Method and device for constructing data warehouse for intelligent network connection automobile scene

Abstract

The embodiment of the invention discloses a data warehouse construction method and device for an intelligent network connection automobile scene. The method comprises the steps of obtaining to-be-stored data; designing a uniform data receiving format, converting each piece of spatio-temporal data in the spatio-temporal data set into data meeting the data receiving format one by one, and pushing the data to a message queue KAKFA; a plurality of calculation tasks are designed on the basis of an Flink calculation framework, the calculation tasks at least comprise binding of a target intelligent network connection vehicle to a lane and a road section, lane index calculation and lane early warning on the basis of data in a message queue KAKFA, and calculation processes of different calculation tasks in the calculation tasks have a dependency relationship; subscribing data in a message queue KAKFA so as to process the plurality of calculation tasks to obtain a plurality of processing result data; and performing hierarchical storage on the data which is converted to meet the data receiving format and a plurality of processing result data in a TimescaleDB time sequence database.

Description

Technical Field

[0001] The present application relates to the field of data warehouse technology, and in particular to a method and device for constructing a data warehouse for intelligent connected vehicle scenarios. Background Art

[0002] A large amount of multi-source heterogeneous data will be generated in the use scenarios of intelligent connected vehicles, such as V2X information, vehicle information, perception information of roadside equipment, etc. Most of these multi-source heterogeneous data contain latitude and longitude information, and all carry the time when the data is generated. In the existing solutions for processing and storing these data, most of them use big data technology systems to process massive multi-source heterogeneous data. For example, in one existing technology, the original collected data is processed by data warehouse technology (ETL) and loaded into the Hadoop platform, and the processed data is stored in the distributed database and the main database through Hadoop. However, the system of this solution has the problems of high complexity and high maintenance cost, and does not process and store the data characteristics generated by the intelligent connected vehicle scenario. The intelligent connected vehicle scenario will generate a large amount of spatiotemporal data, which has time and space dimensional information and needs to be processed and stored according to its characteristics. The traditional big data technology system is not efficient in processing time data, nor does it support the processing and storage of spatial data.

[0003] Therefore, it is necessary to provide a solution that can effectively store the large amount of spatiotemporal data generated by intelligent connected vehicle scenarios. Summary of the invention

[0004] The embodiments of this specification provide a method and device for constructing a data warehouse for an intelligent connected vehicle scenario, so as to provide a highly efficient solution capable of storing the massive spatiotemporal data generated in the intelligent connected vehicle scenario.

[0005] To solve the above technical problems, the embodiments of this specification are implemented as follows:

[0006] According to a first aspect of the present invention, a method for constructing a data warehouse for an intelligent connected vehicle scenario is provided, comprising:

[0007] Acquire the data to be stored at the cloud control platform, the data to be stored is a spatiotemporal data set generated in the use scenario of the intelligent networked vehicle within the monitoring range of the cloud control platform; design a unified data receiving format, the data receiving format at least includes the data generation time attribute, related location attribute, related vehicle heading angle attribute and related vehicle speed attribute, convert each spatiotemporal data in the spatiotemporal data set into data that meets the data receiving format one by one and push it to the message queue KAKFA;

[0008] Several computing tasks are designed based on the Flink computing framework, and the several computing tasks at least include binding the target intelligent connected vehicle to the lane and road section, lane indicator calculation and lane warning based on the data in the message queue KAKFA, and the calculation processes of different computing tasks in the several computing tasks have a dependency relationship; wherein, binding the target intelligent connected vehicle to the lane and road section specifically includes: obtaining the real-time position information of the target intelligent connected vehicle, and after performing spatial calculation based on the real-time position information and a pre-made high-precision map, binding the target intelligent connected vehicle to the lane and road section to which it belongs; the lane indicator calculation specifically includes: obtaining all the intelligent connected vehicles bound to the lane that meet the requirements spatiotemporal data in a data receiving format, and obtaining an indicator calculation result that meets the indicator type to which the lane indicator belongs according to the spatiotemporal data of all the intelligent connected vehicles that meet the data receiving format; the lane warning specifically includes: for any intelligent connected vehicle within the monitoring range, obtaining the real-time position information of any intelligent connected vehicle based on the real-time position information reported by any intelligent connected vehicle, obtaining the target lane information to which it belongs based on the real-time position information of any intelligent connected vehicle, and issuing an early warning to any intelligent connected vehicle according to the indicator calculation result of the target lane; subscribing to the data in the message queue KAKFA so as to process the several computing tasks and obtain several processing result data;

[0009] The data converted into the data receiving format and the processing result data are stored in layers in the TimescaleDB time series database, specifically including: storing the original point data in the ODS layer of the TimescaleDB time series database, and storing the original point data after cleaning including thinning and binding in the OWD layer of the TimescaleDB time series database; and storing the geographic information dimension data in the DWS layer of the TimescaleDB time series database after completing the geographic information dimension data based on the data stored in the OWD layer.

[0010] Preferably, the spatiotemporal data set generated in the intelligent connected vehicle usage scenario includes V2X information, vehicle information, and perception information of roadside equipment generated by the intelligent connected vehicle scenario within the monitoring range of the cloud control platform.

[0011] Preferably, the spatiotemporal data set generated in the intelligent connected vehicle usage scenario also includes the original vehicle-side data reported by different intelligent connected vehicles within the monitoring range of the operation control platform during driving, wherein, for any intelligent connected vehicle among the different intelligent connected vehicles, after any intelligent connected vehicle perceives the data from its vehicle-side perspective based on its own installed body sensors and packages the vehicle-side data into a message that satisfies the MQTT protocol, any intelligent connected vehicle sends the message that satisfies the MQTT protocol to the cloud control platform, so that the cloud control platform collects the message that satisfies the MQTT protocol.

[0012] Preferably, the lane indicators include average traffic flow, average travel speed, free flow speed, and average travel time.

[0013] According to a second aspect of the present invention, there is provided a device for constructing a data warehouse for an intelligent connected vehicle scenario, comprising:

[0014] The original vehicle-side data conversion module is used to obtain the data to be stored at the cloud control platform, and the data to be stored is a spatiotemporal data set generated in the use scenario of the intelligent networked vehicle within the monitoring range of the cloud control platform; design a unified data receiving format, and the data receiving format at least includes the data generation time attribute, related location attribute, related vehicle heading angle attribute and related vehicle speed attribute, and convert each spatiotemporal data in the spatiotemporal data set into data that meets the data receiving format and push it to the message queue KAKFA;

[0015] A computing task processing module is used to design several computing tasks based on the Flink computing framework, wherein the several computing tasks at least include binding the target intelligent connected vehicle to the lane and road section, lane indicator calculation and lane warning based on the data in the message queue KAKFA, and the calculation processes of different computing tasks in the several computing tasks have a dependency relationship; wherein, binding the target intelligent connected vehicle to the lane and road section specifically includes: obtaining the real-time position information of the target intelligent connected vehicle, and after performing spatial calculation based on the real-time position information and a pre-made high-precision map, binding the target intelligent connected vehicle to the lane and road section to which it belongs; the lane indicator calculation specifically includes: obtaining all intelligent connected vehicles bound to the lane The method comprises: obtaining the time-space data of the intelligent connected vehicles that meets the data receiving format, and obtaining the index calculation result that meets the index type to which the lane index belongs according to the time-space data of all the intelligent connected vehicles that meet the data receiving format; the lane warning specifically comprises: for any intelligent connected vehicle within the monitoring range, obtaining the real-time position information of any intelligent connected vehicle based on the real-time position information reported by any intelligent connected vehicle, obtaining the target lane information to which it belongs based on the real-time position information of any intelligent connected vehicle, and giving an early warning to any intelligent connected vehicle according to the index calculation result of the target lane; subscribing to the data in the message queue KAKFA so as to process the plurality of computing tasks and obtain a plurality of processing result data;

[0016] A data storage module is used to perform hierarchical storage of the data converted into the data receiving format and the processing result data in the TimescaleDB time series database, specifically including: storing the original point data in the ODS layer of the TimescaleDB time series database, and storing the original point data after cleaning including thinning and binding in the OWD layer of the TimescaleDB time series database; and storing the geographic information dimension data in the DWS layer of the TimescaleDB time series database after completing the geographic information dimension data based on the data stored in the OWD layer.

[0017] According to a third aspect of the present invention, there is provided a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the method for constructing a data warehouse for intelligent connected vehicle scenarios described above are implemented.

[0018] According to a fourth aspect of the present invention, there is provided a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for constructing a data warehouse for intelligent connected vehicle scenarios as described above.

[0019] One embodiment of the present specification can achieve at least the following beneficial effects: by collecting the original vehicle-side data reported by different intelligent connected vehicles within the monitoring range during driving in real time and converting it into a pre-designed data receiving format, the converted data is pushed to the message queue KAKFA. The subsequent business processing logic can obtain the required data from the message queue KAKFA and obtain the processing results after business processing, and store the collected original vehicle-side data and the processing result data in a layered manner in the TimescaleDB time series database, providing a technical solution that can effectively store a large amount of spatiotemporal data generated by intelligent connected vehicle scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of this specification or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0021] Figure 1 It is a flowchart of a method for constructing a data warehouse for a smart connected vehicle scenario provided by an embodiment of this specification;

[0022] Figure 2 It is a schematic diagram of a hierarchical structure of data storage in a method for constructing a data warehouse for a smart connected vehicle scenario provided in an embodiment of this specification;

[0023] Figure 3 It is a structural diagram of a device for constructing a data warehouse for a smart connected vehicle scenario provided in an embodiment of this specification. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solutions and advantages of one or more embodiments of this specification clearer, the technical solutions of one or more embodiments of this specification will be clearly and completely described below in combination with the specific embodiments of this specification and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of one or more embodiments of this specification.

[0025] It should be understood that although the terms first, second, third, etc. may be used in this application document to describe various information, this information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other.

[0026] As described in the background technology section above, a large amount of multi-source heterogeneous data will be generated in the use scenarios of intelligent connected vehicles, such as V2X information, vehicle information, perception information of roadside equipment, etc. Most of these multi-source heterogeneous data contain latitude and longitude information, and all carry the time when the data was generated. The collection, processing and storage of these multi-source heterogeneous data require a simple, universal, highly reliable, high-performance, and low-latency solution. In the related technology, the Chinese patent "CN106297291A Urban Expressway Traffic Information Collection System" proposes an urban expressway traffic information collection system, including a data collection unit, a data management unit, an information processing unit, an integrated interface unit, a user application unit, and a decision management unit. The data collection unit is used for the collection, aggregation and organization of massive traffic data that is widely distributed and multi-source heterogeneous, the data management unit is used for the distributed storage, query, backup, and cleaning of data, and the information processing unit is used to clean, analyze, and filter heterogeneous data to form information items that meet the standards. However, the data processing and storage of this patent mainly uses the big data technology system, which has high system complexity, high cost, and high latency, and the big data technology system does not support spatial calculation and processing of spatial data. Therefore, it is necessary to provide an efficient solution that can store the massive spatiotemporal data generated in the intelligent connected car scene.

[0027] The embodiments of this specification provide a method for constructing a data warehouse for an intelligent connected vehicle scenario. In the technical solution of the embodiments of this specification, the multi-source heterogeneous massive spatiotemporal data generated in the intelligent connected vehicle scenario are converted according to a unified data receiving format and pushed to the message queue KAKFA, so that some business processing can be performed according to business needs, and then the processed data and the data after format conversion are stored in layers as needed, so that the processing and storage of spatiotemporal data generated by the intelligent connected vehicle scenario can be constructed and maintained at low cost while meeting subsequent business requirements.

[0028] Next, a method for constructing a data warehouse for intelligent connected vehicle scenarios provided in the embodiment of the specification is combined with the attached Figure 1-3 Provide specific instructions.

[0029] Figure 1 The following is a flow chart of a method for constructing a data warehouse for a smart connected vehicle scenario provided in an embodiment of the present specification. From a program perspective, the execution subject of the process can be a program installed on a server of a data service platform.

[0030] like Figure 1 As shown, the process may include the following steps.

[0031] Step 102: Acquire the data to be stored at the cloud control platform, where the data to be stored is a spatiotemporal data set generated in the use scenario of the intelligent connected vehicle within the monitoring range of the cloud control platform; design a unified data receiving format, where the data receiving format at least includes a data generation time attribute, a related location attribute, a related vehicle heading angle attribute, and a related vehicle speed attribute, and convert each spatiotemporal data in the spatiotemporal data set into data that meets the data receiving format and pushes it to the message queue KAKFA.

[0032] In the embodiments of this specification, the cloud control platform can provide dynamic basic data such as vehicle operation, infrastructure, traffic environment, and traffic management for smart cars and their users, management and service agencies, etc. It has basic service mechanisms such as high-performance information sharing, high-real-time cloud computing, big data analysis, and information security, and supports the basic support platform for the actual application needs of smart connected cars. The V2X information, vehicle information, and perception information of roadside equipment generated in the connected car scene will be reported to the cloud control platform. Most of these multi-source heterogeneous data contain longitude and latitude information, and all carry the time when the data is generated, which can be called spatiotemporal data. In the technical solution of the embodiments of this specification, a unified data receiving format can be designed to convert these multi-source heterogeneous data and then process them. In this embodiment, the unified data receiving format can at least include the data generation time attribute, related location attribute, related vehicle heading angle attribute, and related vehicle speed attribute.

[0033] For example, in an optional solution, a data collection gateway AGENT can be provided to uniformly collect the massive spatiotemporal data generated by the intelligent connected car scene, and perform some basic spatial processing, such as coordinate system conversion and encryption of the longitude and latitude of the spatial dimension. In addition, the multi-source heterogeneous spatiotemporal data is structurally analyzed and unified into a GPS data model, mainly the four key attributes of time, position, heading angle, and speed. The remaining dimensional information can be stored in ext, as shown in the following format:

[0034]

[0035]

[0036] KAKFA is a high-throughput distributed subscription message system that provides message persistence through a disk data structure. This structure can maintain long-term stable performance even for TB-scale message storage. In this embodiment, each spatiotemporal data in the spatiotemporal data set can be converted into data that meets the unified data receiving format and then pushed to the message queue KAKFA. Subsequent business systems and logic can be directly processed based on the unified data model without the need to parse the protocol used for spatiotemporal data reporting as in related technologies. Because spatiotemporal data are generally reported by hardware devices, the protocol format will vary depending on the hardware manufacturers and versions. The main data source of this data collection system is MQTT, and the Disruptor asynchronous high-performance memory queue is used internally to realize the collection and forwarding of massive spatiotemporal data.

[0037] Step 104: Design several computing tasks based on the Flink computing framework, wherein the several computing tasks at least include binding the target intelligent connected vehicle to the lane and road section, lane indicator calculation and lane warning based on the data in the message queue KAKFA, and the calculation processes of different computing tasks in the several computing tasks have a dependency relationship; wherein, binding the target intelligent connected vehicle to the lane and road section specifically includes: obtaining the real-time position information of the target intelligent connected vehicle, and after performing spatial calculation based on the real-time position information and a pre-made high-precision map, binding the target intelligent connected vehicle to the lane and road section to which it belongs; the lane indicator calculation specifically includes: obtaining the full range of all intelligent connected vehicles bound to the lane The method comprises the following steps: obtaining the spatiotemporal data that meets the data receiving format, and obtaining the indicator calculation result that meets the indicator type to which the lane indicator belongs according to the spatiotemporal data of all the intelligent connected vehicles that meet the data receiving format; the lane warning specifically comprises: for any intelligent connected vehicle within the monitoring range, obtaining the real-time position information of any intelligent connected vehicle based on the real-time position information reported by any intelligent connected vehicle, obtaining the target lane information to which it belongs based on the real-time position information of any intelligent connected vehicle, and giving an early warning to any intelligent connected vehicle according to the indicator calculation result of the target lane; subscribing to the data in the message queue KAKFA so as to process the several computing tasks and obtain several processing result data.

[0038] In the embodiments of this specification, the Flink computing framework can be used to process and compute massive spatiotemporal data. Flink is a powerful data processing and computing framework suitable for processing large-scale spatiotemporal data. It provides efficient and real-time data processing capabilities, and has fault tolerance and state management mechanisms to ensure the reliability and consistency of the data processing process. In addition, this solution uses the Flink on k8s mode, no longer relying on big data components such as HDFS, and uses k8s to schedule and execute real-time computing tasks, which can reduce the complexity of the system and significantly reduce maintenance costs. Therefore, in the technical solution of this embodiment, the Flink computing framework can be relied upon to process and calculate massive spatiotemporal data, such as binding the target intelligent connected vehicle to the lane and road section, lane indicator calculation and lane warning based on the data in the message queue KAKFA in step 102, wherein the target intelligent connected vehicle may refer to any intelligent connected vehicle within the monitoring range of the cloud control platform. When any intelligent connected vehicle is driving in a certain lane, the surrounding roadside equipment can actively perceive the driving data of any intelligent connected vehicle, such as its real-time speed, real-time position, etc., so that in this step, the target intelligent connected vehicle can be bound to the lane and road section to which it belongs, and other computing tasks can be performed. At the same time, since the calculation process of one of the computing tasks needs to be based on the calculation result of another computing task, that is, the calculation processes of different computing tasks in several computing tasks can have a dependency relationship, so the calculation order between different computing tasks can be designed according to this dependency relationship. Kafka is a high-throughput distributed publish-subscribe messaging system. Since each message published to the Kafka cluster has a category, which is called a Topic, messages of different Topics are physically stored separately. Although messages of a Topic are logically stored on one or more brokers, users only need to specify the Topic of the message to produce or consume data without having to worry about where the data is stored. Therefore, in this embodiment, the Flink computing framework can be combined with the publish-subscribe messaging system Kafka, and the converted data in the message queue KAKFA in step 102 can be used to process related computing tasks.

[0039] Step 106: The data converted into the data receiving format and the processing result data are stored in a layered manner in the TimescaleDB time series database, specifically including: storing the original point data in the ODS layer of the TimescaleDB time series database, and storing the original point data in the OWD layer of the TimescaleDB time series database after performing cleaning processing including thinning and binding; and storing the geographic information dimension data in the DWS layer of the TimescaleDB time series database after completing the geographic information dimension data based on the data stored in the OWD layer.

[0040] Timescale is a database platform designed to provide speed and scale for resource-intensive workloads, which makes it ideal for data such as time series, events, and analytical data. Timescale is built on PostgreSQL, so it can access the entire PostgreSQL ecosystem and simplify database deployment and management through a user-friendly interface. Timescale supports time partitioning and indexing, compresses data, and supports custom data expiration policies. This solution is based on the basic functions provided by postgis. This solution supports the calculation, storage, and query of massive spatial data. For example, querying connected vehicles in a certain range, sending roadside equipment perception data according to the range, providing sliced ​​data maps of road conditions, etc., and making spatial indexes based on spatial types to provide real-time and efficient spatial calculations and queries. In this embodiment, the storage solution of Timescale+postgis only needs the basic relational database PostgreSQL to build. The system complexity is greatly reduced, and the learning, use, and maintenance costs are significantly reduced. The construction threshold of related systems in the field of intelligent connected vehicles is reduced. Relevant technicians only need to understand the basic knowledge of spatiotemporal data to use this solution to build and operate spatiotemporal data systems and handle different intelligent connected vehicle scenario businesses. The solution of this embodiment can well support the storage of massive time series data generated by intelligent network scenarios, and can query time series data in real time, such as quickly querying the latest location of a vehicle, counting traffic flow over a period of time, etc. By classifying different spatiotemporal data types and using the layered concept of data warehouse to store original data, extracted data, multi-dimensional data, and summarized data in layers, it is convenient for query and use of business systems.

[0041] It should be understood that in the methods described in one or more embodiments of this specification, the order of some steps can be adjusted according to actual needs, or some steps can be omitted.

[0042] Figure 1The method in the method collects the original vehicle-side data reported by different intelligent connected vehicles in the monitoring range during driving in real time, converts it into a pre-designed data receiving format, and pushes the converted data to the message queue KAKFA. The subsequent business processing logic can obtain the required data from the message queue KAKFA, and obtain the processing results after business processing, and store the collected original vehicle-side data and processing result data in a layered manner in the TimescaleDB time series database, providing a technical solution that can effectively store a large amount of spatiotemporal data generated by intelligent connected vehicle scenarios. At the same time, the present invention can realize the collection, processing, storage and real-time query of massive spatiotemporal data for intelligent connected vehicle scenarios, and provide real-time data query and spatial computing capabilities for intelligent connected vehicle scenario data for other application systems, such as digital twin large screens and vehicle HMIs.

[0043] based on Figure 1 The method, the examples of this specification also provide some specific implementation plans of the method, which are described below.

[0044] In an optional embodiment, the spatiotemporal data set generated in the intelligent connected vehicle usage scenario may include V2X information, vehicle information, and perception information of roadside equipment generated by the intelligent connected vehicle scenario within the monitoring range of the cloud control platform.

[0045] In an optional embodiment, the spatiotemporal dataset generated in the intelligent connected vehicle usage scenario also includes the original vehicle-side data reported by different intelligent connected vehicles within the monitoring range of the operation control platform during driving, wherein, for any intelligent connected vehicle among the different intelligent connected vehicles, after any intelligent connected vehicle perceives the data from its vehicle-side perspective based on its own installed body sensors and packages the vehicle-side data into a message that satisfies the MQTT protocol, any intelligent connected vehicle sends the message that satisfies the MQTT protocol to the cloud control platform, so that the cloud control platform collects the message that satisfies the MQTT protocol.

[0046] In an optional embodiment, the lane indicators may include average traffic flow, average travel speed, free flow speed, and average travel time.

[0047] Based on the same idea, the embodiments of this specification also provide a device corresponding to the above method. Figure 3 The embodiments of this specification provide corresponding to Figure 1 A schematic diagram of a device for constructing a data warehouse for intelligent connected vehicle scenarios. Figure 3 As shown, the device may include:

[0048] The original vehicle-side data conversion module 302 is used to obtain the data to be stored at the cloud control platform, and the data to be stored is a spatiotemporal data set generated in the use scenario of the intelligent connected vehicle within the monitoring range of the cloud control platform; a unified data receiving format is designed, and the data receiving format at least includes the data generation time attribute, related location attribute, related vehicle heading angle attribute and related vehicle speed attribute, and each spatiotemporal data in the spatiotemporal data set is converted into data that meets the data receiving format one by one and pushed to the message queue KAKFA.

[0049] The computing task processing module 304 is used to design a number of computing tasks based on the Flink computing framework, wherein the computing tasks at least include binding the target intelligent connected vehicle to the lane and road section, lane indicator calculation and lane warning based on the data in the message queue KAKFA, and the computing processes of different computing tasks in the computing tasks have a dependency relationship; wherein, binding the target intelligent connected vehicle to the lane and road section specifically includes: obtaining the real-time location information of the target intelligent connected vehicle, and after performing spatial calculation based on the real-time location information and a pre-made high-precision map, binding the target intelligent connected vehicle to the lane and road section to which it belongs; the lane indicator calculation specifically includes: obtaining all intelligent connected vehicles bound to the lane; The spatiotemporal data of the vehicle that meets the data receiving format, and the indicator calculation result that meets the indicator type to which the lane indicator belongs is obtained according to the spatiotemporal data of all the intelligent connected vehicles that meet the data receiving format; the lane warning specifically includes: for any intelligent connected vehicle within the monitoring range, the real-time position information of any intelligent connected vehicle is obtained based on the real-time position information reported by the any intelligent connected vehicle, the target lane information to which it belongs is obtained based on the real-time position information of the any intelligent connected vehicle, and the any intelligent connected vehicle is warned according to the indicator calculation result of the target lane; subscribe to the data in the message queue KAKFA so as to process the several computing tasks and obtain several processing result data.

[0050] The data storage module 306 is used to perform hierarchical storage of the data converted into the data receiving format and the several processing result data in the TimescaleDB time series database, specifically including: storing the original point data in the ODS layer of the TimescaleDB time series database, and storing the original point data after cleaning including thinning and binding in the OWD layer of the TimescaleDB time series database; and storing the geographic information dimension data in the DWS layer of the TimescaleDB time series database after completing the geographic information dimension data based on the data stored in the OWD layer.

[0051] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the method for constructing a data warehouse for intelligent connected vehicle scenarios described above are implemented.

[0052] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for constructing a data warehouse for intelligent connected vehicle scenarios as described above.

[0053] Those skilled in the art can understand that the accompanying drawings are only schematic diagrams of an embodiment, and the modules or processes in the accompanying drawings are not necessarily required to implement the present invention.

[0054] Those skilled in the art can understand that the modules in the device in the embodiment can be distributed in the device in the embodiment according to the description of the embodiment, or can be changed accordingly and located in one or more devices different from the embodiment. The modules in the above embodiment can be combined into one module, or can be further divided into multiple sub-modules.

[0055] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for constructing a data warehouse for intelligent connected vehicle scenarios, applied to a data service platform, characterized in that: The method comprises: Acquire the data to be stored at the cloud control platform, the data to be stored is a spatiotemporal data set generated in the use scenario of the intelligent networked vehicle within the monitoring range of the cloud control platform; design a unified data receiving format, the data receiving format at least includes the data generation time attribute, related location attribute, related vehicle heading angle attribute and related vehicle speed attribute, convert each spatiotemporal data in the spatiotemporal data set into data that meets the data receiving format one by one and push it to the message queue KAKFA; Several computing tasks are designed based on the Flink computing framework, and the several computing tasks at least include binding the target intelligent connected vehicle to the lane and road section, lane indicator calculation and lane warning based on the data in the message queue KAKFA, and the calculation processes of different computing tasks in the several computing tasks have a dependency relationship; wherein, binding the target intelligent connected vehicle to the lane and road section specifically includes: obtaining the real-time position information of the target intelligent connected vehicle, and after performing spatial calculation based on the real-time position information and a pre-made high-precision map, binding the target intelligent connected vehicle to the lane and road section to which it belongs; the lane indicator calculation specifically includes: obtaining all the intelligent connected vehicles bound to the lane that meet the requirements spatiotemporal data in a data receiving format, and obtaining an indicator calculation result that meets the indicator type to which the lane indicator belongs according to the spatiotemporal data of all the intelligent connected vehicles that meet the data receiving format; the lane warning specifically includes: for any intelligent connected vehicle within the monitoring range, obtaining the real-time position information of any intelligent connected vehicle based on the real-time position information reported by any intelligent connected vehicle, obtaining the target lane information to which it belongs based on the real-time position information of any intelligent connected vehicle, and issuing an early warning to any intelligent connected vehicle according to the indicator calculation result of the target lane; subscribing to the data in the message queue KAKFA so as to process the several computing tasks and obtain several processing result data; The data converted into the data receiving format and the processing result data are stored in layers in the TimescaleDB time series database, specifically including: storing the original point data in the ODS layer of the TimescaleDB time series database, and storing the original point data after cleaning including thinning and binding in the OWD layer of the TimescaleDB time series database; and storing the geographic information dimension data in the DWS layer of the TimescaleDB time series database after completing the geographic information dimension data based on the data stored in the OWD layer.

2. The method for constructing a data warehouse for intelligent connected vehicle scenarios according to claim 1, characterized in that: The spatiotemporal data set generated in the intelligent connected vehicle usage scenario includes V2X information, vehicle information, and perception information of roadside equipment generated by the intelligent connected vehicle scenario within the monitoring range of the cloud control platform.

3. The method for constructing a data warehouse for intelligent connected vehicle scenarios according to claim 2, characterized in that: The spatiotemporal data set generated in the intelligent connected vehicle usage scenario also includes the original vehicle-side data reported by different intelligent connected vehicles within the monitoring range of the operation control platform during driving, wherein, for any intelligent connected vehicle among the different intelligent connected vehicles, after any intelligent connected vehicle perceives the data from its vehicle-side perspective based on the body sensors installed on itself and packages the vehicle-side data into a message that satisfies the MQTT protocol, any intelligent connected vehicle sends the message that satisfies the MQTT protocol to the cloud control platform, so that the cloud control platform collects the message that satisfies the MQTT protocol.

4. The method for constructing a data warehouse for intelligent connected vehicle scenarios according to claim 1, characterized in that: The lane indicators include average traffic flow, average travel speed, free flow speed, and average travel time.

5. A device for constructing a data warehouse for intelligent connected vehicle scenarios, characterized in that: include: The original vehicle-side data conversion module is used to obtain the data to be stored at the cloud control platform, and the data to be stored is a spatiotemporal data set generated in the use scenario of the intelligent networked vehicle within the monitoring range of the cloud control platform; design a unified data receiving format, and the data receiving format at least includes the data generation time attribute, related location attribute, related vehicle heading angle attribute and related vehicle speed attribute, and convert each spatiotemporal data in the spatiotemporal data set into data that meets the data receiving format and push it to the message queue KAKFA; A computing task processing module is used to design several computing tasks based on the Flink computing framework, wherein the several computing tasks at least include binding the target intelligent connected vehicle to the lane and road section, lane indicator calculation and lane warning based on the data in the message queue KAKFA, and the calculation processes of different computing tasks in the several computing tasks have a dependency relationship; wherein, binding the target intelligent connected vehicle to the lane and road section specifically includes: obtaining the real-time position information of the target intelligent connected vehicle, and after performing spatial calculation based on the real-time position information and a pre-made high-precision map, binding the target intelligent connected vehicle to the lane and road section to which it belongs; the lane indicator calculation specifically includes: obtaining all intelligent connected vehicles bound to the lane The method comprises: obtaining the time-space data of the intelligent connected vehicles that meets the data receiving format, and obtaining the index calculation result that meets the index type to which the lane index belongs according to the time-space data of all the intelligent connected vehicles that meet the data receiving format; the lane warning specifically comprises: for any intelligent connected vehicle within the monitoring range, obtaining the real-time position information of any intelligent connected vehicle based on the real-time position information reported by any intelligent connected vehicle, obtaining the target lane information to which it belongs based on the real-time position information of any intelligent connected vehicle, and giving an early warning to any intelligent connected vehicle according to the index calculation result of the target lane; subscribing to the data in the message queue KAKFA so as to process the plurality of computing tasks and obtain a plurality of processing result data; A data storage module is used to perform hierarchical storage of the data converted into the data receiving format and the processing result data in the TimescaleDB time series database, specifically including: storing the original point data in the ODS layer of the TimescaleDB time series database, and storing the original point data after cleaning including thinning and binding in the OWD layer of the TimescaleDB time series database; and storing the geographic information dimension data in the DWS layer of the TimescaleDB time series database after completing the geographic information dimension data based on the data stored in the OWD layer.

6. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the method steps as claimed in claims 1 to 4 are implemented.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method steps as claimed in claims 1 to 4 are implemented.

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