CN115827928A - Vehicle data processing method, device, equipment, medium and product - Google Patents
Vehicle data processing method, device, equipment, medium and product Download PDFInfo
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Abstract
The application provides a vehicle data processing method, a device, equipment, a medium and a product, wherein a preset mapping relation is obtained, the preset mapping relation comprises a plurality of data analysis rules and data coding identifications corresponding to the data analysis rules, each data analysis rule is used for analyzing vehicle data of the corresponding data coding identification, and different data coding identifications correspond to different data formats; then acquiring a data coding identifier of the current driving data of the vehicle, wherein the vehicle data comprises the driving data; then acquiring a first data analysis rule corresponding to the data coding identification of the driving data from a preset mapping relation; and then analyzing the driving data by adopting a first data analysis rule to obtain available data, wherein the available data is used for generating a driving instruction for the vehicle. The embodiment of the application can improve the efficiency and accuracy of data processing.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a method, an apparatus, a device, a medium, and a product for processing vehicle data.
Background
With the development of modern information technology and intelligent networking automobile industry, the role of a cloud control system combining intelligent networking automobile and big data technology will become more and more important. The construction of the cloud control platform is not only to butt joint vehicle data of a single certain vehicle factory, but also to simultaneously access vehicle end data of a plurality of vehicle factories in real time, so that the storage and calculation of big data are carried out on the cloud control platform, but the vehicle data formats of the vehicle factories are various, and in the prior art, when the data are analyzed, most of the data are manually classified according to types of various data, and then each type of data is analyzed. Such manual data analysis is not only inefficient, but also prone to errors.
Disclosure of Invention
The vehicle data processing method, device, equipment, medium and product can improve the efficiency and accuracy of data processing.
In a first aspect, an embodiment of the present application provides a vehicle data processing method, where the method includes:
acquiring a preset mapping relation, wherein the preset mapping relation comprises a plurality of data analysis rules and data coding identifications corresponding to the data analysis rules, each data analysis rule is used for analyzing the vehicle data of the corresponding data coding identification, and different data coding identifications correspond to different data formats;
acquiring a data coding identifier of current driving data of a vehicle, wherein the vehicle data comprises the driving data;
acquiring a first data analysis rule corresponding to a data coding identifier of the driving data from a preset mapping relation;
and analyzing the driving data by adopting a first data analysis rule to obtain available data, wherein the available data is used for generating a driving instruction for the vehicle.
In a second aspect, the present application provides a vehicle data processing apparatus comprising:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring a preset mapping relation, the preset mapping relation comprises a plurality of data analysis rules and data coding identifications corresponding to the data analysis rules, each data analysis rule is used for analyzing vehicle data of the corresponding data coding identification, and different data coding identifications correspond to different data formats;
the second acquisition module is used for acquiring a data coding identifier of the current running data of the vehicle, and the vehicle data comprises the running data;
the third acquisition module is used for acquiring a first data analysis rule corresponding to the data coding identifier of the driving data from the preset mapping relation;
and the analysis module is used for analyzing the driving data by adopting a first data analysis rule to obtain available data, and the available data is used for generating a driving instruction for the vehicle.
In a third aspect, an embodiment of the present application provides an electronic device, including: a processor and a memory storing computer program instructions;
the processor, when executing the computer program instructions, implements a vehicle data processing method as in any one of the embodiments of the first aspect.
In a fourth aspect, the present application provides a computer storage medium having computer program instructions stored thereon, where the computer program instructions, when executed by a processor, implement the vehicle data processing method as in any one of the first aspect.
In a fifth aspect, the present application provides a computer program product, and when executed by a processor of an electronic device, the instructions in the computer program product cause the electronic device to execute a vehicle data processing method implemented in any one of the embodiments of the first aspect.
In the vehicle data processing method, the device, the equipment, the medium and the product provided by the embodiment of the application, a preset mapping relation is obtained, wherein the preset mapping relation comprises a plurality of data analysis rules and data coding identifications corresponding to the data analysis rules, each data analysis rule is used for analyzing vehicle data of the corresponding data coding identification, and different data coding identifications correspond to different data formats; then acquiring a data coding identifier of the current driving data of the vehicle, wherein the vehicle data comprises the driving data; then acquiring a first data analysis rule corresponding to the data coding identification of the driving data from a preset mapping relation; and then analyzing the driving data by adopting a first data analysis rule to obtain available data, wherein the available data is used for generating a driving instruction for the vehicle. By the method, when the vehicle data in different formats are acquired, the preset mapping relation can be inquired according to the respective data coding identification of the vehicle data, the corresponding data analysis rule is selected, the vehicle data can be rapidly analyzed, and the working efficiency and the accuracy of analyzing the vehicle data are improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments of the present application will be briefly described below, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart diagram of a vehicle data processing method provided by an embodiment of the present application;
FIG. 2 is a schematic structural diagram of a vehicle data processing device according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In order that the above objects, features and advantages of the present disclosure may be more clearly understood, aspects of the present disclosure will be further described below. It should be noted that the embodiments and features of the embodiments of the present disclosure may be combined with each other without conflict.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure, but the present disclosure may be practiced in other ways than those described herein; it is to be understood that the embodiments disclosed in the specification are only a few embodiments of the present disclosure, and not all embodiments.
It is noted that, in this document, relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising a … …" does not exclude the presence of another identical element in a process, method, article, or apparatus that comprises the element.
With the development of the intelligent networking automobile industry, the construction of the cloud control platform is not only to butt joint vehicle data of a single certain car factory, but also to simultaneously access vehicle end data of a plurality of car factories in real time, so that the storage and calculation of big data are carried out on the cloud control platform, but the vehicle data formats of the car factories are various, and only the vehicle data in different formats are analyzed to obtain the required data.
In the prior art, the method and the system are mainly proprietary vehicle data access methods and systems developed by each vehicle manufacturer. At present, an access scheme of vehicle data of a vehicle factory provided by a vehicle factory can access the vehicle data of the vehicle factory to a cloud Control platform, a vehicle data format of a specific vehicle factory is single and fixed, common vehicle factory proprietary data formats include xml, json, pbf, binary stream and the like, a Protocol for accessing the vehicle data to the cloud Control platform is also a proprietary Protocol of the specific vehicle factory, common access protocols include a custom application layer Protocol and a Message Queue Telemetry Transport (MQTT) based on a Transmission Control Protocol (TCP), and the data formats and the protocols effectively support that each vehicle factory collects operation data of vehicles of the vehicle factory, but cannot support a scene that multi-vehicle factory multi-format vehicle data needs to be accessed. In addition, in the prior art, one analysis technology can only analyze data in one format, and most analysis systems use a single fixed analysis technology, so that the data in multiple formats cannot be adaptively analyzed.
In order to solve the problems of the prior art, embodiments of the present application provide a method, an apparatus, a device, a medium, and a product for processing vehicle data. The following first describes a vehicle data processing method provided in an embodiment of the present application.
An application scenario according to this embodiment may include a terminal device, a network and a server. The network serves as a medium for providing a communication link between the terminal device and the server. The network may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
A user may use a terminal device to interact with a server over a network to receive or send messages, etc. Various messaging client applications may be installed on the terminal device, such as a shopping application, a web browser application, a search application, an instant messaging tool, a mailbox client, social platform software, and the like (by way of example only).
The terminal device may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The server may be a server providing various services, such as a background management server (for example only) providing support for websites browsed by the user using the terminal device. The background management server may analyze and perform other processing on the received data such as the user request, and feed back a processing result (e.g., a webpage, information, or data obtained or generated according to the user request) to the terminal device.
Fig. 1 shows a schematic flowchart of a vehicle data processing method according to an embodiment of the present application. As shown in fig. 1, the method may specifically include the following steps:
s101, acquiring a preset mapping relation, wherein the preset mapping relation comprises a plurality of data analysis rules and data coding identifications corresponding to the data analysis rules, each data analysis rule is used for analyzing vehicle data of the corresponding data coding identification, and different data coding identifications correspond to different data formats.
Optionally, the data encoding identifies a data format for characterizing the vehicle data. In practical applications, since data formats of vehicle data provided by different vehicle factories are various, the data formats of the vehicle data when the vehicle data is sent to the Yun Kong platform after the different vehicle factories access the cloud control platform are also various. Specifically, the data format of the vehicle data may be xml, json, pbf, binary stream, or the like.
Based on this, in one possible embodiment of the present application, different data coding identifiers may be preset according to different data formats, so as to identify the different data formats through the data coding identifiers, specifically, the different data formats may be represented by using binary; of course, in another possible implementation, the data format may also be determined directly using the number CID of the data frame transmitting the vehicle data itself. It will be readily appreciated that other ways of representing the data encoding indicia may be used in a particular implementation.
Optionally, the cloud control platform is a cloud computing platform for remotely controlling the intelligent networked automobile. When the vehicle and the cloud control platform carry out data interaction, the cloud control platform is a receiver for uploading data of the vehicle, is a sender for sending a decision control instruction to the vehicle, and is a remote service platform in the cloud computing platform. One of the important works for the construction of the cloud control platform is to research and develop intelligent gateway software for receiving real-time data uploaded by a vehicle end and issuing control instructions generated by the cloud platform.
Optionally, in this embodiment, the data parsing rule refers to an extraction rule capable of extracting data content required in the vehicle data. Specifically, in the embodiment of the application, data resolvers corresponding to vehicle data in different data formats may be developed in advance according to different data formats, and then a mapping table of data frame CIDs and data resolvers may be constructed according to data frame CIDs corresponding to vehicle data in different formats and data resolvers corresponding to vehicle data in different formats. Therefore, when the vehicle data is received, the mapping table can be inquired according to the data frame CID of the vehicle data to obtain the corresponding data analyzer, and the required data can be obtained through analysis.
Optionally, in another possible implementation manner of the present application, multiple data parsing rules corresponding to different formats may also be stored in the database, so that vehicle data in different formats may be parsed.
Optionally, in the embodiment of the application, the data analysis rules may be continuously improved or added by the cloud control platform, so that the data analysis rules in the database are updated to analyze the vehicle data in a new format, and thus the cloud control platform can process vehicle data in more formats, and the applicability of analyzing the vehicle data is improved.
S102, acquiring a data code identification of the current running data of the vehicle, wherein the vehicle data comprises the running data.
Optionally, in this embodiment of the application, the same standard protocol may be used to receive vehicle data from different vehicles, so as to implement data interaction between the vehicle and the cloud control platform. Particularly, the vehicle can transmit the driving data of the vehicle to the cloud control platform through different data interfaces, the workload of the cloud control platform for adapting to different vehicle factory custom communication protocols is reduced, and the complexity of the vehicle cloud gateway is also reduced. The Che Yun gateway is a network communication component in the cloud control platform, which implements a vehicle cloud protocol, and supports data interaction between a vehicle and the cloud control platform.
Alternatively, in practical applications, the data interface may be negotiated with different factories in advance, and the data interface may transmit and receive data according to a standard protocol. Wherein, data interface includes at least: multifunctional vehicle bus network interface, WIFI interface, serial interface and bluetooth interface etc..
Alternatively, the travel data may be travel speed information and travel position information of the vehicle, wherein the position information may be acquired by a Global Positioning System (GPS) technology, a global navigation satellite system (GLONASS) technology, a beidou navigation system technology, a Galileo positioning system (Galileo) technology, a quasi-zenith satellite system (QAZZ) technology, a base station positioning technology, a Wi-Fi positioning technology, or the like.
It should be noted that, the vehicles referred to in the embodiments of the present application may include, but are not limited to, electric vehicles, oil-powered vehicles, hybrid vehicles, and other energy-powered vehicles, which all can achieve the purpose of the embodiments of the present application.
S103, acquiring a first data analysis rule corresponding to the data coding identification of the driving data from the preset mapping relation.
Optionally, in this embodiment of the application, the data frame CID of the driving data may be directly obtained, and then the mapping table may be queried according to the data frame CID to obtain the corresponding data parser, so that the data parser (i.e., the data parsing rule) is adaptively selected according to the data format, and the work efficiency of vehicle data parsing is improved.
And S104, analyzing the driving data by adopting a first data analysis rule to obtain available data, wherein the available data is used for generating a driving instruction for the vehicle.
Optionally, in this embodiment, the available data is readable contents of speed information and position information of driving in the driving data transmitted from the vehicle, and the driving data sent by different vehicles is different.
In a specific implementation, the vehicle data generated by the vehicles belonging to the same vehicle factory generally adopt the same data format, that is, the driving data of the vehicles of the same vehicle factory can be analyzed according to the same data analysis rule, but the data content of the available data obtained after analysis can be different.
Alternatively, in the embodiment of the present application, after the data required in the vehicle travel data is obtained by analysis, a different travel command may be generated based on the available data, the travel position information of the vehicle, and the environment information of the actual travel road of the vehicle, so that the automatic driving of the vehicle can be realized.
In these alternative embodiments, corresponding parsers are designed according to different data formats, such as parsers in xml, json, pbf, binary stream, etc., and mapping tables for encoding and parsing of data formats are constructed. When new vehicle data come, the data format code Cid is taken out of the new data, then the mapping table is searched to obtain a corresponding analyzer, and then the analyzer is used for analyzing the data. Therefore, the purpose of adaptively analyzing the data according to the data format is achieved by the method for flexibly obtaining the analyzers corresponding to different formats by the format code Cid in the data.
In the vehicle data processing method provided by the embodiment of the application, a preset mapping relation is obtained, wherein the preset mapping relation comprises a plurality of data analysis rules and data coding identifications corresponding to the data analysis rules, each data analysis rule is used for analyzing vehicle data of the corresponding data coding identification, and different data coding identifications correspond to different data formats; then acquiring a data coding identifier of the current driving data of the vehicle, wherein the vehicle data comprises the driving data; then acquiring a first data analysis rule corresponding to the data coding identification of the driving data from a preset mapping relation; and then analyzing the driving data by adopting a first data analysis rule to obtain available data, wherein the available data is used for generating a driving instruction for the vehicle. By the method, when the vehicle data in different formats are acquired, the preset mapping relation can be inquired according to the respective data coding identification of the vehicle data, the corresponding data analysis rule is selected, the vehicle data can be rapidly analyzed, and the working efficiency and the accuracy of analyzing the vehicle data are improved.
In an embodiment, the step 103 may specifically perform the following steps:
s201, under the condition that the data code identification of the driving data is the same as the data code identification in the mapping relation, acquiring a first data analysis rule corresponding to the data code identification of the driving data from the preset mapping relation.
Optionally, in this embodiment of the application, after obtaining the driving data of the vehicle, it may be determined whether the CID of the data frame of the driving data is in a mapping table between the CID of the data frame and the data parser preset in the application, and only when the CID of the obtained data frame is the same as the CID of the data frame stored in the mapping table, the data parsing rule corresponding to the CID of the data frame may be obtained from the mapping table, so as to avoid that the cloud control platform has an error due to the CID of the data frame not existing in the mapping table, and the cloud control platform always requests to obtain the data parsing rule, which results in a situation that the system runs.
It is easy to understand that, in the embodiment of the present application, if the data frame CID of the driving data obtained in real time is found not in the mapping table, the situation of the obtaining failure at this time may be automatically recorded in the database, and all the data frame CIDs that have failed to be obtained may be subsequently sent to the relevant operation and maintenance personnel, and then a data parsing rule may be newly added for the data frame CIDs that have not obtained the data parsing rule, so as to improve the adaptability and universality of parsing the vehicle data.
In an embodiment, before the step 102, the method may further specifically perform the following steps:
and S301, receiving a data frame of the current driving data of the vehicle, which is sent by at least one cloud platform, by using a data interface according to a preset data access protocol.
Optionally, in this embodiment of the application, in order to interface vehicle cloud gateways of vehicle data in different formats in multiple vehicle factories, a standard vehicle cloud application layer data transmission protocol is designed. After the standard protocol (namely the preset data access protocol) is adopted, vehicles of different vehicle factories can perform data interaction with the cloud according to the same communication protocol, the workload of the cloud control platform for adapting to different vehicle factory custom communication protocols is reduced, and the complexity of a vehicle cloud gateway is also reduced. Considering that the TCP protocol has the advantages of simple data structure, high reliability, low network overhead and the like, the TCP protocol can ensure that a receiving end receives a byte stream sent by a sending end without errors, and can provide reliable communication service, the standard protocol can be based On an application layer protocol of the TCP, and can be adapted to real-time data of a main stream vehicle-mounted Unit (On board Unit, obu), so that vehicles listed with obu of a main stream manufacturer can upload vehicle data to a cloud control base platform through the protocol, and data conforming to the internet communication protocol can be uploaded to a cloud end through mobile communication networks such as 3G, 4G, 5G and the like.
S302, the data frames are sorted according to a preset sorting rule to obtain sorted data frames, and the preset sorting rule is used for sorting and sorting position information and state information in current driving data of vehicles in the data frames and unifying formats of the position information and the state information.
Optionally, in this embodiment of the present application, before transmitting the driving data, the specific contents in the driving data need to be sorted and formatted uniformly to form a data packet meeting the transmission requirement.
In a possible embodiment, it is assumed that the driving data includes VIN (vehicle unique identification code, one VIN corresponds to one car and is composed of 17-bit characters), lat (vehicle latitude), lng (vehicle longitude), and speed (vehicle driving speed), and then the driving data are sorted according to a predetermined sequence, where the predetermined sequence may be selected according to actual needs, and the application does not limit the specific arrangement sequence. It is easy to understand that since the general values of longitude and latitude are decimal point type values, such as 119.753468, we need to convert these decimal point type values into integer type data, such as 119.753468 to 119753468, and put the data converted into integer type into corresponding position according to a predetermined sequence.
In these optional embodiments, the driving data is sorted according to a preset sorting rule to obtain a data packet (sorted data frame) meeting the transmission requirement, so that the receiving end can be ensured to receive the driving data sent by the vehicle without error, and the reliability of driving data transmission is improved.
And S303, uploading the sorted data frames to a central cloud platform.
Optionally, in this embodiment OF the present application, a data Frame may be sequentially composed OF an SOF, a VIN, a CID, a LENGTH, an INFO, and a CHECKSUM, where the SOF is a data Frame START bit flag (START OF Frame) with 4 bytes, and is used to flag the START OF a data Frame; VIN is a 17 byte number vehicle identification code, and the vehicle identification code uniquely identifies a vehicle; CID is a Command Id with 1 byte number, command number in the INFO field below, the INFO field can transmit a plurality of different data structure data, each data structure has an Id, called Command Id, LENGTH is the byte number occupied by the INFO field with 2 byte numbers; the INFO is command information with any byte number, the field can define a data structure and a data format by self, and the selected data format supports binary stream, xml, pbf, json byte stream and the like; the CHECKSUM is a check code with one byte number. It should be noted that the INFO field of the data frame is a place actually carrying the uploaded and issued data in the data frame, and the data structure of this field can be defined by each vehicle manufacturer according to the actual situation of each vehicle, and there may be many fields to upload very detailed vehicle data, and also may define few fields to transmit some critical vehicle data; the data format of the INFO field in the data frame may also be freely defined by each vehicle manufacturer, and may adopt a binary stream format, an xml format, a json format or a pbf format, and the like, where each vehicle manufacturer may flexibly select the data format according to its own actual situation.
Optionally, in another possible implementation manner of the present application, different information may be obtained according to different CID of the data frame, and is not limited to the driving data, the instruction information, and the like of the vehicle. Specifically, when the content of the data frame is to upload real-time data of a vehicle, the CID of the data frame is 0x1h, the direction is uploading, specifically, various data generated in real time in the running process of the vehicle, including position longitude and latitude, speed, course angle and the like, are uploaded, and the field composition of the real-time data can be flexibly selected according to the requirements of various vehicles and factories, and can be few or many; when the content of the data frame is to issue the traffic light real-time data, the CID of the data frame is 0x2h, the direction is to issue, and the traffic light information in front of the vehicle can be specifically sent to the vehicle, so that the vehicle can conveniently master the traffic light condition in front in time; when the content of the data frame is to issue dynamic loop information, the CID of the data frame is 0x3h, and the direction is to issue, specifically, an instruction can be issued for a vehicle, and the vehicle is expected to change lanes in the running process; when the content of the data frame is to issue brake and parking information, the CID of the data frame is 0x4h, and the direction is to issue, specifically, an instruction can be issued for a vehicle to hope to brake the vehicle until the vehicle stops; when the content of the data frame is to issue dynamic vehicle speed information, the CID of the data frame is 0x5h, and the direction is to issue, specifically, an instruction can be issued for a vehicle, and the vehicle is expected to convert the running speed into the speed specified in the instruction. It should be noted that more data frame types can be expanded according to actual needs, more vehicles can issue instruction data frames, and more data frames of vehicle information can also be uploaded, wherein each data frame can freely define respective field number, field sequence, adopted data type, and the like.
S304, acquiring the data coding identification of the data frame from the central cloud platform.
Optionally, after the sorted data frame is stored in the cloud, the data frame CID obtained by sorting can be directly obtained from the cloud, and the corresponding data analysis rule is queried through the CID, so that the purpose of adaptively analyzing data is achieved.
In an embodiment, the step 304 may specifically perform the following steps:
s401, storing the sorted data frames into a cache center of a center cloud platform;
s402, acquiring the sorted data frame from the cache center according to a preset acquisition cycle.
Optionally, in this embodiment of the application, the frequency of sending the driving data transmitted by the vehicle through the transmission protocol is high, however, the capability of the cloud control platform for processing the driving data is limited, and to ensure that all the driving data can be analyzed and processed, the driving data transmitted by the vehicle may be stored in the buffer center first, and the sorted data frame may be retrieved from the buffer center according to a predetermined collection period, so that the cloud control platform may process the sorted data frame in the buffer center in time. The preset acquisition period can be set according to the processing condition of the cloud control platform.
In an embodiment, after the step 104, the method may further specifically perform the following steps:
s501, storing the available data into a data bus of the central cloud platform.
Optionally, in the embodiment of the present application, a high-performance network transport framework Netty is adopted to design and implement the cloud control platform, and the protocol is fully analyzed and decoupled. The Netty is a network transmission programming framework for designing a high-concurrency network communication component, which is a popular network programming framework at present and can support network connection with ultrahigh concurrency under the condition of consuming less computing resources.
Optionally, in this embodiment of the present application, after obtaining the sorted packets, it is necessary to unpack the packets, specifically, cut the binary byte stream into byte blocks forming one data frame. And then decoding the byte blocks of the disassembled data frames, searching a resolver mapping table according to the serial numbers of the data frames, finding out a corresponding resolver, and resolving the byte blocks into usable data. The available data is then stored on the data bus.
And S502, generating a vehicle running instruction corresponding to the available data according to the available data, wherein the vehicle running instruction is used for controlling automatic driving of the vehicle.
Optionally, after the available data is obtained, a vehicle driving instruction may be generated according to the available data road information, for example, if it is determined that the vehicle is about to approach a traffic light according to the longitude and latitude information of the vehicle, and the traffic light is a red light, a stop instruction may be sent to the vehicle, so that the vehicle stops before the traffic light, and intelligent automatic driving of the vehicle is achieved.
And S503, storing the driving instruction into the data bus.
Alternatively, after the travel commands have been received, these travel commands can also be stored in the data bus.
And S504, acquiring the running instruction from the data bus.
Alternatively, after the travel commands are stored in the data bus, they can be retrieved from the data bus into the vehicle data access system.
And S505, packaging and sending the driving instruction to a vehicle platform corresponding to the available data.
Optionally, in this embodiment of the application, the mapping table may be searched according to a data format code in the driving instruction, an encoder corresponding to the data format is found, the data object is encoded into a byte block that can be read by the vehicle, then the byte block of one data object is packed and pushed into the network to be issued to the driving vehicle, and the vehicle may perform corresponding actions on the driving instruction after receiving the driving instruction.
In an embodiment, the step 505 may specifically perform the following steps:
s601, generating a data distribution instruction by the central cloud platform according to the driving instruction;
s602, according to the data distribution instruction, sending a first vehicle running instruction to a corresponding cloud platform, wherein the running instruction comprises the first running instruction, and the cloud platform comprises the vehicle cloud platform and the at least one cloud platform.
In some embodiments of the present application, the data distribution instruction sent by the central cloud platform may be generated according to a driving instruction sent by the central cloud platform. After receiving the data distribution instruction, the cloud gateway can send a vehicle running instruction to the corresponding cloud platform according to the data distribution instruction, that is, to the vehicle platform sending the running data. For example, if there are many vehicles on the road, when the vehicles run in a red light, the relative positions of the vehicles on the road can be determined according to the running data among the vehicles, and the stop instructions are respectively sent in sequence, so that collision among the vehicles is avoided, and the intelligence of automatic driving is improved.
Fig. 2 is a schematic structural diagram of a vehicle data processing device according to another embodiment of the present application, and only a part related to the embodiment of the present application is shown for convenience of description.
Referring to fig. 2, the vehicle data processing apparatus may include:
the first obtaining module 201 is configured to obtain a preset mapping relationship, where the preset mapping relationship includes a plurality of data parsing rules and data coding identifiers corresponding to each data parsing rule, each data parsing rule is used to parse vehicle data of a corresponding data coding identifier, and different data coding identifiers correspond to different data formats;
the second obtaining module 202 is configured to obtain a data code identifier of current driving data of a vehicle, where the vehicle data includes the driving data;
the third obtaining module 203 is configured to obtain a first data parsing rule corresponding to the data coding identifier of the driving data from a preset mapping relationship;
the analysis module 204 is configured to analyze the driving data by using a first data analysis rule to obtain available data, where the available data is used to generate a driving instruction for the vehicle.
In an embodiment, the vehicle data processing apparatus may further include:
and the fourth obtaining module is used for obtaining a first data analysis rule corresponding to the data code identifier of the driving data from the preset mapping relation under the condition that the data code identifier of the driving data is the same as the data code identifier in the mapping relation.
In an embodiment, the vehicle data processing apparatus may further include:
the receiving module is used for receiving a data frame of the current driving data of the vehicle, which is sent by at least one cloud platform, by using a data interface according to a preset data access protocol;
the first determining module is used for sorting the data frames according to a preset sorting rule to obtain sorted data frames, wherein the preset sorting rule is used for sorting and sorting position information and state information in the current driving data of the vehicle in the data frames and unifying formats of the position information and the state information;
the first uploading module is used for uploading the sorted data frames to a central cloud platform;
and the fifth acquisition module is used for acquiring the data coding identifier of the data frame from the central cloud platform.
In an embodiment, the vehicle data processing apparatus may further include:
the first storage module is used for storing the sorted data frames into a cache center of a central cloud platform;
and the sixth acquisition module is used for acquiring the sorted data frames from the cache center according to a preset acquisition cycle.
In an embodiment, the vehicle data processing apparatus may further include:
the second storage module is used for storing the available data into a data bus of the central cloud platform;
the first generation module is used for generating a vehicle running instruction corresponding to the available data according to the available data, and the vehicle running instruction is used for controlling automatic driving of a vehicle;
the third storage module is used for storing the driving instruction into the data bus;
the seventh acquisition module is used for acquiring the driving instruction from the data bus;
and the first packing module is used for packing and sending the driving instruction to the vehicle platform corresponding to the available data.
In an embodiment, the vehicle data processing apparatus may further include:
the second generation module is used for generating a data distribution instruction by the central cloud platform according to the driving instruction;
the first sending module is used for sending a first vehicle running instruction to a corresponding cloud platform according to the data distribution instruction, wherein the running instruction comprises the first running instruction, and the cloud platform comprises the vehicle cloud platform and the at least one cloud platform.
It should be noted that, the contents of information interaction, execution process, and the like between the above-mentioned devices/units are based on the same concept as that of the method embodiment of the present application, and are devices corresponding to the above-mentioned battery thermal runaway early warning method, and all implementation manners in the above-mentioned method embodiment are applicable to the embodiment of the device, and specific functions and technical effects thereof may be specifically referred to in the method embodiment section, and are not described herein again.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules, so as to perform all or part of the functions described above. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. For the specific working processes of the units and modules in the system, reference may be made to the corresponding processes in the foregoing method embodiments, which are not described herein again.
Fig. 3 shows a hardware structure diagram of an electronic device according to an embodiment of the present application.
The device may include a processor 301 and a memory 302 in which program instructions are stored.
The processor 301, when executing the program, implements the steps in any of the various method embodiments described above.
Illustratively, the program may be divided into one or more modules/units, which are stored in the memory 302 and executed by the processor 301 to accomplish the present application. One or more modules/units may be a series of program instruction segments capable of performing certain functions and describing the execution of programs on the device.
Specifically, the processor 301 may include a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or may be configured to implement one or more Integrated circuits of the embodiments of the present Application.
The memory may include Read Only Memory (ROM), random Access Memory (RAM), magnetic disk storage media devices, optical storage media devices, flash memory devices, electrical, optical, or other physical/tangible memory storage devices. Thus, in general, the memory includes one or more tangible (non-transitory) readable storage media (e.g., a memory device) encoded with software comprising computer-executable instructions and when the software is executed (e.g., by one or more processors), it is operable to perform operations described with reference to the method according to an aspect of the disclosure.
The processor 301 implements any of the methods in the above embodiments by reading and executing program instructions stored in the memory 302.
In one example, the electronic device may also include a communication interface 303 and a bus 310. The processor 301, the memory 302, and the communication interface 303 are connected via a bus 310 to complete communication therebetween.
The communication interface 303 is mainly used for implementing communication between modules, apparatuses, units and/or devices in this embodiment.
In addition, in combination with the methods in the foregoing embodiments, the embodiments of the present application may provide a storage medium to implement. The storage medium having stored thereon program instructions; which when executed by a processor implements any of the methods in the embodiments described above.
The embodiment of the present application further provides a chip, where the chip includes a processor and a communication interface, the communication interface is coupled to the processor, and the processor is configured to execute a program or an instruction to implement each process of the foregoing method embodiment, and the same technical effect can be achieved.
It should be understood that the chips mentioned in the embodiments of the present application may also be referred to as a system-on-chip, or a system-on-chip.
Embodiments of the present application provide a computer program product, where the program product is stored in a storage medium, and the program product is executed by at least one processor to implement the processes of the foregoing method embodiments, and achieve the same technical effects, and in order to avoid repetition, details are not described here again.
It is to be understood that the present application is not limited to the particular arrangements and instrumentalities described above and shown in the attached drawings. A detailed description of known methods is omitted herein for the sake of brevity. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present application are not limited to the specific steps described and illustrated, and those skilled in the art can make various changes, modifications, and additions or change the order between the steps after comprehending the spirit of the present application.
The functional blocks shown in the above structural block diagrams may be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, it may be, for example, an electronic circuit, an Application Specific Integrated Circuit (ASIC), suitable firmware, plug-in, function card, or the like. When implemented in software, the elements of the present application are the programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine-readable medium or transmitted by a data signal carried in a carrier wave over a transmission medium or a communication link. A "machine-readable medium" may include any medium that can store or transfer information. Examples of a machine-readable medium include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio Frequency (RF) links, and so forth. The code segments may be downloaded via computer grids such as the internet, intranets, etc.
It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, the present application is not limited to the order of the above-described steps, that is, the steps may be performed in the order mentioned in the embodiments, may be performed in an order different from the order in the embodiments, or may be performed simultaneously.
Aspects of the present disclosure are described above with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions/acts specified in the flowchart and/or block diagram block or blocks. Such a processor may be, but is not limited to, a general purpose processor, a special purpose processor, an application specific processor, or a field programmable logic circuit. It will also be understood that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware for performing the specified functions or acts, or combinations of special purpose hardware and computer instructions.
As will be apparent to those skilled in the art, for convenience and brevity of description, the specific working processes of the systems, modules and units described above may refer to corresponding processes in the foregoing method embodiments, and are not described herein again. It should be understood that the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive various equivalent modifications or substitutions within the technical scope of the present application, and these modifications or substitutions should be covered within the scope of the present application.
Claims (10)
1. A vehicle data processing method, characterized in that the method comprises:
acquiring a preset mapping relation, wherein the preset mapping relation comprises a plurality of data analysis rules and data coding identifications corresponding to the data analysis rules, each data analysis rule is used for analyzing vehicle data of the corresponding data coding identification, and different data coding identifications correspond to different data formats;
acquiring a data coding identifier of current running data of a vehicle, wherein the vehicle data comprises the running data;
acquiring a first data analysis rule corresponding to a data coding identifier of the driving data from the preset mapping relation;
and analyzing the driving data by adopting the first data analysis rule to obtain available data, wherein the available data is used for generating a driving instruction for the vehicle.
2. The method according to claim 1, wherein the obtaining of the first data parsing rule corresponding to the data coding identifier of the driving data from the preset mapping relationship comprises:
and under the condition that the data code identification of the driving data is the same as the data code identification in the mapping relation, acquiring a first data analysis rule corresponding to the data code identification of the driving data from the preset mapping relation.
3. The method of claim 1, wherein prior to said obtaining a data encoded identification of current vehicle travel data, the method further comprises:
receiving a data frame of current vehicle driving data sent by at least one cloud platform by using a data interface according to a preset data access protocol;
sorting the data frames according to a preset sorting rule to obtain sorted data frames, wherein the preset sorting rule is used for sorting and sorting position information and state information in the current driving data of the vehicles in the data frames and unifying formats of the position information and the state information;
uploading the sorted data frame to a central cloud platform;
and acquiring the data coding identification of the data frame from the central cloud platform.
4. The method of claim 3, wherein the obtaining the data encoding identifier in the collated data frame from the central cloud platform comprises:
storing the sorted data frames into a cache center of a center cloud platform;
and acquiring the sorted data frame from the cache center according to a preset acquisition period.
5. The method of claim 3, wherein after the parsing the travel data using the first data parsing rule to obtain available data, the method further comprises:
storing the available data into a data bus of the central cloud platform;
generating a vehicle driving instruction corresponding to the available data according to the available data, wherein the vehicle driving instruction is used for controlling automatic driving of a vehicle;
storing the driving instruction into the data bus;
acquiring the driving instruction from the data bus;
and packaging and sending the driving instruction to a vehicle platform corresponding to the available data.
6. The method of claim 5, wherein the packaging the driving instruction and sending the driving instruction to the vehicle platform corresponding to the available data comprises:
according to the driving instruction, the central cloud platform generates a data distribution instruction;
according to the data distribution instruction, sending a first vehicle driving instruction to a corresponding cloud platform, wherein the driving instruction comprises the first driving instruction, and the cloud platform comprises the vehicle cloud platform and the at least one cloud platform.
7. A vehicular data processing apparatus characterized by comprising:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring a preset mapping relation, the preset mapping relation comprises a plurality of data analysis rules and data coding identifications corresponding to the data analysis rules, each data analysis rule is used for analyzing vehicle data of the corresponding data coding identification, and different data coding identifications correspond to different data formats;
the second acquisition module is used for acquiring a data coding identifier of current running data of a vehicle, wherein the vehicle data comprises the running data;
the third acquisition module is used for acquiring a first data analysis rule corresponding to the data coding identifier of the driving data from the preset mapping relation;
and the analysis module is used for analyzing the driving data by adopting the first data analysis rule to obtain available data, and the available data is used for generating a driving instruction for the vehicle.
8. An electronic device, characterized in that the device comprises: a processor and a memory storing computer program instructions;
the processor, when executing the computer program instructions, implements a vehicle data processing method as claimed in any one of claims 1-6.
9. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon computer program instructions which, when executed by a processor, implement the vehicle data processing method according to any one of claims 1 to 6.
10. A computer program product, wherein instructions in the computer program product, when executed by a processor of an electronic device, cause the electronic device to perform the vehicle data processing method of any one of claims 1-6.
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| CN202211629721.XA CN115827928A (en) | 2022-12-19 | 2022-12-19 | Vehicle data processing method, device, equipment, medium and product |
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| CN202211629721.XA CN115827928A (en) | 2022-12-19 | 2022-12-19 | Vehicle data processing method, device, equipment, medium and product |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117240946A (en) * | 2023-11-13 | 2023-12-15 | 深圳市麦谷科技有限公司 | Equipment analysis method and device, electronic equipment and storage medium |
| CN117675913A (en) * | 2023-12-07 | 2024-03-08 | 上海钒锝科技有限公司 | Laboratory data transmission processing method, device, transmission processing system and medium |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117240946A (en) * | 2023-11-13 | 2023-12-15 | 深圳市麦谷科技有限公司 | Equipment analysis method and device, electronic equipment and storage medium |
| CN117240946B (en) * | 2023-11-13 | 2024-02-13 | 深圳市麦谷科技有限公司 | Equipment analysis method and device, electronic equipment and storage medium |
| CN117675913A (en) * | 2023-12-07 | 2024-03-08 | 上海钒锝科技有限公司 | Laboratory data transmission processing method, device, transmission processing system and medium |
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