A data integration method and system for implementing in-vehicle devices

Abstract

The present invention relates to the field of in-vehicle technologies, and discloses a method and system for realizing data integration of in-vehicle devices, including: First, query the in-vehicle device adaptation application business scenario to set up a data integration framework, construct a simulation environment to test the real-time data transmission performance under different business scenarios, extract interaction parameters to calculate the transmission deviation value, formulate a format conversion strategy accordingly and verify the effectiveness of the strategy through simulation tests, establish a missing index evaluation model to optimize the data guarantee system, and then analyze the response effects under different traffic conditions based on the guarantee system, identify the core change factors in the data flow trend through traffic monitoring and feature analysis, and guide the adaptation connection between the intelligent integration module and the in-vehicle device. Finally, generate a multi-dimensional data integration report by collecting the data of the integrated operation nodes. The present invention can improve the processing efficiency of in-vehicle device data.

Description

Technical Field

[0001] The present invention relates to a data integration method and system for in-vehicle devices, belonging to the field of in-vehicle technologies. Background Art

[0002] In the current era background of the rapid development of the intelligence and networking of the automotive industry, the functions of in-vehicle devices are becoming increasingly complex, and the demand for data interaction has increased significantly. As one of the core technologies for realizing intelligent driving, vehicle networking, and in-vehicle infotainment systems, the data integration of in-vehicle devices is gradually becoming the focus of the industry.

[0003] Currently, there are many challenges in the process of in-vehicle device data integration at the present stage. Traditional integration methods usually have problems such as poor compatibility, low data processing efficiency, and high system complexity. For example, common integration means often have difficulty in realizing the unified management and efficient transmission of multi-source heterogeneous data, resulting in data delay, loss, or redundancy, and cannot meet the requirements of real-time, reliability, and security. In addition, the communication protocols and data formats between different in-vehicle devices vary greatly, further increasing the difficulty of integration. Therefore, a data integration method for in-vehicle devices is needed to improve the processing efficiency of in-vehicle device data. Summary of the Invention

[0004] The present invention provides a data integration method and system for in-vehicle devices, and its main purpose is to improve the processing efficiency of in-vehicle device data.

[0005] To achieve the above purpose, a data integration method for in-vehicle devices provided by the present invention includes:

[0006] Query the application business scenarios corresponding to the in-vehicle devices, and based on the application business scenarios, set the data integration framework adapted to the in-vehicle devices, and based on the data integration framework, construct the simulated integration environment corresponding to the in-vehicle devices;

[0007] Monitor the real-time data transmission situation in the simulated integration environment, extract the detailed interaction parameters in the real-time data transmission situation, and based on the detailed interaction parameters, calculate the transmission deviation values of the in-vehicle devices in different business scenarios;

[0008] Based on the transmission deviation values, formulate the format conversion strategy for the transmitted data in the in-vehicle devices, and based on the format conversion strategy, conduct simulated integration testing on the transmitted data in the in-vehicle devices to obtain simulated test records, calculate the missing indexes corresponding to each record in the simulated test records, and based on the missing indexes, construct the data guarantee system corresponding to the in-vehicle devices;

[0009] Based on the above data security system, analyze the data response effect of the vehicle-mounted device under different data traffic conditions. Based on the data response effect, query the data flow trend of the vehicle-mounted device during the data integration process, and identify the core change factors in the data flow trend;

[0010] Based on the core change factors, test-connect the preset intelligent integration module with the vehicle-mounted device to obtain a measurement integration unit, collect the integration operation nodes of the measurement integration unit during operation, and generate a data integration report corresponding to the vehicle-mounted device based on the integration operation nodes.

[0011] Optionally, constructing a simulation integration environment corresponding to the vehicle-mounted device based on the data integration framework includes:

[0012] Query the framework composition parameters corresponding to the data integration framework;

[0013] Based on the framework composition parameters, determine the framework construction mode corresponding to the data integration framework;

[0014] Based on the framework construction mode, analyze the data interaction requirements corresponding to the vehicle-mounted device;

[0015] According to the data interaction requirements, formulate an environment simulation process corresponding to the vehicle-mounted device;

[0016] Based on the environment simulation process, construct a simulation integration environment corresponding to the vehicle-mounted device.

[0017] Optionally, monitoring the real-time data transmission situation in the simulation integration environment includes:

[0018] Query the transmission protocol type corresponding to the data in the simulation integration environment;

[0019] Based on the transmission protocol type, determine the data monitoring mode corresponding to the simulation integration environment;

[0020] Based on the data monitoring mode, analyze the data transmission characteristics corresponding to the data in the simulation integration environment;

[0021] According to the data transmission characteristics, monitor the real-time data transmission situation in the simulation integration environment.

[0022] Optionally, calculating the transmission deviation value of the vehicle-mounted device under different service scenarios based on the detailed interaction parameters includes:

[0023] Use the following formula to calculate the transmission deviation value of the vehicle-mounted device under different service scenarios:

[0024] ;

[0025] Among them, represents the transmission deviation value of the vehicle-mounted device in different service scenarios, represents the number of scenarios corresponding to the service scenario, represents the number index of the service scenario, represents the number of parameters corresponding to the detailed interaction parameter, represents the number index of the detailed interaction parameter, represents at the th service scenario, the th actual measured value corresponding to the detailed interaction parameter, represents at the th service scenario, the th expected value corresponding to the detailed interaction parameter, represents at the th service scenario, the th variance value corresponding to the detailed interaction parameter, represents the scenario weight corresponding to the th service scenario.

[0026] Optionally, formulating a format conversion strategy for the transmitted data in the vehicle-mounted device based on the transmission deviation value includes:

[0027] Extracting the deviation distribution characteristics corresponding to the transmission deviation value;

[0028] Based on the deviation distribution characteristics, dividing the deviation level interval corresponding to the transmission deviation value;

[0029] Analyzing the format compatibility index corresponding to the deviation level interval;

[0030] Determining the conversion priority corresponding to the format compatibility index;

[0031] Based on the conversion priority, formulating a format conversion strategy for the transmitted data in the vehicle-mounted device.

[0032] Optionally, calculating the missing index corresponding to each record in the simulation test record includes:

[0033] Calculating the missing index corresponding to each record in the simulation test record using the following formula:

[0034] ;

[0035] Among them, MI represents the missing index corresponding to each record in the simulation test record, represents the total number of records corresponding to the simulation test record, represents the number index corresponding to the simulation test record, Indicates the exponential variable corresponding to the th simulation test record, indicates the total number of types corresponding to the simulation test record, indicates the type index corresponding to the simulation test record, Indicates the actual missing value corresponding to the record type, Indicates the theoretical missing value corresponding to the record type, Indicates the average missing ratio corresponding to all record types.

[0036] Optionally, constructing the data guarantee system corresponding to the vehicle-mounted device based on the missing index includes:

[0037] Determining the missing time axis corresponding to the missing index;

[0038] Extracting the time missing nodes in the missing time axis;

[0039] Based on the time missing nodes, constructing the missing distribution matrix corresponding to the missing index;

[0040] Querying the association path between different missing points in the missing distribution matrix;

[0041] Based on the association path, constructing the data guarantee system corresponding to the vehicle-mounted device.

[0042] Optionally, analyzing the data response effect of the vehicle-mounted device under different data traffic conditions based on the data guarantee system includes:

[0043] Deploying traffic monitoring nodes in the data guarantee system;

[0044] Collecting real-time traffic data in the traffic monitoring nodes;

[0045] Converting the real-time traffic data into a feature data stream;

[0046] Generating a traffic distribution map corresponding to the feature data stream;

[0047] Based on the traffic distribution map, analyzing the data response effect of the vehicle-mounted device under different data traffic conditions.

[0048] Optionally, identifying the core change elements in the data flow trend includes:

[0049] Based on the data flow trend, setting the initial analysis parameters corresponding to the data flow;

[0050] Based on the initial analysis parameters, perform a first-round trend analysis on the data flow direction to obtain first-round trend features;

[0051] Based on the first-round trend features, optimize and adjust the analysis environment where the data flow direction is located to obtain a trend optimization environment;

[0052] Based on the trend optimization environment, perform multiple rounds of trend analysis on the data flow direction to obtain multiple rounds of trend records;

[0053] Identify the core change elements in the multiple rounds of trend records.

[0054] To solve the above problems, the present invention also provides a data integration system for implementing in-vehicle devices, and the system includes:

[0055] An environment construction module, configured to query the application service scenarios corresponding to the in-vehicle devices, based on the application service scenarios, set the data integration framework adapted to the in-vehicle devices, and based on the data integration framework, construct a simulated integration environment corresponding to the in-vehicle devices;

[0056] A deviation value calculation module, configured to monitor the real-time data transmission situation in the simulated integration environment, extract the detailed interaction parameters in the real-time data transmission situation, and based on the detailed interaction parameters, calculate the transmission deviation values of the in-vehicle devices under different service scenarios;

[0057] A system construction module, configured to, based on the transmission deviation values, formulate a format conversion strategy for the transmitted data in the in-vehicle devices, based on the format conversion strategy, perform a simulated integration test on the transmitted data in the in-vehicle devices to obtain a simulated test record, calculate the missing indexes corresponding to each record in the simulated test record, and based on the missing indexes, construct a data guarantee system corresponding to the in-vehicle devices;

[0058] An element identification module, configured to, based on the data guarantee system, analyze the data response effects of the in-vehicle devices under different data traffic conditions, based on the data response effects, query the data flow direction trends in the data integration process of the in-vehicle devices, and identify the core change elements in the data flow direction trends;

[0059] A report generation module, configured to, based on the core change elements, test-connect a preset intelligent integration module with the in-vehicle devices to obtain a measurement integration unit, collect the integration operation nodes during the operation of the measurement integration unit, and based on the integration operation nodes, generate a data integration report corresponding to the in-vehicle devices.

[0060] Compared with the problems described in the background art, the present invention can optimize the targeted design of the data integration framework by querying the application service scenarios corresponding to in-vehicle devices, effectively reducing the compatibility risks in actual deployment. At the same time, it provides a scenario-based basis for dynamically adjusting the data format conversion strategy, ultimately enhancing the real-time performance, reliability, and scalability of the system in complex business scenarios. By monitoring the real-time data transmission situation in the simulated integration environment, the present invention can promptly discover potential problems such as protocol conflicts and bandwidth bottlenecks, providing a quantitative basis for optimizing the data routing strategy. Meanwhile, combined with transmission deviation analysis, it can quickly verify the adaptability of the data integration framework in different scenarios. Further, based on the transmission deviation value, the present invention formulates a format conversion strategy for the transmitted data in the in-vehicle device, which can accurately locate data transmission problems and optimize the format specifically, being able to adapt to the requirements of different business scenarios, enhancing the compatibility of in-vehicle devices, ensuring the accurate transfer of data between systems, and providing reliable data support for applications such as intelligent driving. Further, based on the data guarantee system, the present invention analyzes the data response effect of the in-vehicle device under different data traffic conditions, clearly insight into the performance of the device in a complex data environment, accurately locate the data transmission bottleneck and response delay points, and can optimize the data processing process accordingly, rationally allocate resources, and improve the stability and response speed of the device at high and low traffic. Finally, based on the core change factors, the present invention test-connects a preset intelligent integration module with the in-vehicle device to obtain a measurement integration unit, which can accurately adapt the two according to the key characteristics of the data flow direction, ensure that the integration module fits the actual needs of the in-vehicle device, can discover potential compatibility problems in advance, optimize and adjust in a timely manner, and reduce the risks of subsequent large-scale applications. Therefore, a method and system for realizing data integration of in-vehicle devices provided by the embodiments of the present invention can improve the processing efficiency of in-vehicle device data. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 FIG. is a schematic flowchart of a method for realizing data integration of in-vehicle devices provided by an embodiment of the present invention;

[0062] Figure 2 FIG. is a schematic diagram of modules of a system for realizing the data integration of in-vehicle devices provided by an embodiment of the present invention.

[0063] The implementation, functional features, and advantages of the present invention will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0064] 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.

[0065] An embodiment of the present application provides a data integration method for realizing in-vehicle devices. The execution subject of the data integration method for realizing in-vehicle devices includes, but is not limited to, at least one of electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided by the embodiment of the present application. In other words, the data integration method for realizing in-vehicle devices can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to: a single server, a server cluster, a cloud server, or a cloud server cluster, etc.

[0066] Embodiment 1:

[0067] Referring to Figure 1 As shown, it is a schematic flowchart of a data integration method for realizing in-vehicle devices provided by an embodiment of the present invention. In this embodiment, the data integration method for realizing in-vehicle devices includes:

[0068] S1. Query the application service scenario corresponding to the in-vehicle device, set the data integration framework adapted to the in-vehicle device based on the application service scenario, and construct a simulated integration environment corresponding to the in-vehicle device based on the data integration framework.

[0069] By querying the application service scenario corresponding to the in-vehicle device, the present invention can optimize the targeted design of the data integration framework, effectively reduce the compatibility risk in actual deployment, and at the same time provide a scenario-based basis for dynamically adjusting the data format conversion strategy, ultimately improving the real-time performance, reliability, and scalability of the system in complex service scenarios.

[0070] Among them, the in-vehicle device refers to various intelligent electronic devices integrated in a vehicle, covering core components such as sensors (such as cameras, radars, GPS modules), control units (ECUs), infotainment systems, autonomous driving domain controllers, V2X communication modules, etc. Its functions cover data acquisition, real-time processing, instruction execution, and cross-system interaction. The in-vehicle device realizes the interconnection of in-vehicle devices and vehicle-cloud data interaction through communication protocols such as CAN bus, FlexRay, and in-vehicle Ethernet; the application business scenario refers to the set of functional requirements of a vehicle in a specific usage environment, including but not limited to: 1) Urban road autonomous driving scenario (needs to process high-density traffic signals, pedestrian detection, and complex road condition data); 2) Highway intelligent cruise scenario (focuses on long-distance radar data fusion and high-precision map matching); 3) Emergency rescue response scenario (requires millisecond-level fault diagnosis data transmission and security protocol encryption); 4) Remote vehicle health management scenario (involves periodic OBD data upload and OTA upgrade verification); 5) Shared mobility service scenario (needs to be compatible with multi-platform order data interaction and user privacy protection mechanism). Optionally, the query of the application business scenario corresponding to the in-vehicle device can be implemented through a clustering analysis algorithm. For example, according to vehicle speed and position data, the driving state of the vehicle can be clustered into different scenario categories such as urban congestion, highway driving, and suburban driving, and then the corresponding application business scenario can be determined.

[0071] Furthermore, based on the application business scenario, the present invention sets a data integration framework adapted to the in-vehicle device, which can accurately match the real-time performance of data processing for different services. And through predefined data interaction standards and protocol conversion rules, it can effectively reduce the integration complexity of multi-source heterogeneous data, reduce redundant calculations and transmission delays, and provide stable and reliable underlying support for scenarios such as intelligent driving and vehicle networking.

[0072] Among them, the data integration framework refers to a systematic architecture solution designed based on the requirements of application business scenarios. Its core functions include: 1) constructing a unified access standard for multi-source heterogeneous data (such as sensor signals, CAN bus messages, cloud instructions, etc.), and supporting multi-protocol conversion such as FlexRay and in-vehicle Ethernet; 2) optimizing the data routing strategy through a dynamic load balancing mechanism to ensure low-latency transmission of millimeter wave and camera data in the autonomous driving scenario; 3) integrating edge computing nodes to achieve real-time data cleaning and feature extraction, for example, quickly identifying the triggering conditions of the AEB system in the emergency braking scenario; 4) establishing an end-to-end security protection system, including data encryption tunnels, intrusion detection modules, and OTA upgrade verification mechanisms; 5) providing a visual configuration tool to support scenario-based policy orchestration, such as automatically switching the data fusion algorithm for highways and urban roads according to navigation information. Optionally, the data integration framework adapted to the vehicle-mounted device can be implemented through an edge intelligent processing framework, such as: constructing edge computing nodes based on TensorFlow Lite or ONNX Runtime to implement end-side data preprocessing, and finally obtaining the data integration framework.

[0073] Furthermore, based on the data integration framework, the present invention constructs a simulation integration environment corresponding to the vehicle-mounted device, which can reduce the compatibility risk after in-vehicle deployment, support multi-scenario stress testing and dynamic tuning, ensure the reliability of data routing strategies, protocol conversion rules, and security mechanisms under complex working conditions, and be able to monitor real-time data and perform deviation analysis to quickly iterate and optimize the data interaction logic.

[0074] Among them, the simulation integration environment refers to a virtual-real combined verification platform constructed based on the framework, integrating the hardware-in-the-loop (HiL), software-in-the-loop (SiL), and cloud-edge collaborative environments. For example, a full-digital simulation system is deployed through Docker containerization to verify the system stability under 1000+ concurrent connections.

[0075] As an embodiment of the present invention, constructing the simulation integration environment corresponding to the vehicle-mounted device based on the data integration framework includes: querying the framework composition parameters corresponding to the data integration framework; determining the framework construction mode corresponding to the data integration framework based on the framework composition parameters; analyzing the data interaction requirements corresponding to the vehicle-mounted device based on the framework construction mode; formulating an environment simulation process corresponding to the vehicle-mounted device according to the data interaction requirements; and constructing the simulation integration environment corresponding to the vehicle-mounted device based on the environment simulation process.

[0076] The framework composition parameters refer to the core configuration elements of the data integration framework, including protocol adaptation rules, algorithm parameters, hardware resource allocation strategies and security mechanism configuration, such as defining the baud rate of the CAN bus communication protocol and the computing power allocation ratio of the edge computing node; the framework construction mode refers to the architecture design method selected according to the business scenario requirements, covering centralized, distributed or layered hybrid architectures. For example, L2 autonomous driving uses a centralized architecture to achieve unified processing of sensor data, while L4 requires a distributed architecture to support multi-domain controller collaboration; the data interaction requirements refer to the data flow characteristics in a specific scenario, including real-time requirements (such as AEB systems require <10ms delay), throughput requirements (such as 4K cameras require 8Gbps bandwidth), safety levels (such as V2X communications need to meet the ISO 21434 standard) and priority division (such as emergency braking signals occupying bus resources); the environmental simulation process refers to the standardized steps for building a virtual verification environment, including input parameter configuration, scenario data generation, real-time monitoring and result analysis, such as building a vehicle dynamics model through Simulink and injecting sensor noise data on rainy days.

[0077] Furthermore, the query of the framework composition parameters corresponding to the data integration framework can be implemented by what methods, tools or algorithms, such as: it can be implemented by a configuration file parsing method, using Python's ConfigParser library to parse the configuration file and extract the framework composition parameters therefrom; the determination of the framework construction mode corresponding to the data integration framework can be implemented with the help of an architecture evaluation tool, such as: Archimate and other tools, by modeling and evaluating different architecture modes, to determine the most suitable data integration framework construction mode; the analysis of the data interaction requirements corresponding to the vehicle-mounted equipment can be implemented by a traffic analysis method, such as: using Wireshark and other network packet capture tools to capture the data traffic of the vehicle-mounted equipment, analyzing its traffic characteristics, transmission frequency, etc., so as to determine the data interaction requirements; the formulation of the environment simulation process corresponding to the vehicle-mounted equipment can be implemented by using a scripting tool, such as Python, to write scripts to automate the various steps of the environment simulation, and can also use a workflow engine to automatically schedule and execute the environment simulation process according to preset rules and conditions; the construction of the simulated integration environment corresponding to the vehicle-mounted equipment can be implemented by virtualization technology, such as: using VMware, VirtualBox and other tools to create a virtual vehicle-mounted equipment operating environment.

[0078] S2. Monitor the real-time data transmission in the simulated integrated environment, extract detailed interaction parameters in the real-time data transmission, and calculate the transmission deviation value of the vehicle-mounted device in different business scenarios based on the detailed interaction parameters.

[0079] By monitoring the real-time data transmission in the simulated integration environment, the present invention can promptly detect potential problems such as protocol conflicts and bandwidth bottlenecks, providing a quantitative basis for optimizing data routing strategies. Meanwhile, combined with transmission deviation analysis, it can quickly verify the adaptability of the data integration framework under different scenarios.

[0080] Among them, the real-time data transmission situation refers to a dynamic evaluation system constructed based on the monitoring mode and transmission characteristics, and the operating status of each protocol channel is displayed in real time through a visualization tool (such as Grafana). For example, when it is monitored that the delay of the in-vehicle Ethernet channel exceeds the 50ms threshold, the system automatically triggers the switching of the backup route; if it is found that the load rate of the CAN bus continuously exceeds 90%, optimization suggestions (such as splitting redundant signals) are generated.

[0081] As an embodiment of the present invention, monitoring the real-time data transmission in the simulated integration environment includes: querying the transmission protocol type corresponding to the data in the simulated integration environment; determining the data monitoring mode corresponding to the simulated integration environment based on the transmission protocol type; analyzing the data transmission characteristics corresponding to the data in the simulated integration environment based on the data monitoring mode; and monitoring the real-time data transmission in the simulated integration environment according to the data transmission characteristics.

[0082] Among them, the transmission protocol type refers to the standardized communication rules followed by data interaction in the simulated integration environment, including the CAN bus protocol (supporting a multi-master architecture and applicable to chassis control data with high real-time requirements), the FlexRay protocol (having a time-triggered mechanism and meeting the synchronization requirements of autonomous driving sensors), in-vehicle Ethernet (supporting a bandwidth of more than 1Gbps and applicable to high-definition camera data transmission), and security enhancement protocols (such as an encryption communication protocol compliant with the ISO 21434 standard); the data monitoring mode refers to the monitoring strategy designed for a specific protocol type, which is divided into two categories: real-time monitoring and offline analysis. For example, the real-time monitoring mode uses a packet capture tool such as Wireshark or a dedicated protocol analyzer (such as Vector CANoe) to dynamically capture the data stream on the bus and analyze real-time metrics such as frame interval and load rate; the offline analysis mode reproduces historical scenarios through a log playback tool (such as tcpreplay in Linux) and combines Python scripts for in-depth protocol parsing and anomaly detection; the data transmission characteristics refer to the set of quantitative metrics obtained through monitoring, including: 1) bandwidth utilization rate (such as an 80% channel occupancy rate in LTE-V2X communication); 2) end-to-end delay (such as a 12ms delay from camera capture to domain controller processing); 3) packet loss rate (such as a 0.3% packet loss rate of millimeter-wave radar data in a rainstorm environment); 4) throughput (such as a file transfer rate of 20MB / s during OTA upgrade); 5) data burstiness (such as an instantaneous CAN bus traffic of 100 frames per second during an emergency brake).

[0083] Furthermore, querying the transmission protocol type corresponding to the data in the simulation integration environment can be achieved through machine learning classification algorithms, such as: Support Vector Machine (SVM). First, use the data packet samples with known protocol types to train it, and then input the data packets in the simulation integration environment into the trained model. The model can then determine the protocol type based on the features. Determining the data monitoring mode corresponding to the simulation integration environment can be achieved through decision tree algorithms, such as: inputting the relevant data of the simulation integration environment into the decision tree and determining the data monitoring mode according to the output of the decision tree, and finally obtaining the data monitoring mode suitable for this simulation integration environment. Analyzing the data transmission characteristics corresponding to the data in the simulation integration environment can be achieved through clustering algorithms, such as: K-Means algorithm, etc. Cluster the data in the simulation integration environment to identify different data transmission modes and characteristics, and finally obtain comprehensive data transmission characteristics. What methods, tools or algorithms can be used to monitor the real-time data transmission situation in the simulation integration environment, such as: Apache Flink, which can perform real-time processing and analysis on the continuous data stream generated in the simulation integration environment to obtain the real-time data transmission situation.

[0084] By extracting the detailed interaction parameters in the real-time data transmission situation, the present invention can accurately locate the data interaction bottleneck, provide empirical basis for dynamically adjusting the routing strategy and protocol adaptation rules, and at the same time, combined with historical parameter comparison, can predict the performance of the system in extreme scenarios, optimize the resource allocation plan in advance, and ensure the reliable operation of high-real-time scenarios such as the vehicle networking.

[0085] Among them, the detailed interaction parameters refer to the key quantitative indicators generated during the real-time monitoring of data transmission in the simulation integration environment, covering protocol types (such as CAN FD, TSN), end-to-end delay, bandwidth occupancy, data error rate, QoS priority marking, precise timestamp, integrity check value, and encryption algorithm type. These parameters reflect the real-time state and quality characteristics of data interaction, provide quantitative basis for analyzing transmission deviation, optimizing protocol adaptation strategies, and verifying system reliability, and support the dynamic tuning and scenario adaptation of the subsequent data integration framework. Optionally, extracting the detailed interaction parameters in the real-time data transmission situation can be achieved through clustering algorithms, such as: DBSCAN and other algorithms. Use the size of the data packet, transmission time interval, etc. as features, and different types of data transmission modes can be distinguished through the clustering results, and then detailed interaction parameters such as the average transmission rate and the number of data packets in each mode can be extracted.

[0086] Furthermore, based on the detailed interaction parameters, the present invention calculates the transmission deviation values of the in-vehicle device in different service scenarios. It can quantify and analyze key indicators such as protocol compatibility, latency fluctuation, and bandwidth utilization, and can dynamically calibrate the data interaction logic in combination with the scenario-based service requirements, significantly improving the anti-interference ability and real-time response accuracy of the system under complex working conditions.

[0087] Among them, the transmission deviation value refers to the comprehensive deviation degree value between the actual data transmission situation and the expected transmission situation of the in-vehicle device in different service scenarios. It reflects the overall deviation situation of each detailed interaction parameter during the transmission process and is used to evaluate the accuracy and stability of the in-vehicle device data transmission.

[0088] As an embodiment of the present invention, calculating the transmission deviation value of the in-vehicle device in different service scenarios based on the detailed interaction parameters includes:

[0089] Use the following formula to calculate the transmission deviation value of the in-vehicle device in different service scenarios:

[0090] ;

[0091] Wherein, represents the transmission deviation value of the in-vehicle device in different service scenarios, represents the number of scenarios corresponding to the service scenario, represents the number index of the service scenario, represents the number of parameters corresponding to the detailed interaction parameters, represents the number index of the detailed interaction parameters, represents in the th service scenario, the actual measured value corresponding to the th detailed interaction parameter, represents in the th service scenario, the expected value corresponding to the th detailed interaction parameter, represents in the th service scenario, the variance value corresponding to the th detailed interaction parameter, represents the scenario weight corresponding to the th service scenario.

[0092] In detail, the actual measured value refers to the specific value obtained by actual monitoring and measurement, such as the actual transmission delay time, the actual bandwidth occupancy, etc., which reflects the true status of the parameter in the current business scenario; the expected value refers to the ideal value that should be achieved according to theory, design standards or experience, and is a reference standard for measuring whether the actual transmission situation meets the requirements; the variance value refers to the degree of discreteness of the fluctuation of the parameter in multiple measurements or a certain period of time. The larger the variance value, the worse the stability of the parameter; the scenario weight refers to the proportion of importance in the overall evaluation. Different business scenarios have different importance to the operation and function realization of vehicle-mounted equipment. This difference is reflected through the scenario weight so that different scenarios can be given appropriate consideration when calculating the transmission deviation value.

[0093] S3. Based on the transmission deviation value, formulate a format conversion strategy corresponding to the transmission data in the vehicle-mounted device; based on the format conversion strategy, perform a simulated integration test on the transmission data in the vehicle-mounted device to obtain a simulated test record; calculate the missing index corresponding to each record in the simulated test record; and based on the missing index, construct a data assurance system corresponding to the vehicle-mounted device.

[0094] Based on the transmission deviation value, the present invention formulates a format conversion strategy corresponding to the transmission data in the vehicle-mounted device, can accurately locate data transmission problems, optimize the format in a targeted manner, adapt to the needs of different business scenarios, enhance the compatibility of vehicle-mounted equipment, ensure the accurate flow of data between systems, and provide reliable data support for applications such as intelligent driving.

[0095] Among them, the format conversion strategy refers to a series of specific operations and rules formulated for data transmission in vehicle-mounted equipment based on conversion priority, which includes determining which conversion method to use, such as direct mapping conversion, encoding conversion, data structure reorganization, etc.; clarifying the conversion process and steps, from data collection, preprocessing to final format conversion and verification; it also involves resource allocation and scheduling during the conversion process, such as the use of computing resources and storage resources.

[0096] As an embodiment of the present invention, the format conversion strategy corresponding to the transmission data in the vehicle-mounted device is formulated based on the transmission deviation value, including: extracting the deviation distribution characteristics corresponding to the transmission deviation value; dividing the deviation level intervals corresponding to the transmission deviation value based on the deviation distribution characteristics; analyzing the format compatibility index corresponding to the deviation level interval; determining the conversion priority corresponding to the format compatibility index; and formulating the format conversion strategy corresponding to the transmission data in the vehicle-mounted device based on the conversion priority.

[0097] Among them, the deviation distribution characteristic refers to the characteristics presented by the transmission deviation value in terms of numerical range, frequency, aggregation, etc., such as whether it conforms to normal distribution, skewed distribution, etc. Different distribution forms reflect different characteristics of the causes of transmission deviation; it also includes the distribution differences of deviation values in different business scenarios and different data types; the deviation level interval refers to dividing the transmission deviation value into different range levels according to the deviation distribution characteristic. For example, according to the severity of the deviation, it can be divided into a mild deviation interval, a moderate deviation interval, and a severe deviation interval. Each interval corresponds to a different deviation degree. A mild deviation can indicate that the deviation of the transmitted data has little impact on the overall system and basically does not affect the normal use of the data; a moderate deviation requires certain processing to ensure the availability of the data; a severe deviation leads to data transmission failure or seriously affects the system function; the format compatibility index refers to an index that measures the degree of compatibility between the current format and the target format of the data transmitted by the in-vehicle device under different deviation level intervals. For example, within the mild deviation interval, the data format only needs to be simply adjusted to be compatible with the target format, and the format compatibility index is relatively high at this time; while in the severe deviation interval, the data format needs to be massively converted or even re-encoded, and the format compatibility index is relatively low; the conversion priority refers to the order of converting the data format under different deviation level intervals determined according to the format compatibility index. For example, for a deviation level interval with a relatively low format compatibility index, it indicates that the data format conversion is more difficult and has a more serious impact on the system. Therefore, it needs to be processed first and a higher conversion priority is assigned.

[0098] Furthermore, the extraction of the deviation distribution characteristic corresponding to the transmission deviation value can be achieved through a visualization chart method, such as: drawing a histogram, a box plot, etc., to visually present the deviation distribution characteristics such as the distribution range, central tendency, and dispersion degree of the deviation value; the division of the deviation level interval corresponding to the transmission deviation value can be achieved through Python code, such as: using the numpy.percentile() function to calculate the quantiles of the transmission deviation value data, such as the 25%, 50%, and 75% quantiles, and dividing the data into different intervals; the analysis of the format compatibility index corresponding to the deviation level interval can be achieved through a rule engine tool, such as: inputting the deviation level interval and data format-related information into the Drools rule engine, and calculating the corresponding format compatibility index according to the rules; the determination of the conversion priority corresponding to the format compatibility index can be achieved through a sorting algorithm, such as: algorithms such as bubble sort and quick sort; the formulation of the format conversion strategy corresponding to the data transmitted in the in-vehicle device can be achieved through a machine learning algorithm, such as: learning and analyzing a large amount of historical data, mining the potential relationship between the deviation level interval, the format compatibility index, the conversion priority, and the format conversion strategy, so as to formulate the format conversion strategy.

[0099] Based on the format conversion strategy, the present invention conducts simulation integration testing on the transmitted data in the in-vehicle device to obtain simulation test records, which can identify potential conflicts and efficiency bottlenecks in data transmission in advance, ensure the compatibility and stability of the strategy during in-vehicle deployment, thereby improving the accuracy of data transmission and the robustness of the system.

[0100] Among them, the simulation test record refers to a collection of documents that systematically records key information in the whole process when performing in-vehicle device data transmission testing in a simulation integration environment. Its core contents include: 1) Test scenario parameters (such as business scenario characteristics such as vehicle speed and road type); 2) Real-time monitoring data (such as transmission characteristics such as protocol type, delay, and packet loss rate); 3) Deviation analysis results (such as the deviation degree and variance distribution between the actual value and the expected value of each interaction parameter); 4) Format conversion strategy execution log (such as triggered priority rules, adopted conversion algorithms, and resource consumption); 5) Abnormal event records (such as problem phenomena and reproduction conditions such as protocol conflicts and timeout errors). Optionally, the simulation integration testing of the transmitted data in the in-vehicle device can be implemented through simulation test tools, such as tools like CANoe and VT System.

[0101] Furthermore, the present invention can accurately locate weak links in data acquisition in the test process, such as problems like sensor signal loss or abnormal log records, by calculating the missing index corresponding to each record in the simulation test record, and can dynamically optimize the data acquisition strategy and storage mechanism by combining the missing distribution characteristics to ensure the full capture of multi-source heterogeneous data.

[0102] Among them, the missing index is used to measure the missing situation of each record in the simulation test record. It integrates information such as the total number of records, the status of each record, and the difference between the actual and theoretical missing values of different record types, reflecting the integrity degree of the entire simulation test record data. The higher the value, the more serious the data missing problem.

[0103] As an embodiment of the present invention, calculating the missing index corresponding to each record in the simulation test record includes:

[0104] Using the following formula to calculate the missing index corresponding to each record in the simulation test record:

[0105] ;

[0106] Among them, MI represents the missing index corresponding to each record in the simulation test record, represents the total number of records corresponding to the simulation test record, represents the quantity index corresponding to the simulation test record, represents the The exponential variable corresponding to a simulation test record, indicating the total number of types corresponding to the simulation test record, indicating the type index corresponding to the simulation test record, indicating the actual missing value corresponding to the th record type, indicating the theoretical missing value corresponding to the th record type, indicating the average missing ratio corresponding to all

[0107] In detail, the exponential variable refers to the variable set for the th simulation test record. If there is data missing in the th record,it takes the value of 1; if the record is complete and there is no data missing, then it takes the value of 0, which is used to identify the missing status of each record; the actual missing value refers to the quantity or degree of data missing for the th record type in the actual simulation test record. For example, in the sensor data record of a certain type, the number of data that are actually not collected is its actual missing value; the theoretical missing value refers to the quantity or degree of data that should be missing theoretically for the th record type based on the design, expectation or relevant standards of the simulation test. For example, according to the test plan, it is expected that a certain type of data will have a certain proportion of missing under specific conditions, and this expected missing quantity is the theoretical missing value; the average missing ratio is obtained by calculating the missing ratios of all record types and taking the average. It reflects the overall average level of data missing situations of various types in the simulation test records, and is used to measure the deviation degree of the actual missing ratio of different record types from the overall average situation in the formula.

[0108] Furthermore, based on the missing index, the present invention constructs a data guarantee system corresponding to the in-vehicle device, which can accurately locate data missing problems and risk points, such as the vulnerable links of key sensor data being missing, facilitating early prevention, and can specifically optimize data acquisition and storage strategies, such as adjusting the sampling frequency and increasing redundant backups, to improve data integrity.

[0109] Among them, the data guarantee system refers to a set of comprehensive mechanisms and strategies established to ensure the integrity, accuracy and availability of in-vehicle device data. It covers multiple links such as data acquisition, transmission, storage, and processing, including measures for preventing, detecting, repairing and emergency handling of data missing problems, to ensure the stable operation of the in-vehicle device system.

[0110] As an embodiment of the present invention, constructing the data guarantee system corresponding to the vehicle-mounted device based on the missing index includes: determining the missing time axis corresponding to the missing index; extracting the time missing nodes in the missing time axis; constructing the missing distribution matrix corresponding to the missing index based on the time missing nodes; querying the association path between different missing points in the missing distribution matrix; and constructing the data guarantee system corresponding to the vehicle-mounted device based on the association path.

[0111] Among them, the missing time axis refers to a time series that presents the data missing situation in the simulation test record in the dimension of time sequence. It divides the time span of the simulation test and clearly shows the data missing state at different time points; the time missing node refers to the specific time point or time period representing data missing on the missing time axis. These nodes are the key identifiers of data integrity problems; the missing distribution matrix refers to a matrix constructed with time missing nodes as rows and different types of records or data dimensions as columns. The elements in the matrix represent the data missing degree or state at the corresponding time node and data type, intuitively presenting the distribution of data missing in the time and type dimensions; the association path refers to the internal connection path existing between different missing points in the missing distribution matrix. It reflects the propagation or association relationship of data missing between different times and types. For example, whether the missing of certain types of data will trigger the missing of other types of data after a specific time point.

[0112] Further, the determination of the missing time axis corresponding to the missing index can be achieved through time series analysis methods. For example: time series analysis methods can be adopted to arrange the simulation test records in time sequence and determine the missing time axis according to the change of the missing index; the extraction of the time missing nodes in the missing time axis can be achieved through the threshold method. For example: set a suitable missing index threshold, and when the missing index exceeds this threshold, the corresponding time point is the time missing node; the construction of the missing distribution matrix corresponding to the missing index can be achieved through matrix construction methods. For example: taking the time missing nodes as rows and different types of records or data dimensions as columns, and counting the missing situations of each data type at each time node to construct the missing distribution matrix; the query of the association path between different missing points in the missing distribution matrix can be achieved through graph theory algorithms. For example: graph theory algorithms can be used to regard the missing points in the missing distribution matrix as nodes in the graph, calculate the association strength between nodes to construct a graph structure, and then use the shortest path algorithm (such as Dijkstra algorithm) to query the association path; the construction of the data guarantee system corresponding to the vehicle-mounted device can be achieved through system construction methods. For example: comprehensively considering the information obtained above, formulate strategies for data collection, transmission, storage and processing, establish monitoring and warning mechanisms, and construct the data guarantee system.

[0113] S4. Based on the data security system, analyze the data response effect of the in-vehicle device under different data traffic conditions. Based on the data response effect, query the data flow trend of the in-vehicle device during the data integration process, and identify the core change elements in the data flow trend.

[0114] Based on the data security system, the present invention analyzes the data response effect of the in-vehicle device under different data traffic conditions, can clearly insight into the performance of the device in a complex data environment, accurately locate the data transmission bottleneck and response delay points, and can accordingly optimize the data processing process, rationally allocate resources, and improve the stability and response speed of the device at high and low traffic.

[0115] Among them, the different data traffic conditions refer to the differentiated data transmission load scenarios faced by the in-vehicle device during operation, including: low traffic (when the vehicle is driving at a low speed or the network is weak, transmitting a small amount of basic data such as vehicle speed and fuel consumption), medium traffic (when conventional intelligent functions are running, transmitting periodic data such as map updates and audio streams), and high traffic (in the scenarios of advanced driver assistance or vehicle networking, real-time processing of sensor fusion data, high-definition video, and multi-device interaction data), reflecting the processing capacity requirements of the device under different business pressures; the data response effect refers to a quantitative evaluation system established by comparing the traffic distribution map and the device performance indicators (end-to-end delay, throughput, error rate). For example, if it is found in the high-traffic map that the ADAS sensor data delay exceeds the 100ms threshold, the redundant link switching mechanism of the data security system is triggered; if it is detected in the low-traffic map that the GPS data update frequency is lower than the designed value, the sleep-wakeup strategy is optimized to reduce power consumption.

[0116] As an embodiment of the present invention, the analysis of the data response effect of the in-vehicle device under different data traffic conditions based on the data security system includes: deploying traffic monitoring nodes in the data security system; collecting real-time traffic data in the traffic monitoring nodes; converting the real-time traffic data into feature data streams; generating a traffic distribution map corresponding to the feature data streams; and analyzing the data response effect of the in-vehicle device under different data traffic conditions based on the traffic distribution map.

[0117] Among them, the traffic monitoring node refers to an intelligent monitoring device deployed at key nodes of the in-vehicle data transmission link (such as gateways, sensor buses, communication modules), which integrates a high-precision flowmeter and a protocol parsing module. Its core function is to collect dynamic traffic characteristics such as real-time data transmission rate, protocol type ratio, packet size distribution, and QoS level in real time, support fine-grained traffic monitoring in multiple dimensions (time, device, data type), and provide the original data source for subsequent data analysis; the real-time traffic data refers to the original data stream information captured by the monitoring node at a microsecond-level frequency, including metadata such as timestamps, data frame IDs, protocol versions, payload lengths, and priority tags. These data directly reflect the current network load status (such as instantaneous bandwidth occupancy rate, burst traffic peak) and device interaction characteristics, and are the basic inputs for analyzing performance indicators such as data response latency and packet loss rate; the characteristic data stream refers to the dimensionality reduction processing of real-time data through a sliding window algorithm (such as a 500ms window) to extract key feature vectors such as average bandwidth utilization rate, traffic burst intensity, and protocol type entropy value; the traffic distribution map refers to a multi-dimensional visualization model constructed based on the characteristic data stream, using technologies such as heat maps and parallel coordinate plots to present the distribution laws under different traffic conditions. For example, the horizontal axis is the time dimension, the vertical axis is the traffic intensity level, and the color represents the protocol type ratio, which can intuitively identify the phenomenon of a specific protocol traffic surge during peak hours in the morning and evening, or abnormal traffic patterns (such as periodic CAN bus storms).

[0118] Furthermore, the deployment of traffic monitoring nodes in the data security system can be achieved through a deployment interface module. For example, a bus monitoring program is written using the LabVIEW FPGA module to capture the original data frames and mark them with accurate timestamps (with an accuracy of 1 μs), and finally generate traffic monitoring nodes with spatio-temporal correlation characteristics. The acquisition of real-time traffic data in the traffic monitoring nodes can be achieved through CANoe. For example, a multi-bus monitoring project including the ADAS domain, the power domain, and the infotainment domain is created in CANoe, the event trigger conditions are set (such as starting full-volume data recording when the vehicle speed > 120 km / h), and the original data frames are written into a CSV file through CAPL scripts, and finally an original traffic data set with time stamps is obtained. The conversion of the real-time traffic data into a feature data stream can be achieved through an autoencoder. For example, a 1000-dimensional original traffic feature vector is input, compressed to a 10-dimensional feature space through the structure of the autoencoder, the real-time statistics are calculated using the T² control chart, and an anomaly flag is triggered when the control limit is exceeded, and finally a feature data stream including normal / anomaly labels is generated. The generation of the traffic distribution map corresponding to the feature data stream can be achieved through a window aggregation method. For example, the feature data stream is aggregated according to time windows to generate a three-dimensional bar chart (X-axis: time, Y-axis: traffic intensity, Z-axis: protocol type), and the packet loss rate is represented by color gradient, and finally a scalable and interactive real-time traffic distribution map is formed. The analysis of the data response effect of in-vehicle devices under different data traffic conditions can be achieved through machine learning analysis methods. For example, machine learning algorithms (such as decision trees, neural networks, support vector machines, etc.) are used to train these samples to build a prediction model, and then new traffic conditions are input into the model to predict the data response effect of in-vehicle devices.

[0119] Based on the data response effect, the present invention queries the data flow trend of in-vehicle devices during the data integration process, can predict the data surge risk in advance and allocate resources in advance, and by combining historical trend rules, can guide the upgrade of the system architecture, make the data link design more suitable for the data explosion requirements of future intelligent driving, and finally realize the visual management and control and adaptive optimization of the whole-link data flow.

[0120] Among them, the data flow trend refers to the dynamic evolution law of the transmission path and traffic volume change of data among different modules and nodes during the data integration process of in-vehicle devices. Its core includes: 1) the spatio-temporal distribution characteristics of the data flow path, such as the high-frequency transmission path of ADAS sensor data to the central computing unit; 2) the time-series change pattern of multi-dimensional traffic characteristics, such as the periodic surge of vehicle networking data volume during morning and evening rush hours; 3) the evolution trajectory of abnormal flow patterns, such as the abnormal phenomenon that the data flow direction of a certain type of sensor suddenly deviates from the historical average. Optionally, querying the data flow trend of the in-vehicle device during the data integration process can be realized through graph algorithm analysis methods, such as PageRank, breadth-first search, etc., to analyze the data, find out the data flow law and important nodes, so as to determine the data flow trend.

[0121] By identifying the core change factors in the data flow trend, the present invention can early warn of potential faults and formulate emergency strategies; at the same time, combined with business scenario analysis, it can explore the correlation between data flow and functional modules, guide the iteration of the system architecture to adapt to the explosive growth demand of intelligent driving data volume, and finally realize the visual control and adaptive optimization of the whole-link data transfer.

[0122] Among them, the core change factors refer to the key characteristic variables with business significance extracted from multiple rounds of records, such as the driving scenarios that cause traffic surges (such as automatically turning on ADAS on rainy days), the protocol types that cause transmission delays (such as HTTP traffic during OTA upgrades), the node combinations associated with faults (such as sensors A and gateway B being abnormal at the same time), etc.

[0123] As an embodiment of the present invention, identifying the core change factors in the data flow trend includes: setting the initial analysis parameters corresponding to the data flow based on the data flow trend; performing the first-round trend analysis on the data flow based on the initial analysis parameters to obtain the first-round trend characteristics; optimizing and adjusting the analysis environment where the data flow is located based on the first-round trend characteristics to obtain the trend optimization environment; performing multiple rounds of trend analysis on the data flow based on the trend optimization environment to obtain multiple rounds of trend records; and identifying the core change factors in the multiple rounds of trend records.

[0124] Among them, the initial analysis parameters refer to the set of basic conditions preset when conducting data flow trend analysis, including basic configuration items such as time window length (e.g., 5 minutes / hour level), data type filtering rules (e.g., only focusing on ADAS sensor data), traffic threshold (e.g., defining high traffic as >10Mbps), analysis dimension (time / device / protocol), etc.; the first-round trend features refer to the representative statistical features extracted through the preliminary analysis of the data flow set by the initial parameters. For example, the traffic fluctuation period obtained based on time series analysis (e.g., a peak appears every 15 minutes), the burst traffic points identified by the anomaly detection algorithm (e.g., the traffic suddenly increases by 300% at a certain moment), the protocol type distribution feature (e.g., the CAN bus accounts for 80%), etc.; the trend optimization environment refers to the iterative environment formed by dynamically adjusting the analysis parameters according to the obtained features after the first-round analysis. For example, if it is found that the traffic anomaly in a certain period is related to a specific sensor failure, the parameters are optimized to increase the sampling frequency of the sensor data; or the optimal time window length is automatically searched through the genetic algorithm; the multi-round trend records refer to the set of time-series features generated through multi-round iterative analysis, including the trend feature matrix under different parameter combinations. For example, in the first round, a 5-minute window is used to find periodic fluctuations, and in the second round, after adjusting to a 3-minute window, a finer-grained traffic pulse phenomenon is identified. The multi-round records constitute a three-dimensional data cube of time-parameter-feature, providing a data basis for mining deep change rules.

[0125] Furthermore, setting the initial analysis parameters corresponding to the data flow can be achieved through the empirical rule method. For example, according to the experience of previous in-vehicle device data flow analysis, parameters such as time window length, data type range, and traffic threshold are determined. It can also be assisted by a configuration management tool. For example, Ansible can centrally manage and automate the configuration of parameters to finally obtain the initial analysis parameters; conducting the first-round trend analysis on the data flow can be achieved through statistical analysis methods. For example, using the Pandas library in Python for data processing and analysis, combined with the Matplotlib library for visualization, can more intuitively present the trend and finally obtain the first-round trend features; optimizing and adjusting the analysis environment where the data flow is located can be achieved through adaptive algorithms. For example, according to the results of the first-round trend analysis, the genetic algorithm is used to automatically adjust the analysis parameters to achieve a better analysis effect and finally obtain the trend optimization environment; conducting multi-round trend analysis on the data flow can be achieved through iterative analysis methods. For example, after each round of analysis, the parameters are adjusted according to the results of the previous round and analyzed again to gradually deeply explore the data flow trend and finally obtain multi-round trend records; identifying the core change elements in the multi-round trend records can be achieved through feature selection algorithms. For example, algorithms such as chi-square test and information gain are used to screen out the features that have a greater impact on the data flow trend change to finally obtain the core change elements.

[0126] S5. Based on the core change factors, test-connect a preset intelligent integration module with the vehicle-mounted device to obtain a measurement integration unit, collect the integrated operation nodes during the operation of the measurement integration unit, and generate a data integration report corresponding to the vehicle-mounted device based on the integrated operation nodes.

[0127] Based on the core change factors, the present invention test-connects a preset intelligent integration module with the vehicle-mounted device to obtain a measurement integration unit, which can accurately adapt the two according to the key features of the data flow, ensure that the integration module meets the actual needs of the vehicle-mounted device, can discover potential compatibility problems in advance, optimize and adjust in time, and reduce the risk of subsequent large-scale applications.

[0128] Among them, the preset intelligent integration module refers to a modular component that is pre-designed and integrated with functions such as data processing, protocol conversion, and traffic management. Its core role is to achieve a plug-and-play connection with the vehicle-mounted device through a standardized interface. For example, it can integrate a CAN / LIN bus protocol parsing module, an edge computing unit, and an AI inference engine to realize the real-time fusion processing of multi-source heterogeneous data and provide standardized input for the measurement integration unit; the measurement integration unit refers to a closed-loop test system formed after physically connecting the intelligent integration module with the vehicle-mounted device, including hardware interfaces (such as vehicle-mounted Ethernet switches), data acquisition cards (such as NI 9863), and analysis software (such as MATLAB / Simulink). Its core function is to simulate the data flow under real working conditions and verify the improvement effects of the intelligent integration module on key indicators such as the data processing efficiency and response delay of the vehicle-mounted device. Optionally, the test-connection of the preset intelligent integration module with the vehicle-mounted device can be achieved through a hardware connection method. For example: a Kvaser CAN-Ethernet gateway can be used for connection. First, use a wire stripper to strip the outer skin of the CAN bus cable, then use a crimping tool to crimp the cable to the CAN interface of the gateway, and then use a network cable to connect the Ethernet interface of the gateway with the intelligent integration module to complete the hardware connection.

[0129] Furthermore, by collecting the integrated operation nodes during the operation of the measurement integration unit, the present invention can quantitatively evaluate the optimization effects of the intelligent module on indicators such as data transmission efficiency and delay; the accumulated operation node feature library can also provide real-scene training samples for algorithm iteration, improve the robustness of subsequent system integration, and provide data support for the continuous evolution of the vehicle-mounted intelligent system.

[0130] Among them, the integrated operation node refers to a key position, moment, or data point with specific functions or marks during the operation of the measurement integration unit. It can be the interface position where the intelligent integration module interacts with in-vehicle devices, a key calculation step in the data processing flow, or a specific moment when the system state changes, such as the instant when data starts to be transmitted, completed transmission, or protocol conversion occurs. It is a key identification point reflecting the operation state and performance of the measurement integration unit. Optionally, collecting the integrated operation nodes during the operation of the measurement integration unit can be achieved through the event trigger method. For example, a series of event trigger conditions are defined. When these conditions are met during the operation of the measurement integration unit, the corresponding event recording operation is triggered, and the relevant information about the event occurrence is recorded. The points corresponding to this information are the integrated operation nodes.

[0131] Furthermore, based on the integrated operation nodes, the present invention generates a data integration report corresponding to the in-vehicle device, which can verify the optimization effect of the intelligent integration module on indicators such as data transmission efficiency and latency, form a closed-loop verification system, and can explore potential system coordination mechanisms, providing decision-making support for the design and real-time guarantee of the vehicle networking architecture.

[0132] Among them, the data integration report refers to a structured analysis document generated by systematically analyzing the data of the integrated operation nodes collected during the operation of the measurement integration unit. Its core includes: 1) a time-series graph of operation nodes, showing the flow trajectory of data between modules and the association with timestamps; 2) a statistical matrix of performance indicators, quantifying key indicators such as latency, throughput, and packet loss rate; 3) an abnormal diagnosis log, marking the nodes and trigger conditions that deviate from the threshold; 4) a list of optimization suggestions, a system improvement plan proposed based on multi-dimensional analysis. Optionally, generating the data integration report corresponding to the in-vehicle device can be achieved through a report generation tool. For example, using a Web framework to integrate real-time traffic monitoring, highlighting abnormal nodes, and historical data comparison functions, and finally generating a deployable data integration report.

[0133] Compared with the problems described in the background technology, the present invention can optimize the targeted design of the data integration framework by querying the application business scenarios corresponding to the vehicle-mounted equipment, effectively reduce the compatibility risks in actual deployment, and provide a scenario-based basis for dynamically adjusting the data format conversion strategy, ultimately improving the real-time, reliability and scalability of the system in complex business scenarios. The present invention can timely discover potential problems such as protocol conflicts and bandwidth bottlenecks by monitoring the real-time data transmission in the simulated integration environment, and provide a quantitative basis for optimizing data routing strategies. At the same time, combined with the transmission deviation analysis, it can quickly verify the adaptability of the data integration framework in different scenarios. Furthermore, the present invention formulates the format conversion strategy corresponding to the transmission data in the vehicle-mounted equipment based on the transmission deviation value, which can accurately locate data transmission problems, optimize the format in a targeted manner, and can adapt to the needs of different business scenarios. , enhance the compatibility of vehicle-mounted equipment, ensure the accurate flow of data between various systems, and provide reliable data support for applications such as intelligent driving. Furthermore, based on the data security system, the present invention analyzes the data response effect of the vehicle-mounted equipment under different data flow conditions, can clearly understand the performance of the equipment in a complex data environment, accurately locate the data transmission bottleneck and response delay point, and can optimize the data processing process accordingly, reasonably allocate resources, and improve the stability and response speed of the equipment at high and low flow rates. Finally, based on the core change elements, the present invention tests and connects the preset intelligent integration module with the vehicle-mounted equipment to obtain a measurement integration unit, which can accurately adapt the two according to the key characteristics of the data flow direction, ensure that the integration module fits the actual needs of the vehicle-mounted equipment, and can discover potential compatibility problems in advance, optimize and adjust in time, and reduce the risk of subsequent large-scale applications. Therefore, a data integration method and system for realizing vehicle-mounted equipment provided by an embodiment of the present invention can improve the processing efficiency of vehicle-mounted equipment data.

[0134] Embodiment 2:

[0135] like Figure 2 The figure shows a functional module diagram of a data integration system for vehicle-mounted equipment according to the present invention.

[0136] The data integration system 200 for implementing vehicle-mounted equipment described in the present invention can be installed in an electronic device. According to the functions implemented, the data integration system for implementing vehicle-mounted equipment can include an environment construction module 201, a deviation value calculation module 202, a system construction module 203, an element identification module 204 and a report generation module 205. The module described in the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0137] In the embodiment of the present invention, the functions of each module / unit are as follows:

[0138] The environment construction module 201 is configured to query the application service scenarios corresponding to the in-vehicle device, set the data integration framework adapted to the in-vehicle device based on the application service scenarios, and construct a simulated integration environment corresponding to the in-vehicle device based on the data integration framework;

[0139] The deviation value calculation module 202 is configured to monitor the real-time data transmission situation in the simulated integration environment, extract detailed interaction parameters in the real-time data transmission situation, and calculate the transmission deviation values of the in-vehicle device under different service scenarios based on the detailed interaction parameters;

[0140] The system construction module 203 is configured to formulate a format conversion strategy for the transmitted data in the in-vehicle device based on the transmission deviation value, perform simulated integration testing on the transmitted data in the in-vehicle device based on the format conversion strategy to obtain a simulated test record, calculate the missing indexes corresponding to each record in the simulated test record, and construct a data guarantee system corresponding to the in-vehicle device based on the missing indexes;

[0141] The element identification module 204 is configured to analyze the data response effect of the in-vehicle device under different data traffic conditions based on the data guarantee system, query the data flow trend of the in-vehicle device during the data integration process based on the data response effect, and identify the core change elements in the data flow trend;

[0142] The report generation module 205 is configured to test-connect a preset intelligent integration module with the in-vehicle device based on the core change elements to obtain a measurement integration unit, collect the integration operation nodes of the measurement integration unit during operation, and generate a data integration report corresponding to the in-vehicle device based on the integration operation nodes.

[0143] Specifically, each module in the data integration system 200 for implementing the in-vehicle device in the embodiments of the present invention uses the same technical means as those in the Figure 1 a data integration method for implementing an in-vehicle device described above, and can produce the same technical effects, which will not be elaborated here.

[0144] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms.

[0145] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A data integration method for implementing in-vehicle devices, characterized in that, The method includes: Query the application business scenarios corresponding to the in-vehicle device, based on the application business scenarios, set the data integration framework adapted to the in-vehicle device, and based on the data integration framework, construct the simulation integration environment corresponding to the in-vehicle device; Monitor the real-time data transmission situation in the simulation integration environment, extract the detailed interaction parameters in the real-time data transmission situation, and based on the detailed interaction parameters, calculate the transmission deviation values of the in-vehicle device under different business scenarios; Based on the transmission deviation values, formulate the format conversion strategy for the transmitted data in the in-vehicle device, based on the format conversion strategy, conduct simulation integration testing on the transmitted data in the in-vehicle device to obtain simulation test records, calculate the missing indices corresponding to each record in the simulation test records, and based on the missing indices, construct the data guarantee system corresponding to the in-vehicle device; Based on the data guarantee system, analyze the data response effects of the in-vehicle device under different data traffic conditions, based on the data response effects, query the data flow trend of the in-vehicle device during the data integration process, and identify the core change factors in the data flow trend; Based on the core change factors, test-connect the preset intelligent integration module with the in-vehicle device to obtain a measurement integration unit, collect the integration operation nodes during the operation of the measurement integration unit, and based on the integration operation nodes, generate the data integration report corresponding to the in-vehicle device.

2. The data integration method for an in-vehicle device according to claim 1, characterized in that, The constructing the simulation integration environment corresponding to the in-vehicle device based on the data integration framework includes: Query the framework composition parameters corresponding to the data integration framework; Based on the framework composition parameters, determine the framework construction mode corresponding to the data integration framework; Based on the framework construction mode, analyze the data interaction requirements corresponding to the in-vehicle device; According to the data interaction requirements, formulate the environment simulation process corresponding to the in-vehicle device; Based on the environment simulation process, construct the simulation integration environment corresponding to the in-vehicle device.

3. A method for implementing data integration of in-vehicle devices according to claim 1, characterized in that, The monitoring the real-time data transmission situation in the simulation integration environment includes: Query the transmission protocol type of the data in the simulation integration environment; Based on the transmission protocol type, determine the data monitoring mode corresponding to the simulation integration environment; Based on the data monitoring mode, analyze the data transmission characteristics of the data in the simulation integration environment; According to the data transmission characteristics, monitor the real-time data transmission situation in the simulation integration environment.

4. The data integration method for an in-vehicle device according to claim 1, wherein The calculating the transmission deviation values of the in-vehicle device under different business scenarios based on the detailed interaction parameters includes: Calculate the transmission deviation values of the in-vehicle device under different business scenarios using the following formula: ； Among them, represents the transmission deviation value of the in-vehicle device under different service scenarios, represents the number of scenarios corresponding to the service scenario, represents the number index of the service scenario, represents the number of parameters corresponding to the detailed interaction parameter, represents the number index of the detailed interaction parameter, represents at the th service scenario, the th actual measured value corresponding to the detailed interaction parameter, represents at the th service scenario, the th expected value corresponding to the detailed interaction parameter, represents at the th service scenario, the th variance value corresponding to the detailed interaction parameter, represents the scenario weight corresponding to the th service scenario.

5. A data integration method for an in-vehicle device according to claim 1, characterized in that, The formulating the format conversion strategy for the transmitted data in the in-vehicle device based on the transmission deviation values includes: Extract the deviation distribution characteristics corresponding to the transmission deviation values; Based on the deviation distribution characteristics, divide the deviation level intervals corresponding to the transmission deviation values; Analyze the format compatibility indices corresponding to the deviation level intervals; Determine the conversion priorities corresponding to the format compatibility indices; Based on the conversion priority, formulate a format conversion strategy for the transmitted data in the in-vehicle device.

6. The data integration method for an in-vehicle device according to claim 1, characterized in that, Calculating the missing index corresponding to each record in the simulation test record includes: Calculating the missing index corresponding to each record in the simulation test record using the following formula: ； Wherein, MI represents the missing index corresponding to each record in the simulation test record, represents the total number of records corresponding to the simulation test record, represents the quantity index corresponding to the simulation test record, represents the index variable corresponding to the th simulation test record, represents the total number of types corresponding to the simulation test record, represents the type index corresponding to the simulation test record, represents the actual missing value corresponding to the th record type, represents the theoretical missing value corresponding to the th record type, represents the average missing ratio corresponding to all types of record types.

7. A method for implementing data integration of in-vehicle devices according to claim 1, characterized in that, Based on the missing index, constructing a data security system for the in-vehicle device includes: Determine the missing time axis corresponding to the missing index; Extract the time missing nodes in the missing time axis; Based on the time missing nodes, construct a missing distribution matrix corresponding to the missing index; Query the association path between different missing points in the missing distribution matrix; Based on the association path, construct a data security system for the in-vehicle device.

8. A data integration method for an in-vehicle device according to claim 1, characterized in that, Based on the data security system, analyzing the data response effect of the in-vehicle device under different data traffic conditions includes: Deploy traffic monitoring nodes in the data security system; Collect real-time traffic data in the traffic monitoring nodes; Convert the real-time traffic data into a feature data stream; Generate a traffic distribution map corresponding to the feature data stream; Based on the traffic distribution map, analyze the data response effect of the in-vehicle device under different data traffic conditions.

9. A method for implementing data integration of in-vehicle devices according to claim 1, characterized in that, Identifying the core change elements in the data flow trend includes: Based on the data flow trend, set the initial analysis parameters corresponding to the data flow; Based on the initial analysis parameters, conduct a first-round trend analysis on the data flow to obtain first-round trend features; Based on the first-round trend features, optimize and adjust the analysis environment where the data flow is located to obtain a trend optimization environment; Based on the trend optimization environment, conduct multiple rounds of trend analysis on the data flow to obtain multiple rounds of trend records; Identify the core change elements in the multiple rounds of trend records.

10. A data integration system for implementing in-vehicle devices, characterized in that, The system includes: An environment construction module for querying the application business scenarios corresponding to the in-vehicle device, setting a data integration framework adapted to the in-vehicle device based on the application business scenarios, and constructing a simulation integration environment corresponding to the in-vehicle device based on the data integration framework; A deviation value calculation module for monitoring the real-time data transmission situation in the simulation integration environment, extracting detailed interaction parameters in the real-time data transmission situation, and calculating the transmission deviation value of the in-vehicle device in different business scenarios based on the detailed interaction parameters; A system construction module for formulating a format conversion strategy for the transmitted data in the in-vehicle device based on the transmission deviation value, conducting a simulation integration test on the transmitted data in the in-vehicle device based on the format conversion strategy to obtain a simulation test record, calculating the missing index corresponding to each record in the simulation test record, and constructing a data security system corresponding to the in-vehicle device based on the missing index; An element identification module for analyzing the data response effect of the in-vehicle device under different data traffic conditions based on the data security system, querying the data flow trend of the in-vehicle device during the data integration process based on the data response effect, and identifying the core change elements in the data flow trend; A report generation module, configured to test-connect a preset intelligent integration module with the vehicle-mounted device based on the core change factors, obtain a measurement integration unit, collect the integrated operation nodes during the operation of the measurement integration unit, and generate a data integration report corresponding to the vehicle-mounted device based on the integrated operation nodes.

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