CN118295290B - Intelligent connected vehicle remote control data analysis method, system, and a vehicle - Google Patents
Intelligent connected vehicle remote control data analysis method, system, and a vehicleInfo
- Publication number
- CN118295290B CN118295290B CN202410261069.3A CN202410261069A CN118295290B CN 118295290 B CN118295290 B CN 118295290B CN 202410261069 A CN202410261069 A CN 202410261069A CN 118295290 B CN118295290 B CN 118295290B
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
- G05B19/042—Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
- G05B19/0423—Input/output
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/20—Pc systems
- G05B2219/23—Pc programming
- G05B2219/23051—Remote control, enter program remote, detachable programmer
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
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- Automation & Control Theory (AREA)
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Abstract
The invention discloses an intelligent networking automobile remote control data analysis method, a system and a vehicle, wherein the method comprises the steps of obtaining real-time data flow of the Internet of vehicles by utilizing the Internet of vehicles remote control technology; and after analyzing and filtering the batch processing data, calculating the remote control execution success rate and the instruction execution success rate of each trolley. When the remote control instruction acquired by the vehicle is required to be increased or the data stream transmission speed is required to be increased, the real-time processing requirement can be met without excessive reconfiguration.
Description
Technical Field
The invention relates to the technical field of automobiles, in particular to an intelligent network-connected automobile remote control data analysis method, an intelligent network-connected automobile remote control data analysis system and a vehicle.
Background
With the development of technology, intelligent internet-connected automobiles gradually become the development trend of the automobile industry. The intelligent network-connected automobile is an automobile with complex environment sensing, decision making, executing and other capabilities, and intelligent information exchange and sharing among automobiles, automobile and road side infrastructure and automobile and cloud system are realized through an internet technology.
The remote control is an important technology of intelligent network-connected automobiles, can realize remote control of the automobiles, and provides convenience. However, how to analyze remote control data effectively and accurately, and to improve the efficiency and accuracy of remote control, is a great challenge in the current technology.
Therefore, for different remote control command requirements and different signal transmission rates, there is a need to develop a data analysis method that can reduce the computing resources and also can cope with the real-time analysis requirements.
Disclosure of Invention
The invention aims to provide an intelligent network-connected automobile remote control data analysis method, an intelligent network-connected automobile remote control data analysis system and a vehicle, which can meet the real-time processing requirement without excessive reconfiguration when the remote control instruction acquired by the vehicle is required to be increased or the data stream transmission speed is required to be increased.
In order to achieve the above purpose, the present invention provides a method for analyzing remote control data of an intelligent network-connected automobile, comprising:
Acquiring real-time data flow of the Internet of vehicles by utilizing an Internet of vehicles remote control technology;
Combining the real-time data stream of the Internet of vehicles and the historical remote control data of the Internet of vehicles to generate batch processing data;
After analyzing and filtering the batch processing data, calculating the remote control execution success rate and the instruction execution success rate of each trolley.
Further, the acquiring the real-time data stream of the internet of vehicles by using the internet of vehicles remote control technology comprises:
obtaining a real-time data stream of the Internet of vehicles by using a Kafka platform and storing the real-time data stream in a Redis database;
The real-time data stream of the Internet of vehicles comprises running state data, environment data and user behavior data of the automobile.
Further, the merging the real-time data stream of the internet of vehicles and the historical remote control data of the internet of vehicles to generate batch processing data includes:
Acquiring the latest frame data in the Redis database by adopting a Spark-Kafka connection component;
Accessing ClickHouse a database using DBeaver link software and obtaining the historical remote control data in ClickHouse database;
and merging the latest frame data and the historical remote control data, rearranging data items and generating batch processing data.
Further, merging the latest frame data and the historical remote control data, rearranging data items and generating batch processing data, including:
In the merging process, the frame numbers are matched with the corresponding terminal IDs, and all execution module signal data of the single instruction are merged by using the same remote control instruction ID to generate a list of remote control instruction result forms.
Further, after analyzing and filtering the batch data, calculating a remote control execution success rate and an instruction execution success rate of each trolley, including:
analyzing the batch data into analyzable data by using a Spark computing engine to obtain required analysis fields and importing the required analysis fields into a PowerBI analysis system;
Grouping, sequencing and connection calculation are carried out on the required analysis fields through PowerBI analysis system so as to filter out the same event sent by different terminals, and the remote control execution success rate and the instruction execution success rate of each trolley are calculated;
The required analysis fields comprise a frame number, an operator, operation time, a remote control instruction name, a remote control instruction ID, an execution result and a failure reason.
Further, grouping, ordering and connection calculation are performed on the required analysis fields through PowerBI analysis system to filter out the same event sent by different terminals, and calculate the remote control execution success rate and instruction execution success rate of each trolley, including:
Acquiring execution data and a time stamp of each module of each remote control instruction by using each frame number;
Filtering out the same event sent by different terminals according to the frame number and the remote control instruction ID;
combining execution data of each module into a remote control instruction result form through a frame number, and calibrating an execution result, wherein the execution result comprises execution success, execution failure or execution overtime;
Grouping the required analysis fields according to remote control instruction names, calculating remote control execution success rate and instruction execution success rate of each trolley according to the execution results corresponding to the required analysis fields, and storing the remote control execution success rate and the instruction execution success rate into a ClickHouse database.
Further, the acquiring the real-time data stream of the internet of vehicles by using the Kafka platform and storing the real-time data stream in the Redis database comprises:
After an initial Internet of vehicles real-time data stream is acquired, sequentially carrying out data preprocessing and data analysis on the initial Internet of vehicles real-time data stream to acquire a final Internet of vehicles real-time data stream and storing the final Internet of vehicles real-time data stream in a Redis database;
the method for preprocessing the data of the initial Internet of vehicles real-time data stream comprises the steps of cleaning, arranging and formatting the initial Internet of vehicles real-time data stream to obtain the Internet of vehicles real-time data stream after data preprocessing;
The data analysis of the data-preprocessed real-time data stream of the Internet of vehicles comprises classifying and analyzing the data-preprocessed real-time data stream of the Internet of vehicles by utilizing a learning algorithm thereof to obtain a final real-time data stream of the Internet of vehicles.
Based on the same inventive concept, the invention also provides an intelligent network-connected automobile remote control data analysis system, which comprises:
the acquisition unit is used for acquiring the real-time data stream of the Internet of vehicles by utilizing the Internet of vehicles remote control technology;
The merging unit is used for merging the real-time data stream of the Internet of vehicles and the historical remote control data of the Internet of vehicles to generate batch processing data;
And the computing unit is used for computing the remote control execution success rate and the instruction execution success rate of each trolley after analyzing and filtering the batch processing data.
The calculation unit is specifically used for analyzing batch processing data into analyzable data by utilizing a Spark calculation engine, obtaining required analysis fields and importing the required analysis fields into a PowerBI analysis system, grouping, sequencing and connecting the required analysis fields by the PowerBI analysis system so as to filter out the same event sent by different terminals, and calculating the remote control execution success rate and the instruction execution success rate of each trolley;
The required analysis fields comprise a frame number, an operator, operation time, a remote control instruction name, a remote control instruction ID, an execution result and a failure reason.
Based on the same inventive concept, the invention also provides a vehicle, comprising the intelligent network-connected automobile remote control data analysis system.
The remote control system has the technical effects and advantages that the remote control execution success rate of a single car and the execution success rate of the same type of remote control instructions are calculated, meanwhile, the remote control failure reasons are obtained, the remote control system can be well aimed at different conditions, and is unfolded and optimized, so that the remote control service level of a car enterprise is improved, the system has higher practical value and wide application prospect.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention may be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for analyzing remote control data of an intelligent network-connected automobile according to an embodiment of the invention;
FIG. 2 is a detailed flowchart of a method for analyzing remote control data of an intelligent network-connected vehicle according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an intelligent network-connected automobile remote control data analysis system according to an embodiment of the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In order to solve the defects in the prior art, the invention discloses an intelligent network-connected automobile remote control data analysis method, which is shown in fig. 1 and 2 and comprises the following steps:
s1, acquiring real-time data flow of the Internet of vehicles by utilizing an Internet of vehicles remote control technology;
step S2, merging the real-time data stream of the Internet of vehicles and the historical remote control data of the Internet of vehicles to generate batch processing data;
And step S3, after analyzing and filtering the batch processing data, calculating the remote control execution success rate and the instruction execution success rate of each trolley.
In some specific embodiments, step S1, using a remote control technology of the Internet of vehicles, obtains a real-time data stream of the Internet of vehicles, including:
And the Kafka platform is a high-throughput distributed publishing and subscribing message system, and can process all action stream data of consumers in websites.
The real-time data stream of the Internet of vehicles comprises running state data, environment data and user behavior data of the automobile;
The running state data comprise vehicle speed, rotating speed, battery power and the like, the environment data comprise weather, road conditions, traffic conditions and the like, and the user behavior data comprise driving habits, driving routes, parking places and the like.
In some specific embodiments, the internet of vehicles real-time data stream is obtained using the Kafka platform and stored in the Redis database, including:
after an initial Internet of vehicles real-time data stream is acquired, sequentially carrying out data preprocessing and data analysis on the initial Internet of vehicles real-time data stream to acquire a final Internet of vehicles real-time data stream and storing the final Internet of vehicles real-time data stream in a Redis (Remote Dictionary Server, namely a remote dictionary service) database;
The method comprises the steps of carrying out data preprocessing on an initial Internet of vehicles real-time data stream, namely cleaning, arranging and formatting the initial Internet of vehicles real-time data stream (such as removing invalid data, filling missing data, converting data in different formats and the like), and obtaining the Internet of vehicles real-time data stream after data preprocessing so as to carry out subsequent analysis;
the data analysis of the real-time data stream of the Internet of vehicles after the data preprocessing comprises the steps of classifying and analyzing the real-time data stream of the Internet of vehicles after the data preprocessing by utilizing a learning algorithm thereof (for example, classifying and predicting the real-time data stream of the Internet of vehicles by using algorithms such as decision trees, neural networks and the like) so as to identify abnormal data and potential risks and obtain the final real-time data stream of the Internet of vehicles.
In some specific embodiments, step S2, combining the real-time data stream of the Internet of vehicles and the historical remote control data of the Internet of vehicles to generate batch data, comprises:
Acquiring the latest frame data in the Redis database by adopting a Spark-Kafka connection component;
Accessing ClickHouse a database using DBeaver link software and obtaining the historical remote control data in ClickHouse database;
and merging the latest frame data and the historical remote control data, rearranging data items and generating batch processing data.
In some specific embodiments, merging the most recent frame data and the historical remote control data, rearranging data entries and generating batch data includes:
In the merging process, the frame numbers are matched with the corresponding terminal IDs, and all execution module signal data of the single instruction are merged by using the same remote control instruction ID to generate a list of remote control instruction result forms.
In some specific embodiments, step S3, after analyzing and filtering the batch data, calculates a remote control execution success rate and an instruction execution success rate of each trolley, including:
analyzing the batch data into analyzable data by using a Spark computing engine to obtain required analysis fields and importing the required analysis fields into a PowerBI analysis system;
Grouping, sequencing and connection calculation are carried out on the required analysis fields through PowerBI analysis system so as to filter out the same event sent by different terminals, and the remote control execution success rate and the instruction execution success rate of each trolley are calculated;
The required analysis fields comprise a frame number, an operator, operation time, a remote control instruction name, a remote control instruction ID, an execution result and a failure reason.
In some specific embodiments, grouping, ordering and connection calculation are performed on the required analysis fields through PowerBI analysis system to filter out the same event sent by different terminals, and calculate the remote control execution success rate and instruction execution success rate of each trolley, including:
Acquiring execution data and a time stamp of each module of each remote control instruction by using each frame number;
Filtering out the same event sent by different terminals according to the frame number and the remote control instruction ID;
combining execution data of each module into a remote control instruction result form through a frame number, and calibrating an execution result, wherein the execution result comprises execution success, execution failure or execution overtime;
Grouping the required analysis fields according to remote control command names, calculating the remote control execution success rate and the command execution success rate of each trolley according to the execution results corresponding to the required analysis fields, storing the remote control execution success rate and the command execution success rate into a ClickHouse database, preparing for the next calculation and providing data for subsequent display.
After the real-time data stream of the Internet of vehicles is subjected to data analysis to obtain the calculation result of the execution success rate, the result can be fed back to the user and the automobile control system (for example, the automobile condition and the early warning information are displayed to the user through a mobile phone APP or an automobile display screen), so that the user and the automobile control system can make corresponding decisions according to the fed back information.
Based on the same inventive concept, the embodiment of the invention also provides an intelligent network-connected automobile remote control data analysis system, as shown in fig. 3, comprising:
the acquisition unit is used for acquiring the real-time data stream of the Internet of vehicles by utilizing the Internet of vehicles remote control technology;
The merging unit is used for merging the real-time data stream of the Internet of vehicles and the historical remote control data of the Internet of vehicles to generate batch processing data;
And the computing unit is used for computing the remote control execution success rate and the instruction execution success rate of each trolley after analyzing and filtering the batch processing data.
In some specific embodiments, the computing unit is specifically configured to analyze batch data into analyzable data by using a Spark computing engine, obtain a required analysis field, and import the required analysis field into a PowerBI analysis system;
The required analysis fields comprise a frame number, an operator, operation time, a remote control instruction name, a remote control instruction ID, an execution result and a failure reason.
The specific manner in which the respective unit modules perform the operations in the above-described embodiments has been described in detail in relation to the embodiments of the method, and will not be described in detail herein.
Based on the same inventive concept, the embodiment of the invention also provides a vehicle, comprising the intelligent network-connected automobile remote control data analysis system.
The remote control system calculates the execution success rate of the remote control of the single trolley and the execution success rate of the same type of remote control instructions, obtains the reason of remote control failure, can be well developed and optimized for different situations, thereby improving the remote control service level of a vehicle enterprise, has higher practical value and wide application prospect, can more comprehensively know the condition of the vehicle and the requirements of the user by collecting the running state data, the environment data and the user behavior data of the vehicle, can quickly and accurately identify abnormal data and potential risks by data preprocessing and data analysis, improves the efficiency and accuracy of remote control, and can timely transmit the result calculated by an analysis system to the user and the vehicle control system by data feedback, thereby improving the user experience and the driving safety.
It should be noted that the foregoing description is only a preferred embodiment of the present invention, and although the present invention has been described in detail with reference to the foregoing embodiments, it should be understood that modifications, equivalents, improvements and modifications to the technical solution described in the foregoing embodiments may occur to those skilled in the art, and all modifications, equivalents, and improvements are intended to be included within the spirit and principle of the present invention.
Claims (7)
1. An intelligent network-connected automobile remote control data analysis method is characterized by comprising the following steps:
Acquiring real-time data flow of the Internet of vehicles by utilizing an Internet of vehicles remote control technology;
Combining the real-time data stream of the Internet of vehicles and the historical remote control data of the Internet of vehicles to generate batch processing data;
after analyzing and filtering the batch processing data, calculating the remote control execution success rate and the instruction execution success rate of each trolley;
wherein, the
After analyzing and filtering the batch data, calculating the remote control execution success rate and the instruction execution success rate of each trolley, wherein the remote control execution success rate and the instruction execution success rate comprise:
Analyzing batch processing data into analyzable data by utilizing a Spark computing engine, obtaining required analysis fields and importing the required analysis fields into a PowerBI analysis system, grouping, sequencing and connecting the required analysis fields by utilizing the PowerBI analysis system to filter out the same event sent by different terminals and calculate the remote control execution success rate and the instruction execution success rate of each trolley, wherein the required analysis fields comprise a frame number, an operator, operation time, a remote control instruction name, a remote control instruction ID, an execution result and a failure reason;
The method comprises the steps of grouping, ordering and connecting calculation are carried out on required analysis fields through PowerBI analysis systems so as to filter out the same event sent by different terminals, and calculating the remote control execution success rate and the instruction execution success rate of each trolley, wherein the method comprises the following steps:
the method comprises the steps of obtaining execution data and a time stamp of each module of each remote control instruction by using each frame number, filtering out the same event sent by different terminals according to the frame numbers and remote control instruction IDs, merging the execution data of each module into a remote control instruction result form through the frame numbers, calibrating an execution result, grouping required analysis fields according to remote control instruction names, calculating remote control execution success rate and instruction execution success rate of each trolley through the execution result corresponding to the required analysis fields, and storing the remote control execution success rate and the instruction execution success rate into a ClickHouse database, wherein the execution result comprises execution success rate, execution failure or execution overtime.
2. The method for analyzing remote control data of an intelligent network-connected vehicle according to claim 1, wherein the acquiring real-time data stream of the internet of vehicles by using the remote control technology of the internet of vehicles comprises:
obtaining a real-time data stream of the Internet of vehicles by using a Kafka platform and storing the real-time data stream in a Redis database;
The real-time data stream of the Internet of vehicles comprises running state data, environment data and user behavior data of the automobile.
3. The method for analyzing remote control data of an intelligent network-connected vehicle according to claim 1, wherein the merging the real-time data stream of the internet of vehicles and the historical remote control data of the internet of vehicles to generate batch data comprises:
Acquiring the latest frame data in the Redis database by adopting a Spark-Kafka connection component;
Accessing ClickHouse a database using DBeaver link software and obtaining the historical remote control data in ClickHouse database;
and merging the latest frame data and the historical remote control data, rearranging data items and generating batch processing data.
4. A method of analyzing remote control data of an intelligent networked automobile according to claim 3, wherein merging the latest frame data and the historical remote control data, rearranging data entries and generating batch data, comprises:
In the merging process, the frame numbers are matched with the corresponding terminal IDs, and all execution module signal data of the single instruction are merged by using the same remote control instruction ID to generate a list of remote control instruction result forms.
5. The method for analyzing remote control data of intelligent network-connected vehicles according to claim 2, wherein the acquiring real-time data stream of the vehicle network by using the Kafka platform and storing the real-time data stream in the Redis database comprises the following steps:
After an initial Internet of vehicles real-time data stream is acquired, sequentially carrying out data preprocessing and data analysis on the initial Internet of vehicles real-time data stream to acquire a final Internet of vehicles real-time data stream and storing the final Internet of vehicles real-time data stream in a Redis database;
the method for preprocessing the data of the initial Internet of vehicles real-time data stream comprises the steps of cleaning, arranging and formatting the initial Internet of vehicles real-time data stream to obtain the Internet of vehicles real-time data stream after data preprocessing;
The data analysis of the real-time data stream of the Internet of vehicles after the data preprocessing comprises the steps of classifying and analyzing the real-time data stream of the Internet of vehicles after the data preprocessing by utilizing an algorithm to obtain a final real-time data stream of the Internet of vehicles.
6. An intelligent network-connected automobile remote control data analysis system, which is characterized by comprising:
the acquisition unit is used for acquiring the real-time data stream of the Internet of vehicles by utilizing the Internet of vehicles remote control technology;
The merging unit is used for merging the real-time data stream of the Internet of vehicles and the historical remote control data of the Internet of vehicles to generate batch processing data;
The computing unit is used for computing the remote control execution success rate and the instruction execution success rate of each trolley after analyzing and filtering the batch processing data;
wherein, the
The computing unit is specifically used for analyzing batch processing data into analyzable data by utilizing a Spark computing engine, obtaining required analysis fields and importing the required analysis fields into a PowerBI analysis system, and grouping, sequencing and connecting the required analysis fields by the PowerBI analysis system so as to filter out the same event sent by different terminals and calculate the remote control execution success rate and the instruction execution success rate of each trolley, wherein the required analysis fields comprise a frame number, an operator, operation time, a remote control instruction name, a remote control instruction ID, an execution result and a failure reason;
The method comprises the steps of grouping, ordering and connecting calculation are carried out on required analysis fields through PowerBI analysis systems so as to filter out the same event sent by different terminals, and calculating the remote control execution success rate and the instruction execution success rate of each trolley, wherein the method comprises the following steps:
the method comprises the steps of obtaining execution data and a time stamp of each module of each remote control instruction by using each frame number, filtering out the same event sent by different terminals according to the frame numbers and remote control instruction IDs, merging the execution data of each module into a remote control instruction result form through the frame numbers, calibrating an execution result, grouping required analysis fields according to remote control instruction names, calculating remote control execution success rate and instruction execution success rate of each trolley through the execution result corresponding to the required analysis fields, and storing the remote control execution success rate and the instruction execution success rate into a ClickHouse database, wherein the execution result comprises execution success rate, execution failure or execution overtime.
7. A vehicle comprising an intelligent networked automotive remote control data analysis system according to claim 6.
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| US20230128788A1 (en) * | 2021-10-25 | 2023-04-27 | Wejo Ltd. | System and method for processing vehicle event data for improved point snapping of road segments |
| CN114637238A (en) * | 2022-03-10 | 2022-06-17 | 苏州新控域智能科技有限公司 | Intelligent control system and method for intelligent network connection engineering machinery |
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| CN111932236A (en) * | 2020-06-28 | 2020-11-13 | 郑州威科姆科技股份有限公司 | NB-IoT-based vehicle insurance duration charging device and reliability processing method |
| CN113709106A (en) * | 2021-07-22 | 2021-11-26 | 一汽解放汽车有限公司 | Data analysis system and method suitable for commercial vehicle networking data |
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