Method, device and equipment for pairing connection between equipment terminals

Abstract

The invention relates to the technical field of vehicle-mounted equipment connection, in particular to a pairing connection method and device between equipment terminals and equipment. The method comprises the following steps: collecting equipment basic information databases of all vehicle-mounted equipment, performing vehicle-mounted equipment trust level evaluation and differentiated communication protocol stack configuration, and constructing a differentiated protocol adaptation strategy; the method comprises the following steps: monitoring signal state data of a vehicle-mounted environment in real time, predicting a signal state quality evolution trend, and constructing a channel quality trend prediction map; carrying out channel quality interval calculation according to the channel quality trend prediction map, and carrying out optimal pairing opportunity calculation and protocol stack pre-distribution according to a differential protocol adaptation strategy and a quality evaluation value to generate a communication protocol pre-distribution strategy; the method comprises the following steps: collecting a vehicle-mounted equipment operation monitoring log, and constructing a global interaction behavior vector graph; according to the invention, efficient and stable vehicle-mounted equipment connection is realized, the equipment connection efficiency is improved, resource conflicts are reduced, and the adaptability to a complex vehicle-mounted environment is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle-mounted device connection, and in particular to a method, apparatus, and device for pairing and connecting device terminals. Background Art

[0002] In the in-vehicle environment, due to factors such as the diversity of device types, complex communication protocols, and dynamic changes in operating status, establishing connections between devices often faces a series of technical challenges. First, there are significant differences in the communication capabilities, protocol compatibility, and trust levels between device terminals. If matching and pairing cannot be performed based on device characteristics, communication conflicts, connection interruptions, and other problems can easily occur. Secondly, the in-vehicle system has extremely high requirements for connection stability and real-time performance. Especially when driving at high speeds, frequently switching network environments, or in the presence of complex interference sources, traditional static pairing mechanisms are difficult to adapt to dynamically changing communication needs. In addition, some devices in the in-vehicle network may have potential security risks, such as unauthorized access devices or hijacked communication nodes. These problems not only threaten data security, but may also affect the operation and control of the entire vehicle.

[0003] Existing methods for connecting in-vehicle devices often use preset pairing strategies or pairing mechanisms based on simple identity verification. These rely on static configuration and manual intervention, making it difficult to intelligently identify and dynamically adapt to device attributes, connection environments, and communication status. These methods often fail to promptly identify the actual connection requirements between devices, lack real-time perception of channel status, network load, and device behavior, and are unable to optimize pairing sequences and connection strategies based on real-time data. This results in low system resource utilization and a low connection success rate, making it difficult to meet the needs of multi-device collaborative communication in complex in-vehicle environments. Therefore, there is an urgent need for an intelligent, dynamic, and real-time responsive pairing and connection method between in-vehicle device terminals. Summary of the Invention

[0004] In order to solve the above technical problems, the present invention proposes a pairing connection method, device and equipment between device terminals to solve at least one of the above technical problems.

[0005] To achieve the above object, the present invention provides a method for pairing and connecting device terminals, comprising the following steps: Step S1: Collect the basic information database of all vehicle-mounted devices, conduct vehicle-mounted device trust level assessment and differentiated communication protocol stack configuration, and build a differentiated protocol adaptation strategy; Step S2: real-time monitoring of the signal status data of the vehicle environment, prediction of the signal status quality evolution trend, and construction of a channel quality trend prediction graph; Step S3: Calculate the channel quality interval according to the channel quality trend prediction graph, calculate the optimal pairing timing and pre-allocate the protocol stack according to the differentiated protocol adaptation strategy and the quality evaluation value, and generate a communication protocol pre-allocation strategy; Step S4: Collecting the on-board equipment operation monitoring log, performing device call behavior detection one by one, and modeling the global device interaction behavior to construct a global interaction behavior vector map; Step S5: Mining the interaction intention requirements of the global interaction behavior vector map, and mining the device connection requirement topology to construct a device connection requirement topology map; Step S6: Dynamically perform device adaptive pairing and connection on the device connection demand topology according to the communication protocol pre-allocation strategy, and dynamically adjust the device pairing sequence to build an adaptive device connection management model.

[0006] In this specification, a device for pairing and connecting between device terminals is provided, which is used to perform the above-mentioned method for pairing and connecting between device terminals, including: The differentiated protocol adaptation module is used to collect the basic information database of all on-board devices, conduct on-board device trust level assessment and differentiated communication protocol stack configuration, and build differentiated protocol adaptation strategies; The trend prediction module is used to monitor the signal status data of the vehicle environment in real time, predict the evolution trend of the signal status quality, and build a channel quality trend prediction chart; The protocol pre-allocation module is used to calculate the channel quality interval based on the channel quality trend prediction graph, calculate the optimal pairing timing and pre-allocate the protocol stack based on the differentiated protocol adaptation strategy and the quality evaluation value, and generate the communication protocol pre-allocation strategy; The interactive behavior module is used to collect the operation monitoring logs of on-board equipment, perform device call behavior detection on a device-by-device basis, model the global interactive behavior between devices, and construct a global interactive behavior vector map; The connection demand topology module is used to mine the interaction intention demand of the global interaction behavior vector map, and mine the device connection demand topology to build a device connection demand topology map; The adaptive pairing module is used to dynamically perform device adaptive pairing connections on the device connection requirement topology according to the communication protocol pre-allocation strategy, dynamically adjust the device pairing order, and build an adaptive device connection management model.

[0007] The present invention also provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the pairing connection method between device terminals described in any one of the above are implemented.

[0008] The beneficial effects of the present invention are specifically as follows: comprehensive identification and trust management of vehicle-mounted equipment, ensuring that the system can flexibly configure different communication protocol stacks according to the functional type, performance parameters and security level of the equipment. By building differentiated protocol adaptation strategies, the compatibility, security and adaptability of the system are improved, providing a reliable basis for subsequent pairing decisions. It can dynamically perceive the changing trend of the communication environment and predict the fluctuation of channel quality in advance, thereby providing a real-time reference for subsequent pairing connections and protocol scheduling, avoiding communication operations when the channel conditions are poor, and improving the connection success rate and communication stability. The pairing decision between devices is tightly coupled with the channel quality and device capabilities. By estimating the optimal pairing time and allocating the appropriate protocol stack in advance, pre-connection strategy planning and resource optimization are achieved, connection latency is reduced, and communication efficiency and intelligence are improved. Through fine-grained behavior detection and modeling, the actual interaction mode between devices is obtained, forming a panoramic view of device behavior, which helps to identify high-frequency collaborative relationships and abnormal behavior patterns, and provides data support for subsequent intent recognition and connection priority judgment. By deeply exploring the business-driven connection requirements between devices and constructing a connection topology that reflects actual communication intent, this model avoids blind pairing and implements targeted, well-structured device connection paths, providing a structured basis for communication resource allocation. While ensuring channel and protocol compatibility, it dynamically pairs devices and adjusts the connection sequence based on connection requirements, enabling a flexible and adaptive management mechanism for device connections. This model effectively improves system connection efficiency, reduces resource conflicts, and enhances adaptability to complex in-vehicle environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 A schematic flow chart of the steps of a method for pairing and connecting between device terminals according to the present invention; Figure 2 Detailed implementation flow chart of step S1; Figure 3 Detailed implementation flow chart of step S2; Figure 4 Schematic diagram of the detailed implementation steps of step S3. DETAILED DESCRIPTION

[0010] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0011] This application example provides a method, apparatus, and device for pairing and connecting device terminals. The execution entities of the method, apparatus, and device for pairing and connecting device terminals include, but are not limited to, mechanical equipment, data processing platforms, cloud server nodes, network upload devices, etc. that are equipped with the system, which can be regarded as general computing nodes of this application. The data processing platform includes, but is not limited to, at least one of an audio and image management system, an information management system, and a cloud data management system.

[0012] See also Figures 1 to 4 The present invention provides a method for pairing and connecting device terminals, comprising the following steps: Step S1: Collect the basic information database of all vehicle-mounted devices, conduct vehicle-mounted device trust level assessment and differentiated communication protocol stack configuration, and build a differentiated protocol adaptation strategy; Step S2: real-time monitoring of the signal status data of the vehicle environment, prediction of the signal status quality evolution trend, and construction of a channel quality trend prediction graph; Step S3: Calculate the channel quality interval according to the channel quality trend prediction graph, calculate the optimal pairing timing and pre-allocate the protocol stack according to the differentiated protocol adaptation strategy and the quality evaluation value, and generate a communication protocol pre-allocation strategy; Step S4: Collecting the on-board equipment operation monitoring log, performing device call behavior detection one by one, and modeling the global device interaction behavior to construct a global interaction behavior vector map; Step S5: Mining the interaction intention requirements of the global interaction behavior vector map, and mining the device connection requirement topology to construct a device connection requirement topology map; Step S6: Dynamically perform device adaptive pairing and connection on the device connection demand topology according to the communication protocol pre-allocation strategy, and dynamically adjust the device pairing sequence to build an adaptive device connection management model.

[0013] In the embodiment of the present invention, see Figure 1 , is a schematic flow chart of the steps of a method for pairing and connecting between device terminals of the present invention. In this example, the steps of the method for pairing and connecting between device terminals include: Step S1: Collect the basic information database of all vehicle-mounted devices, conduct vehicle-mounted device trust level assessment and differentiated communication protocol stack configuration, and build a differentiated protocol adaptation strategy; In this embodiment, the hardware identification information of all onboard devices is systematically collected through the vehicle main control unit's CAN bus interface and wireless communication module, including the device's MAC address, chip serial number, manufacturer ID, firmware version number, device type identifier, and communication protocol support list. A device basic information database is established, with the data structure comprising a unique device identifier, a hardware capability parameter matrix, a communication protocol compatibility table, and the device's physical location coordinates. Based on the device's historical connection records and security event logs, a device trust level assessment model is constructed using the Analytic Hierarchy Process (AHP). Evaluation metrics include device authentication success rate (weight 0.3), data transmission integrity (weight 0.25), connection stability (weight 0.2), security vulnerability history (weight 0.15), and manufacturer reputation (weight 0.1). Device trust levels are categorized into three levels: high trust (score ≥ 85 points), medium trust (score 60-84 points), and low trust (score < 60 points). Differentiated communication protocol stacks are configured for devices of different trust levels: High-trust devices use an optimized TCP / IP protocol stack, achieving a 35% packet header compression rate and a 20% increase in transmission efficiency; medium-trust devices use a standard UDP protocol stack, maintaining compatibility while ensuring transmission reliability; low-trust devices implement a restricted MQTT protocol stack with added data validation and retransmission mechanisms. By mapping trust levels to protocol stack configurations, a library of differentiated protocol adaptation strategies is constructed, providing a foundation for personalized protocol selection in subsequent steps.

[0014] Step S2: real-time monitoring of the signal status data of the vehicle environment, prediction of the signal status quality evolution trend, and construction of a channel quality trend prediction graph; In this embodiment, multiple distributed signal monitoring nodes are deployed to collect real-time signal strength distribution data in the 2.4 GHz, 5.8 GHz, and millimeter wave frequency bands within the vehicle environment. The sampling frequency is set to 100 Hz. Monitoring parameters include received signal strength indicator (RSSI), signal-to-noise ratio (SNR), packet error rate (PER), multipath delay spread, and Doppler shift. A real-time signal status data monitoring matrix is ​​established, with the data dimensions being a three-dimensional matrix of frequency × time × spatial position. A sliding window technique is used to perform time series analysis on the signal status data, with a time window length of 30 seconds and a step size of 5 seconds. A long short-term memory (LSTM) neural network model is used to predict signal quality evolution trends. The network architecture consists of an input layer with 128 neurons, two hidden layers with 64 neurons each, and an output layer with 32 neurons. The training dataset contains the past 72 hours of historical signal status data. The model is trained using the Adam optimizer with a learning rate of 0.001, a batch size of 32, and 200 training epochs. The model predicts signal quality trends over the next 15 minutes, achieving an accuracy of 92.3%. The prediction results are visualized as a channel quality trend prediction chart, with the vertical axis representing the channel quality index (0-100) and the horizontal axis representing the time series. Different colored curves represent quality trends in different frequency bands. This chart provides the data foundation for calculating the channel quality interval in step S3.

[0015] Step S3: Calculate the channel quality interval according to the channel quality trend prediction graph, calculate the optimal pairing timing and pre-allocate the protocol stack according to the differentiated protocol adaptation strategy and the quality evaluation value, and generate a communication protocol pre-allocation strategy; In this embodiment, based on the channel quality trend prediction chart constructed in step S2, a quantile method is used to categorize channel quality into four levels: excellent (quality index ≥ 75), good (quality index 50-74), fair (quality index 25-49), and poor (quality index < 25). A sliding average algorithm is used to calculate the channel quality stability index for each time period, with a window length set to 10 minutes. The calculation formula is the inverse of the quality variance multiplied by 100. In conjunction with the differentiated protocol adaptation strategy from step S1, an adaptation matrix is ​​established that links channel quality ranges to protocol stack configurations: the excellent range is adapted to a high-throughput protocol stack with a data rate of up to 150 Mbps; the good range selects a balanced protocol stack with a transmission rate of 80 Mbps; the fair range uses a stable protocol stack with a transmission rate of 40 Mbps; and the poor range uses a fault-tolerant protocol stack with a transmission rate of 20 Mbps but a robust retransmission mechanism. The optimal pairing timing is calculated using a multi-objective optimization algorithm, with the optimization objectives of maximizing channel quality, minimizing device energy consumption, and maximizing connection success rate, with weights of 0.4, 0.3, and 0.3, respectively. The algorithm uses a genetic algorithm with a population size of 50, a mutation rate of 0.1, a crossover rate of 0.8, and 100 iterations. Based on the optimization results, protocol stack pre-allocation is performed, pre-allocating the optimal protocol stack configuration for each device in different time periods, with a pre-allocation accuracy of 89.7%. A communication protocol pre-allocation strategy table is generated, containing device ID, time period, pre-allocated protocol type, expected performance parameters, and alternative protocol solutions, providing protocol-level optimization guidance for step S4.

[0016] Step S4: Collecting the on-board equipment operation monitoring log, performing device call behavior detection one by one, and modeling the global device interaction behavior to construct a global interaction behavior vector map; In this embodiment, the on-board diagnostic (OBD) system interface and the device's built-in monitoring agent collect operational monitoring logs from all onboard devices. Log content includes application call timestamps, call frequency, data transmission volume, connection duration, disconnection reason codes, and resource usage. A device operation log data warehouse is established. The data format uses a JSON structure and includes device identification, timestamp, event type, event parameters, and context information. Each device's call behavior is detected individually, and time series analysis is used to identify device usage patterns. The detection algorithm is based on anomaly detection using a sliding window, with a window size of one hour and a detection threshold of three standard deviations of the average call frequency. A behavioral pattern recognition algorithm is used to extract typical device usage scenarios, including four phases: high-frequency startup period, stable operation period, peak usage period, and sleep preparation period. A correlation analysis model for device interaction behavior is established using the Apriori association rule mining algorithm, with a minimum support of 0.3 and a minimum confidence of 0.7, to identify collaborative working patterns and dependencies between devices. A global interaction behavior vector graph is constructed using graph theory. The graph structure includes device nodes, interaction edges, edge weights, and time series labels. Device nodes contain basic device information and a 32-dimensional behavioral feature vector, encompassing parameters such as call frequency, data flow, connection duration, and active time periods. Interaction edges represent collaborative relationships between devices. Edge weights are calculated as the normalized interaction strength divided by the total number of interactions. The global interaction behavior vector map provides the basis for quantitative analysis of inter-device interaction patterns in step S5.

[0017] Step S5: Mining the interaction intention requirements of the global interaction behavior vector map, and mining the device connection requirement topology to construct a device connection requirement topology map; In this embodiment, based on the global interaction behavior vector graph constructed in step S4, a graph neural network (GNN) model from deep learning was used to mine interaction intentions and requirements. The GNN model uses the GraphSAGE architecture and consists of three graph convolutional layers, with 64, 32, and 16 neurons in each layer, respectively. Reluctant Unit (ReLU) is used as the activation function, the learning rate is set to 0.01, and the training batch size is 16. Model training identifies potential connection demand patterns between devices, including four types: data sharing, functional collaboration, resource access, and state synchronization. A quantitative assessment model for intention requirements was established, using the fuzzy analytic hierarchy process to assign weights to different types of requirements: data sharing with a weight of 0.35, functional collaboration with a weight of 0.25, resource access with a weight of 0.22, and state synchronization with a weight of 0.18. A demand strength calculation formula was used to combine historical device interaction data and real-time status information to calculate the connection demand strength between devices. The value range is 0-1, with a threshold of 0.6 or above for strong demand, 0.3-0.6 for medium demand, and below 0.3 for weak demand. Device connection demand topology mining was performed, using the Louvain community discovery algorithm to identify device clusters. Algorithm parameters were set to a resolution of 1.0 and a random seed of 42. The average modularity of the identified device communities reached 0.78. Based on the demand intensity values ​​and device community structure, a device connection demand topology map was constructed. The topology map uses a directed graph structure, with nodes representing devices, edges representing connection demand relationships, and edge weights indicating demand intensity. The topology map includes connection priority markers, demand type labels, and timeliness indicators, providing demand-driven device pairing guidance for step S6.

[0018] Step S6: Dynamically perform device adaptive pairing and connection on the device connection demand topology according to the communication protocol pre-allocation strategy, and dynamically adjust the device pairing sequence to build an adaptive device connection management model.

[0019] In this embodiment, the communication protocol pre-allocation strategy generated in step S3 and the device connection demand topology map constructed in step S5 are integrated to establish a dynamic device pairing decision engine. This decision engine utilizes a multi-constraint optimization model. Constraints include device battery power limits, processor load thresholds, memory usage limits, and bandwidth resource allocation. The optimization objective function is a weighted summation, including maximizing connection success rate (weight 0.4), minimizing system energy consumption (weight 0.3), maximizing user satisfaction (weight 0.2), and optimizing resource utilization (weight 0.1). The optimal pairing solution is solved using a particle swarm optimization algorithm, with algorithm parameters set to 40 particles, a maximum number of iterations of 150, a linearly decreasing inertia weight from 0.9 to 0.4, and a learning factor c1 = c2 = 2.0. Based on the optimization results, the device pairing order is dynamically adjusted, and a pairing priority queue is established. The priority calculation formula comprehensively considers demand intensity, channel quality predictions, device resource status, and protocol adaptation. An adaptive pairing execution mechanism is implemented, using a token bucket algorithm to control the pairing connection frequency. The bucket capacity is set to 10 tokens, and the token generation rate is set to 2 per second to prevent system overload. A real-time connection quality monitoring system was established, with monitoring metrics including connection establishment latency, data transmission throughput, connection stability, and user experience ratings. Adaptive system optimization was achieved through a reinforcement learning algorithm, using a Q-learning algorithm with a learning rate of 0.1, a discount factor of 0.95, and an ε-greedy exploration rate strategy with the ε value decaying from 1.0 to 0.1. An adaptive device connection management model was constructed, comprising a pairing decision module, a connection execution module, a quality monitoring module, and a parameter optimization module, forming a complete closed-loop control system to enable intelligent pairing and connection management between in-vehicle devices.

[0020] In this embodiment, refer to Figure 2 , is a flowchart of the detailed implementation steps of step S1. In this embodiment, the detailed implementation steps of step S1 include: The vehicle-mounted main control unit collects the hardware identification code, signal transmission power, and antenna gain parameters of all vehicle-mounted devices in real time to build a basic equipment information database; Perform multi-dimensional feature extraction on the device basic information database to obtain multi-dimensional features of each device terminal, including device type classification, communication protocol version, and hardware capability level; Evaluate the trust level of the vehicle-mounted devices based on the multi-dimensional features to obtain the trust level of each vehicle-mounted device; the trust level includes a core trust layer, an extended trust layer, and an observation layer; The trust levels perform differentiated communication protocol stack configuration and build differentiated protocol adaptation strategies; the differentiated communication protocol stack configuration is specifically as follows: enabling high-performance direct connection protocols for core trust layer devices, adopting standard compatible protocols for extended trust layer devices, and implementing restricted access protocols for observation layer devices.

[0021] In this embodiment, due to the diverse types and manufacturers of terminal devices in an in-vehicle environment, ensuring effective and secure pairing and communication between devices requires accurate knowledge of each device's underlying hardware information. This step, controlled by the Vehicle Master Control Unit (VMCU), collects the unique hardware identifiers and key communication capability parameters of all devices connected to the in-vehicle network in real time. Specifically, this data includes three pieces of information: First, the device's hardware identifier, typically in the form of a MAC address, IMEI, IMSI, Bluetooth ID, or device ID. This identifier can be proactively reported by the device or obtained by the VMCU through the device management interface. Second, signal transmission power, obtained from the transmit power value (in dBm) periodically reported by the wireless module (such as C-V2X, DSRC, or WiFi), is verified against RSSI (Received Signal Strength Indicator) to characterize the device's signal transmission strength and stability. Third, antenna gain, primarily derived from the device configuration file or the VMCU's built-in antenna performance parameter library, can also be measured in a laboratory environment. During the data collection process, the VMCU uses a polling mechanism to refresh the status of connected devices every 10 seconds and collect dynamic parameter changes within each communication cycle (typically 100ms). In the experiment, 16 device nodes, including a camera module, millimeter-wave radar, telematics unit, and intelligent central control system, were deployed in a typical passenger vehicle. The collected data was recorded in JSON format and stored in a local database, with an average data volume of approximately 5MB per hour. The data collection system is equipped with a data verification module to compare the acquired hardware parameters for consistency and eliminate abnormal or falsified information, ensuring the real-time, accurate, and scalable nature of the constructed device basic information database.

[0022] Device type classification is performed. This classification is based on a comprehensive assessment of features such as the device's hardware identification code prefix compared with the manufacturer's database, interface type identification (such as CAN, Ethernet, Bluetooth, and C-V2X), communication frequency, and message structure. For example, camera modules typically feature high-frequency image streaming, radar devices have periodic, low-latency range data output, and V2X modules periodically transmit Basic Safety Messages (BSMs). Next, communication protocol version identification is performed. Each device must clearly specify protocol version information during handshake communication, such as IEEE 802.11p (for DSRC), 3GPP Release 14 / 15 (for C-V2X), and TCP / IP versions. By capturing the protocol field in the communication initialization frame and combining it with the protocol parsing module, the protocol type and version can be accurately identified, establishing a mapping with hardware compatibility. Finally, hardware capability level assessment is performed. This assessment combines parameters such as the device's processor frequency, memory capacity, communication bandwidth, transmit power stability, and temperature adaptability to construct a capability scoring model. For example, in an experimental environment, the processing delay and packet loss rate of devices were measured at room temperature (25°C), high temperature (60°C), and low temperature (-10°C). Using the Analytic Hierarchy Process (AHP), a multi-metric scoring system was constructed, categorizing devices into three capability levels: high, medium, and low. Ultimately, the system generated a set of multi-dimensional feature vectors that included device type, communication protocol version, and capability level, providing a quantitative basis for subsequent trust assessments.

[0023] Using a combination of a rules engine and a probabilistic model, each in-vehicle device is assigned to a corresponding trust level. The evaluation process first maps a multi-dimensional feature vector into trust assessment factors, such as manufacturer reputation (whether the device is OEM-certified), protocol maturity (using a stable or tested protocol), hardware capability score, historical communication stability, update frequency, and the number of abnormal behaviors. A fuzzy comprehensive evaluation method is then used to construct a trust scoring model. Scores range from 0 to 1, with scores of 0.85 and above designated as the "core trust level," 0.60 to 0.84 as the "extended trust level," and scores below 0.60 as the "observation level." A Bayesian network is introduced to account for the probabilistic relationship between a device's historical behavior and trust evolution, enabling devices with long-term excellent performance to be dynamically promoted from the observation level to the extended or core level. The experiment deployed 320 devices and observed their communication performance in various road scenarios (urban, highway, and tunnel). Approximately 23% of the devices met the core trust level criteria, 56% fell into the extended level, and the remainder were observation level devices. Each time a device is connected to the vehicle system, its trust level will be automatically calculated and recorded in the main control unit. At the same time, it will be periodically re-evaluated based on its changing trend to provide a dynamic basis for communication protocol configuration.

[0024] To implement a trust-driven dynamic pairing communication mechanism, the vehicle's main control system assigns different communication protocol stack configurations to devices based on their trust level, thereby establishing a differentiated protocol adaptation strategy. This strategy aims to balance system openness and security while ensuring efficient communication between core devices. For core trust layer devices, the system uses high-performance direct connection protocols, such as the relayless communication mode based on the C-V2X PC5 interface, the low-latency UDP direct protocol, and the DSRC fast frame exchange mechanism. These devices typically interact with high-bandwidth, low-latency data, such as LiDAR transmitting point cloud data to the central control computing unit. This requires communication latency of less than 10ms and bandwidth exceeding 50Mbps. Therefore, the protocol stack design emphasizes a minimalist processing path, QoS guarantees, and high-speed caching mechanisms. For extended trust layer devices, standard-compatible protocol stacks are used, such as TCP / IP over Ethernet, the C-V2X Uu protocol with authentication handshake support, or CAN FD standard frame transmission, ensuring reliable connectivity while maintaining universal compatibility. These devices are mostly non-critical data nodes, such as navigation information units, infotainment systems, and edge processing modules. Since the observation layer devices have a lower trust level, the system enables restricted access protocols for them, and uses virtual isolation channels, rate limiting mechanisms, read-only mode and other methods to restrict communication permissions. For example, an unverified third-party Bluetooth module is only allowed to broadcast at a rate of 128kbps within a limited time period, and the communication content must be filtered by the master gateway before access is allowed. In the experiment, devices of different trust levels were deployed in 16 test vehicles, and three communication protocol stack configuration schemes were enabled. The results showed that the communication throughput of the core trust layer increased by 22%, while abnormal events in observation layer communications decreased by 46%. This differentiated configuration not only improves communication efficiency, but also enhances the security and controllability of the overall system, laying a solid foundation for subsequent device pairing.

[0025] In this embodiment, refer to Figure 3 , is a flowchart of the detailed implementation steps of step S2. In this embodiment, the detailed implementation steps of step S2 include: Real-time monitoring of the signal status data of the vehicle environment; performing signal strength time series distribution calculation based on the signal status data to generate a vehicle signal time series strength curve; Identifying spectrum occupancy status, multipath interference level, and channel fading characteristics based on the signal status data; Based on the vehicle signal time series strength curve, spectrum occupancy status, multipath interference level and channel fading characteristics, the system perceives the environmental signal status and constructs a real-time channel environment status matrix. Identify abnormal interference sources in signal status data and analyze the interference source distribution pattern to generate a signal environment interference source pattern; Based on the signal environment interference source pattern, the signal state quality evolution trend of the real-time channel environment state matrix is ​​predicted, and a channel quality trend prediction graph is constructed.

[0026] In this embodiment, in complex vehicular environments, dynamic changes in signal quality have a decisive impact on the stability of terminal device pairing connections and communication efficiency. Therefore, the first step is to continuously monitor the signal status data in the vehicle environment in real time through a wireless signal monitoring module deployed in the vehicle's main control unit. Monitoring includes, but is not limited to, RSSI (Received Signal Strength Indicator), SINR (Signal-to-Noise Ratio), BER (Bit Error Rate), frequency density (MHz occupancy), and link quality indicator (LQI). To ensure timeliness and resolution, the system uses a 100ms scan cycle, collecting full-band status data 10 times per second, and performing multi-point sampling based on the device's spatial layout and antenna directivity parameters. The experimental environment selected three typical scenarios: congested urban roads, highways, and mountain tunnels. Four signal detection nodes (front, rear, left, and right) were deployed on each test vehicle to collect real-time signal data in the 5GHz, 3.5GHz, and 2.4GHz frequency bands, respectively. The collected information undergoes preliminary preprocessing (anomaly removal, unit unification, and time synchronization) by the edge computing unit before being transmitted to the central processing module for aggregation and analysis. Through this continuous, wide-coverage monitoring mechanism, the system comprehensively understands the current wireless signal status in the vehicle environment, laying the foundation for subsequent signal modeling and interference analysis. After acquiring large-scale, multi-channel signal status data, the system calculates the time-series distribution of these signal strength values ​​to construct a signal evolution trend curve, known as the "vehicle signal time-series strength curve." The core of this curve construction lies in time-series processing of strength indicators such as RSSI and SINR at each time point, combining their sampling timestamps with a sampling window. A sliding window method (typically 5 seconds in window length and 1 second in step length) is used to smooth consecutive data points and eliminate anomalies. To enhance the curve's sensitivity to small fluctuations, a weighted moving average (WMA) method is introduced to process the strength values, ensuring that sudden interference or signal fading are clearly reflected in the strength curve. The experiment collected 120 minutes of high-speed driving signal data, generating a total of 21,600 time-point signal data sets. Finally, curve fitting was used to identify three typical signal strength variation patterns: stable fluctuations (e.g., open highway sections), periodic decay (e.g., urban areas with tall buildings), and sudden drops (e.g., tunnel entrances). This curve not only reflects channel stability but also serves as a direct basis for assessing connection continuity during pairing communications. For example, if the signal strength curve shows a "sudden drop" pattern during device pairing, the system will automatically trigger the backup link initialization logic to ensure uninterrupted connection.

[0027] To more comprehensively understand the in-vehicle signal environment, the system needs to further analyze the real-time monitored signal status data to identify spectrum occupancy, multipath interference, and channel fading characteristics. First, spectrum occupancy is assessed by performing Fourier transform analysis (FFT) on the signal energy density per unit time within each frequency band to evaluate the signal strength distribution within each frequency band. For example, if energy peaks exceeding -65dBm persist for more than 500ms within the 3.5GHz band, it is considered "high band occupancy." Second, multipath interference is assessed using power delay profile (PDP) analysis. This statistical analysis of signal propagation delays identifies the presence of multiple path signal superposition and calculates the RMS delay spread parameter. If this value exceeds 200ns, significant multipath effects are preliminarily identified. Finally, the channel fading characteristics are fitted using small-scale fading models (such as Rayleigh fading and Rician fading). The channel type is determined by the goodness of fit (R²) and the amplitude change rate. By further structuring and integrating the multi-dimensional signal features extracted above, the system constructs a "real-time channel environment state matrix" representing the overall signal state of the current communication environment. This matrix is ​​structured with time as the horizontal axis and device as the vertical axis. Each matrix element records the comprehensive state value of the channel in which the device is located over a period of time. The state value calculation incorporates signal strength trends (RSSI / SINR mean and fluctuation coefficient), spectrum occupancy (percentage of bandwidth occupied per MHz), multipath interference value (RMS delay spread), and channel fading type (expressed as Rician / Rayleigh fading factors). Each parameter is normalized to form a standard score (0-1), which is ultimately combined into a four-dimensional state vector. For example, the state vector of a device in a given time slice might be [0.82, 0.65, 0.73, 0.45], indicating strong signal strength, moderate spectrum occupancy, mild multipath interference, and mild fading. During the experiment, the matrix generated in each test vehicle was 60×12 in size, representing 60 time slices × 12 devices. The matrix was updated every 5 seconds, retaining the channel status history for the last 10 minutes through a sliding window mechanism. This matrix served as input during the device pairing process, enabling the pairing algorithm to select the optimal device path and dynamically switch link strategies. If a path's matrix score fell below a threshold (e.g., below 0.5), the system automatically evaluated alternative connection channels to improve the stability and robustness of the communication link.

[0028] In vehicular networks, unusual interference sources are a significant factor in causing connection interruptions and sudden drops in signal quality. Therefore, the system must identify and model interference sources using historical and real-time signal status data. First, spectrum analysis algorithms are used to identify unusual characteristics of interference source signals, such as non-protocol frame structures, unusual signal modulation methods (e.g., strong AM / FM interference), and sudden bandwidth occupation. The system uses short-time Fourier transform (STFT) to extract frequency domain spectra and compares them with normal communication patterns to identify suspicious signals. Second, spatial difference detection (comparing signal strength differences between multiple signal nodes) is used to locate the approximate location of the interference source. Combined with GPS positioning information, its occurrence frequency and distribution patterns are recorded. Cluster analysis (using the DBSCAN density clustering algorithm) is then performed on the collected interference events to construct interference source type models, such as "periodic broadcast interference," "sudden strong signal interference," and "mobile interference sources (e.g., external vehicle Wi-Fi)." In experiments, the system successfully identified five major interference source types, each accounting for 72% of spectrum anomalies in urban road tests. Ultimately, the system integrates the time, space, and type characteristics of the interference source to form a "signal environment interference source model." This model visually presents a heat map of interference source activity, frequency band sensitivity distribution, etc., providing decision-making support for avoiding specific frequency bands or areas when pairing devices, significantly reducing the risk of connection failure.

[0029] After completing interference source modeling, the system enters the final critical step: predicting channel quality and generating a "channel quality trend forecast graph." This forecasting process combines the historical channel state matrix with interference source patterns, employing a time series prediction model (such as an LSTM long short-term memory network) to model the short-term evolution of signal states. The LSTM model uses signal strength trends, multipath disturbance intensity, spectral interference density, and interference event type as input variables to generate rolling channel state forecasts for the next 30, 60, and 120 seconds. The forecast results are displayed as a trend graph, with color-coded channel risk levels (e.g., green for excellent, yellow for acceptable, and red for high interference). In an experimental environment, using the state matrix from the past 10 minutes and identified interference source patterns, the model prediction error (MAPE) was kept below 9%. The trend forecast graph identifies areas at risk of impending channel quality degradation, allowing the system to proactively initiate device reselection or initialization of backup links, implementing proactive adjustments to vehicle terminal pairing and connectivity. This prediction mechanism effectively improves the system's connection stability in high-speed mobile scenarios, especially demonstrating good forward-looking decision-making capabilities in platoon communication and vehicle-road collaboration (V2X) scenarios.

[0030] In this embodiment, reference Figure 4 , is a flowchart of the detailed implementation steps of step S3. In this embodiment, the detailed implementation steps of step S3 include: Calculate the channel quality interval based on the channel quality trend prediction graph and extract the channel quality intervals in different time periods; Predicting potential device quality based on the channel quality interval to obtain channel quality intervals for different devices; Evaluate the quality of the channel quality intervals of different devices and generate quality evaluation values ​​for multiple channel quality intervals; The optimal pairing timing is calculated based on the differentiated protocol adaptation strategy and the pros and cons evaluation value, and the protocol stack is pre-allocated to generate the communication protocol pre-allocation strategy.

[0031] In this embodiment, the channel quality trend prediction chart serves as the core data foundation for describing the performance trends of communication channels over the coming period. However, actual pairing decisions often require determining the performance level of the current channel at different moments. Therefore, this step requires segmented calculation of communication quality based on the channel quality trend prediction chart to extract channel quality intervals. Specifically, the mean, variance, and trend of the predicted channel metrics within a sliding time window (e.g., 30 seconds) on the time axis of the prediction chart are calculated, and the results are classified into three intervals: "Excellent (Green)," "Acceptable (Yellow)," and "Poor (Red). This classification criteria combines parameters such as RSSI (>-65dBm), SINR (>15dB), and interference probability (<10%) to determine thresholds. Furthermore, a rate of change metric, ΔQ (the rate of change of channel quality per unit time), is introduced to identify critical, fluctuating channels. Experimental analysis of 100 sets of 120-second prediction charts revealed an average of 4-6 stable intervals, each lasting an average of 18-25 seconds. Each channel quality interval records its start and end time, level, and dominant influencing factors (such as spectrum congestion or multipath interference). This ultimately forms a structured interval data table for subsequent device quality prediction and pairing timing decisions. Through this mechanism, the system converts continuous channel quality predictions into an interval structure that can be used in policy formulation, greatly improving decision-making efficiency and logical clarity of processing.

[0032] After calculating the time-segmented channel quality intervals, the system maps these intervals to the channel environment of each device to be paired, predicting the channel quality trend for each device over the next few time periods. Because different in-vehicle terminals may be located at different locations in the vehicle or use different frequency bands, even within the same macro-channel trend, their perceived signal quality may still vary. Therefore, this step employs a spatial distribution mapping algorithm, combining each device's historical signal detection data (such as RSSI, SINR, and frequency density) with the vehicle's channel quality intervals to predict the channel quality level for each device in each time period. The algorithm first constructs a device correction factor β (reflecting the device's "individual offset" from the central channel) based on the deviation between the device's historical communication parameters and the vehicle-wide channel trend. It then maps the overall channel trend interval to an individual device quality interval. Experiments were conducted on 20 vehicles, with 12 devices per vehicle. After β adjustment, the individual device channel quality intervals matched the measured performance 92.4%. Ultimately, each device will form an independent sequence of channel quality intervals on its timeline. For example, a device may be in the excellent range from t1 to t3 and enter the medium range from t3 to t4. This prediction provides a forward-looking reference for subsequent device quality assessment and optimal pairing window selection.

[0033] To select devices that currently or will soon offer optimal communication conditions from a large number of terminals, the system comprehensively evaluates the performance of all devices within the future channel quality range and generates a specific assessment score. This assessment process uses each device's predicted channel quality range as input and incorporates additional factors such as its hardware capability level, historical communication stability, and protocol compatibility to construct a comprehensive scoring model. The core of the model is a multi-metric weighted scoring system, with the channel quality range level holding the largest weight (approximately 60%). Other factors, such as device capability (20%), historical packet loss rate (10%), and protocol compatibility (10%), are assigned varying weights. Each device receives an assessment score between 0 and 1 during each time interval, indicating its communication potential during that period. For example, in a highway test scenario, the system generated 480 channel quality assessment samples for 120 devices, with approximately 38% of them being high-priority connection candidates (scoring > 0.85) within the next 30 seconds. The system also supports comparing quality scores across frequency bands, preventing the selection of seemingly "high-scoring" devices in high-interference bands. All evaluation values ​​are recorded using device ID, timestamp, and score as core fields, forming a "channel quality profile." This provides a scoring basis for the final pairing strategy and supports quantitative decision-making in the connection optimization mechanism. After determining the device's channel quality evaluation value, the system integrates differentiated protocol adaptation strategies to select the optimal pairing time point and pre-allocate communication protocol stack resources, enabling efficient and dynamic pairing strategy deployment. First, based on each device's channel quality score profile, the system uses a dynamic time-series window algorithm (such as one based on the maximum weighted window value) to identify time periods where the score consistently exceeds a certain threshold (e.g., 0.85) and meets minimum communication requirements (e.g., 15 consecutive seconds). These time periods are considered potential optimal pairing windows. This window is further matched against differentiated protocol strategies. For example, if a device has a high score but only supports an extended trust protocol stack (e.g., standard TCP / IP), it will be excluded from the candidate list for the core direct connection strategy. After the matching is completed, the system will "pre-allocate" the communication protocol stack for the selected device, that is, allocate the corresponding protocol resource pool for the device in the system in advance, including link establishment parameters, identity authentication method, QoS scheduling strategy, etc. In the experiment, 5 protocol stack templates were set to adapt to five types of devices: core trust, high-bandwidth access, compatible connection, temporary access and restricted connection. The connection requirements of 180 groups of devices were simulated and allocated. It was found that the connection success rate based on the predicted pre-allocation strategy increased to 96.7%. The communication protocol pre-allocation strategy is sent to the main control module in the form of a strategy table. The system will automatically trigger the device connection process at the optimal time point to avoid problems such as connection failure retry and link jitter, ensuring that the vehicle terminal achieves optimal pairing and efficient communication in a dynamic environment.

[0034] In this embodiment, step S4 includes the following steps: Collect on-board equipment operation monitoring logs; perform multi-device timestamp recognition on the on-board equipment operation monitoring logs, perform timing alignment processing between devices, and construct timing alignment monitoring logs; Perform device-by-device call behavior detection on the timing alignment monitoring log and extract the call behavior data of all on-board devices; Calculate user interaction frequency and data traffic changes based on the call behavior data; Based on the calculation of user interaction frequency and data traffic changes, interaction frequency distribution analysis and device call pattern mining are performed to generate device-by-device call behavior patterns; Perform statistics on the duration of connections and disconnection frequencies between devices based on the call behavior data, and extract a multi-device connection duration distribution graph; Based on the device-by-device calling behavior pattern, the global device interaction behavior is modeled on the continuous distribution graph of multi-device connections, and a global interaction behavior vector map is constructed.

[0035] In this embodiment, in the vehicle system, device operating status and behavior information is typically generated in real time through logging. These logs not only reflect the device's own operational status but also contain the timing characteristics of inter-device interactions, which are critical for optimizing terminal pairing. Comprehensive monitoring logs generated during the operation of all vehicle-mounted devices are collected. Collection targets include various types of vehicle terminals, such as intelligent central control systems, V2X communication modules, camera modules, millimeter-wave radars, and navigation processors. Collection content includes fields such as event type (startup, call, connection, disconnection, exception, etc.), event timestamp, call object, packet size, and response status. To ensure uniformity in data collection, the system performs synchronous data collection over both the vehicle's Ethernet and CAN bus channels. On average, each device generates approximately 80 to 120 logs per minute. All logs are encoded in a unified Syslog format and written to the edge computing module's log buffer in real time. Timestamp identification and cross-device timing alignment are performed on all collected operational monitoring logs to construct a set of time-aligned monitoring logs based on a unified time base. First, the system identifies the local timestamp field recorded in each log entry and calibrates the device's clock offset. This offset is calculated using a "common event alignment method." For example, a V2X broadcast or central control dispatch call recorded by different devices is used to calculate the clock difference. Experimental verification has shown that under urban road conditions, the maximum clock offset between devices can reach 500-700ms, and the timing alignment algorithm can control the alignment error to within 50ms. After alignment, the system stamps each log with a unified UTC timestamp and constructs a "timing-aligned log queue," which is sorted by chronological order and retains device ID, event type, and interaction partner information. In the experiment, using millimeter-wave radar and V2X communication modules as samples, there was significant cross-sequencing before alignment. After processing, the interaction events in the logs clearly showed a "call-response" structure, laying the timing logic foundation for subsequent behavior extraction and pattern recognition.

[0036] After timestamp alignment, structured analysis can be performed on each device's operational trajectory to identify its specific call behaviors over different time periods. Call behaviors refer to function execution events triggered actively or passively by a device during operation, such as starting navigation, turning on the camera, sending V2X data, or requesting a remote connection. The behavior detection process first categorizes the time-aligned logs by device, dividing them into subsets based on device ID. Each record is then annotated with behavioral attributes based on a predefined event tag library (such as call type field matching rules and event triggering patterns). The system combines rule recognition with sequence matching algorithms to automatically identify high-frequency behavior patterns. For example, a continuous sequence of "radar data upload + central control dispatch processing + camera activation" is identified as a "driving assistance call." In the experiment, 56 different call behavior categories were extracted, and a behavior record table was generated for each device, containing fields such as behavior name, start time, duration, triggering party, and result status. On average, 90 to 130 call behaviors were detected per device per hour. This behavior extraction results serve as the core input for subsequent user interaction frequency analysis, data traffic modeling, and connection stability assessment. After identifying the call behavior of each in-vehicle device, further exploration is needed to explore the interaction characteristics and data load patterns during actual user usage to more accurately infer the device's dependence on connection stability and communication bandwidth. Interaction frequency calculation uses behavioral events as the basis. The number of calls triggered by each device is counted within a time window (e.g., 1 minute or 5 minutes), and the behavior density and rate of change are calculated. The system also calculates the amount of communication data involved in each behavior and, combined with the packet size field and send / receive direction recorded in the device log, constructs a behavioral data traffic curve. For example, if a central control device triggers navigation, voice recognition, and HUD projection calls within 60 seconds, uploading 420KB, 180KB, and 350KB of data, respectively, its interaction frequency during that time period is 3 times / minute, with a total data traffic of 950KB. The experiment analyzed data from 180 devices across 12 test vehicles and found that the camera module and navigation terminal interacted most frequently in urban driving scenarios (>6 times / minute), with an average daily data flow exceeding 800MB. This calculation result can be used to assess device sensitivity to bandwidth and latency during paired communications, serving as a key input parameter for building dynamic communication priorities and resource scheduling strategies.

[0037] Interaction behavior exhibits strong individual differences and temporal regularities across devices. Therefore, this step generates a "call behavior pattern" for each device by performing time-series statistics and cluster analysis on the aforementioned frequency and traffic data. This pattern describes the device's behavioral trends, active periods, and high-frequency functional types across different time periods and scenarios. The system first normalizes the interaction frequency and traffic data, constructing time-series vectors (e.g., number of calls per hour, average traffic) on a daily / hourly basis. The system then uses the K-Means clustering algorithm to classify device behavior patterns. For example, for a particular in-vehicle camera device, its activity was found to be primarily concentrated during daytime driving (7:00 AM - 7:00 PM), with an average of 8.3 calls per hour and peak traffic occurring in densely trafficked areas. The system labeled this pattern a "high-frequency image acquisition device." In the experiment, eight typical device behavior patterns were extracted and annotated, including "navigation-oriented," "passive sensing," "user-triggered," and "periodic broadcast," achieving a classification accuracy of 93.1%. This behavior pattern provides pattern-level prior knowledge for subsequent device connection stability prediction, multi-device coordination optimization, and abnormal behavior identification, and is the basis for personalized customization of connection strategies.

[0038] The stability of device call behavior is closely related to connection persistence. Therefore, the system further analyzes changes in inter-device connection status, extracting the distribution of connection durations and disconnection frequencies between each device and other terminals. First, the system extracts all "connection establishment," "connection maintenance," and "connection disconnection" events from monitoring logs and constructs a lifecycle sequence for each device connection. For each device pair (e.g., V2X module-central control system), the system calculates the duration, number of disconnections, and average reconnection interval for each connection. A "Multi-Device Connection Persistence Distribution Chart" is then constructed based on the timeline, with the connection pair as the vertical axis and time as the horizontal axis. Connection status changes are marked (e.g., green indicates persistence, gray indicates interruption). In the experiment, in a high-density urban environment, the average connection duration between the V2X communication module and the camera module was 185 seconds, with an average daily disconnection frequency of 7.3 times, placing the system in the high-connection stability group. In contrast, the connection persistence of the user's mobile phone Bluetooth module was poor, with an average connection time of 48 seconds and a high disconnection frequency of 14.8 times per day. The statistical results not only reveal the current status of connection reliability, but also reflect which devices are more suitable for participating in long-term pairing connections, guiding the adjustment of protocol allocation strategies and the design of link maintenance mechanisms.

[0039] Based on the behavioral patterns of individual devices and the distribution of inter-device connection states, the system globally models the device interactions across the entire in-vehicle network, constructing a "global interaction behavior vector graph." This graph uses all devices as nodes and their connection behaviors (connection frequency, duration, and data transmission direction) as edges, forming a weighted directed graph representing the dynamic interactions between devices. The weight of each edge is calculated by combining the interaction frequency (f), average data volume (d), and connection stability (s) (e.g., W = αf + βd + γs, where α, β, and γ are empirical weights). Each node in the graph is also accompanied by a behavioral pattern label and a channel adaptability score. The system employs a graph neural network (GNN) architecture to further extract node representations, identifying roles within the network, such as "core connection hub devices," "highly dependent interaction devices," and "weakly connected peripheral devices." This supports pairing priority assignment, primary and backup link planning, and centralized optimization of high-frequency devices.

[0040] In this embodiment, step S5 includes the following steps: Predicting user interaction behaviors on the global interaction behavior vector map to generate user interaction behavior prediction data; Device association identification is performed based on the global interaction behavior vector map, and collaborative work dependency analysis is performed to extract the dependency relationships between devices; Mining user interaction intention requirements based on the inter-device dependency relationship to generate device connection requirements under the interaction intention; The connected devices, connection demand intensity and duration are calculated based on the device connection demand, and the device connection demand topology is mined to construct a device connection demand topology map.

[0041] This embodiment further introduces a user behavior time series modeling mechanism to predict future user interactions. This prediction process is based on feature vectors such as call frequency, connection persistence, and contextual state (e.g., time, location, and driving context) of each device node in the vector graph. Combined with historical user interaction trajectories, the prediction modeling is performed using a fusion model based on a graph convolutional network (GCN) and a long short-term memory network (LSTM). The model inputs include a sequence of interaction graph snapshots from the past hour, as well as current system context information such as navigation path, driving mode (auto / manual), and traffic density. The predicted output is a probability distribution of interaction behavior within a future time window, representing the likelihood of a specific user triggering certain devices or functions within the next 10, 30, or 60 minutes. For example, in a city commuter vehicle, the system predicts the average trigger probabilities for the navigation module, camera module, and voice recognition unit to be 0.72, 0.65, and 0.49, respectively, and the triggering order is consistent with past commuting habits. In experiments, the model was trained and validated using 32 users' vehicle usage logs. The model achieved an accuracy of 87.6% in predicting devices with high-frequency interactions within the next 30 minutes. The predicted data is formatted as a four-tuple structure consisting of user ID, device ID, predicted time period, and interaction probability, providing quantitative input for subsequent device connection demand analysis.

[0042] After identifying user interaction trends, the system further explores the collaborative relationships between devices in the vehicle system. Specifically, it identifies which devices have high functional dependencies, thereby guiding the design of subsequent multi-device pairing and synchronization strategies. This step, based on a global interaction behavior vector graph, employs a graph mining algorithm to identify device combinations with highly frequent interactions. This involves clustering the edge weights between devices (interaction frequency, traffic volume, and connection stability) and introducing a "synergy index" (CI), defined as the percentage of times two devices are simultaneously active within a given time window. In actual calculations, for example, the CI value for the camera and radar modules in driver assistance mode can reach 0.91, indicating high synergy. However, the CI value for the voice recognition and V2X communication modules is only 0.28, indicating lower synergy. Furthermore, the system employs the Apriori algorithm to mine association rules, extracting rules from a large number of device call sequences, such as "If device A is called, there is a greater than 85% probability that device B will be called." The experiment extracted 42 stable device collaboration relationships from 100 hours of interaction logs, including typical patterns such as "navigation-HUD" and "central control-air conditioning system." These inter-device collaboration dependencies were structured into a dependency graph matrix, which served as the core basis for further user intent modeling and device pairing and grouping.

[0043] After identifying the collaborative relationships between devices, the system further infers the user's potential interaction "intent" based on the user's potential future interactions and maps it to corresponding device connection requirements. An interaction intent is a set of targeted actions that the user desires to achieve in a specific scenario, such as "activate driving assistance," "prepare for parking and charging," or "switch to the entertainment system." This step first maps the predicted user interaction behavior data to a set of pre-set intent templates, which are then used by an intent matching engine for pattern recognition. This engine utilizes a hybrid rule-based and machine learning modeling approach. The rule layer is based on interaction probability thresholds and key device activation conditions, while the machine learning layer classifies behavioral combinations using a decision tree model. For example, if the system predicts that the user will activate navigation, switch to autonomous driving mode, and activate the radar system within the next five minutes, it matches the intent template "enter high-speed autonomous driving" and generates a set of connection requirements involving navigation, cameras, millimeter-wave radar, controllers, and other devices. In experiments, the system designed and trained 24 interaction intent templates for 10 typical travel scenarios, achieving an accuracy rate of over 90% for intent recognition. The final output format is: intent ID, list of involved devices, demand trigger time, expected duration and connection importance level, providing a basis for strategic decision-making for the device connection management system.

[0044] After identifying the user's interaction intent and its corresponding device requirements, the system further refines the connection strategy, including determining the list of devices to be connected, the connection requirement strength (i.e., connection priority) for each device, and the expected connection duration. Ultimately, a device connection requirement topology map is generated to guide protocol stack configuration and connection scheduling. In this step, the system first calculates a "connection strength value" based on indicators such as the device's functional criticality in the user's intent, its data communication share, and its historical connection stability. It then estimates the connection duration window based on the predicted timeline. For example, under the "autonomous driving + entertainment switching" intent, the connection strength for the navigation system is 0.94, the camera is 0.87, and the V2X module is 0.72, with expected durations of 600 seconds, 420 seconds, and 360 seconds, respectively. The system then constructs a connection requirement matrix (device ID × strength × duration) and, using a graph construction algorithm, generates a directed graph-like connection topology map. Nodes represent devices, edges represent communication paths, and edge weights represent communication strength. The topology map supports various graph analysis operations, such as calculating the minimum connection cover set, optimizing redundant connections, and extracting communication path dependency chains. In the test scenario, the system constructed corresponding device connection topology maps for 12 typical travel intentions, with an average map depth of 3.4 and an average device connectivity of 5.7. The topology map can be updated in real time and drive the differentiated configuration of the communication protocol stack, ensuring that the pairing connections between on-board devices in dynamic scenarios are predictable, highly reliable, and strategically adaptable.

[0045] In this embodiment, step S6 includes the following steps: Perform dynamic device adaptive pairing and connection based on the device connection requirement topology map according to the communication protocol pre-allocation strategy, and collect real-time device connection network parameters; Perform inter-device connectivity analysis on real-time device connection network parameters to identify paired connection bottleneck nodes and critical connection paths; Calculate the device pairing efficiency based on the paired connection bottleneck nodes and key connection paths to obtain the device pairing efficiency; Connection resources are allocated based on device pairing efficiency, and the device pairing order is dynamically adjusted to build an adaptive device connection management model.

[0046] In this embodiment, based on a generated device connection demand topology map, the system initiates a dynamic, adaptive pairing and connection process according to a previously established communication protocol pre-assignment strategy. First, the protocol stack allocates different communication protocol layers (e.g., high-performance direct connection protocol, standard-compatible protocol, or restricted access protocol) based on the connection strength and connection requirements between devices in the topology map, ensuring protocol matching and device trust levels. The system dynamically initiates device pairing requests through the vehicle's main control unit (MCU) using the real-time connection management module and adjusts connection parameters based on the device's current status (online, power level, hardware capabilities, etc.). During the pairing process, the vehicle bus and wireless module collect real-time network parameters, including link signal strength (RSSI), data throughput (Mbps), latency (ms), and packet loss rate (%), generating real-time connection performance monitoring data. While continuously collecting real-time connection network parameters, the system utilizes graph theory and network analysis algorithms to conduct in-depth analysis of inter-device connectivity. First, the real-time network parameters are mapped back to the edge weights in the device connection demand topology map. The edge weights are dynamically adjusted to reflect the current link quality. For example, the weight of links with severe signal attenuation is reduced, while the weight of links with stable connections is increased. Network flow analysis methods (such as the max-flow-min-cut algorithm) are used to identify bottleneck nodes within the link—i.e., nodes and paths that have the greatest impact on overall connection efficiency. Bottlenecks typically manifest as device modules experiencing link bandwidth limitations, high packet loss rates, high latency, or frequent disconnections. In actual testing, for example, bottlenecks often occur in the wireless link between the in-car entertainment system and the navigation module, resulting in communication latency fluctuations exceeding 15ms. Analysis also revealed that critical connection paths are primarily concentrated between the central control unit and the multimedia interface. Identifying these paths facilitates targeted optimization of connection strategies. This analysis provides a scientific basis for subsequently improving device pairing efficiency and resource allocation.

[0047] After identifying bottleneck nodes and critical paths, the system further constructs a device pairing efficiency metric system to comprehensively evaluate the performance of the pairing process. Pairing efficiency primarily encompasses four dimensions: connection establishment success rate, connection latency, data transmission stability, and energy consumption. In specific implementation, a weighted scoring model is used to calculate the efficiency of individual devices and paths based on real-time collected network parameters and connection logs. For example, the connection success rate accounts for 40%, the average connection latency for 30%, the packet loss rate for 20%, and the device power consumption for 10%. For example, the central control unit (CCU) achieved a connection efficiency score of 85%, while the vehicle camera module scored only 62% due to signal fluctuations. The system continuously tracks device pairing efficiency using a sliding time window to capture dynamic efficiency trends. During the experimental phase, monitoring device pairing efficiency in a multi-vehicle environment resulted in a 7.8% increase in the overall connection success rate and a 12.5% ​​reduction in system latency. These efficiency calculations provide a quantitative reference for resource conflict scheduling and dynamic adjustments.

[0048] Based on the calculated device pairing efficiency, the system dynamically optimizes the allocation and scheduling of connection resources. First, it adjusts the communication frequency band, power, and protocol parameters for inefficient bottleneck nodes to prioritize link stability for critical devices. Resource allocation utilizes a priority-based time slot allocation and channel switching mechanism to prioritize bandwidth and connection time slices for high-priority devices (such as core trust layer devices), reducing the risk of interruptions. Furthermore, the device pairing order is dynamically adjusted based on real-time efficiency changes, prioritizing the connection of high-efficiency devices and reducing overall latency and system resource usage. In terms of model implementation, a feedback control mechanism monitors pairing efficiency metrics in real time and combines it with a reinforcement learning algorithm to optimize the pairing order strategy. Experimental validation demonstrates that this adaptive management model improves overall system resource utilization by 15%, reduces average device connection latency to less than 40ms, and significantly enhances connection stability. This model enables efficient, reliable, and adaptive pairing and connection management between devices in complex and dynamic in-vehicle environments, significantly improving the overall communication performance and user experience of the in-vehicle system.

[0049] In this specification, a device for pairing and connecting between device terminals is provided, which is used to perform the above-mentioned method for pairing and connecting between device terminals, including: The differentiated protocol adaptation module is used to collect the basic information database of all on-board devices, conduct on-board device trust level assessment and differentiated communication protocol stack configuration, and build differentiated protocol adaptation strategies; The trend prediction module is used to monitor the signal status data of the vehicle environment in real time, predict the evolution trend of the signal status quality, and build a channel quality trend prediction chart; The protocol pre-allocation module is used to calculate the channel quality interval based on the channel quality trend prediction graph, calculate the optimal pairing timing and pre-allocate the protocol stack based on the differentiated protocol adaptation strategy and the quality evaluation value, and generate the communication protocol pre-allocation strategy; The interactive behavior module is used to collect the operation monitoring logs of on-board equipment, perform device call behavior detection on a device-by-device basis, model the global interactive behavior between devices, and construct a global interactive behavior vector map; The connection demand topology module is used to mine the interaction intention demand of the global interaction behavior vector map, and mine the device connection demand topology to build a device connection demand topology map; The adaptive pairing module is used to dynamically perform device adaptive pairing connections on the device connection requirement topology according to the communication protocol pre-allocation strategy, dynamically adjust the device pairing order, and build an adaptive device connection management model.

[0050] The present invention also provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the pairing connection method between device terminals described in any one of the above are implemented.

[0051] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced therein.

[0052] The foregoing description is intended only to provide specific embodiments of the present invention, which are intended to enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest manner consistent with the principles and novel features disclosed herein.

Claims

1. A method for pairing and connecting between device terminals, characterized in that: The following steps are involved: Step S1: Collect the basic information database of all vehicle-mounted devices, conduct vehicle-mounted device trust level assessment and differentiated communication protocol stack configuration, and build a differentiated protocol adaptation strategy; Step S2: real-time monitoring of the signal status data of the vehicle environment, prediction of the signal status quality evolution trend, and construction of a channel quality trend prediction graph; Step S3: Calculate the channel quality interval according to the channel quality trend prediction graph, calculate the optimal pairing timing and pre-allocate the protocol stack according to the differentiated protocol adaptation strategy and the quality evaluation value, and generate a communication protocol pre-allocation strategy; Step S4: Collecting the on-board equipment operation monitoring log, performing device call behavior detection one by one, and modeling the global device interaction behavior to construct a global interaction behavior vector map; Step S5: Mining the interaction intention requirements of the global interaction behavior vector map, and mining the device connection requirement topology to construct a device connection requirement topology map; Step S6: Dynamically perform device adaptive pairing and connection on the device connection demand topology according to the communication protocol pre-allocation strategy, and dynamically adjust the device pairing sequence to build an adaptive device connection management model.

2. The method for pairing and connecting between device terminals according to claim 1, wherein: The specific steps of step S1 are: The vehicle-mounted main control unit collects the hardware identification code, signal transmission power, and antenna gain parameters of all vehicle-mounted devices in real time to build a basic equipment information database; Perform multi-dimensional feature extraction on the device basic information database to obtain multi-dimensional features of each device terminal, including device type classification, communication protocol version, and hardware capability level; Performing a trust level assessment on the vehicle-mounted devices based on the multi-dimensional features to obtain a trust level for each vehicle-mounted device; The trust levels include a core trust layer, an extended trust layer, and an observation layer; The trust levels perform differentiated communication protocol stack configuration and build differentiated protocol adaptation strategies; the differentiated communication protocol stack configuration is specifically as follows: enabling high-performance direct connection protocols for core trust layer devices, adopting standard compatible protocols for extended trust layer devices, and implementing restricted access protocols for observation layer devices.

3. The method for pairing and connecting between device terminals according to claim 1, wherein: The specific steps of step S2 are: Real-time monitoring of the signal status data of the vehicle environment; performing signal strength time series distribution calculation based on the signal status data to generate a vehicle signal time series strength curve; Identifying spectrum occupancy status, multipath interference level, and channel fading characteristics based on the signal status data; Based on the vehicle-borne signal time-series strength curve, spectrum occupancy status, multipath interference level, and channel fading characteristics, the system perceives the environmental signal status and constructs a real-time channel environment status matrix. Identify abnormal interference sources in signal status data and analyze the interference source distribution pattern to generate a signal environment interference source pattern; Based on the signal environment interference source pattern, the signal state quality evolution trend of the real-time channel environment state matrix is ​​predicted, and a channel quality trend prediction graph is constructed.

4. The method for pairing and connecting between device terminals according to claim 1, wherein: The specific steps of step S3 are: Calculate the channel quality interval based on the channel quality trend prediction graph and extract the channel quality intervals in different time periods; Predicting potential device quality based on the channel quality interval to obtain channel quality intervals for different devices; Evaluate the quality of the channel quality intervals of different devices and generate quality evaluation values ​​for multiple channel quality intervals; The optimal pairing timing is calculated based on the differentiated protocol adaptation strategy and the pros and cons evaluation value, and the protocol stack is pre-allocated to generate the communication protocol pre-allocation strategy.

5. The method for pairing and connecting between device terminals according to claim 1, wherein: The specific steps of step S4 are: Collect on-board equipment operation monitoring logs; perform multi-device timestamp recognition on the on-board equipment operation monitoring logs, perform timing alignment processing between devices, and construct timing alignment monitoring logs; Perform device-by-device call behavior detection on the timing alignment monitoring log and extract the call behavior data of all on-board devices; Calculate user interaction frequency and data traffic changes based on the call behavior data; Based on the calculation of user interaction frequency and data traffic changes, interaction frequency distribution analysis and device call pattern mining are performed to generate device-by-device call behavior patterns; Perform statistics on the duration of connections and disconnection frequencies between devices based on the call behavior data, and extract a multi-device connection duration distribution graph; Based on the device-by-device calling behavior pattern, the global device interaction behavior is modeled on the continuous distribution graph of multi-device connections, and a global interaction behavior vector map is constructed.

6. The method for pairing and connecting between device terminals according to claim 1, wherein: The specific steps of step S5 are: Predicting user interaction behaviors on the global interaction behavior vector map to generate user interaction behavior prediction data; Device association identification is performed based on the global interaction behavior vector map, and collaborative work dependency analysis is performed to extract the dependency relationships between devices; Mining user interaction intention requirements based on the inter-device dependency relationship to generate device connection requirements under the interaction intention; According to the device connection requirements, the connection devices, connection requirement intensity and duration are calculated, and the device connection requirement topology is mined to construct a device connection requirement topology map.

7. The method for pairing and connecting between device terminals according to claim 1, wherein: The specific steps of step S6 are: Perform dynamic device adaptive pairing and connection based on the device connection requirement topology map according to the communication protocol pre-allocation strategy, and collect real-time device connection network parameters; Perform inter-device connectivity analysis on real-time device connection network parameters to identify paired connection bottleneck nodes and critical connection paths; Calculate the device pairing efficiency based on the paired connection bottleneck nodes and key connection paths to obtain the device pairing efficiency; Connection resources are allocated based on device pairing efficiency, and the device pairing order is dynamically adjusted to build an adaptive device connection management model.

8. A device for pairing and connecting between device terminals, characterized in that: The method for pairing and connecting between device terminals according to claim 1 comprises: The differentiated protocol adaptation module is used to collect the basic information database of all on-board devices, conduct on-board device trust level assessment and differentiated communication protocol stack configuration, and build differentiated protocol adaptation strategies; The trend prediction module is used to monitor the signal status data of the vehicle environment in real time, predict the evolution trend of the signal status quality, and build a channel quality trend prediction chart; The protocol pre-allocation module is used to calculate the channel quality interval based on the channel quality trend prediction graph, calculate the optimal pairing timing and pre-allocate the protocol stack based on the differentiated protocol adaptation strategy and the quality evaluation value, and generate the communication protocol pre-allocation strategy; The interactive behavior module is used to collect the operation monitoring logs of on-board equipment, perform device call behavior detection on a device-by-device basis, model the global interactive behavior between devices, and construct a global interactive behavior vector map; The connection demand topology module is used to mine the interaction intention demand of the global interaction behavior vector map, and mine the device connection demand topology to build a device connection demand topology map; The adaptive pairing module is used to dynamically perform device adaptive pairing connections on the device connection requirement topology according to the communication protocol pre-allocation strategy, dynamically adjust the device pairing order, and build an adaptive device connection management model.

9. A computer device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the computer program, the processor implements the steps of the method for pairing and connecting between device terminals according to any one of claims 1 to 7.

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