CN113709106A - Data analysis system and method suitable for commercial vehicle networking data - Google Patents
Data analysis system and method suitable for commercial vehicle networking data Download PDFInfo
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Abstract
The application relates to a data analysis system and method suitable for commercial vehicle networking data. The system comprises: data acquisition module and data analysis module, wherein: the data acquisition module is used for generating commercial vehicle networking real-time data based on vehicle CAN line signals acquired from a CAN bus and transmitting the commercial vehicle networking real-time data to the data analysis module; the data analysis module is used for carrying out edge calculation on the commercial vehicle networking real-time data based on the received commercial vehicle networking real-time data so as to obtain single vehicle analysis results respectively corresponding to the commercial vehicles; the data analysis module is also used for carrying out background calculation on the commercial vehicle networking real-time data based on the received commercial vehicle networking data and the called background calculation engine so as to obtain a vehicle fleet analysis result corresponding to a corresponding target vehicle fleet; the target fleet includes a plurality of target commercial vehicles of the same vehicle type. Adopt this system can improve commercial car networking data analysis efficiency.
Description
Technical Field
The application relates to the technical field of commercial vehicle networks, in particular to a data analysis system and method suitable for commercial vehicle networking data.
Background
The internet of vehicles means that vehicle-mounted equipment on a vehicle effectively utilizes all vehicle dynamic information in an information network platform through a wireless communication technology so as to provide different functional services in the running process of the vehicle. At present, with the penetration of the car networking to the aspects of research, production, sale, service and the like of commercial car vehicles, the data value of the data of the commercial car networking is in urgent need of development and utilization. At present, data values of commercial vehicle networking data are paid attention to in whole vehicle factories, collected commercial vehicle networking data are stored in commercial vehicle data application systems developed by manufacturers, and corresponding data application modes are not disconnected. However, the vehicle data volume collected based on the internet of vehicles is large, the data format is relatively complex, and the value density is low; therefore, when data analysis is performed based on the collected commercial vehicle networking data, there is a problem that analysis efficiency is low.
Disclosure of Invention
Based on this, it is necessary to provide a data analysis system and method suitable for commercial vehicle networking data, which can improve the efficiency of commercial vehicle networking data analysis, in order to solve the above technical problems.
A data analysis system suitable for commercial car networking data, the system includes data acquisition module and data analysis module, wherein:
the data acquisition module is used for generating commercial vehicle networking real-time data based on vehicle CAN line signals acquired from a CAN bus and transmitting the commercial vehicle networking real-time data to the data analysis module;
the data analysis module is used for carrying out edge calculation on the commercial vehicle networking real-time data based on the received commercial vehicle networking real-time data so as to obtain single vehicle analysis results respectively corresponding to the commercial vehicles;
the data analysis module is further used for carrying out background calculation on the commercial vehicle networking real-time data based on the received commercial vehicle networking data and the called background calculation engine so as to obtain a vehicle fleet analysis result corresponding to a corresponding target vehicle fleet; the target fleet includes a plurality of target commercial vehicles of the same vehicle type.
In one embodiment, the data acquisition module is further configured to encrypt the generated commercial vehicle internet of vehicles real-time data in a preset encryption manner, and transmit the encrypted ciphertext data to the data analysis module;
and the data analysis module is also used for decrypting the received ciphertext data based on a preset decryption mode which is adaptive to the encryption mode so as to obtain corresponding commercial vehicle networking real-time data.
In one embodiment, the vehicle CAN line signal comprises at least one vehicle signal of a meter mileage signal, a meter vehicle speed signal, an engine speed signal, a load signal and a gear signal of the commercial vehicle;
the data analysis module is further used for calculating at least one single-vehicle analysis result of single-vehicle accumulated oil consumption, single-vehicle speed distribution, single-vehicle engine rotating speed distribution, single-vehicle fault distribution, single-vehicle load and single-vehicle gear shifting times which respectively correspond to each commercial vehicle based on the vehicle signals of the commercial vehicles when the edge calculation is carried out on the commercial vehicle networking real-time data;
the data analysis module is further used for calculating at least one fleet analysis result in fleet accumulated oil consumption, fleet speed distribution, fleet engine speed distribution, fleet environment temperature distribution, fleet fault distribution, fleet total load and fleet total gear shifting times corresponding to the target fleet based on the target vehicle signal set of the target fleet when the commercial vehicle networking real-time data is subjected to background calculation; the background computing engine comprises at least one of a Spark computing engine, a Hive computing engine and an Impala computing engine.
In one embodiment, the system further comprises a data storage module, wherein:
the data storage module is used for acquiring and storing vehicle dynamic data and vehicle static data, wherein the vehicle dynamic data comprise the commercial vehicle internet of vehicles real-time data, the single vehicle analysis result and at least one dynamic data in the fleet analysis result, and the vehicle static data comprise attribute information adapted to the dynamic data.
In one embodiment, the system further comprises a data application module, wherein:
the data application module is used for acquiring the single vehicle analysis result and the motorcade analysis result transmitted by the data analysis module;
the data application module is further used for calling corresponding application services to process the obtained single-vehicle analysis result and/or the fleet analysis result when the service request instruction transmitted by the corresponding request terminal is obtained, and feeding back the processed service result to the request terminal; the application service comprises at least one of a data display service, a report making service and a push service.
In one embodiment, the system further comprises an upgrade module, wherein:
the upgrading module is used for acquiring the upgrading packet transmitted by the remote server and transmitting the acquired upgrading packet to the corresponding module to be upgraded so that the module to be upgraded can upgrade the version according to the received upgrading packet, wherein the module to be upgraded comprises at least one of a data acquisition module and a data analysis module.
A method of data analysis adapted for use with the system of any one of the preceding claims, the method comprising:
acquiring vehicle CAN line signals from a CAN bus, and generating commercial vehicle networking real-time data based on the acquired vehicle CAN line signals;
performing edge calculation on the commercial vehicle networking real-time data to obtain single vehicle analysis results corresponding to the commercial vehicles respectively;
performing background calculation on the commercial vehicle networking real-time data based on the called background calculation engine to obtain a vehicle fleet analysis result corresponding to a corresponding target vehicle fleet; the target fleet includes a plurality of target commercial vehicles of the same vehicle type.
A computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the following steps when executing the computer program:
acquiring vehicle CAN line signals from a CAN bus, and generating commercial vehicle networking real-time data based on the acquired vehicle CAN line signals;
performing edge calculation on the commercial vehicle networking real-time data to obtain single vehicle analysis results corresponding to the commercial vehicles respectively;
performing background calculation on the commercial vehicle networking real-time data based on the called background calculation engine to obtain a vehicle fleet analysis result corresponding to a corresponding target vehicle fleet; the target fleet includes a plurality of target commercial vehicles of the same vehicle type.
A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out the steps of:
acquiring vehicle CAN line signals from a CAN bus, and generating commercial vehicle networking real-time data based on the acquired vehicle CAN line signals;
performing edge calculation on the commercial vehicle networking real-time data to obtain single vehicle analysis results corresponding to the commercial vehicles respectively;
performing background calculation on the commercial vehicle networking real-time data based on the called background calculation engine to obtain a vehicle fleet analysis result corresponding to a corresponding target vehicle fleet; the target fleet includes a plurality of target commercial vehicles of the same vehicle type.
According to the data analysis system, the data analysis method, the computer equipment and the storage medium suitable for the commercial vehicle networking data, the data analysis module is adopted to process the commercial vehicle networking real-time data through the edge calculation and the call-based background calculation engine so as to obtain the corresponding single vehicle analysis result or the fleet analysis result, on one hand, the data can be processed in an edge data center or a local data center, and important data can be sequenced from the less important data, so that the task waiting time is reduced, the data processing speed is improved, and the network delay is reduced. The problems that due to the fact that the collected commercial vehicle internet of vehicles is large in real-time data volume, the data format is relatively complex, the value density is low, the user searching mode is complex, and the searching efficiency is low are solved. On the other hand, the problem that a data application mode and a data dimension are single is avoided, and the analysis efficiency of the commercial vehicle networking data is improved under the condition that the user analysis requirements are well mastered.
Drawings
FIG. 1 is a schematic block diagram of a data analysis system suitable for use with commercial Internet of vehicles data in one embodiment;
FIG. 2 is a block diagram of a system architecture of a data analysis system suitable for use with commercial Internet of vehicles data in one embodiment;
FIG. 3 is a schematic diagram of a single-vehicle cumulative fuel consumption distribution of a data analysis system suitable for commercial vehicle networking data in one embodiment;
FIG. 4 is a schematic diagram of a data analysis software architecture of a data analysis system suitable for use with commercial Internet of vehicles data, according to one embodiment;
FIG. 5 is a data processing schematic diagram of a data analysis system suitable for use with commercial Internet of vehicles data in one embodiment;
FIG. 6 is a schematic flow chart diagram of a data analysis method applicable to commercial vehicle networking data in one embodiment;
FIG. 7 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The data analysis system suitable for the commercial vehicle networking data can be applied to a commercial vehicle intelligent query system shown in figure 1. The intelligent query system for the commercial vehicle shown in fig. 1 comprises a data acquisition module and a data analysis module. The data acquisition module is used for generating commercial vehicle networking real-time data based on vehicle CAN line signals acquired from a CAN bus and transmitting the commercial vehicle networking real-time data to the data analysis module. The data analysis module is used for carrying out edge calculation on the real-time data of the commercial vehicle networking based on the received real-time data of the commercial vehicle networking so as to obtain the bicycle analysis results respectively corresponding to the commercial vehicles. The method comprises the steps that a background calculation engine is called to calculate the real-time data of the commercial vehicle networking on the basis of received commercial vehicle networking data to obtain a vehicle fleet analysis result corresponding to a corresponding target vehicle fleet; the target fleet includes a plurality of target commercial vehicles of the same vehicle type.
In one embodiment, the data collection module includes a T-Box module and an MES (Manufacturing Execution System) module, where the T-Box module is configured to provide an information transmission channel for the data collection module and a corresponding data collection object based on a communication method such as 2G, 3G, 4G, and 5G. The MES module is used for connecting theoretical data of the basic information system and actual data of the factory through a real-time database and providing a communication function between the business planning system and the manufacturing control system.
In one embodiment, the background computing engine of the data analysis module application includes at least one of a Spark (an open source clustered computing environment) computing engine, a Hive (data warehouse tool) computing engine, and an Impala (a new query system) computing engine. Wherein:
(1) the Spark calculation engine is a general calculation engine suitable for data mining and machine learning, and can be used for performing SQL (Structured Query Language) Query and text processing in real time.
(2) The Hive computing engine is a data warehouse tool based on Hadoop (distributed system infrastructure), is used for data extraction, transformation and loading, is a mechanism capable of storing, querying and analyzing large-scale data stored in Hadoop, and can realize fast MapReduce (map reduction) statistics through similar SQL statements.
(3) The Impala computing engine can provide SQL semantics and can query large data of a certain magnitude stored in a Hadoop Distributed File System (HDFS) System and a Hadoop Database (HBase) System.
In one embodiment, the intelligent query system for a commercial vehicle further comprises a data storage module and a data application module, wherein:
(1) the data storage module is composed of a Mysql (relational database management system) database, a MongoDB (distributed file storage-based database) database and an HDFS (Hadoop distributed file system) system and is used for storing vehicle dynamic data and vehicle static data, wherein the vehicle dynamic data comprises at least one of commercial vehicle networking real-time data, single vehicle analysis results and fleet analysis results, and the vehicle static data comprises attribute information adaptive to the dynamic data.
(2) The data Application module is suitable for a Web system, a mobile phone APP (Application program) and a vehicle machine APP, a user can input bicycle and fleet query information based on an information input channel provided by the Web system, the mobile phone APP or the vehicle machine APP, the input query information is further fed back to the data storage module, the data storage module queries a bicycle analysis result or a fleet analysis result based on the received query information, the queried result is output to the corresponding Web system, the mobile phone APP or the vehicle machine APP, and the information feedback channel provided by the Web system, the mobile phone APP and the vehicle machine APP is fed back to the user.
In one embodiment, as shown in fig. 2, a data analysis system 200 suitable for commercial vehicle networking data is provided, the system 200 comprising a data acquisition module 201 and a data analysis module 202, wherein:
and the data acquisition module 201 is used for generating the commercial vehicle networking real-time data based on the vehicle CAN line signals acquired from the CAN bus and transmitting the commercial vehicle networking real-time data to the data analysis module.
The CAN bus belongs to the field bus category, and is a serial communication network which effectively supports distributed control or real-time control.
Specifically, the data acquisition module 201 is connected to vehicle devices such as a vehicle instrument and an ECU (Electronic Control Unit) through a CAN bus, and acquires a vehicle CAN line signal through the CAN bus. The collected vehicle CAN line signal comprises at least one signal of an instrument vehicle speed signal, an engine rotating speed signal, an engine torque signal, an instrument mileage signal, an instantaneous fuel consumption signal, an environment temperature signal, a fault signal, a fuel tank liquid level signal, a urea liquid level signal, a load signal, a steering wheel corner signal, a coolant temperature signal, a GPS (Global Positioning System) signal, an acceleration signal and the like.
In one embodiment, the vehicle CAN line signals collected by the data collection module 201 are uploaded to the vehicle networking database, and the vehicle CAN line signals are stored in the vehicle networking database. When the subsequent to-be-analyzed data module 202 needs to call the vehicle CAN line signal, for example, when the data analysis module 202 needs to call the ambient temperature signal, a corresponding query instruction is generated based on the ambient temperature signal, and then the required ambient temperature signal is queried from the vehicle network database based on the query instruction. In one embodiment, if the vehicle networking database uses MySQL database to store the vehicle CAN line signals, the data analysis module 202 may obtain the data to be called through the command prompt, for example, the data analysis module may perform data acquisition by executing "SQL SELECT" command. Alternatively, a PHP (server-side scripting language) script is used to obtain the data that needs to be called, for example, the data analysis module may use the "mysql _ query ()" of the PHP function and perform data acquisition by executing the "SQL SELECT" command.
Like this, through the vehicle networking database vehicle CAN line signal of gathering save to in vehicle CAN line signal to gathering carries out centralized management, has improved data query speed, and is favorable to improving the analysis efficiency of commercial car networking data.
And the data analysis module 202 is configured to perform edge calculation on the real-time data of the commercial vehicle-to-vehicle network based on the received real-time data of the commercial vehicle-to-vehicle network, so as to obtain bicycle analysis results corresponding to the commercial vehicles respectively.
Specifically, the edge calculation method adopted by the data analysis module 202 specifically means that an open platform integrating network, calculation, storage and application core capabilities is adopted on one side close to an object or a data source, so that a nearest-end service is provided nearby. The application program is initiated at the edge side, so that a faster network service response is generated, and the basic requirements of the industry in the aspects of real-time business, application intelligence, safety, privacy protection and the like are met. It should be noted that the edge computation is between the physical entity and the industrial connection, or on top of the physical entity. And the cloud computing still can access the historical data of the edge computing.
In one embodiment, when performing the edge calculation on the real-time data of the commercial vehicle-to-vehicle network, the data analysis module 202 calculates a single-vehicle analysis result corresponding to each commercial vehicle based on the vehicle signal of the commercial vehicle. It should be noted that different types of vehicle signals correspond to different single-vehicle analysis results, where when the vehicle signal is an instrument mileage signal, the calculated single-vehicle analysis result may be a single-vehicle accumulated fuel consumption obtained after performing edge calculation based on the instrument mileage signal (it should be noted that, the distribution of the currently calculated single-vehicle accumulated fuel consumption may specifically refer to fig. 3); when the vehicle signal is the meter vehicle speed signal, the corresponding calculated bicycle analysis result may also be a bicycle speed distribution obtained after the edge calculation based on the meter vehicle speed signal.
Therefore, the data analysis module processes the real-time data of the commercial vehicle internet of vehicles through edge calculation, can process the data in an edge data center or a local data center and sort important data from less important data, reduces task waiting time, improves data processing speed and reduces network delay.
The data analysis module 202 is further configured to perform background calculation on the commercial vehicle networking real-time data based on the received commercial vehicle networking data and the called background calculation engine to obtain a vehicle fleet analysis result corresponding to a corresponding target vehicle fleet; the target fleet comprises a plurality of target commercial vehicles of the same vehicle type
Specifically, the background computing engine adopted by the data analysis module 202 includes at least one of a Spark computing engine, a Hive computing engine and an Impala computing engine; wherein:
(1) the Spark calculation engine is used as a large-scale data calculation framework, and when the Spark calculation engine is called to perform background calculation on the real-time data of the commercial vehicle internet, the Spark calculation engine mainly adopts memory calculation to achieve instant processing of mass data in a short time. It should be noted that the memory calculation refers to a calculation model that uses various memory calculations to allow a Central Processing Unit (CPU) to read and write data from a main memory, rather than a disk, in the calculation process. The memory technology comprises methods of column storage format, data partitioning and compression, incremental writing, no summary table and the like.
(2) The Hive calculation engine supports data processing through a MapReduce calculation engine, wherein the MapReduce calculation engine mainly uses a Map function and a Reduce function to realize a basic parallel calculation task, and can be specifically understood as summarizing a pile of disordered data according to certain characteristics, processing and obtaining a final result; the Map function is faced with disordered and unrelated data, each data is analyzed through the Map function, and keys and values are further extracted, namely the features of the data are extracted. In the Reduce stage, the obtained data are already summarized data, and on the basis, the data analysis module can further process the data so as to obtain a corresponding summarized result.
(3) The Impala computing engine represents the execution plan as a complete execution plan tree, and can distribute the execution plan to each Impala (query engine) to execute the query more naturally, without combining the Impala (a novel query system) into a pipeline type map- > reduce mode like Hive (a data warehouse tool), so that better concurrency of the Impala (a novel query system) is ensured.
In the data analysis system suitable for the commercial vehicle networking data, the data analysis module is adopted to process the commercial vehicle networking real-time data through the edge calculation and the calling-based background calculation engine so as to obtain the corresponding single vehicle analysis result or fleet analysis result, on one hand, the data can be processed in an edge data center or a local data center, and important data can be sequenced from less important data, so that the task waiting time is reduced, the data processing speed is improved, and the network delay is reduced. The problems that due to the fact that the collected commercial vehicle internet of vehicles is large in real-time data volume, the data format is relatively complex, the value density is low, the user searching mode is complex, and the searching efficiency is low are solved. On the other hand, the problem that a data application mode and a data dimension are single is avoided, and the analysis efficiency of the commercial vehicle networking data is improved under the condition that the user analysis requirements are well mastered.
In an embodiment, the data collection module 201 is further configured to encrypt the generated commercial vehicle internet of vehicles real-time data by using a preset encryption method, and transmit the encrypted ciphertext data to the data analysis module.
Specifically, the data acquisition module 201 may encrypt the commercial vehicle-to-vehicle networking real-time data in a PKI (Public Key encryption) encryption system, an asymmetric encryption system, or the like, and transmit the encrypted ciphertext data to the data analysis module 202 in a wireless and/or wired transmission manner when it is determined that the data analysis module is successfully connected to the data acquisition module, so that the data analysis module 202 decrypts the acquired encrypted information, and performs edge calculation or background calculation on the decrypted data.
In one embodiment, when the data acquisition module 201 encrypts the commercial vehicle-to-vehicle networking real-time data by using a PKI encryption system, the method includes:
first, for the data sender (i.e., the data collection module 201), it needs to build a key and publish the key.
Then, the data sending party encrypts the data (i.e. the commercial vehicle internet real-time data) to be transmitted to the data receiving party (i.e. the data analysis module 202) through the currently constructed key, and transmits the corresponding encrypted data to the data receiving party.
Finally, the data receiver decrypts the received encrypted data by using the key disclosed by the data sender, so as to ensure the data transmission security and avoid the interception or midway interception of the commercial vehicle internet transmitted by the data acquisition module 201 by an illegal user.
In one embodiment, the data collection module 201 is further configured to transmit a TCP/IP (Transmission Control Protocol/Internet Protocol) packet to the data analysis module 202, and when receiving a response packet fed back through the data analysis module 202 within a predetermined time, consider that the data collection module is successfully connected to the data analysis module, and then further transmit the data to the data analysis module 202. The data acquisition module 201 may establish data communication with the data analysis module based on 2G, 3G, 4G, and 5G communication modes.
Therefore, before the data acquisition module transmits the data to the data analysis module, the data acquisition module authenticates the communication reliability between the data acquisition module and the data analysis module through a TCP/IP protocol, further ensures that the data can be transmitted to the corresponding data receiving end timely and completely, and improves the communication efficiency.
The data analysis module 202 is further configured to decrypt the received ciphertext data based on a preset decryption manner that is suitable for the encryption manner, so as to obtain corresponding real-time data of the internet of vehicles of the commercial vehicle.
Specifically, after the data analysis module 202 receives the encrypted data, if it is determined that the data acquisition module 201 adopts the PKI encryption system, so as to encrypt the real-time data of the commercial vehicle-to-vehicle network, it may request to acquire a key disclosed in advance by the data acquisition module 201, and decrypt the encrypted data based on the acquired key, so as to acquire the corresponding real-time data of the commercial vehicle-to-vehicle network.
In the embodiment, the data privacy, the data integrity and the identity uniqueness in the data transmission can be ensured based on the PKI encryption system, the data transmission safety is ensured, and the commercial vehicle internet transmitted by the data acquisition module is prevented from being intercepted or midway intercepted by an illegal user.
In one embodiment, the vehicle CAN line signal comprises at least one vehicle signal of a meter range signal, a meter vehicle speed signal, an engine speed signal, a load signal and a gear signal of the commercial vehicle.
The data analysis module 202 is further configured to, when performing edge calculation on the real-time data of the commercial vehicle networking, calculate to obtain at least one single vehicle analysis result of the single vehicle cumulative oil consumption, the single vehicle speed distribution, the single vehicle engine speed distribution, the single vehicle fault distribution, the single vehicle load, and the single vehicle shift frequency, which correspond to each commercial vehicle, based on the vehicle signal of the commercial vehicle.
Specifically, referring to fig. 4, after the data analysis module 202 acquires the data source (i.e., the commercial vehicle-to-vehicle networking real-time data):
first, the data source will be extracted and cleaning transformed based on the ETL (Extract Transform Load) tool.
And then loading the obtained cleaning data to a data warehouse so as to integrate scattered, disordered and non-uniform data. In this case, the data source may be stored in real time by specifying Hadoop, NoSQL (Not Only SQL, a non-relational database), MongoDB, or the like integrated in the big data system.
And subsequently, when analysis and mining are required, loading target cleaning data based on the data warehouse, and performing edge calculation on the currently loaded target cleaning data to obtain a corresponding single-vehicle analysis result. And the finally analyzed bicycle analysis result is further fed back to the user to help the user to master the running condition of the commercial vehicle.
In one embodiment, the vehicle CAN line signal further includes an instantaneous oil consumption signal, an ambient temperature signal, a fault signal, an oil tank liquid level signal, a urea liquid level signal, a steering wheel corner signal, a gas station GPS positioning signal, an acceleration signal, and the like, and the corresponding obtained analysis result of the single vehicle corresponding to each of the commercial vehicles further includes a current instantaneous oil consumption corresponding to the instantaneous oil consumption signal, a current ambient temperature distribution corresponding to the ambient temperature signal, a current fault distribution corresponding to the fault signal, a fuel filling amount distribution corresponding to the oil tank liquid level signal, a urea injection frequency corresponding to the urea liquid level signal, an emergency turn frequency corresponding to the steering wheel corner signal, a gas station list corresponding to the GPS positioning signal, and an emergency acceleration frequency and an emergency deceleration frequency corresponding to the acceleration signal.
Therefore, data extraction and data cleaning conversion are carried out based on the ETL tool, the change data can be accurately identified, the ETL tool does not need to be deployed on a core server, and pressure is not caused on the core server. By specifying the Hadoop, NoSQL database and MongoDB database integrated in the big data system, the data source is stored instantly, dynamic mobile data can be carried out among nodes, the dynamic balance of each node is ensured, the processing speed is high, and the data processing efficiency can be effectively improved.
The data analysis module 202 is further configured to calculate, based on a target vehicle signal set of a target fleet, at least one fleet analysis result of fleet cumulative oil consumption, fleet speed distribution, fleet engine speed distribution, fleet environment temperature distribution, fleet fault distribution, fleet total load and fleet total shift times corresponding to the target fleet when performing background calculation on the commercial vehicle networking real-time data; the background computing engine comprises at least one of a Spark computing engine, a Hive computing engine and an Impala computing engine.
Specifically, referring to FIG. 5, the big data analysis platform provided by the embodiment of the application comprises a data conversion matcher for performing data type conversion, a connector library for connecting the database, a data integration engine for logically or physically organically concentrating data with different sources, formats and characteristic properties, a data processing operation library for providing operation channels such as data operation and drawing, a data processing engine for providing processing channels such as query and storage, a data analysis engine, a machine learning algorithm library for providing machine learning and data analysis algorithms and the like, an intelligent display engine for providing a data display channel, an intelligent analysis recommender for providing automatic product analysis and allowing user operation, and a visual graph library for providing data prediction/cluster analysis/correlation analysis/combination graph and the like.
In one embodiment, the commercial vehicle networking real-time data transmitted by the data acquisition module 201 is integrated in a Kafka (distributed publish-subscribe messaging system) message queue, and the data analysis module 202 invokes a background computing engine to perform distributed computation based on each functional component provided in the big data analysis platform to obtain a corresponding fleet analysis result.
In the embodiment, the commercial vehicle networking real-time data is integrated in the Kafka message queue, so that the key component can withstand the sudden access pressure without completely collapsing due to the sudden overload request; and the background computing engine is called to perform distributed computing, so that rare resources can be shared, computing loads can be balanced on a plurality of computers, and the analysis efficiency of the commercial vehicle networking data is improved.
In one embodiment, the system further comprises a data storage module, wherein: and the data storage module is used for acquiring and storing vehicle dynamic data and vehicle static data, wherein the vehicle dynamic data comprises at least one dynamic data of commercial vehicle networking real-time data, a single vehicle analysis result and a fleet analysis result, and the vehicle static data comprises attribute information adaptive to the dynamic data.
Specifically, the data storage module may use a Hadoop, NoSQL, or MongoDB database to store the vehicle dynamic data and the vehicle static data in real time. When the data analysis module needs to call the vehicle dynamic data and the vehicle static data from the data storage module, the data analysis module only needs to set a query instruction based on the vehicle dynamic data and the vehicle static data, and then queries the required data from the data storage module based on the query instruction.
In one embodiment, the data storage module may also use a PKI encryption system to encrypt the data of the vehicle dynamic data and the vehicle static data that are currently stored, so that data privacy may be ensured, and identity uniqueness may also be ensured, so that even when an illegal user illegally obtains the access right of the data storage module, the user cannot obtain what data is stored in the current data storage module without knowing a decryption rule.
In the embodiment, the Hadoop, NoSQL database or MongoDB database is used for storing the dynamic data and the static data of the vehicle in real time, dynamic mobile data are carried out among the nodes, the dynamic balance of each node is ensured, the processing speed is high, and the data processing efficiency can be effectively improved. The data security is further ensured by a preset encryption mode, for example, a PKI encryption system carries out data encryption on the vehicle dynamic data and the vehicle static data which are stored at present.
In one embodiment, the system further comprises a data application module, wherein: and the data application module is used for acquiring the single vehicle analysis result and the motorcade analysis result transmitted by the data analysis module.
Specifically, when the data analysis module determines that the data analysis module is successfully connected with the data application module, the data analysis module transmits the single-vehicle analysis result and the fleet analysis result to the data application module based on a corresponding communication transmission protocol, such as a 2G communication protocol or a 3G communication protocol, so that the data application module calls a corresponding application service, processes the obtained single-vehicle analysis result and the fleet analysis result, and obtains a corresponding service result.
The data application module is also used for calling corresponding application services to process the obtained single-vehicle analysis result and/or the fleet analysis result when the service request instruction transmitted by the corresponding request terminal is obtained, and feeding back the processed service result to the request terminal; the application service comprises at least one of a data presentation service, a report making service and a push service.
Specifically, the data application module is suitable for a Web system, a mobile phone APP and a car machine APP, and a user can input single-car and fleet query information based on information input channels (for example, voice query and text input query) provided by the Web system, the mobile phone APP or the car machine APP, and the input query information:
in one embodiment, the information is further fed back to the data storage module, so that the data storage module queries the single vehicle analysis result or the fleet analysis result based on the received query information, and feeds back the queried result to the user through an information feedback channel provided by the Web system, the mobile phone APP and the vehicle APP.
In one embodiment, the input query information can be fed back to the data analysis module, the data analysis module matches the currently received query information with the analyzed single-vehicle analysis result or the analyzed vehicle fleet analysis result, and when the matching is successful, the corresponding matching result is fed back to the user through an information feedback channel provided by the Web system, the mobile phone APP and the vehicle APP.
In one embodiment, the information input channels provided by the Web system, the mobile phone APP and the car machine APP comprise keyboard input and voice input. The user can input the vehicle ID, the time range and select the query content through a keyboard input channel; the data application module may bind the fuzzy matching algorithm to the voice input channel for voice-to-text conversion. It should be noted that the fuzzy matching algorithm may be further understood as using a part of the parameters to perform the search of the related data. For example, a query by name, specifying only a certain portion of the name, such as the last name or a certain word in the first name or a combination thereof, can find the data associated therewith, mainly to find the required data as much as possible with little known information. In the current embodiment, the method can be set to trigger the data analysis module to perform data query when the keyword 'hello' is identified; the method can also be set to trigger the data analysis module to inquire the vehicle speed distribution data when the keyword 'vehicle speed' is identified; it may also be arranged to trigger the data analysis module to end the keyword recognition thread upon determining that no keyword has been recognized within 1 minute.
In the embodiment, the data analysis module performs data query based on the fuzzy matching algorithm, so that less memory is occupied, interaction is reduced (a user can input more relevant information), and the accuracy of data search is further ensured.
In one embodiment, the system further comprises an upgrade module, wherein: and the upgrading module is used for acquiring the upgrading packet transmitted by the remote server and transmitting the acquired upgrading packet to the corresponding module to be upgraded so that the module to be upgraded carries out version upgrading according to the received upgrading packet, wherein the module to be upgraded comprises at least one of a data acquisition module and a data analysis module.
Specifically, the upgrade module may implement version upgrade of a corresponding module to be upgraded by using an OTA (Over-the-Air Technology) upgrade technique. The OTA upgrading technology is a technology that a terminal downloads an upgrading package on a remote server through a wireless network and upgrades a system or application.
In one embodiment, as shown in fig. 6, a data analysis method applicable to the data analysis system disclosed in any one of the above embodiments, the method includes:
and step S602, acquiring vehicle CAN line signals from the CAN bus, and generating the commercial vehicle networking real-time data based on the acquired vehicle CAN line signals.
And step S604, performing edge calculation on the real-time data of the commercial vehicle networking to obtain the single vehicle analysis results respectively corresponding to the commercial vehicles.
Step S606, performing background calculation on the commercial vehicle networking real-time data based on the called background calculation engine to obtain a vehicle fleet analysis result corresponding to a corresponding target vehicle fleet; the target fleet includes a plurality of target commercial vehicles of the same vehicle type.
In one embodiment, the vehicle CAN line signal comprises at least one vehicle signal of an instrument mileage signal, an instrument vehicle speed signal, an engine speed signal, a load signal and a gear signal of the corresponding commercial vehicle; carry out edge calculation to commercial car networking real-time data to obtain the bicycle analysis result that each commercial vehicle corresponds respectively, include: when the edge calculation is carried out on the real-time data of the commercial vehicle networking, at least one single vehicle analysis result of single vehicle accumulated oil consumption, single vehicle speed distribution, single vehicle engine rotating speed distribution, single vehicle fault distribution, single vehicle load and single vehicle gear shifting times which are respectively corresponding to each commercial vehicle is obtained through calculation based on vehicle signals of the commercial vehicles.
Background calculation is carried out to commercial car networking real-time data based on backstage computational engine that calls to obtain the motorcade analysis result that corresponding target motorcade corresponds, includes: when background calculation is carried out on the commercial vehicle networking real-time data, at least one vehicle fleet analysis result of vehicle fleet accumulated oil consumption, vehicle fleet speed distribution, vehicle fleet engine rotating speed distribution, vehicle fleet environment temperature distribution, vehicle fleet fault distribution, vehicle fleet total load and vehicle fleet total gear shifting times corresponding to a target vehicle fleet is obtained through calculation based on a target vehicle signal set of the target vehicle fleet; the background computing engine comprises at least one of a Spark computing engine, a Hive computing engine and an Impala computing engine.
In one embodiment, the method further comprises: the trigger data acquisition module encrypts the generated commercial vehicle internet of vehicles real-time data in a preset encryption mode and transmits the encrypted ciphertext data to the data analysis module; and the trigger data analysis module decrypts the received ciphertext data based on a preset decryption mode which is adaptive to the encryption mode so as to obtain corresponding commercial vehicle internet real-time data.
In one embodiment, the method further comprises: and the trigger data storage module acquires and stores vehicle dynamic data and vehicle static data, wherein the vehicle dynamic data comprises at least one dynamic data of commercial vehicle networking real-time data, a single vehicle analysis result and a fleet analysis result, and the vehicle static data comprises attribute information adaptive to the dynamic data.
In one embodiment, the method further comprises: the trigger data application module acquires the single vehicle analysis result and the motorcade analysis result transmitted by the data analysis module; when a service request instruction transmitted by a corresponding request terminal is acquired, a data application module is triggered to call corresponding application service to process the calculated single vehicle analysis result and/or the vehicle fleet analysis result, and the service result obtained by processing is fed back to the request terminal; the application service comprises at least one of a data presentation service, a report making service and a push service.
In one embodiment, the method further comprises: and triggering the upgrading module to acquire the upgrading packet transmitted by the remote server, and transmitting the acquired upgrading packet to the corresponding module to be upgraded so that the module to be upgraded performs version upgrading according to the received upgrading packet, wherein the module to be upgraded comprises at least one of a data acquisition module and a data analysis module.
According to the data analysis method, the data analysis module is adopted to process the real-time data of the commercial vehicle internet of vehicles through the edge calculation and the call-based background calculation engine so as to obtain the corresponding single vehicle analysis result or fleet analysis result, on one hand, the data can be processed in an edge data center or a local data center, and the important data can be sequenced from the less important data, so that the task waiting time is reduced, the data processing speed is increased, and the network delay is reduced. The problems that due to the fact that the collected commercial vehicle internet of vehicles is large in real-time data volume, the data format is relatively complex, the value density is low, the user searching mode is complex, and the searching efficiency is low are solved. On the other hand, the problem that a data application mode and a data dimension are single is avoided, and the analysis efficiency of the commercial vehicle networking data is improved under the condition that the user analysis requirements are well mastered.
It should be understood that, although the steps in the flowchart of fig. 6 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a portion of the steps in fig. 6 may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed in turn or alternately with other steps or at least a portion of the other steps or stages.
For specific limitations of the data analysis method, see the above limitations of the data analysis system, which are not repeated herein. The various modules in the data analysis system may be implemented in whole or in part by software, hardware, and combinations thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a terminal or a server, and its internal structure diagram may be as shown in fig. 7. The computer device includes a processor, a memory, and a communication interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless communication can be realized through WIFI, an operator network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement a data analysis method suitable for commercial vehicle networking data.
Those skilled in the art will appreciate that the architecture shown in fig. 7 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having a computer program stored therein, the processor implementing the following steps when executing the computer program: acquiring vehicle CAN line signals from a CAN bus, and generating commercial vehicle networking real-time data based on the acquired vehicle CAN line signals; performing edge calculation on the real-time data of the commercial vehicle networking to obtain the single vehicle analysis results corresponding to the commercial vehicles respectively; performing background calculation on the commercial vehicle networking real-time data based on the called background calculation engine to obtain a vehicle fleet analysis result corresponding to a corresponding target vehicle fleet; the target fleet includes a plurality of target commercial vehicles of the same vehicle type.
In one embodiment, the vehicle CAN line signal comprises at least one vehicle signal selected from a meter mileage signal, a meter vehicle speed signal, an engine speed signal, a load signal and a gear signal of the corresponding commercial vehicle; the processor, when executing the computer program, further performs the steps of: when the edge calculation is carried out on the real-time data of the commercial vehicle networking, at least one single vehicle analysis result of single vehicle accumulated oil consumption, single vehicle speed distribution, single vehicle engine rotating speed distribution, single vehicle fault distribution, single vehicle load and single vehicle gear shifting times which are respectively corresponding to each commercial vehicle is obtained through calculation based on vehicle signals of the commercial vehicles.
Background calculation is carried out to commercial car networking real-time data based on backstage computational engine that calls to obtain the motorcade analysis result that corresponding target motorcade corresponds, includes: when background calculation is carried out on the commercial vehicle networking real-time data, at least one vehicle fleet analysis result of vehicle fleet accumulated oil consumption, vehicle fleet speed distribution, vehicle fleet engine rotating speed distribution, vehicle fleet environment temperature distribution, vehicle fleet fault distribution, vehicle fleet total load and vehicle fleet total gear shifting times corresponding to a target vehicle fleet is obtained through calculation based on a target vehicle signal set of the target vehicle fleet; the background computing engine comprises at least one of a Spark computing engine, a Hive computing engine and an Impala computing engine.
In one embodiment, the processor, when executing the computer program, further performs the steps of: the trigger data acquisition module encrypts the generated commercial vehicle internet of vehicles real-time data in a preset encryption mode and transmits the encrypted ciphertext data to the data analysis module; and the trigger data analysis module decrypts the received ciphertext data based on a preset decryption mode which is adaptive to the encryption mode so as to obtain corresponding commercial vehicle internet real-time data.
In one embodiment, the processor, when executing the computer program, further performs the steps of: and the trigger data storage module acquires and stores vehicle dynamic data and vehicle static data, wherein the vehicle dynamic data comprises at least one dynamic data of commercial vehicle networking real-time data, a single vehicle analysis result and a fleet analysis result, and the vehicle static data comprises attribute information adaptive to the dynamic data.
In one embodiment, the processor, when executing the computer program, further performs the steps of: the trigger data application module acquires the single vehicle analysis result and the motorcade analysis result transmitted by the data analysis module; when a service request instruction transmitted by a corresponding request terminal is acquired, a data application module is triggered to call corresponding application service to process the calculated single vehicle analysis result and/or the vehicle fleet analysis result, and the service result obtained by processing is fed back to the request terminal; the application service comprises at least one of a data presentation service, a report making service and a push service.
In one embodiment, the processor, when executing the computer program, further performs the steps of: and triggering the upgrading module to acquire the upgrading packet transmitted by the remote server, and transmitting the acquired upgrading packet to the corresponding module to be upgraded so that the module to be upgraded performs version upgrading according to the received upgrading packet, wherein the module to be upgraded comprises at least one of a data acquisition module and a data analysis module.
According to the computer equipment, the data analysis module is adopted to process the real-time data of the commercial vehicle internet of vehicles through the edge calculation and the calling-based background calculation engine so as to obtain the corresponding single vehicle analysis result or fleet analysis result, on one hand, the data can be processed in an edge data center or a local data center, and important data can be sequenced from less important data, so that the task waiting time is reduced, the data processing speed is increased, and the network delay is reduced. The problems that due to the fact that the collected commercial vehicle internet of vehicles is large in real-time data volume, the data format is relatively complex, the value density is low, the user searching mode is complex, and the searching efficiency is low are solved. On the other hand, the problem that a data application mode and a data dimension are single is avoided, and the analysis efficiency of the commercial vehicle networking data is improved under the condition that the user analysis requirements are well mastered.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of: acquiring vehicle CAN line signals from a CAN bus, and generating commercial vehicle networking real-time data based on the acquired vehicle CAN line signals; performing edge calculation on the real-time data of the commercial vehicle networking to obtain the single vehicle analysis results corresponding to the commercial vehicles respectively; performing background calculation on the commercial vehicle networking real-time data based on the called background calculation engine to obtain a vehicle fleet analysis result corresponding to a corresponding target vehicle fleet; the target fleet includes a plurality of target commercial vehicles of the same vehicle type.
In one embodiment, the vehicle CAN line signal comprises at least one vehicle signal selected from a meter mileage signal, a meter vehicle speed signal, an engine speed signal, a load signal and a gear signal of the corresponding commercial vehicle; the computer program when executed by the processor further realizes the steps of: when the edge calculation is carried out on the real-time data of the commercial vehicle networking, at least one single vehicle analysis result of single vehicle accumulated oil consumption, single vehicle speed distribution, single vehicle engine rotating speed distribution, single vehicle fault distribution, single vehicle load and single vehicle gear shifting times which are respectively corresponding to each commercial vehicle is obtained through calculation based on vehicle signals of the commercial vehicles.
Background calculation is carried out to commercial car networking real-time data based on backstage computational engine that calls to obtain the motorcade analysis result that corresponding target motorcade corresponds, includes: when background calculation is carried out on the commercial vehicle networking real-time data, at least one vehicle fleet analysis result of vehicle fleet accumulated oil consumption, vehicle fleet speed distribution, vehicle fleet engine rotating speed distribution, vehicle fleet environment temperature distribution, vehicle fleet fault distribution, vehicle fleet total load and vehicle fleet total gear shifting times corresponding to a target vehicle fleet is obtained through calculation based on a target vehicle signal set of the target vehicle fleet; the background computing engine comprises at least one of a Spark computing engine, a Hive computing engine and an Impala computing engine.
In one embodiment, the computer program when executed by the processor further performs the steps of: the trigger data acquisition module encrypts the generated commercial vehicle internet of vehicles real-time data in a preset encryption mode and transmits the encrypted ciphertext data to the data analysis module; and the trigger data analysis module decrypts the received ciphertext data based on a preset decryption mode which is adaptive to the encryption mode so as to obtain corresponding commercial vehicle internet real-time data.
In one embodiment, the computer program when executed by the processor further performs the steps of: and the trigger data storage module acquires and stores vehicle dynamic data and vehicle static data, wherein the vehicle dynamic data comprises at least one dynamic data of commercial vehicle networking real-time data, a single vehicle analysis result and a fleet analysis result, and the vehicle static data comprises attribute information adaptive to the dynamic data.
In one embodiment, the computer program when executed by the processor further performs the steps of: the trigger data application module acquires the single vehicle analysis result and the motorcade analysis result transmitted by the data analysis module; when a service request instruction transmitted by a corresponding request terminal is acquired, a data application module is triggered to call corresponding application service to process the calculated single vehicle analysis result and/or the vehicle fleet analysis result, and the service result obtained by processing is fed back to the request terminal; the application service comprises at least one of a data presentation service, a report making service and a push service.
In one embodiment, the computer program when executed by the processor further performs the steps of: and triggering the upgrading module to acquire the upgrading packet transmitted by the remote server, and transmitting the acquired upgrading packet to the corresponding module to be upgraded so that the module to be upgraded performs version upgrading according to the received upgrading packet, wherein the module to be upgraded comprises at least one of a data acquisition module and a data analysis module.
According to the storage medium, the data analysis module is used for processing the real-time data of the commercial vehicle internet of vehicles through the edge calculation and the calling-based background calculation engine to obtain the corresponding single vehicle analysis result or the fleet analysis result, on one hand, the data can be processed in an edge data center or a local data center, and the important data can be sequenced from the less important data, so that the task waiting time is reduced, the data processing speed is increased, and the network delay is reduced. The problems that due to the fact that the collected commercial vehicle internet of vehicles is large in real-time data volume, the data format is relatively complex, the value density is low, the user searching mode is complex, and the searching efficiency is low are solved. On the other hand, the problem that a data application mode and a data dimension are single is avoided, and the analysis efficiency of the commercial vehicle networking data is improved under the condition that the user analysis requirements are well mastered.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database or other medium used in the embodiments provided herein can include at least one of non-volatile and volatile memory. Non-volatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical storage, or the like. Volatile Memory can include Random Access Memory (RAM) or external cache Memory. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. The utility model provides a data analysis system suitable for commercial car networking data, a serial communication port, the system includes data acquisition module and data analysis module, wherein:
the data acquisition module is used for generating commercial vehicle networking real-time data based on vehicle CAN line signals acquired from a CAN bus and transmitting the commercial vehicle networking real-time data to the data analysis module;
the data analysis module is used for carrying out edge calculation on the commercial vehicle networking real-time data based on the received commercial vehicle networking real-time data so as to obtain single vehicle analysis results respectively corresponding to the commercial vehicles;
the data analysis module is further used for carrying out background calculation on the commercial vehicle networking real-time data based on the received commercial vehicle networking data and the called background calculation engine so as to obtain a vehicle fleet analysis result corresponding to a corresponding target vehicle fleet; the target fleet includes a plurality of target commercial vehicles of the same vehicle type.
2. The system according to claim 1, wherein the data acquisition module is further configured to encrypt the generated commercial vehicle internet of vehicles real-time data in a preset encryption manner, and transmit the encrypted ciphertext data to the data analysis module;
and the data analysis module is also used for decrypting the received ciphertext data based on a preset decryption mode which is adaptive to the encryption mode so as to obtain corresponding commercial vehicle networking real-time data.
3. The system of claim 1, wherein the vehicle CAN line signal comprises at least one vehicle signal of a meter range signal, a meter vehicle speed signal, an engine speed signal, a load signal, and a gear signal of the commercial vehicle;
the data analysis module is further used for calculating at least one single-vehicle analysis result of single-vehicle accumulated oil consumption, single-vehicle speed distribution, single-vehicle engine rotating speed distribution, single-vehicle fault distribution, single-vehicle load and single-vehicle gear shifting times which respectively correspond to each commercial vehicle based on the vehicle signals of the commercial vehicles when the edge calculation is carried out on the commercial vehicle networking real-time data;
the data analysis module is further used for calculating at least one fleet analysis result in fleet accumulated oil consumption, fleet speed distribution, fleet engine speed distribution, fleet environment temperature distribution, fleet fault distribution, fleet total load and fleet total gear shifting times corresponding to the target fleet based on the target vehicle signal set of the target fleet when the commercial vehicle networking real-time data is subjected to background calculation; the background computing engine comprises at least one of a Spark computing engine, a Hive computing engine and an Impala computing engine.
4. The system of claim 1, further comprising a data storage module, wherein:
the data storage module is used for acquiring and storing vehicle dynamic data and vehicle static data, wherein the vehicle dynamic data comprise the commercial vehicle internet of vehicles real-time data, the single vehicle analysis result and at least one dynamic data in the fleet analysis result, and the vehicle static data comprise attribute information adapted to the dynamic data.
5. The system of claim 1, further comprising a data application module, wherein:
the data application module is used for acquiring the single vehicle analysis result and the motorcade analysis result transmitted by the data analysis module;
the data application module is further used for calling corresponding application services to process the obtained single-vehicle analysis result and/or the fleet analysis result when the service request instruction transmitted by the corresponding request terminal is obtained, and feeding back the processed service result to the request terminal; the application service comprises at least one of a data display service, a report making service and a push service.
6. The system of claim 1, further comprising an upgrade module, wherein:
the upgrading module is used for acquiring the upgrading packet transmitted by the remote server and transmitting the acquired upgrading packet to the corresponding module to be upgraded so that the module to be upgraded can upgrade the version according to the received upgrading packet, wherein the module to be upgraded comprises at least one of a data acquisition module and a data analysis module.
7. A method of data analysis adapted for use in a system according to any one of claims 1 to 6, the method comprising:
acquiring vehicle CAN line signals from a CAN bus, and generating commercial vehicle networking real-time data based on the acquired vehicle CAN line signals;
performing edge calculation on the commercial vehicle networking real-time data to obtain single vehicle analysis results corresponding to the commercial vehicles respectively;
performing background calculation on the commercial vehicle networking real-time data based on the called background calculation engine to obtain a vehicle fleet analysis result corresponding to a corresponding target vehicle fleet; the target fleet includes a plurality of target commercial vehicles of the same vehicle type.
8. The method of claim 7, wherein the vehicle CAN line signal comprises at least one vehicle signal selected from the group consisting of a meter range signal, a meter vehicle speed signal, an engine speed signal, a load signal, and a gear signal of the respective commercial vehicle;
the right commercial vehicle networking real-time data carries out marginal calculation to obtain the bicycle analysis result that each commercial vehicle corresponds respectively, include:
when the edge calculation is carried out on the commercial vehicle networking real-time data, at least one single vehicle analysis result of single vehicle accumulated oil consumption, single vehicle speed distribution, single vehicle engine speed distribution, single vehicle fault distribution, single vehicle load and single vehicle gear shifting times which are respectively corresponding to each commercial vehicle is obtained through calculation based on vehicle signals of the commercial vehicles;
the background calculation engine based on calling carries out background calculation on the commercial vehicle networking real-time data to obtain a vehicle fleet analysis result corresponding to a corresponding target vehicle fleet, and the method comprises the following steps:
when background calculation is carried out on the commercial vehicle networking real-time data, at least one vehicle fleet analysis result of vehicle fleet accumulated oil consumption, vehicle fleet speed distribution, vehicle fleet engine rotating speed distribution, vehicle fleet environment temperature distribution, vehicle fleet fault distribution, vehicle fleet total load and vehicle fleet total gear shifting times corresponding to the target vehicle fleet is obtained through calculation based on a target vehicle signal set of the target vehicle fleet; the background computing engine comprises at least one of a Spark computing engine, a Hive computing engine and an Impala computing engine.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor realizes the steps of the method of any one of claims 7 to 8 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 7 to 8.
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