CN117149860A - Driving data mining method and system for automatic driving vehicle - Google Patents
Driving data mining method and system for automatic driving vehicle Download PDFInfo
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- 238000007418 data mining Methods 0.000 title claims abstract description 74
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- 238000005065 mining Methods 0.000 claims abstract description 264
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Abstract
The application relates to the technical field of data mining, and particularly discloses a driving data mining method and system of an automatic driving vehicle. According to the method, the data mining requirements are acquired, the requirement scene analysis is carried out, and the scene mining characteristics and a plurality of requirement mining characteristics are acquired; according to scene mining features, initially mining vehicle running data to obtain scene mining data; determining idle time of the vehicle; and in idle time of the vehicle, deep mining is carried out according to the plurality of requirement mining features to obtain requirement mining data. The method has the advantages that the requirement scene analysis can be carried out on the data mining requirements, the scene mining characteristics and the multiple requirement mining characteristics are obtained, the vehicle driving data are initially mined according to the scene mining characteristics, the scene mining data are deeply mined according to the multiple requirement mining characteristics in the idle time of the vehicle, excessive vehicle-machine resources are prevented from being occupied in the working process of the automatic driving vehicle during the data mining, the normal running of the automatic driving is ensured, and the potential safety hazard is eliminated.
Description
Technical Field
The application belongs to the technical field of data mining, and particularly relates to a driving data mining method and system for an automatic driving vehicle.
Background
Data mining is a data processing technique that extracts potentially useful information and knowledge from a large, incomplete, noisy, ambiguous, random data that is implicit therein, not known a priori. Data mining is generally related to computer science and achieves the needs and goals of mining by a number of methods such as statistics, online analytical processing, intelligence retrieval, machine learning, expert systems, and pattern recognition.
In the prior art, for the driving data mining of an automatic driving vehicle, because data are generated in real time and are huge, cloud processing of the data mining cannot be performed, and in general, a vehicle machine performs instant data mining without primary and secondary and stage, so that excessive vehicle machine resources are easily occupied in the working process of the automatic driving vehicle, the normal operation of the automatic driving is influenced, and certain potential safety hazards are provided.
Disclosure of Invention
The embodiment of the application aims to provide a driving data mining method and system for an automatic driving vehicle, and aims to solve the problems in the background technology.
In order to achieve the above object, the embodiment of the present application provides the following technical solutions:
a method of driving data mining for an autonomous vehicle, the method comprising the steps of:
acquiring data mining requirements, carrying out requirement scene analysis, and acquiring scene mining features and a plurality of requirement mining features;
according to the scene mining characteristics, vehicle running data are initially mined, and scene mining data are obtained;
performing vehicle-mounted resource monitoring, acquiring resource monitoring data, performing resource usage analysis, and determining idle time of the vehicle-mounted;
and in the idle time of the vehicle, deep mining is carried out on the scene mining data according to a plurality of the requirement mining features to obtain the requirement mining data.
As a further limitation of the technical solution of the embodiment of the present application, the acquiring data mining requirements, performing requirement scene analysis, and acquiring scene mining features and multiple requirement mining features specifically includes the following steps:
receiving mining request operation of a user, and creating a demand interaction interface;
acquiring data mining requirements through the requirement interaction interface;
performing scene analysis on the data mining requirements to obtain scene mining features;
and carrying out demand analysis on the data mining demands to acquire a plurality of demand mining features.
As a further limitation of the technical solution of the embodiment of the present application, the initial mining of the vehicle driving data according to the scene mining feature, to obtain scene mining data specifically includes the following steps:
performing management acquisition to obtain management acquisition data;
performing management verification on the management acquisition data, and judging whether the management acquisition data has management authority;
when the management right exists, acquiring vehicle running data;
and performing initial mining according to the scene mining features to obtain scene mining data.
As further defined by the technical solution of the embodiment of the present application, the initial mining is performed according to the scene mining feature, and the obtaining of the scene mining data specifically includes the following steps:
acquiring a scene data type of the scene mining feature;
extracting scene type data from the vehicle driving data according to the scene data type;
and according to the scene mining characteristics, carrying out initial mining on the scene type data to obtain scene mining data.
As a further limitation of the technical solution of the embodiment of the present application, the performing vehicle-mounted resource monitoring, obtaining resource monitoring data, performing resource usage analysis, and determining the idle time of the vehicle-mounted specifically includes the following steps:
generating a resource monitoring instruction;
according to the resource monitoring instruction, monitoring the vehicle-mounted resource to acquire resource monitoring data;
comparing the resource monitoring data with preset idle standard resources to generate a resource comparison result;
and determining the idle time of the vehicle machine according to the resource comparison result.
As further defined by the technical solution of the embodiment of the present application, in the idle time of the vehicle-mounted device, according to a plurality of the requirement mining features, deep mining is performed on the scene mining data, and the requirement mining data is obtained specifically including the following steps:
generating a deep mining instruction in the idle time of the vehicle;
importing scene excavation data according to the depth excavation instruction;
and performing deep mining on the scene mining data according to the plurality of the requirement mining features to obtain the requirement mining data.
A travel data mining system for an autonomous vehicle, the system comprising a demand scenario analysis module, a data initial mining module, a resource usage analysis module, and a data depth mining module, wherein:
the demand scene analysis module is used for acquiring data mining demands, carrying out demand scene analysis and acquiring scene mining features and a plurality of demand mining features;
the data initial mining module is used for initially mining the vehicle running data according to the scene mining characteristics to obtain scene mining data;
the resource use analysis module is used for monitoring the vehicle-mounted machine resources, acquiring resource monitoring data, carrying out resource use analysis and determining the idle time of the vehicle-mounted machine;
and the data depth mining module is used for performing depth mining on the scene mining data according to a plurality of the requirement mining features in the idle time of the vehicle and machine to obtain the requirement mining data.
As a further limitation of the technical solution of the embodiment of the present application, the demand scene analysis module specifically includes:
the interface generation unit is used for receiving the mining request operation of the user and creating a demand interaction interface;
the requirement acquisition unit is used for acquiring data mining requirements through the requirement interaction interface;
the scene analysis unit is used for carrying out scene analysis on the data mining requirements and obtaining scene mining features;
the demand analysis unit is used for carrying out demand analysis on the data mining demands and acquiring a plurality of demand mining features.
As a further limitation of the technical solution of the embodiment of the present application, the data initial mining module specifically includes:
the management acquisition unit is used for carrying out management acquisition and acquiring management acquisition data;
the management verification unit is used for performing management verification on the management acquisition data and judging whether the management acquisition data has management authority;
a data acquisition unit for acquiring vehicle running data when the management right is provided;
and the initial mining unit is used for performing initial mining according to the scene mining characteristics to obtain scene mining data.
As a further limitation of the technical solution of the embodiment of the present application, the resource usage analysis module specifically includes:
the instruction generation unit is used for generating a resource monitoring instruction;
the resource monitoring unit is used for monitoring the vehicle-mounted machine resources according to the resource monitoring instruction and acquiring resource monitoring data;
the resource comparison unit is used for comparing the resource monitoring data with preset idle standard resources to generate a resource comparison result;
and the idle determining unit is used for determining the idle time of the vehicle machine according to the resource comparison result.
Compared with the prior art, the application has the beneficial effects that:
according to the embodiment of the application, the data mining requirements are acquired, the requirement scene analysis is carried out, and the scene mining characteristics and a plurality of requirement mining characteristics are acquired; according to scene mining features, initially mining vehicle running data to obtain scene mining data; determining idle time of the vehicle; and in idle time of the vehicle, deep mining is carried out according to the plurality of requirement mining features to obtain requirement mining data. The method has the advantages that the requirement scene analysis can be carried out on the data mining requirements, the scene mining characteristics and the multiple requirement mining characteristics are obtained, the vehicle driving data are initially mined according to the scene mining characteristics, the scene mining data are deeply mined according to the multiple requirement mining characteristics in the idle time of the vehicle, excessive vehicle-machine resources are prevented from being occupied in the working process of the automatic driving vehicle during the data mining, the normal running of the automatic driving is ensured, and the potential safety hazard is eliminated.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following description will briefly introduce the drawings that are needed in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the present application.
Fig. 1 shows a flowchart of a method provided by an embodiment of the present application.
Fig. 2 shows a flowchart of a demand scene analysis in a method according to an embodiment of the present application.
Fig. 3 shows a flowchart of initial mining of vehicle travel data in the method provided by the embodiment of the application.
Fig. 4 shows a flowchart of obtaining scene mining data in the method provided by the embodiment of the application.
Fig. 5 shows a flowchart of monitoring vehicle resources in the method provided by the embodiment of the application.
Fig. 6 shows a flowchart of deep mining of scene mining data in the method provided by the embodiment of the present application.
Fig. 7 shows an application architecture diagram of a system provided by an embodiment of the present application.
Fig. 8 shows a block diagram of a demand scene analysis module in the system according to an embodiment of the present application.
Fig. 9 shows a block diagram of the data initial mining module in the system according to the embodiment of the present application.
Fig. 10 is a block diagram illustrating a structure of a resource usage analysis module in the system according to an embodiment of the present application.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
It can be understood that in the prior art, for the driving data mining of the automatic driving vehicle, because the data is numerous and miscellaneous and is generated in real time, the cloud processing of the data mining cannot be performed, and in general, the vehicle machine performs the instant data mining without primary and secondary and stage, so that excessive vehicle machine resources are easily occupied in the working process of the automatic driving vehicle, the normal running of the automatic driving is affected, and a certain potential safety hazard exists.
In order to solve the problems, the embodiment of the application performs demand scene analysis by acquiring data mining demands, and acquires scene mining features and a plurality of demand mining features; according to scene mining features, initially mining vehicle running data to obtain scene mining data; monitoring vehicle-mounted resources and determining idle time of the vehicle-mounted; and in idle time of the vehicle, deep mining is carried out according to the plurality of requirement mining features to obtain requirement mining data. The method has the advantages that the requirement scene analysis can be carried out on the data mining requirements, the scene mining characteristics and the multiple requirement mining characteristics are obtained, the vehicle driving data are initially mined according to the scene mining characteristics, the scene mining data are deeply mined according to the multiple requirement mining characteristics in the idle time of the vehicle, excessive vehicle-machine resources are prevented from being occupied in the working process of the automatic driving vehicle during the data mining, the normal running of the automatic driving is ensured, and the potential safety hazard is eliminated.
Fig. 1 shows a flowchart of a method provided by an embodiment of the present application.
In particular, in a preferred embodiment provided by the present application, a method for mining travel data of an autonomous vehicle, the method comprising the steps of:
step S101, acquiring data mining requirements, carrying out requirement scene analysis, and acquiring scene mining features and a plurality of requirement mining features.
In the embodiment of the application, when a user has a data mining requirement on an automatic driving vehicle, the mining request operation can be remotely or directly carried out on the automatic driving vehicle, a corresponding requirement interaction interface is created by receiving the mining request operation of the user, the requirement interaction interface is displayed for the user, the user can carry out requirement input on the displayed requirement interaction interface, the data mining requirement of the user is obtained, further, scene mining characteristics are obtained by carrying out scene analysis on the data mining requirement, and a plurality of requirement mining characteristics are obtained by carrying out the requirement analysis on the data mining requirement, wherein the scene mining characteristics are scenes (such as rainy days, working hours and the like) corresponding to the data mining requirement; demand mining features are specific requirements for data mining (e.g., vehicle speed, temperature, drive record video, etc.).
Specifically, fig. 2 shows a flowchart of a requirement scene analysis in the method provided by the embodiment of the present application.
In the preferred embodiment provided by the application, the acquiring the data mining requirement, performing requirement scene analysis, acquiring scene mining characteristics and a plurality of requirement mining characteristics specifically includes the following steps:
step S1011, receiving mining request operation of a user and creating a demand interaction interface;
step S1012, acquiring data mining requirements through the requirement interaction interface;
step S1013, performing scene analysis on the data mining requirements to obtain scene mining features;
step S1014, performing a requirement analysis on the data mining requirement, to obtain a plurality of requirement mining features.
Further, the driving data mining method of the automatic driving vehicle further comprises the following steps:
step S102, according to the scene mining features, initial mining is carried out on the vehicle running data to obtain scene mining data.
In the embodiment of the application, a user is managed and acquired (can be fingerprint acquisition), management and acquisition data (can be fingerprint acquisition data) are acquired, management and authentication are carried out on the management and acquisition data according to preset authentication backup data (can be fingerprint backup data) (can be characteristic authentication is carried out on the fingerprint acquisition data according to the fingerprint backup data), whether management authority is available or not is judged (specifically, when the characteristic authentication of the fingerprint acquisition data passes, the management authority is judged), when the management authority is available, vehicle running data are acquired, then scene data types (such as video data) of scene mining characteristics are acquired, the vehicle running data are preprocessed according to the scene data types, corresponding scene type data are extracted from the vehicle running data, and then initial mining is carried out on the scene type data according to the scene mining characteristics, so that the scene mining data are obtained.
It can be appreciated that the initial mining can be performed by using a plurality of algorithms such as a neural network method, a decision tree method, a genetic algorithm, a rough set method, a fuzzy set method, an association rule method and the like.
Specifically, fig. 3 shows a flowchart of initial mining of vehicle driving data in the method provided by the embodiment of the present application.
In a preferred embodiment of the present application, the initial mining of the vehicle driving data according to the scene mining feature, to obtain scene mining data specifically includes the following steps:
step S1021, management acquisition is carried out to obtain management acquisition data;
step S1022, performing management verification on the management acquisition data, and judging whether the management authority exists;
step S1023, when the management right is provided, acquiring vehicle running data;
step S1024, performing initial mining according to the scene mining features to obtain scene mining data.
Specifically, fig. 4 shows a flowchart of obtaining scene mining data in the method provided by the embodiment of the present application.
In a preferred embodiment of the present application, the initial mining is performed according to the scene mining feature, and the obtaining scene mining data specifically includes the following steps:
step S10241, obtaining the scene data type of the scene mining feature;
step S10242, extracting scene type data from the vehicle running data according to the scene data type;
and step S10243, performing initial mining on the scene type data according to the scene mining characteristics to obtain scene mining data.
Further, the driving data mining method of the automatic driving vehicle further comprises the following steps:
and step S103, monitoring the vehicle-mounted machine resources, acquiring resource monitoring data, analyzing the resource use, and determining the idle time of the vehicle-mounted machine.
In the embodiment of the application, a resource monitoring instruction is generated, then, according to the resource monitoring instruction, the vehicle-to-vehicle machine resource monitoring (including the monitoring of resources such as CPU, GPU, memory and hard disk) is carried out on the automatic driving vehicle, the resource monitoring data is obtained, the resource monitoring data is compared with the preset idle standard resources to generate a resource comparison result, and the idle time of the vehicle-to-vehicle machine is determined according to the resource comparison result, specifically: in the resource monitoring data, when the utilization rate of all the resources is smaller than the utilization rate of the idle standard resources, judging that the vehicle is in the idle time of the vehicle; and in the resource monitoring data, when the utilization rate of any resource is not smaller than the utilization rate of the idle standard resource, judging that the resource is not in the idle time of the vehicle.
Specifically, fig. 5 shows a flowchart of monitoring vehicle-to-machine resources in the method provided by the embodiment of the application.
In the preferred embodiment provided by the application, the vehicle-mounted resource monitoring, the resource monitoring data acquisition, the resource usage analysis and the determination of the idle time of the vehicle-mounted comprise the following steps:
step S1031, generating a resource monitoring instruction;
step S1032, carrying out vehicle-to-vehicle resource monitoring according to the resource monitoring instruction to acquire resource monitoring data;
step S1033, comparing the resource monitoring data with preset idle standard resources to generate a resource comparison result;
and step S1034, determining the idle time of the vehicle machine according to the resource comparison result.
Further, the driving data mining method of the automatic driving vehicle further comprises the following steps:
and step S104, deep mining is carried out on the scene mining data according to a plurality of the requirement mining features in the idle time of the vehicle to obtain the requirement mining data.
In the embodiment of the application, a depth mining instruction is generated in idle time of a vehicle, scene mining data is imported according to the depth mining instruction, and further the scene mining data is subjected to depth mining according to a plurality of requirement mining features to obtain the requirement mining data, wherein the depth mining can also be performed by adopting a plurality of algorithms such as a neural network method, a decision tree method, a genetic algorithm, a rough set method, a fuzzy set method, a correlation rule method and the like.
Specifically, fig. 6 shows a flowchart of deep mining on scene mining data in the method provided by the embodiment of the present application.
In the preferred embodiment of the present application, in the idle time of the vehicle, according to a plurality of the requirement mining features, deep mining is performed on the scene mining data, and the requirement mining data is obtained specifically including the following steps:
step S1041, generating a depth excavation instruction in the idle time of the vehicle;
step S1042, importing scene excavation data according to the depth excavation instruction;
step S1043, performing deep mining on the scene mining data according to a plurality of the requirement mining features to obtain requirement mining data.
Further, fig. 7 shows an application architecture diagram of the system provided by the embodiment of the present application.
In another preferred embodiment of the present application, a driving data mining system for an autonomous vehicle includes:
the requirement scene analysis module 101 is configured to obtain a data mining requirement, perform requirement scene analysis, and obtain a scene mining feature and a plurality of requirement mining features.
In the embodiment of the application, when a user has a data mining requirement on an automatic driving vehicle, the mining request operation can be remotely or directly performed on the automatic driving vehicle, the requirement scene analysis module 101 creates a corresponding requirement interaction interface by receiving the mining request operation of the user and displays the requirement interaction interface to the user, the user can perform requirement input on the displayed requirement interaction interface, the requirement scene analysis module 101 obtains the data mining requirement of the user, further obtains scene mining characteristics by performing scene analysis on the data mining requirement, and obtains a plurality of requirement mining characteristics by performing the requirement analysis on the data mining requirement, wherein the scene mining characteristics are scenes corresponding to the data mining requirement (such as rainy days, working hours and the like); demand mining features are specific requirements for data mining (e.g., vehicle speed, temperature, drive record video, etc.).
Specifically, fig. 8 shows a block diagram of a demand scene analysis module 101 in the system according to an embodiment of the present application.
In a preferred embodiment of the present application, the demand scene analysis module 101 specifically includes:
the interface generating unit 1011 is configured to receive an excavation request operation of a user, and create a demand interactive interface;
a requirement acquisition unit 1012, configured to acquire a data mining requirement through the requirement interaction interface;
a scene analysis unit 1013, configured to perform scene analysis on the data mining requirement, and obtain a scene mining feature;
a requirement analysis unit 1014, configured to perform requirement analysis on the data mining requirement, and acquire a plurality of requirement mining features.
Further, the driving data mining system of the autonomous vehicle further includes:
the data initial mining module 102 is configured to perform initial mining on the vehicle driving data according to the scene mining feature, so as to obtain scene mining data.
In the embodiment of the present application, the data initial mining module 102 performs management collection (may be fingerprint collection) on a user, acquires management collection data (may be fingerprint collection data), performs management verification on the management collection data according to preset verification backup data (may be fingerprint backup data), performs feature verification on the fingerprint collection data according to the fingerprint backup data, determines whether management authority is available (specifically, determines that management authority is available when the feature verification of the fingerprint collection data is passed), acquires vehicle driving data when the management authority is available, acquires a scene data type (for example, video data) of scene mining features, performs preprocessing on the vehicle driving data according to the scene data type, extracts corresponding scene type data from the vehicle driving data, and performs initial mining on the scene type data according to the scene mining features to obtain the scene mining data.
Specifically, fig. 9 shows a block diagram of the data initial mining module 102 in the system according to the embodiment of the present application.
In a preferred embodiment of the present application, the data initial mining module 102 specifically includes:
the management acquisition unit 1021 is used for performing management acquisition and acquiring management acquisition data;
a management verification unit 1022, configured to perform management verification on the management collected data, and determine whether the management authority is available;
a data acquisition unit 1023 for acquiring vehicle travel data when having a management right;
and an initial mining unit 1024, configured to perform initial mining according to the scene mining feature, to obtain scene mining data.
Further, the driving data mining system of the autonomous vehicle further includes:
the resource usage analysis module 103 is configured to monitor the vehicle-mounted resource, acquire resource monitoring data, perform resource usage analysis, and determine the idle time of the vehicle-mounted.
In the embodiment of the present application, the resource usage analysis module 103 generates a resource monitoring instruction, and then monitors the vehicle-to-vehicle resources (including monitoring of resources such as CPU, GPU, memory, hard disk, etc.) of the automatic driving vehicle according to the resource monitoring instruction, obtains resource monitoring data, compares the resource monitoring data with a preset idle standard resource, generates a resource comparison result, and determines the idle time of the vehicle-to-vehicle according to the resource comparison result, specifically: in the resource monitoring data, when the utilization rate of all the resources is smaller than the utilization rate of the idle standard resources, judging that the vehicle is in the idle time of the vehicle; and in the resource monitoring data, when the utilization rate of any resource is not smaller than the utilization rate of the idle standard resource, judging that the resource is not in the idle time of the vehicle.
Specifically, fig. 10 shows a block diagram of the resource usage analysis module 103 in the system according to the embodiment of the present application.
In a preferred embodiment provided by the present application, the resource usage analysis module 103 specifically includes:
an instruction generation unit 1031 for generating a resource monitoring instruction;
the resource monitoring unit 1032 is configured to monitor the vehicle resource according to the resource monitoring instruction, and obtain resource monitoring data;
a resource comparison unit 1033, configured to compare the resource monitoring data with a preset idle standard resource, and generate a resource comparison result;
and an idle determining unit 1034, configured to determine an idle time of the vehicle according to the resource comparison result.
Further, the driving data mining system of the autonomous vehicle further includes:
and the data depth mining module 104 is configured to perform depth mining on the scene mining data according to a plurality of the requirement mining features in idle time of the vehicle to obtain requirement mining data.
In the embodiment of the application, during idle time of a vehicle, the data depth mining module 104 generates a depth mining instruction, introduces scene mining data according to the depth mining instruction, and further performs depth mining on the scene mining data according to a plurality of requirement mining features to obtain the requirement mining data, wherein the depth mining can also perform mining processing by adopting a plurality of algorithms such as a neural network method, a decision tree method, a genetic algorithm, a rough set method, a fuzzy set method, an association rule method and the like.
It should be understood that, although the steps in the flowcharts of the embodiments of the present application are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in various embodiments may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor do the order in which the sub-steps or stages are performed necessarily performed in sequence, but may be performed alternately or alternately with at least a portion of the sub-steps or stages of other steps or other steps.
Those skilled in the art will appreciate that all or part of the processes in the methods of the above embodiments may be implemented by a computer program for instructing relevant hardware, where the program may be stored in a non-volatile computer readable storage medium, and where the program, when executed, may include processes in the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The technical features of the above-described embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above-described embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples illustrate only a few embodiments of the application and are described in detail herein without thereby limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.
The foregoing description of the preferred embodiments of the application is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the application.
Claims (10)
1. A method of driving data mining for an autonomous vehicle, the method comprising the steps of:
acquiring data mining requirements, carrying out requirement scene analysis, and acquiring scene mining features and a plurality of requirement mining features;
according to the scene mining characteristics, vehicle running data are initially mined, and scene mining data are obtained;
performing vehicle-mounted resource monitoring, acquiring resource monitoring data, performing resource usage analysis, and determining idle time of the vehicle-mounted;
and in the idle time of the vehicle, deep mining is carried out on the scene mining data according to a plurality of the requirement mining features to obtain the requirement mining data.
2. The method for mining travel data of an autonomous vehicle according to claim 1, wherein the acquiring data mining requirements, performing a requirement scene analysis, acquiring scene mining features and a plurality of requirement mining features specifically includes the steps of:
receiving mining request operation of a user, and creating a demand interaction interface;
acquiring data mining requirements through the requirement interaction interface;
performing scene analysis on the data mining requirements to obtain scene mining features;
and carrying out demand analysis on the data mining demands to acquire a plurality of demand mining features.
3. The method for mining travel data of an automatically driven vehicle according to claim 1, wherein the initial mining of the vehicle travel data according to the scene mining features to obtain scene mining data specifically comprises the following steps:
performing management acquisition to obtain management acquisition data;
performing management verification on the management acquisition data, and judging whether the management acquisition data has management authority;
when the management right exists, acquiring vehicle running data;
and performing initial mining according to the scene mining features to obtain scene mining data.
4. The method for mining travel data of an automatically driven vehicle according to claim 3, wherein the initial mining according to the scene mining features to obtain scene mining data comprises the steps of:
acquiring a scene data type of the scene mining feature;
extracting scene type data from the vehicle driving data according to the scene data type;
and according to the scene mining characteristics, carrying out initial mining on the scene type data to obtain scene mining data.
5. The method for mining travel data of an automatically driven vehicle according to claim 1, wherein the steps of performing vehicle-to-vehicle resource monitoring, acquiring resource monitoring data, performing resource usage analysis, and determining a vehicle-to-vehicle idle time specifically include the steps of:
generating a resource monitoring instruction;
according to the resource monitoring instruction, monitoring the vehicle-mounted resource to acquire resource monitoring data;
comparing the resource monitoring data with preset idle standard resources to generate a resource comparison result;
and determining the idle time of the vehicle machine according to the resource comparison result.
6. The method for mining travel data of an autonomous vehicle according to claim 1, wherein the step of deep mining the scene mining data according to a plurality of the requirement mining features during the idle time of the vehicle machine to obtain the requirement mining data specifically comprises the steps of:
generating a deep mining instruction in the idle time of the vehicle;
importing scene excavation data according to the depth excavation instruction;
and performing deep mining on the scene mining data according to the plurality of the requirement mining features to obtain the requirement mining data.
7. A driving data mining system of an automatic driving vehicle, the system comprising a demand scene analysis module, a data initial mining module, a resource usage analysis module and a data depth mining module, wherein:
the demand scene analysis module is used for acquiring data mining demands, carrying out demand scene analysis and acquiring scene mining features and a plurality of demand mining features;
the data initial mining module is used for initially mining the vehicle running data according to the scene mining characteristics to obtain scene mining data;
the resource use analysis module is used for monitoring the vehicle-mounted machine resources, acquiring resource monitoring data, carrying out resource use analysis and determining the idle time of the vehicle-mounted machine;
and the data depth mining module is used for performing depth mining on the scene mining data according to a plurality of the requirement mining features in the idle time of the vehicle and machine to obtain the requirement mining data.
8. The system for mining travel data of an autonomous vehicle of claim 7, wherein the demand scenario analysis module specifically comprises:
the interface generation unit is used for receiving the mining request operation of the user and creating a demand interaction interface;
the requirement acquisition unit is used for acquiring data mining requirements through the requirement interaction interface;
the scene analysis unit is used for carrying out scene analysis on the data mining requirements and obtaining scene mining features;
the demand analysis unit is used for carrying out demand analysis on the data mining demands and acquiring a plurality of demand mining features.
9. The system for mining travel data of an autonomous vehicle of claim 7, wherein said data initial mining module comprises:
the management acquisition unit is used for carrying out management acquisition and acquiring management acquisition data;
the management verification unit is used for performing management verification on the management acquisition data and judging whether the management acquisition data has management authority;
a data acquisition unit for acquiring vehicle running data when the management right is provided;
and the initial mining unit is used for performing initial mining according to the scene mining characteristics to obtain scene mining data.
10. The system for mining travel data of an autonomous vehicle of claim 7, wherein the resource usage analysis module specifically comprises:
the instruction generation unit is used for generating a resource monitoring instruction;
the resource monitoring unit is used for monitoring the vehicle-mounted machine resources according to the resource monitoring instruction and acquiring resource monitoring data;
the resource comparison unit is used for comparing the resource monitoring data with preset idle standard resources to generate a resource comparison result;
and the idle determining unit is used for determining the idle time of the vehicle machine according to the resource comparison result.
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