Domestic time sequence database docking integration method of Internet of Vehicles industry

Abstract

The invention discloses a domestic time sequence database docking integration method for the Internet of Vehicles industry, and the method comprises the steps: S1, evaluating a domestic database, and selecting a domestic time sequence database suitable for the Internet of Vehicles industry; s2, designing a data docking strategy, obtaining vehicle data and loading the vehicle data into a time sequence database; s3, constructing a data integration platform, wherein the Internet of Vehicles platform is seamlessly connected with the domestic time sequence database; s4, analyzing the data and constructing a visual interface for real-time viewing; and S5, integrating a block chain technology, deploying an edge computing node, and realizing local rapid processing of the data. According to the scheme, through integration of the domestic time sequence database and the Internet of Vehicles platform, efficient storage, rapid query and intelligent analysis of data are realized, and the high requirement of the Internet of Vehicles industry for data processing is met.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of database processing, in particular to a domestic time series database interfacing and integrating method for the Internet of Vehicles industry. BACKGROUND

[0002] The Internet of Vehicles industry has a large amount of data and various data types, including vehicle status, driving trajectory, environmental monitoring and other time series data, which need to be stored and analyzed by efficient and stable database systems. Existing database systems cannot meet the high requirements of the Internet of Vehicles industry for time series data processing. SUMMARY

[0003] In view of the above technical problems, the present application provides a domestic time series database interfacing and integrating method for the Internet of Vehicles industry.

[0004] The present application is implemented by the following technical scheme: a domestic time series database interfacing and integrating method for the Internet of Vehicles industry, comprising the following steps: Step S1: evaluate the domestic database and select a domestic time series database suitable for the Internet of Vehicles industry; Step S2: design a data interfacing strategy, obtain vehicle data and load it into the time series database; Step S3: build a data integration platform, seamlessly interface the Internet of Vehicles platform with the domestic time series database; Step S4: analyze the data and build a visual interface for real-time viewing; Step S5: integrate blockchain technology, deploy edge computing nodes and realize local fast processing of data.

[0005] Specifically, the database evaluation of step S1 specifically includes: Evaluate the read / write speed and query response time key performance indicators of the database; Investigate the fault recovery capability and data consistency guarantee of the database; Analyze the horizontal and vertical expansion capability of the database.

[0006] Specifically, the data interfacing of step S2 specifically includes: Step S21: design an efficient data collection mechanism to obtain vehicle data in real time; Step S22: develop a data cleaning tool to remove invalid and redundant data; Step S23: data format conversion to ensure smooth data import into the target database; Step S24: build a data loading process to load the cleaned and converted data into the time series database.

[0007] Specifically, the seamless interfacing of the Internet of Vehicles platform with the domestic time series database of step S3 specifically includes: The integrated data extraction, conversion and loading function realizes the automatic processing of data; Ensure real-time synchronization of data between the Internet of Vehicles platform and the database.

[0008] Specifically, the docking integration method further comprises: using encryption technology to ensure the security during data transmission; and implementing strict access control policies to protect user privacy data from being leaked.

[0009] The beneficial effects of the present application are: the present application constructs an efficient, safe and intelligent unified management and operation system. The scheme aims to comprehensively solve the problems of dispersion, security monitoring blind area, complicated operation process and lack of intelligent application in current Internet of Vehicles data management. Through the integration of domestic time sequence database and Internet of Vehicles platform, efficient storage, rapid query and intelligent analysis of data are realized to meet the high requirements of the Internet of Vehicles industry on data processing. BRIEF DESCRIPTION OF DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced as follows. Obviously, the drawings in the following description only show some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of the drawings shown.

[0011] Figure 1 The docking integration method flowchart in the embodiments of the present application. DETAILED DESCRIPTION

[0012] In order to make the purpose, technical scheme and advantages of the embodiments of the present application more clear, the technical scheme in the embodiments of the present application will be clearly and completely described below in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are some of the embodiments of the present application, not all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations.

[0013] It should be noted that: similar reference numbers and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0014] The technical scheme of the present application will be described below in combination with the drawings. Figure 1 Some embodiments of the present application will be described in detail. The following embodiments and features in the embodiments can be combined with each other without conflict.

[0015] The application provides a domestic time series database docking integration method for the Internet of Vehicles industry. In a preferred embodiment, the docking integration method comprises the following steps: Step S1: evaluating the domestic database and selecting a domestic time series database suitable for the Internet of Vehicles industry; Step S2: designing a data docking strategy, obtaining vehicle data and loading it into the time series database; Step S3: building a data integration platform, seamlessly docking the Internet of Vehicles platform with the domestic time series database; Step S4: analyzing the data and building a visual interface for real-time viewing; Step S5: integrating blockchain technology, deploying edge computing nodes, and realizing local rapid processing of data.

[0016] In this embodiment, the database evaluation in step S1 specifically comprises: Evaluating the read-write speed and query response time key performance indicators of the database; Investigating the fault recovery capability and data consistency guarantee of the database; Analyzing the horizontal and vertical expansion capability of the database.

[0017] In this embodiment, the data docking in step S2 specifically comprises: Step S21: designing an efficient data collection mechanism to obtain vehicle data in real time; Step S22: developing a data cleaning tool to remove invalid data and redundant data; Step S23: data format conversion to ensure smooth data import into the target database; Step S24: building a data loading process to load the cleaned and converted data into the time series database.

[0018] In this embodiment, the seamless docking of the Internet of Vehicles platform with the domestic time series database in step S3 specifically comprises: Integrating data extraction, transformation and loading functions to realize automatic data processing; Ensuring real-time synchronization of data between the Internet of Vehicles platform and the database.

[0019] In this embodiment, the docking integration method further comprises: using encryption technology to ensure the security of data transmission; implementing strict access control strategies to protect user privacy data from being leaked.

[0020] In another embodiment, the present solution focuses on the innovative application of domestic time series databases in the Internet of Vehicles industry, with the core being the construction of an efficient, secure, and intelligent unified management and control system. This solution aims to comprehensively address the core challenges of current Internet of Vehicles data management, such as decentralization, security monitoring blind spots, complex operation processes, and lack of intelligent applications. The following is a detailed description of this technical solution: I. Selection and evaluation of domestic time series databases Objective: Select a suitable domestic time series database for the Internet of Vehicles industry.

[0021] Evaluation criteria: Performance: Evaluate key performance indicators such as read-write speed, query response time, etc.

[0022] Stability: Investigate the database's fault recovery capability, data consistency guarantee, etc.

[0023] Scalability: Analyze the database's horizontal and vertical scalability, including node expansion testing (gradually increasing the number of nodes to monitor performance changes), data sharding testing (verifying automatic sharding capabilities under different data set sizes), and network overhead evaluation (measuring inter-node communication delays). Vertical scalability analysis covers resource upgrade testing (upgrading single-node configurations to identify bottleneck points) and storage optimization evaluation (the impact of different media and compression algorithms). The complete analysis process should include demand analysis, test environment setup, benchmark testing execution (such as TSBS and Internet of Vehicles scenario simulation), extreme stress testing, and result analysis report, with special attention to geographical distributed deployment, elastic scaling mechanism, and mixed load processing specific to the Internet of Vehicles, to comprehensively evaluate the database's horizontal and vertical scalability, providing scientific basis for technology selection and ensuring the ability to meet future business growth demands.

[0024] II. Data connection strategy Steps: Data collection: Design an efficient data collection mechanism to acquire real-time vehicle data (such as collecting engine speed, vehicle speed, etc. through the OBD interface, and obtaining vehicle status information through the CAN bus, with a sampling frequency of 100ms / second).

[0025] Data cleaning: Develop data cleaning tools to remove invalid and redundant data, improving data quality.

[0026] Data conversion: Implement data format conversion, convert raw JSON data into Line Protocol format supported by time series databases, convert timestamps to ISO 8601 standard format, force type conversion of field values ​​(e.g., string to floating-point number), add necessary tag fields (e.g., vehicle_id, region, etc.) to ensure that data can be successfully imported into the target database.

[0027] Data loading: Construct a data loading process to load the cleaned and transformed data into the time series database.

[0028] III. Construction of Data Integration Platform Objective: To achieve seamless integration between the vehicle networking platform and domestically produced time-series databases.

[0029] Function: ETL process: Integrates data extraction, transformation, and loading functions to achieve automated data processing.

[0030] Data Synchronization: Ensure real-time data synchronization between the vehicle networking platform and the database. Change DataCapture (CDC) technology is used to capture data changes, a message queue (such as Kafka) is used as a data pipeline, a dual-write verification mechanism is set up to ensure data consistency, and a breakpoint resume function is implemented to handle network anomalies.

[0031] IV. Data Analysis and Visualization Development content: Data analysis tools: Develop tools that support complex data analysis needs, and provide a wealth of analysis algorithms and models.

[0032] Visual interface: Build an intuitive and easy-to-use visual interface that supports real-time data analysis and historical data query.

[0033] V. Security and Privacy Protection measure: Data transmission security: Encryption technology is used to ensure the security of data transmission.

[0034] Data storage security: Implement strict access control policies, set role permissions based on the RBAC model (such as administrator, analyst, and ordinary user), implement field-level data anonymization (such as partially hiding license plate numbers), set up a data access whitelist mechanism, and conduct full auditing and tracing of operation logs to protect user privacy data from being leaked.

[0035] VI. Innovative Technology Applications Blockchain technology: Integrating blockchain technology ensures the immutability and transparency of data, thereby enhancing data credibility.

[0036] Machine learning algorithms: Applying machine learning algorithms to predict and analyze vehicle behavior patterns to improve driving safety.

[0037] Edge computing technology: Deploying edge computing nodes enables rapid local data processing, reducing latency and improving response speed. For example, real-time traffic flow statistics can be implemented in roadside units (RSUs), vehicle terminals can locally recognize emergency braking behavior, regional servers can preprocess vehicle trajectory compression, and edge nodes can cache frequently queried data.

[0038] In this embodiment, the implementation process is as follows: Database performance evaluation: assessing database performance and selecting the best database product.

[0039] Data acquisition module design: Design a data acquisition module to realize the real-time acquisition and transmission of vehicle data.

[0040] Data cleaning and transformation tool development: Data cleaning and transformation tools to ensure that data quality meets analytical requirements.

[0041] Data integration platform construction: Build a data integration platform to realize the ETL process of data and ensure that data can be successfully imported into the time series database.

[0042] Data analysis tool development: Develop data analysis and visualization tools to support complex data analysis needs and provide intuitive visualization interfaces.

[0043] Data security and privacy protection mechanisms are implemented to ensure the security of data transmission and storage.

[0044] Blockchain technology integration: Integrating blockchain technology ensures data integrity and security, and enhances data credibility.

[0045] Application of machine learning algorithms: Applying machine learning algorithms to predict and analyze vehicle behavior patterns to improve driving safety.

[0046] Edge computing node deployment: Deploy edge computing nodes to enable rapid local data processing, reduce latency, and improve system response speed.

[0047] For the foregoing embodiments, in order to simplify the description, they are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to this application, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions involved are not necessarily essential to this application.

[0048] The above embodiments describe the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Modifications and variations made by those skilled in the art without departing from the spirit and scope of the invention should be within the protection scope of the appended claims.

Claims

1. A method for integrating domestically produced time-series databases in the Internet of Vehicles (IoV) industry, characterized in that: Includes the following steps: Step S1: Evaluate domestic databases and select a suitable domestic time-series database for the Internet of Vehicles industry; Step S2: Design a data integration strategy, acquire vehicle data, and load it into the time-series database; Step S3: Build a data integration platform, seamlessly connecting the vehicle networking platform with the domestic time-series database; Step S4: Analyze the data and build a visualization interface for real-time viewing; Step S5: Integrate blockchain technology and deploy edge computing nodes to achieve rapid local data processing.

2. The method for integrating domestic time-series databases in the vehicle networking industry as described in claim 1, characterized in that, The database evaluation in step S1 specifically includes: Evaluate key performance indicators of database read / write speed and query response time; Examine the database's fault recovery capabilities and data consistency assurance; Analyze the horizontal and vertical scalability of the database.

3. The method for integrating domestic time-series databases in the vehicle networking industry as described in claim 1, characterized in that, The data docking step S2 specifically includes: Step S21: Design an efficient data acquisition mechanism to acquire vehicle data in real time; Step S22: Develop data cleaning tools to remove invalid and redundant data; Step S23: Data format conversion to ensure that the data can be successfully imported into the target database; Step S24: Construct a data loading process to load the cleaned and transformed data into the time series database.

4. The method for integrating domestic time-series databases in the vehicle networking industry as described in claim 1, characterized in that, The step S3, seamless integration of the vehicle networking platform with the domestic time-series database, specifically includes: It integrates data extraction, transformation, and loading functions to achieve automated data processing; Ensure real-time data synchronization between the vehicle networking platform and the database.

5. The method for integrating domestic time-series databases in the vehicle networking industry as described in claim 1, characterized in that, The integration method also includes: employing encryption technology to ensure the security of data transmission; and implementing strict access control policies to protect user privacy data from being leaked.

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