CN115576991A - Multi-source real-time data fusion method based on FLink - Google Patents
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Abstract
The invention belongs to the technical field of data fusion, and particularly relates to a multi-source real-time data fusion method based on Flink, which comprises the following steps: acquiring a plurality of real-time vehicle end data; adding corresponding dimension information to each real-time vehicle end data to obtain intermediate data; and traversing each intermediate data, and fusing all intermediate data containing preset information to obtain target data. The invention performs multi-source real-time data fusion at the cloud based on the Flink, has higher timeliness, ensures the latest state information of the target data to be acquired, can support the processing of hundreds of millions of data volumes, has higher efficiency in the aspect of data processing speed, and has obvious technical effect and great significance.
Description
Technical Field
The invention belongs to the technical field of data fusion, and particularly relates to a multi-source real-time data fusion method based on Flink.
Background
With the updating iteration of the communication technology and the continuous development of the car networking technology, more and more car owners of intelligent networked cars purchasing the car networking service are provided, the intelligent networked cars can generate a large amount of signal data in the use process, and the data sources are various and the data structures are different, such as original car condition data, user behavior logs, car machine embedded data and the like. Under the background, the operation business needs to begin to mine internet connection real-time behavior data, such as a GPS real-time position, an automatic assistant driving IACC function use condition, a real-time vehicle condition, DTC fault information, and the like, and integrate the internet connection data meeting business requirements to support the business requirements.
However, in the prior art, the completion and fusion timeliness of real-time data are low, and yesterday data is usually completed and fused today, so that the obtained target data state information is delayed, meanwhile, the data volume related to completion cannot be too large, only million-level data volume can be processed generally, and the processing efficiency is low.
Disclosure of Invention
In view of the above-mentioned shortcomings of the prior art, the present invention provides a method for fusing real-time data to solve the above-mentioned technical problems.
The invention provides a multi-source real-time data fusion method based on Flink, which comprises the following steps: acquiring a plurality of real-time vehicle end data; adding corresponding dimension information to each real-time vehicle end data to obtain intermediate data; and traversing each intermediate data, and fusing all intermediate data containing preset information to obtain target data.
According to a specific embodiment of the present invention, the step of acquiring a plurality of real-time vehicle-side data includes: acquiring real-time vehicle-side data and uploading the real-time vehicle-side data to Kafka, wherein the real-time vehicle-side data adopts a Json character string format; and acquiring a plurality of real-time vehicle-end data in the Kafka through Flink.
According to an embodiment of the present invention, the step of obtaining the plurality of real-time vehicle-side data in Kafka through Flink includes: reading a plurality of Json character strings in the Kafka through the Flink; parseobject converts the Json string into a recognizable POJO class object.
According to a specific embodiment of the present invention, the step of adding corresponding dimension information to each piece of real-time vehicle-side data to obtain intermediate data includes: inquiring corresponding dimension information in MySQL according to the identification of the real-time vehicle end data; and adding the dimension information into the Flink, and integrating the dimension information with the corresponding real-time vehicle-end data to obtain the intermediate data.
According to a specific embodiment of the invention, corresponding dimension information is added to each real-time vehicle-side data in a mode of combining the asynchronous I/O mechanism of the Flank and the Google guava to obtain intermediate data.
According to a specific embodiment of the present invention, the step of traversing each of the intermediate data and fusing all the intermediate data including preset information to obtain target data includes: traversing each of the intermediate data; screening out the intermediate data containing preset information; and fusing all the intermediate data containing the preset information to obtain the target data.
According to a specific embodiment of the invention, the intermediate data containing the preset information is screened out through a Join algorithm.
A multi-source real-time data fusion system based on Flink comprises: the information acquisition module is used for acquiring a plurality of real-time vehicle end data; the information completion module is used for adding corresponding dimension information to each piece of real-time vehicle-side data to obtain intermediate data; and the information fusion module is used for traversing each piece of intermediate data and fusing all pieces of intermediate data containing preset information to obtain target data.
A multi-source real-time data fusion device based on Flink comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the processor realizes the steps of any one of the methods when executing the computer program.
A computer readable medium having stored thereon instructions which are loaded by a processor and which perform the method of any of the above.
The method has the technical effects that the real-time vehicle-side data are acquired and immediately processed after being uploaded to the Kafka, wherein the Kafka can store a large amount of data information; supplementing real-time dimension information to the FLink through an asynchronous I/O mechanism of the FLink, wherein the asynchronous I/O mechanism of the FLink supports simultaneous processing of a plurality of pieces of real-time vehicle-end data, processing of hundreds of millions of data volumes is achieved, and the speed is more efficient; and finally, fusing corresponding real-time vehicle-side data according to requirements to obtain target data. The real-time data fusion method can process the data immediately after the data are acquired, almost no delay exists, higher timeliness is achieved, meanwhile, data information with larger dimensionality can be acquired by data fusion according to requirements, and the problems that in the prior art, the data fusion processing amount is small, timeliness is low, and the data dimensionality is incomplete are solved better.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application. It is obvious that the drawings in the following description are only some embodiments of the application, and that for a person skilled in the art, other drawings can be derived from them without inventive effort. In the drawings:
FIG. 1 is a flow chart of a multi-source real-time data fusion method based on Flink according to an embodiment of the present invention;
FIG. 2 is a flow chart of a multi-source real-time data fusion system based on Flink according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a multi-source real-time data fusion device based on Flink according to an embodiment of the present invention.
Detailed Description
Other advantages and effects of the present invention will become apparent to those skilled in the art from the disclosure herein, wherein the embodiments of the present invention are described in detail with reference to the accompanying drawings and preferred embodiments. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It should be understood that the preferred embodiments are illustrative of the invention only and are not limiting upon the scope of the invention.
It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention, and the drawings only show the components related to the present invention rather than being drawn according to the number, shape and size of the components in actual implementation, and the type, amount and proportion of each component in actual implementation can be changed freely, and the layout of the components can be more complicated.
In the following description, numerous details are set forth to provide a more thorough explanation of embodiments of the present invention, however, it will be apparent to one skilled in the art that embodiments of the present invention may be practiced without these specific details, and in other embodiments, well-known structures and devices are shown in block diagram form, rather than in detail, in order to avoid obscuring embodiments of the present invention.
It should be noted that, with the update iteration of the communication technology and the continuous development of the car networking technology, the intelligent internet car generates a large amount of data information in the using process and transmits the data information to the vehicle-mounted end or the cloud end for the user to inquire and browse, so as to pay attention to the using condition of the car. Because the volume of the generated data information is huge, the related data information needs to be fused so as to be convenient for viewing. At present, fusion processing cannot be carried out immediately after vehicle-end data are acquired, so that overdue performance of the data is caused, and abnormal information of a vehicle in use cannot be found in time. Therefore, the multi-source real-time data fusion method based on the Flink is particularly important, has a remarkable effect, can be used for immediately processing the obtained real-time data, and guarantees timeliness of data information.
In order to make the technical solutions in the embodiments of the present application better understood, the technical solutions in the embodiments of the present application are clearly and completely described.
The system architecture of the application can include local end equipment and a cloud server, and the local end equipment comprises: a vehicle-mounted TBOX and a vehicle-mounted terminal; the cloud server comprises: kafka, flink, and MySQL; the local end equipment and the cloud server are directly connected through a network, and can be connected through wired communication or wireless communication.
The user can use the vehicle-mounted terminal to interact with the cloud server through the network so as to receive or send messages. The vehicle-mounted terminal can be provided with a display screen and can support the electronic equipment cloud server for web browsing to be one or multiple, and Kafka, flink and MySQL can be erected on one cloud server or multiple cloud servers can be erected independently.
It should be noted that the fusion method for the multi-source real-time data based on the Flink provided in the embodiment of the present application is generally executed by the cloud server, and accordingly, the fusion device and the medium for the multi-source real-time data based on the Flink are generally disposed in the cloud server.
The embodiment of the application acquires real-time vehicle-side data of a vehicle through the vehicle-mounted TBOX and uploads the real-time vehicle-side data to the Kafka, the data in the Kafka are read through Flink to be processed, real-time dimension information in MySQL is called to achieve completion and fusion of the real-time vehicle-side data, and the fused data are stored in the cloud server so that the vehicle-mounted terminal can view and browse at any time.
Kafka is an open source stream processing platform developed by the Apache software foundation, written in Scala and Java. Kafka is a high-throughput distributed publish-subscribe messaging system that can handle all the action flow data of a consumer in a web site. The method can be used as an information transmission platform to efficiently transmit information.
Preferably, due to the large amount of data information generated by the intelligent internet vehicle, kafka is adopted as a storage end of real-time vehicle-end data for more efficient throughput data processing in the embodiment of the application.
Apache Flink is an open source stream processing framework developed by the Apache software foundation, at the heart of which is a distributed stream data stream engine written in Java and Scala. Flink executes arbitrary stream data programs in a data parallel and pipelined manner, and Flink's pipelined runtime system can execute batch and stream processing programs.
Preferably, in order to be able to handle the huge amount of data throughput of Kafka, single-line data processing is too inefficient, so the embodiment of the present application adopts Flink as the processing end of real-time vehicle-end data.
MySQL is a relational database management system developed by MySQL AB, sweden, and belongs to the product under Oracle flag. Relational databases maintain data in different tables rather than placing all data in one large repository, which increases speed and flexibility. And multithreading is supported, and CPU resources are fully utilized. The optimized SQL query algorithm effectively improves the query speed. It can be implemented in a client server network environment as a stand-alone application or embedded in other software as a library. And a large database is supported, and the large database with tens of millions of records can be processed.
Preferably, according to the MySQL, more data can be stored, multi-thread operation is supported, and the query speed is high, so that the MySQL is adopted as a dimension information storage end of real-time vehicle end data in the embodiment of the application.
Example 1
Referring to fig. 1, a method for fusing multi-source real-time data based on Flink includes:
and S10, acquiring a plurality of real-time vehicle end data.
Specifically, real-time vehicle-side data are collected through a vehicle-mounted TBOX and uploaded to Kafka, wherein the real-time vehicle-side data are in a Json character string format.
And acquiring a plurality of real-time vehicle end data in the Kafka by using the Flink, wherein the Flink is used as a data processing party and can read the data stored in the Kafka. Reading a plurality of Json character strings in the Kafka, and converting the Json character strings into recognizable POJO objects by adopting Json.
The Json character strings stored in the Kafka as real-time vehicle-end data can be subjected to subsequent data completion and fusion only by converting the Json character strings into identifiable objects. Parseobject is the conversion of Json strings to corresponding objects.
Further, the step S10 may further include: and collecting real-time dimension information and uploading the dimension information to MySQL. Dimension expansion is carried out on the real-time vehicle-end data through real-time dimension information, so that the finally obtained target data is more accurate, higher timeliness is achieved, and detailed information corresponding to the real-time vehicle-end data can be fed back more truly.
Wherein, real-time car end data includes: current vehicle behavior information, real-time vehicle conditions, current vehicle machine fault information, GPS positioning information, and the like.
The real-time dimension information comprises: current weather conditions, temperature and humidity factors, surrounding area information, and the like.
And S20, adding corresponding dimension information to each piece of real-time vehicle-end data to obtain intermediate data.
Specifically, the corresponding dimension information in MySQL is inquired according to the identification of the real-time vehicle end data. And inquiring the dimension information corresponding to the real-time vehicle end data through the index function of the MySQL to enlarge the dimension of the real-time vehicle end data.
And simultaneously adding the dimension information into the Flink, and integrating the dimension information with the corresponding real-time vehicle-side data to obtain the intermediate data.
The obtained real-time vehicle-side data is usually concise and contains less information, so that the data information needs to be completely expanded and completed. For example. The acquired real-time vehicle end data is the opening or closing of the vehicle door, the data information at the moment is simple, and the vehicle door is completely closed or not, the vehicle door is locked or not, the feedback information of a vehicle door handle, the specific time for executing corresponding operation and the like are completed through dimensional information such as corresponding specific operation, time and the like. And after the data expansion completion is finished, the detailed information of the corresponding real-time vehicle-side data can be obtained.
Specifically, in the application, because the speed of the single data processing mode is slow, and in the face of huge data volume generated at the moment of intelligent network connection, if the real-time vehicle-side data processing is performed in such a mode, the real-time vehicle-side data can be received in a delayed mode, and more data information can be accumulated, so that the data information loses corresponding timeliness.
Preferably, the corresponding operation in step S20 is implemented by combining the asynchronous I/O mechanism of the flag with Google guava, so as to implement fast expansion of real-time vehicle-side data dimension.
The main purpose of AsyncI/O is to solve the problem that network delay becomes a system bottleneck when interacting with external systems.
When Flink is used for stream data calculation, interaction with an external system (such as a database, a Redis, a Hive, an HBase and other storage systems) is needed many times. Attention is often paid to whether intersystem communication delays will slow down the entire Flink job, affecting overall throughput and real-time. In order to solve the problem of synchronous access, an asynchronous mode can concurrently process a plurality of requests and replies, that is, you can continuously send the requests of the users a, b, c, d, and the like to the database, and at the same time, which request replies are returned first to process which replies, so that no blocking wait is needed between continuous requests, synchronous processing of a plurality of requests is realized, and processing of data volume is increased.
Guava is an open source Java library, and the Google Guava open source library is used for providing practical methods of collection, caching, original sentence support, concurrency, common annotation, character string processing, I/O and verification.
The real-time vehicle-side data are subjected to dimensionality completion by combining an asynchronous I/O mechanism with a Google Guava open source library, a large amount of data information can be processed simultaneously, time is saved, efficiency is improved, real-time vehicle-side data generated at any time are processed quickly, corresponding vehicle state information is obtained in time, and timeliness and accuracy of the data are guaranteed.
And S30, traversing each intermediate data, and fusing all intermediate data containing preset information to obtain target data.
Specifically, each intermediate data is traversed, the intermediate data containing preset information is screened out, and finally all the intermediate data containing the preset information are fused to obtain the target data.
And finding out associated segments of all the intermediate data, namely preset information according to business requirements, and fusing the intermediate data with the associated segments to obtain target data. For example, the intermediate data at the same time or place are fused to obtain the required target data, i.e., the information such as behavior, failure, and condition of the vehicle, based on the current time or place as the related segment.
Specifically, in the application, the opening and closing data information of the vehicle door can be fused with the opening and closing data information of the vehicle window to check the safety information of the vehicle, and whether the vehicle door is closed and locked when the vehicle door leaves the vehicle. Or the fault information of each device of the vehicle is fused according to the detection requirement so as to obtain complete vehicle fault abnormal information.
Wherein, the screening of the intermediate data with the associated segment is completed through the Join algorithm. Since various paradigms are required to avoid data redundancy when designing a database, it is possible to design a piece of related data in different data tables, and therefore, it is necessary to associate different table data according to conditions when querying. And the integration of data is realized through the Join algorithm.
It should be noted that, the steps of the above methods are divided for clarity, and the implementation may be combined into one step or split some steps, and the steps are divided into multiple steps, and as long as the steps contain the same logical relationship, the steps are within the scope of the present patent; it is within the scope of this patent to add insignificant modifications or introduce insignificant designs to the algorithms or processes, but not to change the core designs of the algorithms and processes.
Example 2
Referring to fig. 2, an embodiment of the present application further provides a system for fusing multi-source real-time data based on Flink, including:
the information acquisition module 10 is used for acquiring a plurality of real-time vehicle end data;
the information complementing module 20 is configured to add corresponding dimension information to each piece of real-time vehicle-side data to obtain intermediate data;
and the information fusion module 30 is configured to traverse each piece of the intermediate data, and fuse all pieces of the intermediate data that include preset information to obtain target data.
It should be noted that the multi-source real-time data fusion system based on Flink provided in the foregoing embodiment and the multi-source real-time data fusion method based on Flink provided in the foregoing embodiment 1 belong to the same concept, and specific ways for each module and unit to execute operations have been described in detail in the method embodiment, and are not described herein again. In practical application, the Flink-based multi-source real-time data fusion method provided in embodiment 1 may be implemented by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to implement all or part of the above-described functions, which is not limited herein.
Example 3
Referring to fig. 3, an embodiment of the present application further provides a Flink-based multi-source real-time data fusion device, which includes a memory 2, a processor 1, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of any one of the above methods when executing the computer program.
Wherein the memory includes at least one type of readable storage medium including flash memory, removable hard disks, multimedia cards, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disks, optical disks, etc. The memory may in some embodiments be an internal storage unit of the electronic device, for example a removable hard disk of the electronic device. The memory may also be an external storage device of the electronic device in other embodiments, such as a plug-in removable hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like provided on the electronic device. Further, the memory may also include both an internal storage unit and an external storage device of the electronic device. The memory may be used not only to store application software installed in the electronic device and various types of data, but also to temporarily store data that has been output or will be output.
A processor may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same or different functions, including one or more Central Processing Units (CPUs), microprocessors, digital Processing chips, graphics processors, and combinations of various control chips. The processor is a Control Unit (Control Unit) of the electronic device, connects various components of the entire electronic device by using various interfaces and lines, and executes various functions and processes data of the electronic device by running or executing programs or modules stored in the memory and calling data stored in the memory.
The processor executes an operating system of the electronic device and various installed application programs. The processor executes the application program to realize the steps in the above-mentioned lithium power battery cold joint detection method embodiments.
Illustratively, the computer program may be partitioned into one or more modules that are stored in the memory and executed by the processor to implement the invention. The one or more modules may be a series of computer program instruction segments capable of performing certain functions, which are used to describe the execution of the computer program in the electronic device.
The integrated unit implemented in the form of a software functional module may be stored in a computer-readable storage medium. The software functional module is stored in a storage medium and includes several instructions to enable a computer device (which may be a personal computer, a computer device, or a network device) or a processor (processor) to execute part of the functions of the method for detecting cold joint of a lithium battery according to various embodiments of the present invention.
In conclusion, the method has the technical effects that the real-time vehicle-side data are acquired and immediately processed after being uploaded to the Kafka, wherein the Kafka can store a large amount of data information; supplementing real-time dimension information to the FLink through an asynchronous I/O mechanism of the FLink, wherein the asynchronous I/O mechanism of the FLink supports simultaneous processing of a plurality of pieces of real-time vehicle-side data, processing of hundred million-level data volume is achieved, and the speed is more efficient; and finally, fusing corresponding real-time vehicle-side data according to requirements to obtain target data. The real-time data fusion method can be used for processing immediately after data are acquired, almost no delay exists, higher timeliness is achieved, meanwhile, data information with larger dimensionality can be acquired by data fusion according to requirements, and the problems that in the prior art, data fusion processing amount is small, timeliness is low, and data dimensionality is incomplete are solved better.
The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.
Claims (10)
1. A multi-source real-time data fusion method based on Flink is characterized by comprising the following steps:
acquiring a plurality of real-time vehicle end data;
adding corresponding dimension information to each real-time vehicle end data to obtain intermediate data;
traversing each intermediate data, and fusing all intermediate data containing preset information to obtain target data.
2. The Flink-based multi-source real-time data fusion method according to claim 1, wherein the step of obtaining a plurality of real-time vehicle-side data comprises:
acquiring real-time vehicle-side data and uploading the real-time vehicle-side data to Kafka, wherein the real-time vehicle-side data adopts a Json character string format;
and acquiring a plurality of real-time vehicle-end data in the Kafka through Flink.
3. The method for fusion of Flink-based multi-source real-time data according to claim 2, wherein the step of obtaining a number of the real-time vehicle-end data in Kafka via Flink comprises:
reading a plurality of Json character strings in the Kafka through the Flink;
the Json string is converted into a recognizable POJO class object by Json.
4. The Flink-based multi-source real-time data fusion method according to claim 1, wherein the step of adding corresponding dimension information to each real-time vehicle-side data to obtain intermediate data comprises:
inquiring corresponding dimension information in MySQL according to the identification of the real-time vehicle end data;
and adding the dimension information into the Flink, and integrating the dimension information with the corresponding real-time vehicle-end data to obtain the intermediate data.
5. The Flink-based multi-source real-time data fusion method according to claim 1, wherein corresponding dimension information is added to each real-time vehicle-side data in a mode of combining an asynchronous I/O mechanism of the Flink with Google guava to obtain intermediate data.
6. The Flink-based multi-source real-time data fusion method according to claim 1, wherein the step of traversing each of the intermediate data and fusing all the intermediate data containing preset information to obtain target data comprises:
traversing each of the intermediate data;
screening out the intermediate data containing preset information;
and fusing all the intermediate data containing the preset information to obtain the target data.
7. The Flink-based multi-source real-time data fusion method according to claim 6, wherein the intermediate data containing preset information is screened out through a Join algorithm.
8. A multi-source real-time data fusion system based on Flink is characterized by comprising:
the information acquisition module is used for acquiring a plurality of real-time vehicle end data;
the information completion module is used for adding corresponding dimension information to each piece of real-time vehicle-side data to obtain intermediate data;
and the information fusion module is used for traversing each piece of intermediate data and fusing all pieces of intermediate data containing preset information to obtain target data.
9. A Flink-based multi-source real-time data fusion device, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method according to any one of claims 1 to 7 when executing the computer program.
10. A computer-readable medium having stored thereon instructions which are loaded by a processor and which perform the method of any one of claims 1 to 7.
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