisions.

[0084] Reference Figure 2 The hardware system used for this real-time method can adopt a vehicle-cloud collaborative architecture. The in-vehicle dual-mode communication T-Box (in-vehicle terminal) 101 serves as the execution terminal, responsible for data collection, local decision-making, and communication execution. Its hardware foundation can be upgraded based on the existing in-vehicle T-Box to enhance the computing power of the in-vehicle T-Box main control unit. The cloud-based policy management platform 102 is responsible for macro-level policy management, such as: setting differentiated cost control policies for fleets of different vehicle models and in different regions; collecting massive amounts of vehicle communication feedback data to train and distribute better decision-making algorithm models; and monitoring the overall network communication quality and cost. The emergency call center 103 receives emergency call requests and MSD data from ground or satellite links. The in-vehicle dual-mode communication T-Box 101 and the cloud-based policy management platform 102 maintain a heartbeat connection through ground base station 104 or satellite 105 for policy synchronization. In an emergency, the in-vehicle dual-mode communication T-Box 101 establishes a voice or data channel directly to the emergency call center 103 based on local decisions.

[0085] The following is an example of how the method provided in this embodiment is applied to trigger an emergency call after a vehicle collision. The specific process is as follows:

[0086] 1. The collision sensor signal is sent to the vehicle dual-mode communication T-box101 via the CAN bus, and the service is determined to be the highest level of emergency voice call and MSD transmission.

[0087] 2. Immediately collect current network parameters: terrestrial 4G signal is weak (Q_g=40 after mapping) with high latency; satellite Tiantong-1 signal is good (Q_s=85 after mapping). In the cost strategy, emergency calls are "at all costs".

[0088] 3. After weighted calculation, the satellite link's overall score is significantly higher than that of the terrestrial link, leading to the decision to use the satellite link to establish a voice call. Based on this, control commands are generated: activate the satellite module, establish a voice channel to the emergency call center, and configure routing.

[0089] 4. Simultaneously, the engine determined that the MSD data packet was small and the dual-network parallel strategy was triggered (due to the extremely high urgency). Therefore, the engine further controlled the ground module to establish a data channel, simultaneously transmitting voice and MSD data via satellite and also transmitting a copy of the MSD data via the ground network as a redundant backup.

[0090] 5. During the data establishment process, the vehicle entered the tunnel and the satellite signal was lost. Real-time monitoring showed that Q_s dropped sharply to 10, while Q_g remained unchanged (40), triggering the optimal switching logic. The engine started the backup data channel establishment process of the ground network, and after it was ready, it performed the application layer switching, and attempted to switch the data link to the ground network in the background. Since the ground network was still connected, the switching process was fast and the user's call interruption was minimal.

[0091] 6. After the transmission is completed, the decision parameters, results and switching events are recorded. Then, when the vehicle reports its status again, the data will be uploaded to the cloud-based strategy management platform 102 along with the historical logs for analysis to optimize future decision parameters.

[0092] To verify the effectiveness of this invention, it was compared with traditional cold backup solutions and existing predictive solutions. The following typical test scenario was set: a vehicle collides on a remote mountain road, triggering an emergency call. The terrestrial 4G signal in this area is extremely weak and unstable (RSRP = -125 dBm), while the satellite signal is good. The switching parameters are shown in the table below:

[0093]

[0094] As shown in the table above, this invention, by introducing a cost dimension and a business-adaptive dynamic weighting mechanism, achieves intelligent cost control for non-critical business operations while ensuring the highest reliability and timeliness of emergency calls. Compared to traditional and predictive solutions, the real-time weighted scoring model makes decisions more directly and quickly when dealing with sudden, irregular weak signal scenarios. Furthermore, it forms a closed loop of continuous improvement through cloud-based feedback learning, resulting in comprehensive and outstanding technical effects.

[0095] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0096] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0097] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for intelligent handover and cooperative communication between satellite and terrestrial networks, characterized in that, The method includes: S1. Real-time acquisition of multi-dimensional parameters; S2. Use the vehicle-mounted terminal to make decisions based on multi-dimensional parameters and output the decision results; S3. Based on the decision results, control the switching between the ground communication module and the satellite communication module in the vehicle terminal, establish a connection with the ground satellite network and / or satellite network and send data; S4. Record the results of each transmission and the input parameters during decision-making, and upload them to the cloud-based policy management platform.

2. The intelligent handover and cooperative communication method between satellite and terrestrial networks according to claim 1, characterized in that: The multi-dimensional parameters mentioned in step S1 include network status parameters, service characteristic parameters, and cost strategy parameters.

3. The intelligent handover and cooperative communication method between satellite and terrestrial networks according to claim 2, characterized in that: The network status parameters include the signal quality of satellite and terrestrial networks and the latency of satellite or terrestrial networks; the cost strategy parameters include the network tariff unit price and the cost control strategy type; and the service characteristic parameters include data type, urgency, and data volume.

4. The intelligent handover and cooperative communication method between satellite and terrestrial networks according to claim 3, characterized in that: Step S2 employs a weighted scoring method for decision-making. Includes the following processes: S2.1 Parameter Normalization: The multi-dimensional parameters are mapped to the range of 0 to 100 according to their values ​​or importance, respectively, to obtain the terrestrial network quality index Q_g, satellite network quality index Q_s, cost index C and service urgency index U; S2.2 Determine the current weight coefficient W=[W_q, W_c, W_u] based on the current service type, where W_q, W_c and W_u are the network quality weight, cost weight and service urgency weight under the current service type, respectively, and W_q+W_c+W_u=1; S2.3 Calculate the terrestrial network comprehensive index Score_g and the satellite network comprehensive index Score_s using the following formulas: Score_g=W_q*Q_g+W_c*C+W_u*U, Score_s=W_q*Q_s+W_c*C+W_u*U; S2.

4. Make a decision by combining the terrestrial network comprehensive index Score_g and the satellite network comprehensive index Score_s.

5. The intelligent handover and cooperative communication method between satellite and terrestrial networks according to claim 4, characterized in that: In step S2.1, the cost index C is calculated using the following mapping function: When Price_current <Price_threshold: C=C_max*(1-Price_current / Price_threshold), When Price_current ≥ Price_threshold: C=0； Where C_max is the maximum value of 100, Price_current is the estimated unit cost of transmitting current service data on this link, which is calculated by combining the network tariff unit price and the cost control strategy type, and Price_threshold is the highest cost threshold.

6. The intelligent handover and cooperative communication method between satellite and terrestrial networks according to claim 4, characterized in that: Step S2.4 executes the decision-making process in a hierarchical order, including the following procedures: a. First-level adjudication, ultimate reliability guarantee adjudication: This level of adjudication is triggered if and only if both of the following conditions are met simultaneously: Business urgency condition: The current business urgency index U is higher than the preset value; Network availability conditions: Both the terrestrial network quality index Q_g and the satellite network quality index Q_s are higher than the preset values; The ruling is as follows: A dual-network parallel transmission strategy will be adopted, establishing two transmission channels simultaneously on the ground and satellite for the current business data, and sending identical copies of the data. Degradation process: If only the business urgency condition is met, but the network availability condition is not met, then the process proceeds to the second-level decision-making stage. b. Second-level decision-making, single-link intelligent selection decision: Let the network comprehensive index of the currently used link be Score_x, and the network comprehensive index of another link be Score_y. When Score_y - Score_x > the switching trigger threshold, the decision is to select the other link. When Score_x < the lower limit of link quality, a handover assessment is forcibly triggered, that is, to assess whether Score_y - Score_x > the handover trigger threshold. If it is true, another link is selected.

7. The intelligent handover and cooperative communication method between satellite and terrestrial networks according to claim 6, characterized in that: In the second-level adjudication, if the service data to be transmitted consists of multiple logically separable sub-data packets with different service characteristics, then each sub-data packet is independently adjudicated and executed using the single-link intelligent selection adjudication logic.

8. The intelligent handover and cooperative communication method between satellite and terrestrial networks according to claim 1, characterized in that: In step S4, the cloud-based strategy management platform analyzes the results and input parameters during decision-making, optimizes the input parameters during decision-making, and sends updates to the vehicle terminal via OTA.

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