CN116703349A - Vehicle service recommendation method, system, electronic equipment and storage medium - Google Patents

Vehicle service recommendation method, system, electronic equipment and storage medium Download PDF

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CN116703349A
CN116703349A CN202210168462.9A CN202210168462A CN116703349A CN 116703349 A CN116703349 A CN 116703349A CN 202210168462 A CN202210168462 A CN 202210168462A CN 116703349 A CN116703349 A CN 116703349A
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data
vehicle
service
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value data
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徐娟娟
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Shanghai Qwik Smart Technology Co Ltd
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    • G06Q10/00—Administration; Management
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60S—SERVICING, CLEANING, REPAIRING, SUPPORTING, LIFTING, OR MANOEUVRING OF VEHICLES, NOT OTHERWISE PROVIDED FOR
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    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0631—Item recommendations
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/30—Computing systems specially adapted for manufacturing

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Abstract

The application provides a vehicle service recommendation method, a system, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring full life cycle data of a vehicle; carrying out data management on the full life cycle data; modeling a service scene based on the data treatment result to obtain a value data pool corresponding to the service scene; and recommending vehicle service based on the value data pool. The application can improve marketing efficiency and customer experience.

Description

Vehicle service recommendation method, system, electronic equipment and storage medium
Technical Field
The present application relates to the field of big data technologies, and in particular, to a vehicle service recommendation method, a system, an electronic device, and a storage medium.
Background
At present, in order to continuously follow up the subsequent value added service and the renewal project of the car owners, 80% of after-sales departments of the brands of passenger cars need to arrange appointed after-sales personnel, and follow up the service condition of each car in a mode of adding a car owner WeChat and the like, when the after-sales personnel service is not timely followed or the after-sales personnel service is not timely delivered, valuable car owners are lost easily, and the operation effect is poor.
In addition, when the car owner uses the car, can not in time pay attention to the various problems that the car needs to do little maintenance, need do big maintenance, need do the follow-up insurance of annual inspection when need do, and whether the car size trouble needs to overhaul immediately in the car using links such as, probably can feel anxiety and helplessness because of can't find the professional people in the first time help oneself solve the problem, influence user experience.
Disclosure of Invention
In order to overcome the defects that in the prior art, the operation effect of the vehicle owner service is poor and the user experience is influenced, the application provides a vehicle service recommending method, a system, electronic equipment and a storage medium.
In order to achieve the above purpose, the present application adopts the following technical scheme:
in a first aspect, the present application provides a vehicle service recommendation method, including:
acquiring full life cycle data of a vehicle;
carrying out data management on the full life cycle data;
modeling a service scene based on the data treatment result to obtain a value data pool corresponding to the service scene;
and recommending vehicle service based on the value data pool.
Preferably, the full life cycle data of the vehicle includes: vehicle manufacturing data and vehicle part failure history data;
the modeling of the service scene based on the data governance result to obtain a value data pool corresponding to the service scene includes:
and modeling the production and manufacturing scene based on the data governance result to obtain a value data pool corresponding to the production and manufacturing scene, wherein the value data pool comprises the part failure prediction data of the vehicle.
Preferably, the recommending the vehicle service based on the value data pool includes:
recommending corresponding part goods adjusting information to corresponding maintenance shops according to the part fault prediction data; and/or
And recommending corresponding part transformation information to corresponding manufacturers according to the part fault prediction data.
Preferably, the full life cycle data of the vehicle comprises at least one of the following: commemorative day data related to the vehicle, software data of the vehicle-mounted terminal, abnormal data of the vehicle, annual inspection period and expiration data of the vehicle;
the modeling of the service scene based on the data governance result to obtain a value data pool corresponding to the service scene includes:
and carrying out vehicle scene modeling based on the data management result to obtain a value data pool corresponding to the vehicle scene, wherein the value data pool comprises: target care subjects, and care data corresponding to each target care subject.
Preferably, the recommending the vehicle service based on the value data pool includes:
and outputting the corresponding care data to each target care object.
Preferably, the full life cycle data of the vehicle includes: vehicle history driving data and vehicle history maintenance data;
the modeling of the service scene based on the data governance result to obtain a value data pool corresponding to the service scene includes:
modeling the after-sales scene based on the data governance result to obtain a value data pool corresponding to the after-sales scene, the value data pool comprising: expired or otherwise non-serviced vehicles.
Preferably, the recommending the vehicle service based on the value data pool includes:
and recommending corresponding maintenance prompt information to the owners of the vehicles which are out of date or have not been maintained in the temporary period.
Preferably, the full life cycle data of the vehicle includes: vehicle manufacturing data, vehicle sales data, vehicle history maintenance data, vehicle history insurance data, and vehicle history driving data;
the modeling of the service scene based on the data governance result to obtain a value data pool corresponding to the service scene includes:
performing insurance scene modeling based on the data governance result to obtain a value data pool corresponding to the insurance scene, wherein the value data pool comprises: vehicles for which no insurance has been purchased for an expiration date or expiration, recommended insurance policies, and insurance prices.
Preferably, the recommending the vehicle service based on the value data pool includes:
recommending corresponding insurance risk and insurance price to the vehicle owners of the vehicles which do not purchase insurance in the period of time or expiration.
Preferably, the full life cycle data of the vehicle includes: real-time meteorological data, real-time position data and POI position data corresponding to the vehicle;
the modeling of the service scene based on the data governance result to obtain a value data pool corresponding to the service scene includes:
performing insurance scene modeling based on the data governance result to obtain a value data pool corresponding to the insurance scene, wherein the value data pool comprises: the vehicle for which insurance is required, the recommended insurance policy, and the insurance price are predicted.
Preferably, the recommending the vehicle service based on the value data pool includes:
and recommending corresponding insurance risk and insurance price to the vehicle owners of the vehicles which are predicted to need insurance.
Preferably, the full life cycle data of the vehicle includes: deadline data of an ordered service corresponding to a vehicle and service data of an unactivated service corresponding to the vehicle;
the modeling of the service scene based on the data governance result to obtain a value data pool corresponding to the service scene includes:
performing ecological scene modeling based on the data governance result to obtain a value data pool corresponding to the ecological scene, wherein the value data pool comprises: a target service object.
Preferably, the recommending the vehicle service based on the value data pool includes:
and recommending corresponding service expiration information or service activation prompt information to the target service object.
Preferably, the full life cycle data of the vehicle includes: historical running data of the vehicle, current condition data of the vehicle and historical vehicle browsing data of a corresponding vehicle owner;
the modeling of the service scene based on the data governance result to obtain a value data pool corresponding to the service scene includes:
performing replacement scene modeling based on the data governance result to obtain a value data pool corresponding to the replacement scene, wherein the value data pool comprises: target to-be-replaced vehicles and corresponding target recommended vehicles.
Preferably, the recommending the vehicle service based on the value data pool includes:
and recommending corresponding target recommended vehicles to the owners of the target vehicles to be replaced.
Preferably, the vehicle service makes a recommendation by at least one of a telephone call, a client prompt and an in-vehicle device prompt.
Preferably, the service scenario modeling based on the data governance result includes:
and carrying out service scene modeling through a data mining or machine learning algorithm based on the data management result.
In a second aspect, the present application provides a vehicle service recommendation system, comprising:
the data acquisition module is used for acquiring full life cycle data of the vehicle;
the data management module is used for carrying out data management on the full life cycle data;
the modeling module is used for carrying out service scene modeling based on the data treatment result so as to obtain a value data pool corresponding to the service scene;
and the recommending module is used for recommending the vehicle service based on the value data pool.
In a third aspect, the application provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the aforementioned method when executing the computer program.
In a fourth aspect, the application provides a computer readable storage medium having stored thereon a computer program, characterized in that the computer program, when executed by a processor, implements the aforementioned method.
By adopting the technical scheme, the application has the following beneficial effects:
according to the application, the full life cycle data of the vehicle is subjected to data management, and the service scene modeling is performed based on the data management result, so that the value data pool corresponding to the service scene is obtained, and then the vehicle service is recommended based on the value data pool, so that the operation closed-loop efficiency can be improved, and the user experience degree can be improved.
Drawings
Fig. 1 is a flow chart of a vehicle service recommendation method according to embodiment 1 of the present application;
FIG. 2 is a block diagram showing a vehicle service recommendation system according to embodiment 2 of the present application;
fig. 3 is a hardware architecture diagram of an electronic device according to embodiment 3 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. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used in this disclosure and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any or all possible combinations of one or more of the associated listed items.
Example 1
The embodiment provides a vehicle service recommendation method, as shown in fig. 1, which specifically includes the following steps:
s1, acquiring full life cycle data of a vehicle.
In particular, the full lifecycle data can include vehicle overview data, real-time vehicle condition data, vehicle owner user representation data, driving behavior data, vehicle linkage behavior data, service ecology data 6 major vehicle data dimension. For example, the vehicle overview data includes relevant data from vehicle manufacturing, channel sales, vehicle activation, vehicle binding, vehicle operation 5 major vehicle link monitoring; the real-time vehicle condition data comprise real-time instrument panel data, real-time track data, real-time fault report data, real-time value reminding data (such as 30 days apart from minor maintenance) and the like of the vehicle.
In this embodiment, for example, the monitoring of the full life cycle data CAN be realized through the CAN and the buried point, then the data is accessed through the Kafka and the Sqoop, then the data is stored through the HDFS, and the original table data of the ODS layer is created.
S2, carrying out data management on the full life cycle data.
Specifically, dirty data in full life cycle data is cleaned through Spark SQL, hive SQL and the like, then data is stored through an HDFS, and wide table data of an ODM layer is created.
And S3, modeling the service scene based on the data treatment result to obtain a value data pool corresponding to the service scene.
Specifically, service scene modeling can be performed through algorithms such as data mining and/or machine learning, so as to obtain value data pools corresponding to different service scenes respectively.
And S4, recommending vehicle service based on the value data pool.
In this embodiment, the corresponding vehicle service may be automatically recommended to the vehicle owner based on the different value data pools.
According to the embodiment, the full life cycle data of the vehicle is subjected to data management, the service scene modeling is performed based on the data management result, so that a value data pool corresponding to the service scene is obtained, and then the vehicle service is automatically recommended based on the value data pool, so that the operation closed-loop efficiency can be improved, and the user experience degree is improved.
In an alternative embodiment, the full life cycle data acquired in step S1 includes: vehicle manufacturing data and vehicle component failure history data. The vehicle manufacturing data may include, for example, production time, vehicle model, vehicle class, engine model, transmission type, etc.; the vehicle component failure history data may include, for example, historical engine intake pressure abnormality data, engine speed abnormality data, accelerator pedal abnormality data, nitrogen oxide emission abnormality data, coolant liquid level abnormality data, injector metering abnormality data, and the like.
On this basis, step S3 specifically includes: and modeling the production and manufacturing scene based on the data governance result to obtain a value data pool corresponding to the production and manufacturing scene, wherein the value data pool comprises the part failure prediction data of the vehicle. The step S4 specifically comprises the following steps: recommending corresponding part goods adjusting information to corresponding maintenance shops according to the part fault prediction data; and/or recommending corresponding part transformation information to corresponding manufacturers according to the part fault prediction data.
Specifically, the present embodiment first establishes a failure prediction model corresponding to a production and manufacturing scenario to acquire component failure prediction data of each vehicle. Then, corresponding part goods-adjusting information can be generated by combining the part fault prediction data and the inventory of the parts of the corresponding regional repair shops so as to inform the corresponding repair shops of goods-adjusting in advance, and the vehicle repair operation efficiency is improved; the vulnerable parts can be determined according to the part fault prediction data, and corresponding part transformation information is generated to remind manufacturers to transform the parts, so that the fault rate of the parts is reduced.
In an alternative embodiment, the full life cycle data of the vehicle includes at least one of the following: commemorative day data related to the vehicle, software data of the in-vehicle terminal, abnormal data of the vehicle, annual inspection date and expiration data of the vehicle. The commemorative day data related to the vehicle comprises a vehicle owner birthday, a vehicle owner child birthday, a vehicle purchasing commemorative day and the like; the software data of the vehicle-mounted terminal comprise software versions and the like; the abnormal data of the vehicle include real-time battery deficiency data, tire under-pressure data, and the like.
On this basis, step S3 specifically includes: and carrying out vehicle scene modeling based on the data management result to obtain a value data pool corresponding to the vehicle scene, wherein the value data pool comprises: target care subjects, and care data corresponding to each target care subject. The step S4 specifically comprises the following steps: and outputting the corresponding care data to each target care object.
For example, the target care-subject may include an owner of a vehicle who has passed a birthday on the same day, the corresponding care data being birthday blessing care data; the target care object can also comprise an owner of the vehicle which needs to be upgraded but does not upgrade the software yet, and the corresponding care data is software upgrade reminding care data; the target care object may further include an owner of the vehicle in which the abnormality occurs, and the corresponding care data is abnormality alert care data; the target care subject may also include a yearly test period and an expired vehicle owner, and the corresponding care data is yearly test reminder care data.
According to the embodiment, care greetings of the vehicle owners can be automatically triggered, the user experience is improved, and the operation efficiency is further improved.
In an alternative embodiment, the full life cycle data of the vehicle includes: vehicle history maintenance data. The vehicle historical maintenance data may include, for example, historical large maintenance data and historical small maintenance data.
On this basis, step S3 specifically includes: modeling the after-sales scene based on the data governance result to obtain a value data pool corresponding to the after-sales scene, the value data pool comprising: expired or otherwise non-serviced vehicles. The step S4 specifically comprises the following steps: and recommending corresponding maintenance prompt information to the owners of the vehicles which are out of date or have not been maintained in the temporary period.
According to the vehicle maintenance reminding method and device, the vehicle owner can be reminded of automatically carrying out vehicle maintenance, the user experience degree is improved, and further the operation efficiency is improved.
In an alternative embodiment, the full life cycle data of the vehicle includes: vehicle manufacturing data, vehicle sales data, vehicle history maintenance data, vehicle history insurance data, and vehicle history driving data. The vehicle manufacturing data may include, for example: production time, vehicle model, vehicle class, engine model, gearbox type, etc.; the vehicle sales data may include, for example, sales time, sales price, etc.; the vehicle history maintenance data includes history maintenance times, maintenance objects and the like; the vehicle history insurance data includes the insurance category of history purchase, purchase price and purchase time; the vehicle history driving data includes accumulated driving mileage.
On this basis, S3 specifically includes: performing insurance scene modeling based on the data governance result to obtain a value data pool corresponding to the insurance scene, wherein the value data pool comprises: vehicles for which no insurance has been purchased for an expiration date or expiration, recommended insurance policies, and insurance prices. The step S4 specifically comprises the following steps: recommending corresponding insurance risk and insurance price to the vehicle owners of the vehicles which do not purchase insurance in the period of time or expiration. The recommended insurance risk and insurance price are predicted by the established model.
According to the embodiment, the matched insurance risk and insurance price can be automatically recommended to the user, so that the user experience is improved, and the operation efficiency is further improved.
In an alternative embodiment, the full life cycle data of the vehicle includes: real-time weather data, real-time position data and POI (point of interest) position data corresponding to the vehicle.
On this basis, step S3 specifically includes: performing insurance scene modeling based on the data governance result to obtain a value data pool corresponding to the insurance scene, wherein the value data pool comprises: the vehicle for which insurance is required, the recommended insurance policy, and the insurance price are predicted. The step S4 specifically comprises the following steps: and recommending corresponding insurance risk and insurance price to the vehicle owners of the vehicles which are predicted to need insurance.
According to the embodiment, the appropriate scene risk and price can be automatically recommended to the user, so that the user experience is improved, and the operation efficiency is further improved.
In an alternative embodiment, the full life cycle data of the vehicle includes: deadline data of an ordered service corresponding to a vehicle, and service data of an unactivated service corresponding to the vehicle. The service herein may be a car service, an entertainment service, or a life service, for example, the car service includes "stop simple", "camptotheck", "le Che Bang", and the like; entertainment services include "loving art", "cool me music", "love", "himalaya", and the like; life services include "listening to the head bar", "carrying the journey", etc. The term data includes, for example, a annuity expiration date. On this basis, step S3 includes: performing ecological scene modeling based on the data governance result to obtain a value data pool corresponding to the ecological scene, wherein the value data pool comprises: a target service object. The step S4 includes: and recommending corresponding service expiration information or service activation prompt information to the target service object. For example, the target service object may be a vehicle owner who is about to expire or has expired corresponding to the annual service fee, to which corresponding service expiration date or expiration information is recommended; the target service object may also be a vehicle owner who does not activate the corresponding service, and then a corresponding service activation prompt message is recommended to the vehicle owner.
According to the method and the device, the intelligent ecological service client pool can be accurately obtained, and marketing efficiency is improved.
In an alternative embodiment, the full life cycle data of the vehicle includes: historical running data of the vehicle, current condition data of the vehicle and historical vehicle browsing data of a corresponding vehicle owner. Wherein, the historical operation data can comprise historical operation mileage and the like; the current condition data may include condition data of critical components of the vehicle, etc.; the historical vehicle browsing data may include the model, brand, category, etc. of vehicles historically browsed at the terminal.
On this basis, step S3 specifically includes: performing replacement scene modeling based on the data governance result to obtain a value data pool corresponding to the replacement scene, wherein the value data pool comprises: target to-be-replaced vehicles and corresponding target recommended vehicles. The step S4 specifically comprises the following steps: and recommending corresponding target recommended vehicles to the owners of the target vehicles to be replaced.
According to the method and the device for recommending the target to be replaced, the target to be replaced vehicles possibly needing replacing can be automatically obtained, and the target recommended vehicles possibly interested can be recommended to the vehicle owners, so that marketing efficiency can be improved.
In an optional implementation manner, step S4 specifically performs vehicle service recommendation by at least one of a telephone outbound call, a client prompt and a vehicle-mounted device prompt, so as to implement automatic access of the value data.
In an alternative implementation, after performing step S4, the method of this embodiment further includes: the conversion result of the recommended vehicle service is traced back and visually displayed, so that the operation effect is conveniently monitored.
Example 2
The present embodiment provides a vehicle service recommendation system, as shown in fig. 2, which specifically includes:
a data acquisition module 11 for acquiring full life cycle data of the vehicle;
a data management module 12, configured to perform data management on the full life cycle data;
the modeling module 13 is used for performing service scene modeling based on the data treatment result to obtain a value data pool corresponding to the service scene;
a recommendation module 14 for recommending vehicle services based on the pool of value data.
In an alternative embodiment, the full life cycle data of the vehicle includes: vehicle manufacturing data and vehicle component failure history data. The modeling module 13 performs production manufacturing scenario modeling based on the data governance results to obtain a value data pool corresponding to the production manufacturing scenario, the value data pool including part failure prediction data of the vehicle. The recommending module 14 recommends corresponding part goods-adjusting information to corresponding maintenance shops according to the part fault prediction data; and/or recommending corresponding part transformation information to corresponding manufacturers according to the part fault prediction data.
In an alternative embodiment, the full life cycle data of the vehicle includes at least one of the following: commemorative day data related to the vehicle, software data of the in-vehicle terminal, abnormal data of the vehicle, annual inspection date and expiration data of the vehicle. The modeling module 13 performs vehicle scene modeling based on the data governance result to obtain a value data pool corresponding to the vehicle scene, the value data pool including: target care subjects, and care data corresponding to each target care subject. Recommendation module 14 outputs the corresponding care data to each of the target care subjects.
In an alternative embodiment, the full life cycle data of the vehicle includes: vehicle historical driving data and vehicle historical maintenance data. The modeling module 13 performs after-sales scenario modeling based on the data governance results to obtain a value data pool corresponding to the after-sales scenario, the value data pool including: expired or otherwise non-serviced vehicles. The recommendation module 14 recommends corresponding maintenance prompts to the owners of the expired or non-serviced vehicles.
In an alternative embodiment, the full life cycle data of the vehicle includes: vehicle manufacturing data, vehicle sales data, vehicle history maintenance data, vehicle history insurance data, and vehicle history driving data. The modeling module 13 performs insurance scene modeling based on the data governance results to obtain a value data pool corresponding to the insurance scene, the value data pool including: vehicles for which no insurance has been purchased for an expiration date or expiration, recommended insurance policies, and insurance prices. The recommendation module 14 recommends the corresponding insurance risk and insurance price to the owners of the vehicles that have not purchased insurance for the expiration date or expiration.
In an alternative embodiment, the full life cycle data of the vehicle includes: real-time weather data, real-time position data and POI position data corresponding to the vehicle. The modeling module 13 performs insurance scene modeling based on the data governance results to obtain a value data pool corresponding to the insurance scene, the value data pool including: the vehicle for which insurance is required, the recommended insurance policy, and the insurance price are predicted. The recommendation module 14 recommends the respective insurance policy and insurance price to the owner of the vehicle for which insurance is predicted to be required.
In an alternative embodiment, the full life cycle data of the vehicle includes: deadline data of an ordered service corresponding to a vehicle, and service data of an unactivated service corresponding to the vehicle. The modeling module 13 performs ecological scene modeling based on the data governance results to obtain a value data pool corresponding to the ecological scene, the value data pool including: a target service object. The recommendation module 14 recommends corresponding service expiration or expiration information, or service activation hint information, to the target service object.
In an alternative embodiment, the full life cycle data of the vehicle includes: historical running data of the vehicle, current condition data of the vehicle and historical vehicle browsing data of a corresponding vehicle owner. The modeling module 13 performs replacement scenario modeling based on the data governance result to obtain a value data pool corresponding to the replacement scenario, the value data pool including: target to-be-replaced vehicles and corresponding target recommended vehicles. The recommendation module 14 recommends a corresponding target recommended vehicle to the owner of the target vehicle to be replaced.
In an alternative embodiment, the vehicle service is recommended by at least one of a telephone outbound call, a client prompt, and an in-vehicle device prompt.
In an alternative embodiment, the modeling of the service scenario based on the data governance results includes:
and carrying out service scene modeling through a data mining or machine learning algorithm based on the data management result.
According to the embodiment, the full life cycle data of the vehicle is subjected to data management, the service scene modeling is performed based on the data management result, so that a value data pool corresponding to the service scene is obtained, and then the vehicle service is automatically recommended based on the value data pool, so that the operation closed-loop efficiency can be improved, and the user experience degree is improved.
Example 3
The present embodiment provides an electronic device, which may be expressed in the form of a computing device (for example, may be a server device), including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor may implement the method provided in embodiment 1 when executing the computer program.
Fig. 3 shows a schematic diagram of the hardware structure of the present embodiment, and as shown in fig. 3, the electronic device 9 specifically includes:
at least one processor 91, at least one memory 92, and a bus 93 for connecting the different system components (including the processor 91 and the memory 92), wherein:
the bus 93 includes a data bus, an address bus, and a control bus.
The memory 92 includes volatile memory such as Random Access Memory (RAM) 921 and/or cache memory 922, and may further include Read Only Memory (ROM) 923.
Memory 92 also includes a program/utility 925 having a set (at least one) of program modules 924, such program modules 924 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.
The processor 91 executes various functional applications and data processing, such as the method provided in embodiment 1 of the present application, by running a computer program stored in the memory 92.
The electronic device 9 may further communicate with one or more external devices 94 (e.g., keyboard, pointing device, etc.). Such communication may occur through an input/output (I/O) interface 95. Also, the electronic device 9 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN) and/or a public network, such as the Internet, through a network adapter 96. The network adapter 96 communicates with other modules of the electronic device 9 via the bus 93. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in connection with the electronic device 9, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, data backup storage systems, and the like.
It should be noted that although several units/modules or sub-units/modules of an electronic device are mentioned in the above detailed description, such a division is merely exemplary and not mandatory. Indeed, the features and functionality of two or more units/modules described above may be embodied in one unit/module in accordance with embodiments of the present application. Conversely, the features and functions of one unit/module described above may be further divided into ones that are embodied by a plurality of units/modules.
Example 4
The present embodiment provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method provided by embodiment 1.
More specifically, among others, readable storage media may be employed including, but not limited to: portable disk, hard disk, random access memory, read only memory, erasable programmable read only memory, optical storage device, magnetic storage device, or any suitable combination of the foregoing.
In a possible embodiment, the application may also be implemented in the form of a program product comprising program code for causing a terminal device to carry out the method as described in embodiment 1, when said program product is run on the terminal device.
Wherein the program code for carrying out the application may be written in any combination of one or more programming languages, which program code may execute entirely on the user device, partly on the user device, as a stand-alone software package, partly on the user device and partly on the remote device or entirely on the remote device.
While specific embodiments of the application have been described above, it will be appreciated by those skilled in the art that this is by way of example only, and the scope of the application is defined by the appended claims. Various changes and modifications to these embodiments may be made by those skilled in the art without departing from the principles and spirit of the application, but such changes and modifications fall within the scope of the application.

Claims (20)

1. A vehicle service recommendation method, characterized by comprising:
acquiring full life cycle data of a vehicle;
carrying out data management on the full life cycle data;
modeling a service scene based on the data treatment result to obtain a value data pool corresponding to the service scene;
and recommending vehicle service based on the value data pool.
2. The vehicle service recommendation method according to claim 1, the full life cycle data of the vehicle comprising: vehicle manufacturing data and vehicle part failure history data;
the modeling of the service scene based on the data governance result to obtain a value data pool corresponding to the service scene includes:
and modeling the production and manufacturing scene based on the data governance result to obtain a value data pool corresponding to the production and manufacturing scene, wherein the value data pool comprises the part failure prediction data of the vehicle.
3. The vehicle service recommendation method according to claim 2, the recommending vehicle service based on the value data pool, comprising:
recommending corresponding part goods adjusting information to corresponding maintenance shops according to the part fault prediction data; and/or
And recommending corresponding part transformation information to corresponding manufacturers according to the part fault prediction data.
4. The vehicle service recommendation method of claim 1, the full life cycle data of the vehicle comprising at least one of: commemorative day data related to the vehicle, software data of the vehicle-mounted terminal, abnormal data of the vehicle, annual inspection period and expiration data of the vehicle;
the modeling of the service scene based on the data governance result to obtain a value data pool corresponding to the service scene includes:
and carrying out vehicle scene modeling based on the data management result to obtain a value data pool corresponding to the vehicle scene, wherein the value data pool comprises: target care subjects, and care data corresponding to each target care subject.
5. The vehicle service recommendation method according to claim 4, the recommending vehicle service based on the value data pool, comprising:
and outputting the corresponding care data to each target care object.
6. The vehicle service recommendation method according to claim 1, the full life cycle data of the vehicle comprising: vehicle history driving data and vehicle history maintenance data;
the modeling of the service scene based on the data governance result to obtain a value data pool corresponding to the service scene includes:
modeling the after-sales scene based on the data governance result to obtain a value data pool corresponding to the after-sales scene, the value data pool comprising: expired or otherwise non-serviced vehicles.
7. The vehicle service recommendation method according to claim 6, the recommending vehicle service based on the value data pool, comprising:
and recommending corresponding maintenance prompt information to the owners of the vehicles which are out of date or have not been maintained in the temporary period.
8. The vehicle service recommendation method according to claim 1, the full life cycle data of the vehicle comprising: vehicle manufacturing data, vehicle sales data, vehicle history maintenance data, vehicle history insurance data, and vehicle history driving data;
the modeling of the service scene based on the data governance result to obtain a value data pool corresponding to the service scene includes:
performing insurance scene modeling based on the data governance result to obtain a value data pool corresponding to the insurance scene, wherein the value data pool comprises: vehicles for which no insurance has been purchased for an expiration date or expiration, recommended insurance policies, and insurance prices.
9. The vehicle service recommendation method according to claim 8, the recommending vehicle service based on the value data pool, comprising:
recommending corresponding insurance risk and insurance price to the vehicle owners of the vehicles which do not purchase insurance in the period of time or expiration.
10. The vehicle service recommendation method according to claim 1, the full life cycle data of the vehicle comprising: real-time meteorological data, real-time position data and POI position data corresponding to the vehicle;
the modeling of the service scene based on the data governance result to obtain a value data pool corresponding to the service scene includes:
performing insurance scene modeling based on the data governance result to obtain a value data pool corresponding to the insurance scene, wherein the value data pool comprises: the vehicle for which insurance is required, the recommended insurance policy, and the insurance price are predicted.
11. The vehicle service recommendation method according to claim 10, the recommending vehicle service based on the value data pool, comprising:
and recommending corresponding insurance risk and insurance price to the vehicle owners of the vehicles which are predicted to need insurance.
12. The vehicle service recommendation method according to claim 1, the full life cycle data of the vehicle comprising: deadline data of an ordered service corresponding to a vehicle and service data of an unactivated service corresponding to the vehicle;
the modeling of the service scene based on the data governance result to obtain a value data pool corresponding to the service scene includes:
performing ecological scene modeling based on the data governance result to obtain a value data pool corresponding to the ecological scene, wherein the value data pool comprises: a target service object.
13. The vehicle service recommendation method according to claim 12, the recommending vehicle service based on the value data pool, comprising:
and recommending corresponding service expiration information or service activation prompt information to the target service object.
14. The vehicle service recommendation method according to claim 1, the full life cycle data of the vehicle comprising: historical running data of the vehicle, current condition data of the vehicle and historical vehicle browsing data of a corresponding vehicle owner;
the modeling of the service scene based on the data governance result to obtain a value data pool corresponding to the service scene includes:
performing replacement scene modeling based on the data governance result to obtain a value data pool corresponding to the replacement scene, wherein the value data pool comprises: target to-be-replaced vehicles and corresponding target recommended vehicles.
15. The vehicle service recommendation method according to claim 14, the recommending vehicle service based on the value data pool, comprising:
and recommending corresponding target recommended vehicles to the owners of the target vehicles to be replaced.
16. The vehicle service recommendation method according to claim 1, wherein the vehicle service is recommended by at least one of a telephone outbound call, a client prompt, and an in-vehicle device prompt.
17. The vehicle service recommendation method according to claim 1, wherein the service scenario modeling based on the data governance results includes:
and carrying out service scene modeling through a data mining or machine learning algorithm based on the data management result.
18. A vehicle service recommendation system, comprising:
the data acquisition module is used for acquiring full life cycle data of the vehicle;
the data management module is used for carrying out data management on the full life cycle data;
the modeling module is used for carrying out service scene modeling based on the data treatment result so as to obtain a value data pool corresponding to the service scene;
and the recommending module is used for recommending the vehicle service based on the value data pool.
19. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any one of claims 1 to 17 when the computer program is executed by the processor.
20. A computer readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, implements the method of any one of claims 1 to 17.
CN202210168462.9A 2022-02-23 2022-02-23 Vehicle service recommendation method, system, electronic equipment and storage medium Pending CN116703349A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117422232A (en) * 2023-10-13 2024-01-19 上海复通软件技术有限公司 Vehicle owner and customer operation method and system based on AIGC

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117422232A (en) * 2023-10-13 2024-01-19 上海复通软件技术有限公司 Vehicle owner and customer operation method and system based on AIGC
CN117422232B (en) * 2023-10-13 2024-04-16 上海复通软件技术有限公司 AIGC-based vehicle owner and customer operation method and system

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