CN115206100A - Driving data sharing method and device based on cloud big data - Google Patents
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Abstract
The application discloses driving data sharing method and device based on cloud big data, wherein the method comprises the following steps: collecting vehicle data, driver operation data and/or road traffic environment data of a plurality of automobiles; generating shared data corresponding to the vehicles, the drivers and the traffic system according to the vehicle data, the driver operation data and/or the road traffic environment data of each vehicle; and respectively sending the shared data to the corresponding ToC end, toB end and ToG end to respectively generate an active reminding mechanism strategy, a vehicle-enterprise vehicle performance and product optimization strategy and an intelligent control strategy for a traffic management system and a signal lamp system. Therefore, the technical problem that in the related technology, the application degree of data generated by vehicle driving to ToC, toB and ToG is very low, and great resource waste is caused is solved.
Description
Technical Field
The application relates to the technical field of vehicle networking, in particular to a driving data sharing method and device based on cloud big data.
Background
With the development of economy, the number of automobiles kept increases year by year, and these vehicles generate a large amount of data during driving, including data of the vehicles themselves, driver operation data, road traffic environment data, and the like. However, in the related art, the data has a low application degree To the car owner ToC (To Consumer), the car enterprise ToB (To Business), and the government functional department ToG (To Governmetal facing government), which causes a great waste of resources and needs To be improved.
Disclosure of Invention
The application provides a driving data sharing method and device based on cloud big data, and aims to solve the technical problem that in the related art, the application degree of data generated by vehicle driving to ToC, toB and ToG is very low, so that great resource waste is caused.
The embodiment of the first aspect of the application provides a driving data sharing method based on cloud big data, which comprises the following steps: collecting vehicle data, driver operation data and/or road traffic environment data of a plurality of automobiles; generating shared data corresponding to the vehicles, the drivers and the traffic system according to the vehicle data, the driver operation data and/or the road traffic environment data of each vehicle; and respectively sending the shared data to the corresponding ToC end, toB end and ToG end to respectively generate an active reminding mechanism strategy, a vehicle-enterprise vehicle performance and product optimization strategy and an intelligent control strategy for a traffic management system and a signal lamp system.
Optionally, in an embodiment of the present application, the generating shared data corresponding to the vehicle, the driver, and the traffic system according to the vehicle data, the driver operation data, and/or the road traffic environment data of each vehicle includes: performing data definition and processing on the vehicle data, the driver operation data and/or the road traffic environment data, and screening out processing data meeting preset conditions; and inputting the processed data into a pre-trained data sharing model, and outputting the shared data corresponding to the vehicle, the driver and the traffic system, wherein the data sharing model is obtained from a training data set with a label.
Optionally, in an embodiment of the present application, the method further includes: detecting an actual category of the processed data; and determining the optimal storage scheme of the processing data according to the actual category, and storing the processing data according to the optimal storage scheme.
Optionally, in an embodiment of the present application, the method further includes: performing label construction on the shared data to generate label information of the shared data; and matching the best application result of the shared data according to the label information.
An embodiment of a second aspect of the present application provides a driving data sharing device based on cloud big data, including: the acquisition module is used for acquiring vehicle data, driver operation data and/or road traffic environment data of a plurality of automobiles; the generating module is used for generating shared data corresponding to the vehicles, the drivers and the traffic system according to the vehicle data, the driver operation data and/or the road traffic environment data of each vehicle; and the sharing module is used for respectively sending the shared data to the corresponding ToC end, toB end and ToG end so as to respectively generate an active reminding mechanism strategy, an optimization strategy of the vehicle enterprises on vehicle performance and products and an intelligent control strategy of the traffic management system and the signal lamp system.
Optionally, in an embodiment of the present application, the generating module includes: the processing unit is used for carrying out data definition and processing on the vehicle data, the driver operation data and/or the road traffic environment data, and screening out processing data meeting preset conditions; and the calculation unit is used for inputting the processing data into a pre-trained data sharing model and outputting the sharing data corresponding to the vehicle, the driver and the traffic system, wherein the data sharing model is obtained by a training data set with a label.
Optionally, in an embodiment of the present application, the method further includes: a detection module for detecting an actual category of the processed data; and the processing module is used for determining the optimal storage scheme of the processed data according to the actual category and storing the processed data according to the optimal storage scheme.
Optionally, in an embodiment of the present application, the method further includes: the tag module is used for performing tag construction on the shared data to generate tag information of the shared data; and the matching module is used for matching the optimal application result of the shared data according to the label information.
An embodiment of a third aspect of the present application provides a server, including: the driving data sharing method based on the cloud big data comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the processor executes the program to realize the driving data sharing method based on the cloud big data.
A fourth aspect of the present application provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the driving data sharing method based on cloud big data is implemented.
According to the embodiment of the application, the corresponding shared data can be generated according to the vehicle data, the driver operation data and/or the road traffic environment data of each automobile, and the shared data is respectively sent to the corresponding ToC end, toB end and ToG end, so that a corresponding strategy is generated, reasonable application of data generated in the vehicle driving process is realized, and resource waste is avoided. Therefore, the technical problem that in the related technology, the application degree of data generated by vehicle driving to ToC, toB and ToG is very low, and great resource waste is caused is solved.
Additional aspects and advantages of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the present application.
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The above and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
fig. 1 is a flowchart of a driving data sharing method based on cloud big data according to an embodiment of the present application;
fig. 2 is a schematic diagram illustrating a principle of a driving data sharing method based on cloud big data according to an embodiment of the present application;
fig. 3 is a schematic diagram illustrating data acquisition and data storage of a driving data sharing method based on cloud big data according to an embodiment of the present application;
fig. 4 is a schematic diagram illustrating data processing of a driving data sharing method based on cloud big data according to an embodiment of the present application;
fig. 5 is a schematic diagram illustrating data calculation and data application of a driving data sharing method based on cloud big data according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of a driving data sharing device based on cloud big data according to an embodiment of the present application;
fig. 7 is a schematic structural diagram of a server according to an embodiment of the present application.
Detailed Description
Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative and intended to explain the present application and should not be construed as limiting the present application.
The following describes a driving data sharing method and device based on cloud big data according to an embodiment of the present application with reference to the accompanying drawings. In order to solve the technical problems that in the related technology mentioned in the background technology center, the application degree of data generated by vehicle driving to ToC, toB and ToG is very low, and great resource waste is caused, the application provides a driving data sharing method based on cloud big data. Therefore, the technical problem that in the related technology, the application degree of data generated by vehicle driving to ToC, toB and ToG is very low, and great resource waste is caused is solved.
Specifically, fig. 1 is a schematic flow diagram of a driving data sharing method based on cloud big data according to an embodiment of the present disclosure.
As shown in fig. 1, the driving data sharing method based on cloud big data includes the following steps:
in step S101, vehicle data, driver operation data, and/or road traffic environment data of a plurality of automobiles are collected.
In the actual execution process, the embodiment of the application can be a cloud big data platform which is built in advance so as to achieve the purposes of collecting and storing data by a user, modeling and carrying out algorithm analysis on the data, distributing the data to the outside and providing data service.
In some embodiments, data of components, functional modules or software operations, road condition information, GPS positions, environments, driving behaviors, and the like in the vehicle may be acquired through a plurality of vehicle acquisition devices, such as laser radars, sensors, T-BOX, GPS, cameras, and the like, to obtain vehicle data, driver operation data, and/or road traffic environment data of a plurality of vehicles.
In step S102, shared data corresponding to the vehicle, the driver, and the traffic system is generated according to the vehicle data, the driver operation data, and/or the road traffic environment data of each vehicle.
As a possible implementation manner, in the embodiment of the present application, after vehicle data, driver operation data, and/or road traffic environment data of a plurality of vehicles are collected, shared data corresponding to the vehicles, the drivers, and the traffic systems may be generated based on the vehicle data, the driver operation data, and/or the road traffic environment data of each vehicle.
Optionally, in an embodiment of the present application, generating shared data corresponding to the vehicle, the driver, and the traffic system according to vehicle data, driver operation data, and/or road traffic environment data of each vehicle includes: performing data definition and processing on vehicle data, driver operation data and/or road traffic environment data, and screening out processing data meeting preset conditions; and inputting the processed data into a pre-trained data sharing model, and outputting the shared data corresponding to the vehicle, the driver and the traffic system, wherein the data sharing model is obtained by a training data set with a label.
Particularly, the embodiment of the application can carry out data clearness and processing on vehicle data, driver operation data and/or road traffic environment data, so that data enhancement and data format conversion are realized, processed data meeting preset conditions are screened out, the data are conveniently transmitted and applied subsequently, analysis errors caused by unclear data are avoided, the influence of error data on data integrity is reduced, and the final strategy is influenced.
Further, the embodiment of the application can build a data sharing model based on various application scenes, perform simulation training through machine learning, perform algorithm analysis, input the processed data into the data sharing model, and output the shared data corresponding to the vehicle, the driver and the traffic system, for example: and (3) building a model through the road intersection, the traffic flow of each entrance, the passing time, the traffic light interval, the number of vehicles at busy intersections, the number of vehicles at idle intersections and the like, performing algorithm analysis based on various factors and authorities, and calculating the reasonable control time of the traffic lights at the intersection.
It should be noted that the preset conditions can be set by those skilled in the art according to practical situations, and are not limited in particular.
In step S103, the shared data is respectively sent to the corresponding ToC terminal, toB terminal, and ToG terminal, so as to respectively generate an active reminding mechanism policy, a vehicle performance and product optimization policy for the vehicle enterprise, and an intelligent control policy for the traffic management system and the signal lamp system.
Specifically, for the ToC terminal, the embodiment of the application can generate an active reminding mechanism strategy, that is, a prejudge result is obtained based on data calculation, information push is performed on a vehicle driver, and information such as vehicle conditions and road conditions is informed to the vehicle driver in time;
for the ToB terminal, the optimization strategy of the vehicle enterprise on the vehicle performance and products can be generated, namely the vehicle enterprise masters vehicle information, user driving habits and behavior preferences based on vehicle data, driving data, user operation data and the like, so that data decision suggestions can be provided for vehicle remote diagnosis, maintenance suggestions, vehicle function optimization and the like;
for the ToG end, an intelligent control strategy for a traffic management system and a signal lamp system can be generated, namely, a basis for decision change is provided for a traffic command system, for example, the reasonable control time length of traffic lights at intersection calculated in the steps can be obtained, data can be distributed to the traffic command system, and the traffic command system can automatically adjust the control time length of the traffic lights after receiving the control time length of the signal lights, so that vehicles at busy intersections can pass through as soon as possible, and congestion caused by overstocking of the vehicles due to waiting for the red lights is avoided.
Optionally, in an embodiment of the present application, the method further includes: detecting an actual category of the processed data; and determining an optimal storage scheme for the processed data according to the actual category, and storing the processed data according to the optimal storage scheme.
In an actual execution process, the cloud server in the embodiment of the application can receive the acquired data and store the received data according to the modules, the actual category of the processed data can be detected, and classified storage can be performed according to a control main body of the data, such as driver operation data, vehicle response data and the like; the data processing method and the data processing device can also perform classified storage according to the actual state of the data, and if the data which needs to be cleaned and processed, the data which can be directly used and the like are needed, the optimal storage scheme for processing the data can be determined according to the actual category, the processed data can be stored according to the optimal storage scheme, reasonable planning and storage of the data are achieved, and quick searching and calling of the data are achieved.
Optionally, in an embodiment of the present application, the method further includes: carrying out label construction on the shared data to generate label information of the shared data; and matching the best application result of the shared data according to the label information.
As a possible implementation manner, the embodiment of the application may perform label construction on shared data to generate label information of the shared data, for example, perform label construction on an automobile, a driver, and the like, and further generate label information of the shared data according to different labels, so as to match an optimal application result of the shared data, and implement shared data application of the ToC end, the ToB end, and the ToG end.
With reference to fig. 2 to 5, an embodiment of the present application is used to describe in detail a working principle of a driving data sharing method based on cloud big data.
As shown in fig. 2, the embodiment of the present application may include the following steps:
s1: and collecting original data. In the actual execution process, the embodiment of the application can be a cloud big data platform which is built in advance so as to achieve the purposes of collecting and storing data by a user, modeling and carrying out algorithm analysis on the data, distributing the data to the outside and providing data service.
As shown in fig. 3, in the embodiment of the application, in the driving process of the vehicle, data such as operation of each component, functional module or software, road condition information, a GPS position, an environment, driving behavior and the like in the vehicle can be acquired through a laser radar, a sensor, a T-BOX, a GPS, a camera and the like of the vehicle, and the newly acquired data is synchronized to the cloud in real time.
S2: the embodiment of the application can synchronize various data collected in the vehicle running process to the cloud big data platform in real time, and the cloud server stores the received data according to the module.
S3: the big data center can wash and process the data of the cloud, store the data according to rules, and build labels for automobiles, drivers and the like.
Specifically, the data sharing model can be built based on various application scenes, and the simulation training and algorithm analysis are performed through machine learning.
Specifically, as shown in fig. 4, the cloud big data platform can store the acquired data according to scenes such as vehicles, vehicle owners, driving behaviors, road conditions and the like, and perform data cleaning and processing, slightly summarize the data and the like to form a relatively complete and standardized data warehouse system, as shown in fig. 5, the cloud big data platform can perform label construction, data model construction, algorithm analysis and the like on the data based on the data warehouse system, and finally can output the calculation result data of the application when driving.
S4: and storing the calculation result and issuing data.
S5: data application, the embodiment of the present application may provide data application to the TOC end, the TOB end, and the TOG end based on a predeterminable result obtained by data calculation.
Specifically, for the ToC terminal, the embodiment of the application can generate an active reminding mechanism strategy, that is, a prejudge result is obtained based on data calculation, information push is performed on a vehicle driver, and information such as vehicle conditions and road conditions is informed to the vehicle driver in time;
for the ToB terminal, the optimization strategy of the vehicle enterprise on the vehicle performance and products can be generated, namely the vehicle enterprise masters the vehicle information, the driving habits of the user and the behavior preference based on the vehicle data, the driving data, the user operation data and the like, so that data decision suggestions can be provided for vehicle remote diagnosis, maintenance suggestions, vehicle function optimization and the like;
for the ToG end, the embodiment of the application can generate an intelligent control strategy for a traffic management system and a signal lamp system, that is, a basis for decision change is provided for a traffic command system.
According to the driving data sharing method based on the cloud big data, corresponding shared data can be generated according to vehicle data, driver operation data and/or road traffic environment data of each automobile, the shared data are sent to the corresponding ToC end, toB end and ToG end respectively, a corresponding strategy is generated, reasonable application of data generated in the driving process of the automobile is achieved, and resource waste is avoided. Therefore, the technical problem that in the related technology, the application degree of data generated by vehicle driving to ToC, toB and ToG is very low, and great resource waste is caused is solved.
Next, a driving data sharing device based on cloud big data according to an embodiment of the present application is described with reference to the drawings.
Fig. 6 is a schematic block diagram of a driving data sharing device based on cloud big data according to an embodiment of the present application.
As shown in fig. 6, the cloud big data based driving data sharing device 10 includes: an acquisition module 100, a generation module 200 and a sharing module 300.
Specifically, the collection module 100 is configured to collect vehicle data, driver operation data, and/or road traffic environment data of a plurality of vehicles.
The generating module 200 is configured to generate shared data corresponding to the vehicle, the driver, and the traffic system according to vehicle data, driver operation data, and/or road traffic environment data of each vehicle.
The sharing module 300 is configured to send the shared data to the corresponding ToC end, toB end, and ToG end, respectively, so as to generate an active reminding mechanism policy, an optimization policy of a vehicle enterprise on vehicle performance and products, and an intelligent control policy of a traffic management system and a signal lamp system.
Optionally, in an embodiment of the present application, the generating module 200 includes: a processing unit and a calculating unit.
The processing unit is used for carrying out data definition and processing on the vehicle data, the driver operation data and/or the road traffic environment data, and screening out processing data meeting preset conditions.
And the computing unit is used for inputting the processing data into a pre-trained data sharing model and outputting the sharing data corresponding to the vehicle, the driver and the traffic system, wherein the data sharing model is obtained by a training data set with a label.
Optionally, in an embodiment of the present application, the driving data sharing device 10 based on cloud big data further includes: the device comprises a detection module and a processing module.
The detection module is used for detecting the actual type of the processed data.
And the processing module is used for determining the optimal storage scheme of the processing data according to the actual category and storing the processing data according to the optimal storage scheme.
Optionally, in an embodiment of the present application, the driving data sharing device 10 based on cloud big data further includes: the device comprises a label module and a matching module.
The tag module is used for carrying out tag construction on the shared data and generating tag information of the shared data.
And the matching module is used for matching the optimal application result of the shared data according to the label information.
It should be noted that the explanation of the embodiment of the driving data sharing method based on cloud big data is also applicable to the driving data sharing device based on cloud big data of the embodiment, and is not repeated herein.
According to the driving data sharing device based on the cloud big data, the corresponding shared data can be generated according to the vehicle data, the driver operation data and/or the road traffic environment data of each automobile, the shared data are respectively sent to the corresponding ToC end, the corresponding ToB end and the corresponding ToG end, a corresponding strategy is further generated, reasonable application of data generated in the driving process of the automobile is achieved, and resource waste is avoided. Therefore, the technical problem that in the related technology, the application degree of data generated by vehicle driving to ToC, toB and ToG is very low, and great resource waste is caused is solved.
Fig. 7 is a schematic structural diagram of a server according to an embodiment of the present application. The server may include:
The processor 702 implements the driving data sharing method based on the cloud big data provided in the above embodiments when executing the program.
Further, the server further comprises:
a communication interface 703 for communication between the memory 701 and the processor 702.
A memory 701 for storing computer programs operable on the processor 702.
The memory 701 may comprise high-speed RAM memory, and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
If the memory 701, the processor 702 and the communication interface 703 are implemented independently, the communication interface 703, the memory 701 and the processor 702 may be connected to each other through a bus and perform communication with each other. The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in FIG. 7, but this is not intended to represent only one bus or type of bus.
Alternatively, in specific implementation, if the memory 701, the processor 702, and the communication interface 703 are integrated on one chip, the memory 701, the processor 702, and the communication interface 703 may complete mutual communication through an internal interface.
The processor 702 may be a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits configured to implement embodiments of the present Application.
The embodiment of the application further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the driving data sharing method based on cloud big data is implemented.
In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or N embodiments or examples. Moreover, various embodiments or examples and features of various embodiments or examples described in this specification can be combined and combined by one skilled in the art without being mutually inconsistent.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "N" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or N executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present application.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or N wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or combination of the following technologies, which are well known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc. Although embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application, and that variations, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present application.
Claims (10)
1. A driving data sharing method based on cloud big data is characterized by comprising the following steps:
collecting vehicle data, driver operation data and/or road traffic environment data of a plurality of automobiles;
generating shared data corresponding to the vehicles, the drivers and the traffic system according to the vehicle data, the driver operation data and/or the road traffic environment data of each vehicle; and
and respectively sending the shared data to the corresponding ToC end, toB end and ToG end so as to respectively generate an active reminding mechanism strategy, a vehicle-enterprise vehicle performance and product optimization strategy and an intelligent control strategy for a traffic management system and a signal lamp system.
2. The method according to claim 1, wherein the generating of the shared data corresponding to the vehicles, drivers and traffic systems from the vehicle data, driver operation data and/or road traffic environment data of each vehicle comprises:
performing data definition and processing on the vehicle data, the driver operation data and/or the road traffic environment data, and screening out processing data meeting preset conditions;
and inputting the processed data into a pre-trained data sharing model, and outputting the shared data corresponding to the vehicle, the driver and the traffic system, wherein the data sharing model is obtained from a training data set with a label.
3. The method of claim 2, further comprising:
detecting an actual category of the processed data;
and determining the optimal storage scheme of the processing data according to the actual category, and storing the processing data according to the optimal storage scheme.
4. The method of claim 1, further comprising:
performing label construction on the shared data to generate label information of the shared data;
and matching the best application result of the shared data according to the label information.
5. The utility model provides a driving data sharing device based on big data in high in clouds which characterized in that includes:
the acquisition module is used for acquiring vehicle data, driver operation data and/or road traffic environment data of a plurality of automobiles;
the generating module is used for generating shared data corresponding to the vehicles, the drivers and the traffic system according to the vehicle data, the driver operation data and/or the road traffic environment data of each vehicle; and
and the sharing module is used for respectively sending the shared data to the corresponding ToC end, toB end and ToG end so as to respectively generate an active reminding mechanism strategy, an optimization strategy of the vehicle enterprises on vehicle performance and products and an intelligent control strategy of the traffic management system and the signal lamp system.
6. The apparatus of claim 5, wherein the generating module comprises:
the processing unit is used for carrying out data definition and processing on the vehicle data, the driver operation data and/or the road traffic environment data, and screening out processing data meeting preset conditions;
and the calculation unit is used for inputting the processing data into a pre-trained data sharing model and outputting the sharing data corresponding to the vehicle, the driver and the traffic system, wherein the data sharing model is obtained by a training data set with a label.
7. The apparatus of claim 6, further comprising:
a detection module for detecting an actual category of the processed data;
and the processing module is used for determining the optimal storage scheme of the processed data according to the actual category and storing the processed data according to the optimal storage scheme.
8. The apparatus of claim 5, further comprising:
the tag module is used for carrying out tag construction on the shared data to generate tag information of the shared data;
and the matching module is used for matching the optimal application result of the shared data according to the label information.
9. A server, comprising: the vehicle data sharing method based on the cloud big data comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the program to achieve the vehicle data sharing method based on the cloud big data according to any one of claims 1 to 4.
10. A computer-readable storage medium, on which a computer program is stored, wherein the program is executed by a processor, so as to implement the cloud big data-based driving data sharing method according to any one of claims 1 to 4.
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