Wireless communication method and system for vehicle diagnostic system

Abstract

The present invention relates to the field of communication optimization technology, and in particular to a wireless communication method and system for a vehicle diagnostic system. The method comprises the following steps: identifying multiple signal sources in the environment and three-dimensional spatial data of the current position of the vehicle; performing a comprehensive evaluation of the scene interference intensity of the multiple signal sources in the environment, thereby obtaining an evaluation value of the current scene interference intensity of the vehicle; performing a spatial layout analysis of environmental obstacles on the three-dimensional spatial data of the current position of the vehicle, and performing three-dimensional modeling of the environment to construct a three-dimensional point cloud model of the vehicle environment; performing signal multipath propagation evolution according to the evaluation value of the current scene interference intensity of the vehicle and the three-dimensional point cloud model of the vehicle environment, and performing an optimal signal frequency band analysis to extract the optimal signal frequency band parameters; performing dynamic transmission frequency modulation and signal adaptive coding based on the optimal signal frequency band parameters to construct a dynamic signal transmission strategy. The present invention realizes efficient and stable wireless communication, and improves the performance of vehicle-mounted communication.

Description

Technical Field

[0001] The present invention relates to the technical field of communication optimization, and in particular to a wireless communication method and system for a vehicle diagnostic system. Background Art

[0002] With the rapid development of the modern automobile industry, the intelligence level of vehicles is constantly improving, and the on-board diagnostics system (OBD) has become an indispensable and important tool in automobile management and fault detection. The vehicle diagnostic system monitors the various operating parameters of the vehicle in real time, and conducts health status assessment and fault alarm for key components such as the engine, emission system, and brake system. With the popularization of automobile networking technology, more and more vehicles use wireless communication to achieve real-time data transmission with external devices, cloud platforms, and remote diagnostic centers. This wireless communication technology not only improves the efficiency of fault diagnosis, but also facilitates the real-time grasp of the health status of vehicles by car owners and maintenance personnel.

[0003] However, with the increasing demand for wireless communications, traditional vehicle diagnostic systems face a series of problems during wireless communications. First, the vehicle environment is complex, and the on-board communication network is affected by factors such as electromagnetic interference and signal blocking, resulting in instability in signal transmission, which in turn affects the accuracy and real-time nature of diagnostic data. Secondly, existing wireless communication technologies often have problems such as insufficient bandwidth and long transmission delays. Especially in the case of large amounts of data, this may lead to bottlenecks in information transmission, affecting the timeliness of vehicle fault detection and fault warning. Furthermore, due to the high dynamics of vehicle driving, the frequent changes in the on-board wireless communication link increase the uncertainty of communication and reduce the reliability of the system.

[0004] Traditional vehicle diagnostic systems rely on fixed wireless communication protocols and conventional data transmission methods, often ignoring the dynamic changes in the vehicle during driving and the impact of different environments on wireless signals. Therefore, how to improve the stability of wireless communication, reduce latency, and increase bandwidth utilization in complex vehicle environments has become a technical problem that needs to be solved urgently. Summary of the invention

[0005] In order to solve the above technical problems, the present invention proposes a wireless communication method and system for a vehicle diagnostic system to solve at least one of the above technical problems.

[0006] To achieve the above object, the present invention provides a wireless communication method for a vehicle diagnostic system, comprising the following steps:

[0007] Step S1: Identify multiple signal sources in the environment and three-dimensional spatial data of the current position of the vehicle; perform a comprehensive evaluation of the scene interference intensity of the multiple signal sources in the environment, thereby obtaining an evaluation value of the current scene interference intensity of the vehicle;

[0008] Step S2: performing a spatial layout analysis of environmental obstacles on the three-dimensional spatial data of the vehicle's current position, and performing three-dimensional modeling of the environment to construct a three-dimensional point cloud model of the vehicle environment;

[0009] Step S3: performing signal multipath propagation evolution according to the vehicle's current scene interference intensity evaluation value and the vehicle environment three-dimensional point cloud model, and performing optimal signal frequency band analysis to extract optimal signal frequency band parameters;

[0010] Step S4: Perform dynamic transmission frequency modulation and signal adaptive coding based on the optimal signal frequency band parameters to build a dynamic signal transmission strategy;

[0011] Step S5: Plan the optimal relay transmission path for the dynamic signal transmission strategy, optimize the relay cooperative transmission, and build a cooperative transmission optimization strategy;

[0012] Step S6: Based on the collaborative transmission optimization strategy, the whole process of vehicle wireless communication is controlled, and then adaptive communication optimization is performed to construct an adaptive communication optimization model.

[0013] The present invention helps to evaluate the signal interference situation of the vehicle's current environment through comprehensive evaluation of scene interference intensity, providing a basis for subsequent communication optimization. Obtaining the vehicle's current scene interference intensity evaluation value can help the system dynamically adjust communication parameters to cope with different interference situations. Environmental obstacle spatial layout analysis and three-dimensional point cloud model construction help understand the obstacle distribution in the vehicle's surrounding environment and provide an environmental reference for communication signal transmission. Construction of a three-dimensional point cloud model of the vehicle environment can provide accurate environmental data support for subsequent signal propagation simulation and optimization. Through signal multi-path propagation evolution and optimal signal frequency band analysis, the signal propagation characteristics can be better understood, the transmission efficiency and stability of the communication signal can be improved, and the extraction of optimal signal frequency band parameters can help determine the communication frequency band most suitable for the current environment and optimize communication. Quality and reliability, dynamic transmission frequency modulation and signal adaptive coding can adjust communication parameters in real time according to environmental changes and channel conditions, improve communication efficiency and stability, and build a dynamic signal transmission strategy to make the communication system more flexible and adaptable, suitable for different communication scenarios, optimal relay transmission path planning and relay cooperative transmission optimization can help improve communication coverage and transmission efficiency, ensure communication quality, and cooperative transmission optimization strategy can optimize the relay transmission process and improve the overall performance and stability of the communication system. Vehicle wireless communication full process control and adaptive communication optimization can help to achieve comprehensive monitoring and optimization of the communication process, improve communication efficiency and reliability, and build an adaptive communication optimization model to dynamically adjust the communication strategy according to the real-time environment and communication needs to ensure communication stability and performance.

[0014] Preferably, step S1 comprises the following steps:

[0015] Step S11: identifying the current vehicle position, and based on the current vehicle position identifying multiple signal sources in the environment and three-dimensional spatial data of the vehicle's current position;

[0016] Step S12: performing signal feature analysis on multiple signal sources in the environment, and extracting signal feature parameters of each signal source;

[0017] Step S13: Tracking dynamic signal changes of signal characteristic parameters of each signal source to generate dynamic change characteristics of each signal source;

[0018] Step S14: performing time-domain mutation fluctuation identification on the dynamic change characteristics of each signal source to obtain the time-domain mutation fluctuation data of each signal source;

[0019] Step S15: Perform a comprehensive evaluation of the scene interference intensity based on the time-domain mutation fluctuation data of each signal source, so as to obtain an evaluation value of the vehicle's current scene interference intensity.

[0020] The present invention achieves a comprehensive understanding of the surrounding communication environment by identifying multiple signal sources in the environment based on the current vehicle position, providing accurate basic data for subsequent communication optimization. Acquiring the three-dimensional spatial data of the vehicle's current position helps to build an accurate environmental model, providing support for the optimization and adjustment of the communication system. Signal feature analysis and parameter extraction can help understand the characteristics of each signal source, providing important information for subsequent signal processing and optimization. Extracting the signal feature parameters of each signal source helps to distinguish different signal sources, providing a basis for subsequent interference evaluation and signal optimization. Dynamic signal change tracking and feature generation help to monitor the changes of signal sources in real time, providing support for timely response to signal changes, and generating The dynamic change characteristics of each signal source can help the system better adapt to the dynamic changes of signals and improve the robustness and stability of the communication system. Through time domain mutation and fluctuation identification, the time domain characteristic changes of the signal source signal can be captured, providing important data support for interference assessment and signal optimization. Obtaining the time domain mutation and fluctuation data of each signal source is helpful to analyze the stability and reliability of the signal source and provide a reference for communication quality. By evaluating the scene interference intensity of the time domain mutation and fluctuation data of each signal source, we can fully understand the interference situation of the vehicle's current environment. Obtaining the vehicle's current scene interference intensity evaluation value is helpful to dynamically adjust the communication strategy, improve communication quality and reliability, and ensure the communication success rate.

[0021] Preferably, the specific steps of step S12 are:

[0022] Capturing signals of multiple signal sources in the environment based on the vehicle-mounted radio frequency sensor to obtain the original signal of each signal source;

[0023] Perform signal filtering on the original signal of each signal source to obtain a filtered optimized signal of each signal source;

[0024] Perform spectrum analysis on the filtered optimized signal of each signal source and classify the signal to obtain the signal type of each signal source;

[0025] Performing time domain signal feature analysis on the filtered optimized signal of each signal source, thereby generating the time domain signal feature of each signal source; the time domain signal feature analysis includes amplitude calculation, phase calculation and pulse characteristic analysis;

[0026] Performing frequency domain signal feature analysis on the filtered optimized signal of each signal source to generate the frequency domain signal feature of each signal source; the time domain signal feature analysis includes signal center frequency calculation, bandwidth calculation and spectral power distribution analysis;

[0027] According to the signal type of each signal source, the signal modulation characteristic parameter analysis is performed on the time domain signal characteristics of each signal source and the frequency domain signal characteristics of each signal source, and the signal characteristic parameters of each signal source are extracted.

[0028] The present invention can obtain the original signals of multiple signal sources in the surrounding environment in real time by using the vehicle-mounted radio frequency sensor for signal capture, and provide basic data for subsequent processing. Obtaining the original signal of each signal source is helpful for a comprehensive understanding of the communication environment, and provides necessary information for signal optimization and interference identification. Signal filtering processing can remove noise and interference, improve signal quality, and provide a clear signal basis for subsequent analysis and identification. Obtaining the filtered optimized signal of each signal source is helpful for accurately analyzing signal characteristics and improving the accuracy and reliability of signal processing. Spectral analysis and signal classification can help distinguish the characteristics of different signal sources, thereby identifying the signal type of each signal source, providing a basis for subsequent processing. Obtaining the signal type of each signal source is helpful for corresponding processing and optimization of different types of signals, and improving the adaptability of the communication system. Time domain signal feature analysis Including amplitude, phase and pulse characteristic analysis, we can fully understand the characteristics of the signal in the time domain, provide a basis for signal identification and processing, generate time domain signal characteristics of each signal source, help to deeply understand the dynamic characteristics of the signal, and provide support for subsequent signal modulation and demodulation. Frequency domain signal characteristic analysis includes signal center frequency, bandwidth and power spectrum distribution analysis, which helps to understand the characteristics of the signal in the frequency domain and provide a reference for signal optimization. Obtaining the frequency domain signal characteristics of each signal source can help with spectrum management and optimization, and improve the efficiency and performance of the communication system. Analyzing the time domain and frequency domain signal characteristics according to the signal type can extract the characteristic parameters of each signal source, which provides an important reference for signal demodulation and processing. Extracting the signal characteristic parameters of each signal source helps to accurately identify and analyze the signal, and provides support for the optimization and adjustment of the communication system.

[0029] Preferably, step S2 specifically comprises the following steps:

[0030] Step S21: performing obstacle recognition on the three-dimensional spatial data of the current position of the vehicle, and marking a plurality of environmental obstacle nodes;

[0031] Step S22: Calculate the spatial position of each of the multiple environmental obstacle nodes one by one to obtain the spatial position coordinates of each obstacle;

[0032] Step S23: performing an environmental obstacle spatial layout analysis based on the spatial position coordinates of each obstacle to generate obstacle spatial layout data;

[0033] Step S24: performing a three-dimensional structure analysis of the environment on the three-dimensional spatial data of the current position of the vehicle to generate a three-dimensional structure feature of the environment;

[0034] Step S25: Perform three-dimensional modeling of the environment based on the three-dimensional structural features of the environment based on the obstacle spatial layout data, and construct a three-dimensional point cloud model of the vehicle environment.

[0035] The present invention can help the vehicle system avoid obstacle interference in signal transmission and improve the reliability and stability of communication by identifying and marking obstacle nodes in the environment. Calculating the spatial position coordinates of obstacles can help the vehicle-mounted signal transmission system accurately evaluate the impact of obstacles on signal transmission, so as to take corresponding optimization measures. By analyzing the spatial layout of obstacles, the vehicle-mounted communication system can optimize the signal transmission path, reduce attenuation and interference in signal transmission, and improve communication quality. Analyzing the three-dimensional structural characteristics of the vehicle's surrounding environment can help the system better understand the signal transmission environment and provide a basis for signal optimization and adjustment. Environmental three-dimensional modeling based on obstacle spatial layout data can help the vehicle-mounted communication system simulate different signal transmission paths, optimize signal transmission plans, and improve communication efficiency and stability.

[0036] Preferably, step S3 specifically comprises the following steps:

[0037] Step S31: performing signal transmission frequency band sensing processing on the vehicle diagnostic system to obtain multiple signal transmission frequency bands;

[0038] Step S32: performing interference calculation on multiple signal transmission frequency bands one by one according to the interference strength evaluation value of the current scene of the vehicle, and extracting the signal interference strength of each transmission frequency band;

[0039] Step S33: filtering abnormal interference frequency bands based on the signal interference strength of each transmission frequency band, and extracting available signal transmission frequency bands;

[0040] Step S34: performing signal multipath propagation evolution on the three-dimensional point cloud model of the vehicle environment according to the available signal transmission frequency band to generate environmental signal multipath propagation effect characteristics;

[0041] Step S35: Perform optimal signal frequency band analysis on the multipath propagation effect characteristics of the environmental signal, thereby extracting optimal signal frequency band parameters.

[0042] The present invention can obtain multiple signal transmission frequency bands in the environment in real time by performing signal transmission frequency band perception processing on the vehicle diagnostic system, thereby providing basic data for subsequent processing.

[0043] Obtaining multiple signal transmission frequency bands helps to understand the distribution of communication frequency bands and provide necessary information for interference calculation and signal optimization. Performing band-by-band interference calculation on multiple signal transmission frequency bands based on the vehicle's current scene interference strength assessment value can evaluate the interference situation of each transmission frequency band.

[0044] Extracting the signal interference strength of each transmission frequency band helps identify potential interference sources and provides a basis for subsequent signal optimization and interference elimination. Filtering abnormal interference frequency bands based on the signal interference strength of each transmission frequency band can extract available signal transmission frequency bands and reduce the impact of interference on communications.

[0045] Extracting available signal transmission frequency bands helps ensure communication quality and stability and improve the reliability and performance of the communication system. Evolving the signal multipath propagation of the three-dimensional point cloud model of the vehicle environment based on the available signal transmission frequency bands can simulate the propagation of signals in the environment.

[0046] Generating the multipath propagation effect characteristics of environmental signals helps to understand the complexity of signal propagation and provides a reference for signal optimization and coverage. Performing optimal signal frequency band analysis on the multipath propagation effect characteristics of environmental signals can extract the optimal signal frequency band parameters and optimize signal transmission performance.

[0047] Extracting the optimal signal frequency band parameters helps to select the most suitable frequency band for communication, improve communication efficiency and reliability, and reduce the impact of interference.

[0048] Preferably, the specific steps of step S34 are:

[0049] Performing signal propagation simulation on the three-dimensional point cloud model of the vehicle environment according to the available signal transmission frequency bands to generate signal propagation simulation data of multiple transmission frequency bands;

[0050] Performing signal propagation path identification on signal propagation simulation data of multiple transmission frequency bands to obtain multiple signal propagation paths;

[0051] The influence of obstacle propagation reflection and refraction on multiple signal propagation paths is analyzed to obtain the characteristics of obstacle propagation influence;

[0052] Calculating the path propagation delay difference for multiple signal propagation paths to generate multiple propagation path delay difference parameters;

[0053] Performing signal phase analysis on multiple signal propagation paths to generate multipath signal phase parameters;

[0054] Performing a multipath comprehensive signal quality assessment on multiple propagation path delay difference parameters and multipath signal phase parameters to generate a multipath propagation signal quality assessment value;

[0055] The multipath propagation effect analysis is performed based on the multipath propagation signal quality evaluation value and the obstacle propagation impact characteristics to generate the multipath propagation effect characteristics of the environmental signal.

[0056] The present invention can better understand the propagation characteristics of signals in different frequency bands by simulating the propagation of signals in a vehicle environment. Generating signal propagation simulation data of multiple transmission frequency bands is helpful for analyzing the coverage, attenuation and possible interference sources of signals. Identifying multiple signal propagation paths can help determine the path of signal propagation and possible propagation obstacles. Understanding multiple signal propagation paths can help optimize signal transmission paths and improve communication quality and stability. Analyzing the effects of reflection and refraction of obstacle propagation can help understand the degree of influence of obstacles on signal propagation. Obtaining the characteristics of obstacle propagation influence can help optimize signal transmission paths and reduce interference and attenuation in signal propagation. Calculating the delay difference parameters of multiple propagation paths can evaluate the time difference of signal propagation on different paths and generate delay Difference parameters help adjust the signal transmission timing and improve the synchronization and accuracy of communication. Signal phase analysis can help understand the phase changes of signals on different paths. Generating multipath signal phase parameters helps optimize the signal phase adjustment and improve the reliability and anti-interference ability of communication. Comprehensive evaluation of multipath propagation signal quality can comprehensively consider the propagation characteristics and influencing factors of different paths. Generating multipath propagation signal quality evaluation values ​​can help select the best transmission path and improve communication quality and performance. Multipath propagation effect analysis based on signal quality evaluation values ​​and obstacle propagation impact characteristics can further optimize signal transmission plans. Generating environmental signal multipath propagation effect characteristics can help to gain a deeper understanding of the complexity of signal propagation and provide a basis for signal optimization and interference elimination.

[0057] Preferably, the specific steps of step S4 are:

[0058] Step S41: Calculating the transmission signal-to-noise ratio based on the optimal signal frequency band parameters to generate the current environment signal-to-noise ratio;

[0059] Step S42: dynamically modulate the transmission frequency of the current frequency band signal-to-noise ratio to generate a dynamic modulation signal transmission frequency;

[0060] Step S43: performing signal adaptive coding on the optimal signal frequency band parameters to generate a signal adaptive coding strategy;

[0061] Step S44: Make intelligent transmission decisions on the signal adaptive coding strategy and the dynamic modulation signal transmission frequency to build a dynamic signal transmission strategy.

[0062] The present invention can evaluate the quality and stability of signal transmission in the current environment by calculating the transmission signal-to-noise ratio. Generating the current environment signal-to-noise ratio is helpful to understand the interference degree of signal transmission and provide basic data for subsequent transmission strategies. By dynamically modulating the signal transmission frequency, the signal transmission parameters can be adjusted according to the real-time signal-to-noise ratio to improve communication efficiency and reliability. Generating the dynamically modulated signal transmission frequency is helpful to achieve better signal adaptation and transmission optimization in different environments. The use of the signal adaptive coding strategy can dynamically adjust the coding method according to the channel conditions and transmission requirements to improve the adaptability and stability of signal transmission. Generating the signal adaptive coding strategy is helpful to optimize the transmission efficiency, reduce the bit error rate, and improve the communication quality. Through intelligent transmission decision-making, the signal adaptive coding strategy and the dynamically modulated signal transmission frequency can be comprehensively considered to construct a dynamic signal transmission strategy that adapts to the current environment. Constructing the dynamic signal transmission strategy is helpful to maximize the signal transmission efficiency and improve the performance and stability of the communication system.

[0063] Preferably, the specific steps of step S5 are:

[0064] Step S51: Identify surrounding relay network nodes based on the vehicle diagnostic system;

[0065] Step S52: Calculate the node bandwidth of the surrounding relay network nodes to obtain bandwidth parameters of the surrounding relay nodes;

[0066] Step S53: performing signal transmission delay analysis on the surrounding relay network nodes to obtain signal transmission delay data of each node;

[0067] Step S54: performing optimal relay transmission path planning based on the bandwidth parameters of the surrounding relay nodes and the signal transmission delay data of each node, thereby obtaining the optimal relay transmission path;

[0068] Step S55: Optimize the relay cooperative transmission of the dynamic signal transmission strategy based on the optimal relay transmission path, and construct a cooperative transmission optimization strategy.

[0069] By identifying the surrounding relay network nodes, the present invention can enable the vehicle to obtain the information of the available communication nodes in the surrounding area. Identifying the relay network nodes helps the vehicle to establish an effective communication foundation and provide necessary support for the subsequent communication process. Through node bandwidth calculation, the vehicle can understand the communication capability and resource allocation of the surrounding relay nodes. Obtaining the bandwidth parameters of the surrounding relay nodes helps to select the best relay node to optimize the communication transmission quality and speed. Through signal transmission delay analysis, the vehicle can evaluate the communication delay of each node. Obtaining the signal transmission delay data of each node helps to select nodes with lower delay and improve communication efficiency and real-time performance. The optimal relay transmission path planning can effectively select the relay nodes suitable for the current communication needs. Obtaining the optimal relay transmission path helps to optimize the communication transmission process and improve the reliability and efficiency of data transmission. Relay collaborative transmission optimization can utilize the optimal relay transmission path to further improve the signal transmission efficiency and quality. Constructing a collaborative transmission optimization strategy helps to achieve the collaborative work of the vehicle communication system and improve communication performance and stability.

[0070] Preferably, the specific steps of step S6 are:

[0071] Step S61: Control the entire process of vehicle wireless communication based on the collaborative transmission optimization strategy, and monitor the wireless communication control response data in real time;

[0072] Step S62: quantifying the communication signal performance of the wireless communication control response data to generate a communication signal performance quantization value;

[0073] Step S63: performing dynamic transfer learning according to the communication signal performance quantization value to obtain dynamic transfer learning data;

[0074] Step S64: Adaptively optimize the collaborative transmission optimization strategy based on the dynamic transfer learning data, thereby constructing an adaptive communication optimization model.

[0075] Through full-process control and real-time monitoring, the present invention can enable vehicles to track and manage the wireless communication process in real time. Real-time monitoring of wireless communication control response data is helpful to timely discover and solve communication problems, and ensure the stability and reliability of the communication process. Through the quantification of communication signal performance, the performance of the communication signal can be converted into a quantifiable indicator. Generating a communication signal performance quantification value is helpful to objectively evaluate the quality and performance of the communication signal, and provide data support for subsequent optimization. Dynamic transfer learning can realize dynamic adjustment and learning of the communication system according to the communication signal performance quantification value. Obtaining dynamic transfer learning data is helpful to adjust the communication strategy according to real-time communication needs and performance changes, and improve the adaptability and flexibility of the communication system. Adaptive communication optimization can adjust the collaborative transmission optimization strategy according to the dynamic transfer learning data, and realize adaptive optimization of the communication system. Constructing an adaptive communication optimization model is helpful to adjust the communication strategy according to real-time performance data, and improve communication efficiency and quality.

[0076] In this specification, a wireless communication system of a vehicle diagnostic system is provided, which is used to execute the wireless communication method of the vehicle diagnostic system as described above, including:

[0077] The scene signal interference module identifies multiple signal sources in the environment and the three-dimensional spatial data of the vehicle's current position; comprehensively evaluates the scene interference intensity of the multiple signal sources in the environment, thereby obtaining an evaluation value of the vehicle's current scene interference intensity;

[0078] The environment three-dimensional structure module performs a spatial layout analysis of the environment obstacles on the three-dimensional spatial data of the current position of the vehicle, performs three-dimensional modeling of the environment, and constructs a three-dimensional point cloud model of the vehicle environment;

[0079] The optimal frequency band module performs signal multipath propagation evolution and optimal signal frequency band analysis based on the vehicle's current scene interference intensity assessment value and the vehicle environment 3D point cloud model, thereby extracting the optimal signal frequency band parameters;

[0080] Dynamic signal transmission module, which performs dynamic transmission frequency modulation and signal adaptive coding based on the optimal signal frequency band parameters to build a dynamic signal transmission strategy;

[0081] The relay transmission optimization module is used to plan the optimal relay transmission path for the dynamic signal transmission strategy, optimize the relay cooperative transmission, and build a cooperative transmission optimization strategy;

[0082] The adaptive communication optimization module is used to control the entire process of vehicle wireless communication based on the collaborative transmission optimization strategy, and then perform adaptive communication optimization to build an adaptive communication optimization model.

[0083] The present invention helps to understand the communication environment around the vehicle by identifying multiple signal sources in the environment and the three-dimensional spatial data of the vehicle's current position. The comprehensive evaluation of scene interference intensity provides a quantitative evaluation of the interference degree of the current communication environment, providing a basis for subsequent optimization. The spatial layout analysis and three-dimensional modeling of environmental obstacles help to understand the physical environment around the vehicle. The construction of a three-dimensional point cloud model of the vehicle environment provides environmental information for subsequent signal propagation and optimization. Through the evolution of signal multi-path propagation and the analysis of the optimal signal frequency band, the optimal signal frequency band parameters are extracted to optimize the signal transmission quality. Obtaining the optimal signal frequency band parameters helps to improve communication efficiency and reduce interference effects. Dynamic transmission frequency modulation and signal adaptive coding construct dynamic signal transmission strategies for different scenarios, improve the adaptability of the communication system, and construct dynamic signal transmission strategies to help cope with changes and interference in the signal transmission process and improve communication quality and stability. Optimal relay transmission path planning and relay cooperative transmission optimization improve the efficiency and reliability of signal transmission. Constructing a cooperative transmission optimization strategy helps to optimize the signal transmission path and improve communication performance. Vehicle wireless communication full process control and adaptive communication optimization construct an adaptive communication optimization model for real-time communication needs. Constructing an adaptive communication optimization model helps to achieve intelligent adjustment and optimization of the communication system and improve the adaptability and performance of the communication system. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] Figure 1 A schematic flow chart of the steps of a wireless communication method of a vehicle diagnostic system according to the present invention;

[0085] Figure 2 Detailed implementation flow chart of step S1;

[0086] Figure 3 Detailed implementation flow chart of step S2;

[0087] Figure 4 Detailed implementation flow chart of step S3. DETAILED DESCRIPTION

[0088] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0089] The present application example provides a wireless communication method and system for a vehicle diagnostic system. The execution subject of the wireless communication method and system for the vehicle diagnostic system includes but is not limited to: mechanical equipment, data processing platform, cloud server node, network upload device, etc. equipped with the system can be regarded as the general computing node of the present application, and 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.

[0090] See also Figures 1 to 4The present invention provides a wireless communication method for a vehicle diagnostic system, the wireless communication method for a vehicle diagnostic system comprising the following steps:

[0091] Step S1: Identify multiple signal sources in the environment and three-dimensional spatial data of the current position of the vehicle; perform a comprehensive evaluation of the scene interference intensity of the multiple signal sources in the environment, thereby obtaining an evaluation value of the current scene interference intensity of the vehicle;

[0092] Step S2: performing a spatial layout analysis of environmental obstacles on the three-dimensional spatial data of the vehicle's current position, and performing three-dimensional modeling of the environment to construct a three-dimensional point cloud model of the vehicle environment;

[0093] Step S3: performing signal multipath propagation evolution according to the vehicle's current scene interference intensity evaluation value and the vehicle environment three-dimensional point cloud model, and performing optimal signal frequency band analysis to extract optimal signal frequency band parameters;

[0094] Step S4: Perform dynamic transmission frequency modulation and signal adaptive coding based on the optimal signal frequency band parameters to build a dynamic signal transmission strategy;

[0095] Step S5: Plan the optimal relay transmission path for the dynamic signal transmission strategy, optimize the relay cooperative transmission, and build a cooperative transmission optimization strategy;

[0096] Step S6: Based on the collaborative transmission optimization strategy, the whole process of vehicle wireless communication is controlled, and then adaptive communication optimization is performed to construct an adaptive communication optimization model.

[0097] The present invention helps to evaluate the signal interference situation of the vehicle's current environment through comprehensive evaluation of scene interference intensity, providing a basis for subsequent communication optimization. Obtaining the vehicle's current scene interference intensity evaluation value can help the system dynamically adjust communication parameters to cope with different interference situations. Environmental obstacle spatial layout analysis and three-dimensional point cloud model construction help understand the obstacle distribution in the vehicle's surrounding environment and provide an environmental reference for communication signal transmission. Construction of a three-dimensional point cloud model of the vehicle environment can provide accurate environmental data support for subsequent signal propagation simulation and optimization. Through signal multi-path propagation evolution and optimal signal frequency band analysis, the signal propagation characteristics can be better understood, the transmission efficiency and stability of the communication signal can be improved, and the extraction of optimal signal frequency band parameters can help determine the communication frequency band most suitable for the current environment and optimize communication. Quality and reliability, dynamic transmission frequency modulation and signal adaptive coding can adjust communication parameters in real time according to environmental changes and channel conditions, improve communication efficiency and stability, and build a dynamic signal transmission strategy to make the communication system more flexible and adaptable, suitable for different communication scenarios, optimal relay transmission path planning and relay cooperative transmission optimization can help improve communication coverage and transmission efficiency, ensure communication quality, and cooperative transmission optimization strategy can optimize the relay transmission process and improve the overall performance and stability of the communication system. Vehicle wireless communication full process control and adaptive communication optimization can help to achieve comprehensive monitoring and optimization of the communication process, improve communication efficiency and reliability, and build an adaptive communication optimization model to dynamically adjust the communication strategy according to the real-time environment and communication needs to ensure communication stability and performance.

[0098] In the embodiment of the present invention, refer to Figure 1 , is a schematic flow chart of the steps of a wireless communication method of a vehicle diagnostic system of the present invention. In this example, the steps of the wireless communication method of the vehicle diagnostic system include:

[0099] Step S1: Identify multiple signal sources in the environment and three-dimensional spatial data of the current position of the vehicle; perform a comprehensive evaluation of the scene interference intensity of the multiple signal sources in the environment, thereby obtaining an evaluation value of the current scene interference intensity of the vehicle;

[0100] In this embodiment, a wireless signal scanning device is selected, such as a software defined radio (SDR) or a spectrum analyzer. These devices can capture various signals in the surrounding environment, including Wi-Fi, Bluetooth, cellular signals, etc. The signal scanning system of the vehicle is started, and the device is set to perform real-time scanning within a specific frequency range. The device will record all detected signal sources, including the type, frequency, strength and relative position of the signal source. The collected signal source data is imported into a data processing software (such as MATLAB or Python), and analyzed through a signal processing algorithm to identify a variety of signal sources and their characteristics. During the data sorting process, the signal source information needs to be structured for subsequent processing. The global positioning system (GPS) module is used to obtain the real-time position of the vehicle, including longitude, latitude and altitude information. At the same time, the inertial navigation system (INS) data is combined to improve the positioning accuracy and stability. The GPS and INS data are integrated to form the real-time position data of the vehicle in three-dimensional space, ensuring the accuracy and real-time nature of the data to support the subsequent interference intensity evaluation. The interference evaluation method based on signal strength and propagation model is adopted, and the empirical formula or machine learning model can be used to comprehensively evaluate various signals. The interference intensity of the signal source on the vehicle's position is calculated based on the identified signal sources and their recorded signal strengths. The interference contribution of each signal source to the vehicle's current position is calculated. Common calculation methods include path loss model and multipath propagation model. Considering the impact of environmental characteristics such as buildings and terrain on signal propagation, the interference intensity of all signal sources is weighted averaged to obtain the total interference intensity assessment value of the vehicle's current scene. Statistical methods such as weighted sum or least squares method can be used to conduct a comprehensive analysis of the interference. The calculated scene interference intensity assessment value is recorded in the database to form an interference intensity data set, and a visual report is generated to show the interference situation of the signal source in the current environment, providing a basis for subsequent decision support.

[0101] Step S2: performing a spatial layout analysis of environmental obstacles on the three-dimensional spatial data of the vehicle's current position, and performing three-dimensional modeling of the environment to construct a three-dimensional point cloud model of the vehicle environment;

[0102] In this embodiment, devices such as laser radar (LiDAR), cameras and ultrasonic sensors are used to collect data on the surrounding environment. These sensors can capture information about obstacles around the vehicle, including their position, shape and size. Data from different sensors are fused to improve the accuracy of environmental perception. Algorithms such as Kalman filter or particle filter can be used to process sensor data, eliminate noise and errors, and generate a more accurate environmental model. Computer vision and deep learning technology are used to analyze the fused data and identify obstacles in the environment. Deep learning models such as convolutional neural network (CNN) can be used to classify and segment image and point cloud data to accurately locate obstacles. Based on the environmental data obtained by the sensor, a three-dimensional point cloud around the vehicle is generated, each point containing spatial coordinates (x, y, z) and other features (such as color, intensity, etc.) to form a complete three-dimensional data set. The generated point cloud data is preprocessed to remove noise and redundant data. Voxel grid filtering or statistical outlier removal can be used. Removal) and other technologies to improve the quality of point clouds. If multiple sensors are used or data is collected at different times, point cloud registration is required. The iterative closest point (ICP) algorithm can be used to ensure alignment between different point cloud data sets and merge them into a unified 3D model. Select appropriate 3D modeling software or library (such as PCL, Open3D or MATLAB), import the processed point cloud data, and build a 3D model. The software should have good point cloud visualization and processing capabilities. Use surface reconstruction algorithms (such as Poisson Surface Reconstruction or Alpha Shapes) to process the point cloud data and generate a continuous 3D surface model. This step converts the point cloud into a visual 3D object for subsequent analysis. Optimize the generated 3D model to reduce redundant patches and data redundancy to improve the rendering efficiency of the model. Simplification algorithms (such as Quadric Error Metrics) can be used to reduce the complexity of the model. Save the final 3D point cloud model in a standard format (such as PLY, OBJ or STL) to facilitate subsequent analysis and application, and ensure that the model can be used in other software or systems.

[0103] Step S3: performing signal multipath propagation evolution according to the vehicle's current scene interference intensity evaluation value and the vehicle environment three-dimensional point cloud model, and performing optimal signal frequency band analysis to extract optimal signal frequency band parameters;

[0104] In this embodiment, the multipath propagation theory is adopted to establish a signal propagation model based on ray tracing or a multipath propagation model (such as the Saleh-Valenzuela model). These theories can accurately simulate multiple propagation paths caused by reflection, refraction and scattering of signals in complex environments. The three-dimensional point cloud model of the vehicle environment is combined with the interference intensity evaluation value of the current scene to form a comprehensive environment model, ensuring that the environment model contains all obstacles and interference sources that may affect signal propagation. According to known signal frequency, transmission power, receiving sensitivity and other information, the input parameters of the multipath propagation model are set. These parameters will be used to simulate the propagation characteristics of the signal in the environment. The ray tracing algorithm is used to calculate all possible propagation paths from the signal source to the receiver. For each path, its propagation distance, number of reflections, refraction conditions and interaction with obstacles are recorded. When calculating each propagation path, the interference intensity evaluation value in the environment is applied to the model to analyze its impact on the signal strength. The attenuation model (such as the free space path loss model) is used to calculate the signal attenuation of each path. The signal strength of the propagation path is synthesized, and the final signal strength received by the receiver is obtained by weighting the signal strength of each path. This step involves the processing of signal coherence and phase information, and the analysis of the propagation characteristics of different signal frequency bands, including the attenuation characteristics, reflectivity and penetration ability of the signal. Through simulation or experimental data, the effectiveness of signal propagation of each frequency band in the current environment is evaluated, and the performance of each frequency band in multi-path propagation simulation is compared to select the frequency band with the best signal strength and the least interference. Statistical analysis methods (such as mean and variance analysis) can be used to evaluate the performance of each frequency band, and the key parameters of the selected frequency band, including frequency, bandwidth, signal characteristics (such as modulation method and coding strategy), etc., are extracted to provide a basis for subsequent signal transmission optimization and communication system configuration. The optimal signal frequency band parameters and related performance data obtained by simulation are recorded in the database to form a complete signal frequency band evaluation report. These data will be used for subsequent communication optimization and decision support.

[0105] Step S4: Perform dynamic transmission frequency modulation and signal adaptive coding based on the optimal signal frequency band parameters to build a dynamic signal transmission strategy;

[0106] In this embodiment, according to the optimal signal frequency band parameters extracted in the previous step, the frequency band applicable in the current environment is confirmed, including the frequency range and bandwidth. This step ensures that the selected frequency band can minimize interference and improve signal quality. According to the transmission requirements and environmental characteristics, a suitable modulation method is selected. Common modulation technologies include sinusoidal modulation, phase modulation (PM), frequency modulation (FM) and amplitude modulation (AM). In an environment with large interference, a modulation method with strong anti-interference ability is selected, such as QAM (quadrature amplitude modulation) or OFDM (orthogonal frequency division multiplexing). A digital signal processor (DSP) or software defined radio (SDR) device is used to implement dynamic frequency modulation. During the transmission process, the system dynamically adjusts the transmission frequency according to the channel conditions (such as signal-to-noise ratio, interference intensity, etc.) monitored in real time, uses feedback control mechanism to ensure that the system can adapt to environmental changes, optimize frequency settings in real time, design signal adaptive coding strategies, and dynamically select coding methods based on real-time channel conditions and transmission requirements. Commonly used adaptive coding technologies include convolutional coding, Turbo coding, and LDPC (low-density parity check) coding. These coding methods can optimize the reliability and efficiency of data transmission according to channel quality. During the signal transmission process, the channel conditions (such as signal-to-noise ratio and delay) are monitored in real time, and the system automatically selects the appropriate coding method according to the preset adaptive coding strategy, using DSP Or FPGA (field programmable gate array) to achieve adaptive coding function, ensure that the coding method can be quickly switched under different channel conditions to improve the robustness of data transmission, package the encoded data and prepare for sending, it is necessary to ensure that the data format is compatible with the transmission protocol and meets the network transmission standard, use an appropriate protocol stack (such as TCP / IP, UDP, etc.) to handle data transmission, ensure data integrity and reliability, integrate dynamic frequency modulation and adaptive coding strategies to form a complete dynamic signal transmission strategy, which should be able to automatically select suitable frequencies and coding methods under different channel conditions to achieve optimal communication performance, establish a real-time monitoring system, and continuously collect performance data during signal transmission, such as transmission rate, delay, packet loss rate, etc., through the feedback mechanism, adjust the dynamic transmission strategy in real time to cope with environmental changes and fluctuations in channel conditions, test the dynamic signal transmission strategy in the actual environment, evaluate the effectiveness of the strategy by comparing the signal transmission performance indicators before and after the test, and continuously adjust and optimize the transmission strategy according to the test results to ensure high communication quality under various environmental conditions.

[0107] Step S5: Plan the optimal relay transmission path for the dynamic signal transmission strategy, optimize the relay cooperative transmission, and build a cooperative transmission optimization strategy;

[0108] In this embodiment, the data related to the current dynamic signal transmission strategy of the vehicle is collected, including signal frequency band, transmission rate, interference intensity, etc. At the same time, the environmental model data, especially the location, bandwidth, delay and other information of the relay node, is obtained, and the relay node information in the environment is integrated into a unified model to form a topological diagram of the relay network. This model not only includes the physical connection between nodes, but also needs to consider the propagation characteristics and interference of the signal, and select a suitable path planning algorithm, such as Dijkstra algorithm or A* Algorithms, which can calculate the optimal relay transmission path based on the characteristics of relay nodes (including bandwidth, delay and signal quality, etc.), input the data of dynamic signal transmission strategy and relay network model into the path planning algorithm, and calculate the optimal transmission path from the source node to the target node. In this process, the algorithm will comprehensively consider the performance indicators of each relay node to ensure that the selected path is optimal in signal quality and transmission efficiency, design a collaborative transmission mechanism between relay nodes to ensure that each relay node can work effectively together during signal transmission. Centralized or distributed collaborative strategies can be adopted to ensure that information can be efficiently shared between relay nodes. Based on the signal propagation model and the relay network model, a relay collaborative transmission optimization model is constructed. The model will consider the bandwidth, delay and signal quality of the relay node, and introduce optimization objectives, such as minimizing transmission delay Or maximize network throughput, select appropriate optimization algorithms, such as genetic algorithms, particle swarm optimization (PSO) or ant colony algorithms. These algorithms are suitable for solving complex nonlinear problems and can find the optimal solution under multiple constraints. Through the optimization algorithm, adjust the parameters of the relay cooperative transmission strategy to ensure that under the guidance of the dynamic signal transmission strategy, the collaborative work of each relay node can achieve the best effect. Continuously iterate the optimization until the set performance indicators are met. According to the optimization results, formulate a specific relay cooperative transmission optimization strategy. This strategy should include the selection of relay nodes, signal scheduling schemes, frequency allocation and data flow control to ensure the efficiency and reliability of the overall transmission. Establish a real-time monitoring system to continuously track the performance indicators of relay transmission (such as delay, packet loss rate, signal strength, etc.). The system will provide data support for dynamically adjusting the cooperative transmission strategy.

[0109] Step S6: Based on the collaborative transmission optimization strategy, the whole process of vehicle wireless communication is controlled, and then adaptive communication optimization is performed to construct an adaptive communication optimization model.

[0110] In this embodiment, an integrated control system is designed. The system combines the collaborative transmission optimization strategy and can monitor and control all aspects of vehicle wireless communication in real time. The system should include two parts: hardware and software. The hardware part includes a wireless communication module (such as SDR), a sensor (such as GPS, LiDAR) and a processing unit (such as an embedded system or a cloud server). The collaborative transmission optimization strategy is implemented in the vehicle's wireless communication module, and appropriate signal frequency, modulation mode and coding mode are configured to ensure that the wireless communication system can automatically adjust the communication parameters according to real-time environmental changes to improve signal stability and transmission efficiency. Sensors and signal monitoring equipment are used to collect performance data of wireless communication in real time, such as signal strength, delay, bandwidth utilization, bit error rate, etc. These data will be used for subsequent optimization and adjustment. Based on the real-time collected data, an adaptive communication optimization strategy is designed. The strategy should include a mechanism for dynamically adjusting signal transmission parameters (such as frequency, power, and coding mode) to adapt to different environmental conditions and communication requirements. Machine learning or deep learning algorithms (such as neural networks and decision trees) are used to analyze historical communication data, identify key factors affecting communication performance, and establish a model. According to the analysis results, an adaptive communication optimization model is constructed. The model should be able to predict the best communication parameter configuration under different environmental conditions to achieve the optimization of communication performance. The adaptive optimization model is embedded in the wireless communication system, and dynamic adjustments are made using real-time monitoring data. A feedback control mechanism is used to continuously optimize signal transmission parameters according to real-time performance indicators (such as signal strength, delay, etc.), and an effective feedback loop is established. The actual performance data monitored is compared with the model prediction, and the model parameters are adjusted in time to improve the accuracy of the prediction and the effectiveness of the optimization. The adaptive communication optimization model is verified in the actual environment. By comparing the communication performance indicators before and after optimization (such as data transmission rate, signal quality, delay, etc.), the effectiveness of the model is evaluated. According to the verification results, the model parameters are continuously adjusted, and the algorithms and strategies are optimized to ensure that the model can maintain excellent performance under different environmental conditions. The construction process, parameter settings, verification results, etc. of the adaptive communication optimization model are fully recorded to provide a reference for subsequent maintenance and upgrades. The adaptive communication optimization model is formally deployed in the vehicle wireless communication system to ensure that it can execute the optimization strategy in real time in actual operation and provide support for the intelligent communication of the vehicle.

[0111] In this embodiment, refer to Figure 2 , is a flowchart of detailed implementation steps of step S1. In this embodiment, the detailed implementation steps of step S1 include:

[0112] Step S11: identifying the current vehicle position, and based on the current vehicle position identifying multiple signal sources in the environment and three-dimensional spatial data of the vehicle's current position;

[0113] Step S12: performing signal feature analysis on multiple signal sources in the environment, and extracting signal feature parameters of each signal source;

[0114] Step S13: Tracking dynamic signal changes of signal characteristic parameters of each signal source to generate dynamic change characteristics of each signal source;

[0115] Step S14: performing time-domain mutation fluctuation identification on the dynamic change characteristics of each signal source to obtain the time-domain mutation fluctuation data of each signal source;

[0116] Step S15: Perform a comprehensive evaluation of the scene interference intensity based on the time-domain mutation fluctuation data of each signal source, so as to obtain an evaluation value of the vehicle's current scene interference intensity.

[0117] In this embodiment, GPS, inertial navigation system (INS) and other positioning technologies (such as ground base stations, Wi-Fi positioning, etc.) are used to obtain the real-time position of the vehicle. By integrating these data, the accuracy and reliability of the position are ensured. According to the position of the vehicle, a three-dimensional spatial data model is constructed. This model should include the current position of the vehicle, the surrounding environment and the positions of its related signal sources. It can be visualized using three-dimensional modeling software (such as Blender or Unity). Around the current vehicle position, multiple signal sources in the environment are identified through signal scanning and analysis. These signal sources may include radio transmitters, Wi-Fi Hotspots, Bluetooth devices, traffic lights, etc., integrate the signal source location and related information into the three-dimensional spatial data model to form a comprehensive model that includes vehicle, signal source and environmental characteristics for subsequent analysis. The signal data of each signal source is collected through appropriate receiving equipment (such as software-defined radio, signal analyzer), ensuring that the signal collection process meets relevant standards to ensure data quality. Select appropriate signal feature extraction methods, such as Fourier transform (FFT) for frequency domain analysis, or wavelet transform for time-frequency analysis to extract the signal's frequency component, amplitude, phase and other characteristic parameters. The collected signals are feature extracted to generate signal characteristic parameters for each signal source. These characteristic parameters Including signal strength, frequency bandwidth, modulation mode, signal-to-noise ratio, etc., to form a signal feature database, regularly record the signal feature parameters of each signal source, collect data according to the set time interval (such as every second, every minute) to form time series data, and select appropriate dynamic change analysis methods, such as autoregressive moving average model (ARIMA) or Kalman filtering, to perform time series analysis on signal features. Through the analysis of time series data, the dynamic change characteristics of each signal source are generated. These characteristics may include parameters such as signal strength change rate, frequency drift, signal stability, etc., and select appropriate fluctuation recognition algorithms, such as CUSUM (cumulative sum control chart) or change point detection (Change Point Detection) algorithm is used to identify sudden fluctuations in signal characteristics, the dynamic change characteristics of each signal source are input into the selected fluctuation recognition algorithm, the sudden change points of signal characteristic parameters are detected, the rapid changes of signal strength, frequency or other characteristics are identified, and the time points of these sudden changes are recorded. The identified sudden fluctuation data are integrated into the signal characteristic database to provide basic data for subsequent interference intensity assessment, and appropriate interference intensity assessment methods are selected, such as multivariate analysis (such as principal component analysis PCA) or weighted scoring method, to comprehensively evaluate the interference intensity of each signal source, and a comprehensive evaluation is performed based on the time domain sudden fluctuation data of each signal source, and the interference intensity of each signal source to the environment is calculated. The weighted scoring method can be used to assign weights according to the importance of signal characteristics, generate a comprehensive interference intensity assessment value, output the interference intensity assessment value of the current scene, and form a report or visual display.Display the interference intensity of different signal sources through charts or dashboards to provide a basis for further decision-making.

[0118] In this embodiment, the specific steps of step S12 are:

[0119] Capturing signals of multiple signal sources in the environment based on the vehicle-mounted radio frequency sensor to obtain the original signal of each signal source;

[0120] Perform signal filtering on the original signal of each signal source to obtain a filtered optimized signal of each signal source;

[0121] Perform spectrum analysis on the filtered optimized signal of each signal source and classify the signal to obtain the signal type of each signal source;

[0122] Performing time domain signal feature analysis on the filtered optimized signal of each signal source, thereby generating the time domain signal feature of each signal source; the time domain signal feature analysis includes amplitude calculation, phase calculation and pulse characteristic analysis;

[0123] Performing frequency domain signal feature analysis on the filtered optimized signal of each signal source to generate the frequency domain signal feature of each signal source; the time domain signal feature analysis includes signal center frequency calculation, bandwidth calculation and spectral power distribution analysis;

[0124] According to the signal type of each signal source, the signal modulation characteristic parameter analysis is performed on the time domain signal characteristics of each signal source and the frequency domain signal characteristics of each signal source, and the signal characteristic parameters of each signal source are extracted.

[0125] In this embodiment, a suitable vehicle-mounted RF sensor is selected to ensure that it has sufficient frequency range and sensitivity to capture the original signals of various signal sources in the environment. These signal sources may include radio transmitters, Wi-Fi, Bluetooth devices, etc. During driving, the RF sensor is used to capture real-time signals, the working mode of the sensor is set (such as broadband or narrowband mode), and the sampling rate is configured to ensure that the captured signal has sufficient resolution. The captured signal is stored in a local memory or a cloud database through a data acquisition system to form an original signal data set. This data will be used for subsequent signal processing and analysis. A suitable filter type is selected, such as a low-pass, high-pass, band-pass or band-stop filter. The selection of the filter should be based on the characteristics of the captured signal and the target signal frequency band. Digital signal processing software (such as MATLAB, Python's SciPy) is used to process the captured signal. library) to filter the original signal, apply the selected filter to remove noise and unnecessary signal components, retain the target signal, verify the filtering results through visualization tools (such as spectrograms), ensure that the filtered signal is clear and meets the expected spectrum characteristics, select appropriate spectrum analysis tools, such as fast Fourier transform (FFT), to convert the time domain signal into a frequency domain signal, perform FFT processing on the filtered optimized signal, calculate its spectrum to identify the frequency components of the signal, generate a spectrogram, analyze the energy distribution of the signal at different frequencies, classify the signal according to the spectrum analysis results, and classify the signal. The signal source can be divided into different types (such as AM, FM, digital signals, etc.) according to specific frequency characteristics, modulation methods, and signal strength. The signal type and related characteristics of each signal source are recorded in the database for subsequent analysis. Select a method for time domain signal feature analysis, including amplitude calculation, phase calculation, and pulse characteristic analysis. These calculations can be performed using signal processing software. Calculate the amplitude and phase characteristics of the filtered optimized signal, which usually involves the calculation of the maximum value, mean, and root mean square value of the signal, as well as the analysis of the phase change rate. Analyze the pulse characteristics of the signal, including pulse width, interval, and Repetition frequency, etc. These characteristics help to identify the modulation mode and application scenario of the signal. The calculated time domain characteristic parameters are integrated into the signal characteristic database for subsequent analysis. The frequency domain signal characteristic analysis method is selected, including signal center frequency calculation, bandwidth calculation and power spectrum distribution analysis. The center frequency and bandwidth of each signal source are calculated, which usually involves determining the center of gravity of the energy distribution in the spectrum and the frequency range of the signal. The power spectral density (PSD) of the signal is calculated, and the power distribution of the signal at different frequencies is analyzed, which can help identify the strength and nature of the signal. The frequency domain characteristics are integrated into the database to form a complete signal characteristic description of each signal source. The appropriate signal modulation characteristic parameter analysis method is selected, and feature extraction is performed according to the signal type (such as extracting frequency deviation for FM signals and extracting modulation depth for AM signals). For the signal type of each signal source, the corresponding modulation characteristic parameter analysis is performed.Extract characteristic parameters, such as frequency deviation, modulation depth, signal duration, etc., integrate all signal characteristic parameters into a unified data structure, form a detailed signal characteristic description, including time domain characteristics, frequency domain characteristics and modulation characteristics, and output the analysis results to a report or database for subsequent use and reference.

[0126] In this embodiment, refer to Figure 3 , is a flowchart of detailed implementation steps of step S2. In this embodiment, the detailed implementation steps of step S2 include:

[0127] Step S21: performing obstacle recognition on the three-dimensional spatial data of the current position of the vehicle, and marking a plurality of environmental obstacle nodes;

[0128] Step S22: Calculate the spatial position of each of the multiple environmental obstacle nodes one by one to obtain the spatial position coordinates of each obstacle;

[0129] Step S23: performing an environmental obstacle spatial layout analysis based on the spatial position coordinates of each obstacle to generate obstacle spatial layout data;

[0130] Step S24: performing a three-dimensional structural analysis of the environment on the three-dimensional spatial data of the current position of the vehicle to generate a three-dimensional structural feature of the environment;

[0131] Step S25: Perform three-dimensional modeling of the environment based on the three-dimensional structural features of the environment based on the obstacle spatial layout data, and construct a three-dimensional point cloud model of the vehicle environment.

[0132] In this embodiment, sensors such as laser radar (LiDAR), stereo cameras or depth cameras are used to collect three-dimensional spatial data of the current position of the vehicle. These sensors can provide high-precision environmental information, including distance, depth and object contours. Suitable obstacle recognition algorithms are selected, such as point cloud-based clustering algorithms (such as DBSCAN or K-means) and machine learning algorithms (such as convolutional neural networks (CNNs)). These algorithms can effectively identify and classify obstacles in three-dimensional space. The collected three-dimensional spatial data is input into the selected recognition algorithm to perform obstacle detection and marking. During the recognition process, the system will analyze the point cloud data, extract obstacle nodes with certain volumes and features, and mark their positions. Visual tools (such as PCL and Matplotlib) are used to display the recognition results to ensure the accuracy and completeness of the recognition. The algorithm parameters are adjusted to optimize the recognition effect. Geometric calculation methods are used to extract spatial coordinates from the identified obstacle nodes. Coordinate transformation and projection algorithms can be used to convert the three-dimensional point cloud coordinates into position representations in a standard coordinate system. For each marked obstacle node, its spatial coordinate information (x, y,z), which can be calculated by writing data processing scripts (such as using NumPy and PCL libraries in Python), organizing the spatial position coordinates of each obstacle extracted into structured data (such as CSV, JSON format) for subsequent analysis and processing, selecting appropriate spatial layout analysis methods, such as Voronoi diagram, Delaunay triangulation or spatial clustering analysis, which can effectively describe the relative position and distribution characteristics of obstacles, based on the extracted obstacle spatial position coordinates, perform spatial layout analysis, calculate the distance and relative position relationship between each obstacle, and generate spatial layout data, organize the analysis results into layout data, including the relative position, proximity relationship and spatial distribution characteristics of each obstacle, and use charts or data files to present the layout analysis results, select appropriate 3D structure analysis methods, such as voxelization or 3D mesh generation, which can extract the structural characteristics of the environment from 3D point cloud data, analyze the 3D spatial data of the vehicle's current position, and extract the 3D structural characteristics of the environment, which includes the height information, width, depth and shape of the main features (such as walls, obstacles, roads, etc.) of the environment The generated three-dimensional structural features of the environment are stored as structured data, including the geometric shape, size and feature description of the environment, for subsequent use. Suitable three-dimensional modeling methods are selected, such as point cloud merging, meshing or three-dimensional modeling software (such as Blender, SketchUp) for environmental modeling. Based on the obstacle space layout data and the three-dimensional structural features of the environment, a three-dimensional point cloud model of the vehicle environment is constructed. The extracted layout data and structural features are imported using the modeling software to create a corresponding three-dimensional model. The generated three-dimensional point cloud model is optimized to ensure the accuracy and usability of the model. The integrity and accuracy of the model are checked through visualization tools, and adjustments and corrections are made when necessary. The final generated three-dimensional point cloud model is exported to a standard format (such as PLY, OBJ) for further analysis, simulation or application in other systems.

[0133] In this embodiment, refer to Figure 4 , is a flowchart of detailed implementation steps of step S3. In this embodiment, the detailed implementation steps of step S3 include:

[0134] Step S31: performing signal transmission frequency band sensing processing on the vehicle diagnostic system to obtain multiple signal transmission frequency bands;

[0135] Step S32: performing interference calculation on multiple signal transmission frequency bands one by one according to the interference strength evaluation value of the current scene of the vehicle, and extracting the signal interference strength of each transmission frequency band;

[0136] Step S33: filtering abnormal interference frequency bands based on the signal interference strength of each transmission frequency band, and extracting available signal transmission frequency bands;

[0137] Step S34: performing signal multipath propagation evolution on the three-dimensional point cloud model of the vehicle environment according to the available signal transmission frequency band to generate environmental signal multipath propagation effect characteristics;

[0138] Step S35: Perform optimal signal frequency band analysis on the multipath propagation effect characteristics of the environmental signal, thereby extracting optimal signal frequency band parameters.

[0139] In this embodiment, a suitable signal analysis device is selected, such as a software defined radio (SDR) or a spectrum analyzer. These devices can monitor and analyze signals in different frequency bands. The selected device is used to monitor the signals around the vehicle in real time, capture the signal transmission conditions of different frequency bands, set the frequency range of the device to ensure that all possible signal frequency bands (such as VHF, UHF, Wi-Fi, Bluetooth, etc.) are covered, and the captured signal data is transmitted to the data processing system. The spectrum analysis tool is used to process the signal, extract the information of multiple signal transmission frequency bands, and display the signal strength and spectrum distribution of different frequency bands through visualization tools to ensure that the identified frequency bands are accurate, and select a suitable interference calculation model. , such as the signal-to-noise ratio (SNR) model or the power spectral density (PSD) model. These models can help evaluate the signal interference strength of each frequency band. For each signal transmission frequency band, combined with the interference strength evaluation value of the vehicle's current scenario, interference calculation is performed, and the signal strength of each frequency band is evaluated using an algorithm to calculate the signal interference strength of each frequency band. The signal interference strength of each frequency band is recorded in the database to form a detailed interference strength data set for subsequent analysis and processing. The standard for abnormal interference is defined. For example, a frequency band with an interference strength exceeding a certain threshold is set as an abnormal interference frequency band. This can be determined through historical data analysis and statistical methods. According to the set abnormal interference standard, the recorded signal The signal interference intensity data is filtered to identify the frequency bands whose interference intensity exceeds the threshold and mark them as abnormal frequency bands. The frequency bands that are not marked as abnormal are extracted from all signal transmission frequency bands to form a list of available signal transmission frequency bands. This list will be used for subsequent signal processing and analysis. Appropriate multipath propagation models are selected, such as ray tracing models or climate propagation models. These models can simulate the propagation characteristics of signals in complex environments. The selected model is used to simulate the multipath propagation effect of the signal based on the available signal transmission frequency bands and the three-dimensional point cloud model of the vehicle environment. The obstacles, reflective surfaces and other factors in the environment are taken into account to generate the signal propagation path and reflection and refraction characteristics. The environmental signal multipath is extracted from the simulation results. The characteristic parameters of multipath propagation effects, such as propagation delay, multipath component, path loss, etc., are used to form an environmental signal propagation characteristic data set, and a suitable optimal frequency band analysis method is selected, such as multivariable optimization technology (such as genetic algorithm) or signal quality assessment model (such as minimum mean square error). Based on the extracted characteristic data of multipath propagation effects of environmental signals, the available signal transmission frequency bands are evaluated, the signal quality and reliability of each frequency band are determined, the performance of each frequency band in signal transmission is analyzed, and the optimal signal frequency band is extracted. The identified optimal signal frequency band and its characteristic parameters are recorded in the database, and an analysis report is generated for subsequent use. This information will provide support for the vehicle's communication system optimization and signal transmission strategy.

[0140] In this embodiment, the specific steps of step S34 are:

[0141] Performing signal propagation simulation on the three-dimensional point cloud model of the vehicle environment according to the available signal transmission frequency bands to generate signal propagation simulation data of multiple transmission frequency bands;

[0142] Performing signal propagation path identification on signal propagation simulation data of multiple transmission frequency bands to obtain multiple signal propagation paths;

[0143] The influence of obstacle propagation reflection and refraction on multiple signal propagation paths is analyzed to obtain the characteristics of obstacle propagation influence;

[0144] Calculating the path propagation delay difference for multiple signal propagation paths to generate multiple propagation path delay difference parameters;

[0145] Performing signal phase analysis on multiple signal propagation paths to generate multipath signal phase parameters;

[0146] Performing a multipath comprehensive signal quality assessment on multiple propagation path delay difference parameters and multipath signal phase parameters to generate a multipath propagation signal quality assessment value;

[0147] The multipath propagation effect analysis is performed based on the multipath propagation signal quality evaluation value and the obstacle propagation impact characteristics to generate the multipath propagation effect characteristics of the environmental signal.

[0148] In this embodiment, a suitable signal propagation model is selected, such as a ray tracing model or a multipath propagation model. These models can simulate the characteristics of signal propagation in a complex environment, especially in a city or complex terrain. The available signal transmission frequency band and the three-dimensional point cloud model of the vehicle environment are input into a propagation simulation tool (such as MATLAB, Python's SciPy library, or dedicated signal propagation simulation software), the signal propagation process is simulated in the three-dimensional model, and the propagation path of the signal from the transmitting point to the receiving point is calculated, considering factors including multipath effects, reflection, refraction and diffraction, recording the signal strength, propagation path and other key parameters under multiple transmission frequency bands, generating and storing the signal propagation simulation data for each transmission frequency band, including the signal strength, propagation path and other related information, for subsequent analysis and use, and selecting a suitable path identification algorithm, such as the Dijkstra algorithm or A*. Algorithms, which can effectively find the optimal propagation path from the transmitting point to the receiving point, input the generated signal propagation simulation data into the path identification algorithm, identify the signal propagation path of each transmission frequency band, analyze the signal strength and environmental characteristics, identify the key obstacles and reflection surfaces that affect propagation, and record the identified multiple signal propagation paths and their related parameters (such as path length, propagation loss, etc.) in the database for subsequent analysis and optimization. Reflection and refraction models (such as Fresnel equations) are used to calculate the propagation characteristics of the signal when it passes through obstacles. These models can simulate the changes in the energy distribution of the signal when it encounters obstacles. For each identified signal propagation path, the impact of the obstacle on the signal propagation is calculated, including the reflection coefficient and the refraction angle. The model evaluates the attenuation and direction-changing characteristics of the signal when passing through obstacles, organizes the propagation impact characteristics of obstacles, including signal strength changes caused by reflection and refraction, path adjustment and other information, and forms a detailed propagation impact characteristic data set. The delay is calculated based on the length of the propagation path and the signal propagation speed. For each propagation path, the signal propagation delay is calculated and the delay parameters of each path are recorded. These parameters will reflect the different times when the signal arrives at the receiving point. Considering the impact of multipath propagation, the calculated delay difference parameters of multiple propagation paths are recorded in the database to form a delay characteristic data set for subsequent analysis. The calculation method of phase analysis is selected. The signal phase is usually calculated based on the propagation time and frequency of the signal. The formula is: φ=2πft, where f is the signal frequency, t is the propagation delay, for each propagation path, the corresponding signal phase parameter is calculated according to the calculated delay and signal frequency, and the phase information of each path is recorded to facilitate the subsequent signal quality evaluation. The signal phase parameters of all propagation paths are organized into structured data to form a multipath signal phase parameter set for subsequent analysis. A signal quality evaluation model is selected, such as the signal-to-noise ratio (SNR) comprehensive evaluation or the minimum mean square error (MMSE) model.These models can comprehensively consider the impact of delay difference and phase information on signal quality, conduct comprehensive signal quality evaluation for each propagation path based on delay difference parameters and phase parameters, calculate the effectiveness and reliability of the signal, generate signal quality evaluation values ​​for each path, and record the generated multipath propagation signal quality evaluation values ​​in the database, and form a signal quality evaluation report for subsequent use and analysis. Select a suitable environmental signal multipath propagation effect model, such as a statistical model or a machine learning-based model, to analyze the propagation effect of the signal in the environment. Combine the multipath propagation signal quality evaluation value with the obstacle propagation impact characteristics to perform multipath propagation effect analysis. Evaluate the propagation effect of the signal in the environment, including attenuation, distortion, and multipath interference. Organize the generated environmental signal multipath propagation effect characteristics to form a detailed propagation effect feature data set as a basis for subsequent optimization and decision support. ,

[0149] In this embodiment, the specific steps of step S4 are:

[0150] Step S41: Calculating the transmission signal-to-noise ratio based on the optimal signal frequency band parameters to generate the current environment signal-to-noise ratio;

[0151] Step S42: dynamically modulate the transmission frequency of the current frequency band signal-to-noise ratio to generate a dynamic modulation signal transmission frequency;

[0152] Step S43: performing signal adaptive coding on the optimal signal frequency band parameters to generate a signal adaptive coding strategy;

[0153] Step S44: Make intelligent transmission decisions on the signal adaptive coding strategy and the dynamic modulation signal transmission frequency to build a dynamic signal transmission strategy.

[0154] In this embodiment, the calculation formula of the signal-to-noise ratio (SNR) is adopted, which is usually SNR=Ps / Pn, where Ps is the signal power and Pn is the noise power. It is necessary to obtain relevant data within the selected optimal signal frequency band, and use a signal analysis device (such as a spectrum analyzer or SDR) to monitor the signal strength and background noise level of the selected frequency band in the current environment in real time, ensuring that the device has sufficient sensitivity and dynamic range to accurately capture signal and noise data. According to the collected signal power and noise power data, the signal-to-noise ratio calculation is implemented through programming to generate the signal-to-noise ratio value of the current environment. Data processing software (such as MATLAB) can be used. or Python) for calculation and visualization, record the calculated current environmental signal-to-noise ratio value in the database to form a signal-to-noise ratio data set for subsequent analysis and decision support, design a dynamic modulation strategy, dynamically adjust the signal transmission frequency according to the real-time change of the signal-to-noise ratio, and select frequency hopping modulation (FDM) or phase modulation (PM) and other methods. According to the real-time monitored signal-to-noise ratio value, dynamically adjust the signal transmission frequency. For example, when the signal-to-noise ratio is high, a higher frequency can be selected for transmission; when the signal-to-noise ratio is low, adjust to a lower frequency to enhance the signal's anti-interference ability. Use the modulation strategy to generate a dynamic modulation signal transmission frequency, and use a signal processing device (such as a digital signal processor) to achieve real-time frequency modulation. Record and output the generated dynamic modulation signal transmission frequency for subsequent use. Formulate an adaptive coding strategy, select a suitable coding method (such as convolutional coding, Turbo coding, or LDPC coding) based on the optimal signal frequency band parameters and signal-to-noise ratio. The adaptive coding strategy should take into account the signal quality and transmission conditions, and adaptively encode the signal in the optimal signal frequency band to automatically select the optimal coding method according to the real-time channel conditions. Programming tools (such as Python) can be used. Or MATLAB) to realize the adaptive coding function, record the generated signal adaptive coding strategy and related parameters in the database to form a coding strategy data set to support subsequent transmission decisions and select a suitable intelligent transmission decision model. A rule-based decision model or machine learning algorithm (such as decision tree, support vector machine, etc.) can be used to formulate the signal transmission strategy. Combined with factors such as signal adaptive coding strategy, dynamic modulation signal frequency and current signal-to-noise ratio, a comprehensive transmission decision is made. The best signal transmission strategy is calculated through the decision model, including the selection of the optimal frequency band, coding method and modulation method. The generated dynamic signal transmission strategy is implemented in the signal transmission system, and real-time monitoring and adjustment are carried out. The implementation results are recorded for subsequent analysis and optimization.

[0155] In this embodiment, step S4 includes the following steps:

[0156] Step S51: Identify surrounding relay network nodes based on the vehicle diagnostic system;

[0157] Step S52: Calculate the node bandwidth of the surrounding relay network nodes to obtain bandwidth parameters of the surrounding relay nodes;

[0158] Step S53: performing signal transmission delay analysis on the surrounding relay network nodes to obtain signal transmission delay data of each node;

[0159] Step S54: performing optimal relay transmission path planning based on the bandwidth parameters of the surrounding relay nodes and the signal transmission delay data of each node, thereby obtaining the optimal relay transmission path;

[0160] Step S55: Optimize the relay cooperative transmission of the dynamic signal transmission strategy based on the optimal relay transmission path, and construct a cooperative transmission optimization strategy.

[0161] In this embodiment, wireless signal scanning technology is used in combination with the vehicle's diagnostic system to identify surrounding relay network nodes through devices (such as software defined radio, Wi-Fi or LTE scanners). These nodes include base stations, signal boosters and other communication devices. The vehicle's diagnostic system is started to scan wireless signals around the vehicle, and the information of the discovered relay network nodes, including the node's unique identifier (such as MAC address), signal strength and type, is recorded. The collected relay node information is organized into structured data to form a relay network node list to provide basic data for subsequent analysis. Visual tools (such as GIS software or custom applications) are used to display the identified relay node locations to ensure the accuracy and completeness of node identification. Scan parameters are adjusted as needed to optimize the identification effect. A model for calculating node bandwidth is selected, such as an evaluation model based on traffic monitoring and network performance indicators. Network traffic analysis tools (such as Wireshark) can be used. or NetFlow) to measure bandwidth, and conduct bandwidth tests on each identified relay network node. Bandwidth test tools (such as iPerf) can be used to initiate data flow tests from the vehicle to measure the actual bandwidth of each node, and record the bandwidth parameters of each relay node, including uplink bandwidth and downlink bandwidth, to form a detailed bandwidth data set. The measured bandwidth parameters are organized into a report for subsequent path planning to ensure the accuracy and availability of the data for decision support. Delay measurement tools (such as ping or traceroute commands) are used to evaluate the signal transmission delay with each relay network node. This method is suitable for simple network delay tests. A test signal is sent from the vehicle to each relay node, and the response time is recorded. The average delay value of each node is obtained through multiple consecutive measurements to reduce the impact of accidental factors. The measured signal transmission delay data is organized into a structured format, including information such as the average delay, maximum delay, and minimum delay of each node. The signal transmission delay data of all relay nodes are recorded in a database to form a delay feature data set for subsequent path planning. The shortest path algorithm (such as Dijkstra algorithm or A*) is used. Algorithm), combines bandwidth and delay information to plan the optimal relay transmission path, designs a comprehensive evaluation standard to weigh bandwidth and delay, inputs the bandwidth parameters and signal transmission delay data of the surrounding relay nodes into the path planning algorithm, and calculates the optimal relay transmission path from the vehicle to the target node. The algorithm will consider factors such as bandwidth, delay and network topology, generate a transmission path diagram, record the calculated optimal relay transmission path in the database, and form a path planning report, which will be provided to subsequent transmission optimization decisions, and select a suitable collaborative transmission optimization model. The model can optimize the signal transmission strategy based on game theory, machine learning or dynamic programming methods. Based on the optimal relay transmission path, combined with the current environmental signal quality and bandwidth delay data, the dynamic signal transmission strategy is optimized.Considering the collaborative work of relay nodes, design reasonable signal allocation and scheduling strategies to improve the overall transmission efficiency, record the generated collaborative transmission optimization strategy in the system, monitor the implementation effect in real time, and adjust the strategy according to the feedback data to ensure the best signal transmission quality.

[0162] In this embodiment, step S6 includes the following steps:

[0163] Step S61: Control the entire process of vehicle wireless communication based on the collaborative transmission optimization strategy, and monitor the wireless communication control response data in real time;

[0164] Step S62: quantifying the communication signal performance of the wireless communication control response data to generate a communication signal performance quantization value;

[0165] Step S63: performing dynamic transfer learning according to the communication signal performance quantization value to obtain dynamic transfer learning data;

[0166] Step S64: Adaptively optimize the collaborative transmission optimization strategy based on the dynamic transfer learning data, thereby constructing an adaptive communication optimization model.

[0167] In this embodiment, according to the designed cooperative transmission optimization strategy, the wireless communication system of the vehicle is configured, including signal transmission, receiving frequency, modulation mode and coding strategy, to ensure that the system can dynamically adjust communication parameters according to real-time environmental conditions, select appropriate monitoring tools (such as network monitoring software or a customized monitoring platform) for real-time collection and analysis of wireless communication control response data, to ensure that the monitoring system can capture signal quality, delay, bandwidth and other key performance indicators, and in the process of wireless communication, record the response data of the system in real time, including signal strength, signal quality, delay and data packet loss rate, and use data acquisition equipment (such as sensors or data loggers) to automate the collection process, and use data processing software (such as MATLAB) to automatically collect and analyze the control response data of wireless communication. or Python) to analyze the collected control response data, identify potential problems and optimization opportunities, ensure the accuracy and completeness of the data, so as to provide a reliable basis for subsequent steps, determine the key indicators used to quantify the communication signal performance, such as signal strength, signal-to-noise ratio, latency, throughput, and data packet loss rate, etc. Each indicator should be strictly defined according to its impact on the communication quality, and a standardized method should be used to quantify each performance indicator into a unified performance value. Normalization technology can be used to convert each indicator value into a proportional value between 0 and 1 for comprehensive comparison, process the wireless communication control response data obtained by real-time monitoring, calculate the quantitative value of each performance indicator, and integrate these quantitative values ​​into a comprehensive communication performance score to form the final communication signal performance quantitative value for subsequent analysis, and select a suitable transfer learning framework, such as TensorFlow Or PyTorch, use the existing model for dynamic transfer learning. The selected model should be able to effectively process the communication signal performance data and adapt to the changes in the new environment. Combine the generated communication signal performance quantification value with historical data to build a data set for transfer learning. Make sure that the data set contains communication performance data in a variety of scenarios so that the model can learn and adapt. Use the sorted data set to train the transfer learning model. The model will learn how to dynamically adjust its own parameters according to the communication signal performance quantification value to improve communication performance. Through continuous iterative optimization, the model can be self-improved. Record the results of dynamic transfer learning, including the performance improvement and adaptability changes of the model, to ensure Ensure that the transfer learning data can provide support for subsequent adaptive communication optimization, design an adaptive communication optimization model framework, combine the data and model output of dynamic transfer learning, and form a feedback loop system. The system can dynamically adjust the optimization strategy according to the real-time communication performance feedback, apply the model obtained by dynamic transfer learning to the collaborative transmission optimization strategy, and adjust the communication parameters (such as frequency, modulation method and coding strategy) in real time to improve communication quality and efficiency. Establish a feedback mechanism to feed back real-time communication performance data to the adaptive model, so that the model can continuously learn and adjust the strategy to cope with the ever-changing environment and needs, and evaluate the effect of the adaptive communication optimization model.By comparing the communication performance indicators before and after optimization, the effectiveness of the model is verified, and the model and strategy are further optimized based on the evaluation results.

[0168] In this embodiment, a wireless communication system of a vehicle diagnostic system is provided, which is used to execute the wireless communication method of the vehicle diagnostic system as described above, including:

[0169] The scene signal interference module identifies multiple signal sources in the environment and the three-dimensional spatial data of the vehicle's current position; comprehensively evaluates the scene interference intensity of the multiple signal sources in the environment, thereby obtaining an evaluation value of the vehicle's current scene interference intensity;

[0170] The environment three-dimensional structure module performs a spatial layout analysis of the environment obstacles on the three-dimensional spatial data of the current position of the vehicle, performs three-dimensional modeling of the environment, and constructs a three-dimensional point cloud model of the vehicle environment;

[0171] The optimal frequency band module performs signal multipath propagation evolution and optimal signal frequency band analysis based on the vehicle's current scene interference intensity assessment value and the vehicle environment 3D point cloud model, thereby extracting the optimal signal frequency band parameters;

[0172] Dynamic signal transmission module, which performs dynamic transmission frequency modulation and signal adaptive coding based on the optimal signal frequency band parameters to build a dynamic signal transmission strategy;

[0173] The relay transmission optimization module is used to plan the optimal relay transmission path for the dynamic signal transmission strategy, optimize the relay cooperative transmission, and build a cooperative transmission optimization strategy;

[0174] The adaptive communication optimization module is used to control the entire process of vehicle wireless communication based on the collaborative transmission optimization strategy, and then perform adaptive communication optimization to build an adaptive communication optimization model.

[0175] The present invention helps to understand the communication environment around the vehicle by identifying multiple signal sources in the environment and the three-dimensional spatial data of the vehicle's current position. The comprehensive evaluation of scene interference intensity provides a quantitative evaluation of the interference degree of the current communication environment, providing a basis for subsequent optimization. The spatial layout analysis and three-dimensional modeling of environmental obstacles help to understand the physical environment around the vehicle. The construction of a three-dimensional point cloud model of the vehicle environment provides environmental information for subsequent signal propagation and optimization. Through the evolution of signal multi-path propagation and the analysis of the optimal signal frequency band, the optimal signal frequency band parameters are extracted to optimize the signal transmission quality. Obtaining the optimal signal frequency band parameters helps to improve communication efficiency and reduce interference effects. Dynamic transmission frequency modulation and signal adaptive coding construct dynamic signal transmission strategies for different scenarios, improve the adaptability of the communication system, and construct dynamic signal transmission strategies to help cope with changes and interference in the signal transmission process and improve communication quality and stability. Optimal relay transmission path planning and relay cooperative transmission optimization improve the efficiency and reliability of signal transmission. Constructing a cooperative transmission optimization strategy helps to optimize the signal transmission path and improve communication performance. Vehicle wireless communication full process control and adaptive communication optimization construct an adaptive communication optimization model for real-time communication needs. Constructing an adaptive communication optimization model helps to achieve intelligent adjustment and optimization of the communication system and improve the adaptability and performance of the communication system.

[0176] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0177] The above is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be 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 will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A wireless communication method for a vehicle diagnostic system, characterized in that: The following steps are involved: Step S1: Identify multiple signal sources in the environment and three-dimensional spatial data of the vehicle's current position; Performing a comprehensive evaluation of scene interference intensity of multiple signal sources in the environment, thereby obtaining a current scene interference intensity evaluation value of the vehicle; Step S2: Performing a spatial layout analysis of environmental obstacles on the three-dimensional spatial data of the vehicle's current position, and performing three-dimensional modeling of the environment to construct a three-dimensional point cloud model of the vehicle environment; The specific steps of step S2 are: Step S21: performing obstacle recognition on the three-dimensional spatial data of the current position of the vehicle, and marking a plurality of environmental obstacle nodes; Step S22: Calculate the spatial position of each of the multiple environmental obstacle nodes one by one to obtain the spatial position coordinates of each obstacle; Step S23: performing an environmental obstacle spatial layout analysis based on the spatial position coordinates of each obstacle to generate obstacle spatial layout data; Step S24: performing a three-dimensional structure analysis of the environment on the three-dimensional spatial data of the current position of the vehicle to generate a three-dimensional structure feature of the environment; Step S25: Performing three-dimensional modeling of the environment based on the three-dimensional structural features of the environment based on the obstacle spatial layout data, and constructing a three-dimensional point cloud model of the vehicle environment; Step S3: performing signal multipath propagation evolution according to the vehicle's current scene interference intensity evaluation value and the vehicle environment three-dimensional point cloud model, and performing optimal signal frequency band analysis to extract optimal signal frequency band parameters; Step S4: Perform dynamic transmission frequency modulation and signal adaptive coding based on the optimal signal frequency band parameters to build a dynamic signal transmission strategy; Step S5: Plan the optimal relay transmission path for the dynamic signal transmission strategy, optimize the relay cooperative transmission, and build a cooperative transmission optimization strategy; Step S6: Based on the collaborative transmission optimization strategy, the whole process of vehicle wireless communication is controlled, and then adaptive communication optimization is performed to construct an adaptive communication optimization model.

2. The wireless communication method of the vehicle diagnostic system according to claim 1, characterized in that: The specific steps of step S1 are: Step S11: identifying the current vehicle position, and based on the current vehicle position identifying multiple signal sources in the environment and three-dimensional spatial data of the vehicle's current position; Step S12: performing signal feature analysis on multiple signal sources in the environment, and extracting signal feature parameters of each signal source; Step S13: Tracking dynamic signal changes of signal characteristic parameters of each signal source to generate dynamic change characteristics of each signal source; Step S14: performing time-domain mutation fluctuation identification on the dynamic change characteristics of each signal source to obtain the time-domain mutation fluctuation data of each signal source; Step S15: Perform a comprehensive evaluation of the scene interference intensity based on the time-domain mutation fluctuation data of each signal source, so as to obtain an evaluation value of the vehicle's current scene interference intensity.

3. The wireless communication method of the vehicle diagnostic system according to claim 2, characterized in that: The specific steps of step S12 are: Capturing signals of multiple signal sources in the environment based on the vehicle-mounted radio frequency sensor to obtain the original signal of each signal source; Perform signal filtering on the original signal of each signal source to obtain a filtered optimized signal of each signal source; Perform spectrum analysis on the filtered optimized signal of each signal source and classify the signal to obtain the signal type of each signal source; Performing time domain signal feature analysis on the filtered optimized signal of each signal source, thereby generating the time domain signal feature of each signal source; The time domain signal characteristic analysis includes amplitude calculation, phase calculation and pulse characteristic analysis; Performing frequency domain signal feature analysis on the filtered optimized signal of each signal source to generate frequency domain signal features of each signal source; The time domain signal feature analysis includes signal center frequency calculation, bandwidth calculation and spectral power distribution analysis; According to the signal type of each signal source, the signal modulation characteristic parameter analysis is performed on the time domain signal characteristics of each signal source and the frequency domain signal characteristics of each signal source, and the signal characteristic parameters of each signal source are extracted.

4. The wireless communication method of the vehicle diagnostic system according to claim 1, characterized in that: The specific steps of step S3 are: Step S31: performing signal transmission frequency band sensing processing on the vehicle diagnostic system to obtain multiple signal transmission frequency bands; Step S32: performing interference calculation on multiple signal transmission frequency bands one by one according to the interference strength evaluation value of the current scene of the vehicle, and extracting the signal interference strength of each transmission frequency band; Step S33: filtering abnormal interference frequency bands based on the signal interference strength of each transmission frequency band, and extracting available signal transmission frequency bands; Step S34: performing signal multipath propagation evolution on the three-dimensional point cloud model of the vehicle environment according to the available signal transmission frequency band to generate environmental signal multipath propagation effect characteristics; Step S35: Perform optimal signal frequency band analysis on the multipath propagation effect characteristics of the environmental signal, thereby extracting optimal signal frequency band parameters.

5. The wireless communication method of the vehicle diagnostic system according to claim 4, characterized in that: The specific steps of step S34 are: Performing signal propagation simulation on the three-dimensional point cloud model of the vehicle environment according to the available signal transmission frequency bands to generate signal propagation simulation data of multiple transmission frequency bands; Performing signal propagation path identification on signal propagation simulation data of multiple transmission frequency bands to obtain multiple signal propagation paths; The influence of obstacle propagation reflection and refraction on multiple signal propagation paths is analyzed to obtain the characteristics of obstacle propagation influence; Calculating the path propagation delay difference for multiple signal propagation paths to generate multiple propagation path delay difference parameters; Performing signal phase analysis on multiple signal propagation paths to generate multipath signal phase parameters; Performing a multipath comprehensive signal quality assessment on multiple propagation path delay difference parameters and multipath signal phase parameters to generate a multipath propagation signal quality assessment value; Multipath propagation effect analysis is performed based on the multipath propagation signal quality evaluation value and obstacle propagation impact characteristics to generate environmental signal multipath propagation effect characteristics.

6. The wireless communication method of the vehicle diagnostic system according to claim 1, characterized in that: The specific steps of step S4 are: Step S41: Calculating the transmission signal-to-noise ratio based on the optimal signal frequency band parameters to generate the current environment signal-to-noise ratio; Step S42: dynamically modulate the transmission frequency of the current frequency band signal-to-noise ratio to generate a dynamic modulation signal transmission frequency; Step S43: performing signal adaptive coding on the optimal signal frequency band parameters to generate a signal adaptive coding strategy; Step S44: Make intelligent transmission decisions on the signal adaptive coding strategy and the dynamic modulation signal transmission frequency to build a dynamic signal transmission strategy.

7. The wireless communication method of the vehicle diagnostic system according to claim 1, characterized in that: The specific steps of step S5 are: Step S51: Identify surrounding relay network nodes based on the vehicle diagnostic system; Step S52: Calculate the node bandwidth of the surrounding relay network nodes to obtain bandwidth parameters of the surrounding relay nodes; Step S53: performing signal transmission delay analysis on the surrounding relay network nodes to obtain signal transmission delay data of each node; Step S54: performing optimal relay transmission path planning based on the bandwidth parameters of the surrounding relay nodes and the signal transmission delay data of each node, thereby obtaining the optimal relay transmission path; Step S55: Optimize the relay cooperative transmission of the dynamic signal transmission strategy based on the optimal relay transmission path, and construct a cooperative transmission optimization strategy.

8. The wireless communication method of the vehicle diagnostic system according to claim 1, characterized in that: The specific steps of step S6 are: Step S61: Control the entire process of vehicle wireless communication based on the coordinated transmission optimization strategy, and monitor the wireless communication control response data in real time; Step S62: quantifying the communication signal performance of the wireless communication control response data to generate a communication signal performance quantization value; Step S63: performing dynamic transfer learning according to the communication signal performance quantization value to obtain dynamic transfer learning data; Step S64: Adaptively optimize the collaborative transmission optimization strategy based on the dynamic transfer learning data, thereby constructing an adaptive communication optimization model.

9. A wireless communication system for a vehicle diagnostic system, characterized in that: A wireless communication method for executing a vehicle diagnostic system as claimed in any one of claims 1 to 8, comprising: The scene signal interference module identifies multiple signal sources in the environment and the three-dimensional spatial data of the vehicle's current position; comprehensively evaluates the scene interference intensity of the multiple signal sources in the environment, thereby obtaining an evaluation value of the vehicle's current scene interference intensity; The environment three-dimensional structure module performs a spatial layout analysis of the environment obstacles on the three-dimensional spatial data of the current position of the vehicle, performs three-dimensional modeling of the environment, and constructs a three-dimensional point cloud model of the vehicle environment; The optimal frequency band module performs signal multipath propagation evolution and optimal signal frequency band analysis based on the vehicle's current scene interference intensity assessment value and the vehicle environment 3D point cloud model, thereby extracting the optimal signal frequency band parameters; Dynamic signal transmission module, which performs dynamic transmission frequency modulation and signal adaptive coding based on the optimal signal frequency band parameters to build a dynamic signal transmission strategy; The relay transmission optimization module is used to plan the optimal relay transmission path for the dynamic signal transmission strategy, optimize the relay cooperative transmission, and build a cooperative transmission optimization strategy; The adaptive communication optimization module is used to control the entire process of vehicle wireless communication based on the collaborative transmission optimization strategy, and then perform adaptive communication optimization to build an adaptive communication optimization model.

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