CN114429364A - Business data management method and device, storage medium and electronic equipment - Google Patents
Business data management method and device, storage medium and electronic equipment Download PDFInfo
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Abstract
The invention discloses a business data management method and device, a storage medium and electronic equipment, and is applied to the field of Internet of vehicles. Wherein, the method comprises the following steps: acquiring target vehicle service data to be managed; extracting a management dimension label corresponding to the vehicle service attribute from the target vehicle service data; constructing a vehicle business data chain based on the incidence relation among the management dimension labels; respectively counting the target vehicle service data according to different time granularities based on the management dimension label and the vehicle service data chain to obtain statistical result data; and clustering the statistical result data according to different vehicle service themes to obtain a plurality of vehicle service data sets, wherein each vehicle service data corresponds to one vehicle service theme. The invention solves the technical problem of low management efficiency of the service data in the prior art.
Description
Technical Field
The invention relates to the field of vehicle networking, in particular to a business data management method and device, a storage medium and electronic equipment.
Background
Nowadays, vehicles are becoming a transportation tool for more and more users or families, and the sales competition among different vehicle-enterprise dealers is also getting stronger. In order to avoid the loss of the vehicle purchasing user, the consultant of the vehicle enterprise dealer is often required to accurately locate the real purchasing demand of the user as soon as possible.
In order to manage the business data corresponding to the vehicle purchasing user, the business data are generally classified according to different classification conditions, and the classified business data are dispersedly stored in different business databases. However, when the business data is statistically analyzed in an online analysis processing manner, since different business databases are stored separately, the same index data is repeatedly calculated, which not only consumes computing resources and increases computing pressure of the business databases, but also fails to ensure accuracy of the computed result.
That is, the way of managing data separately in different service databases proposed in the related art has a problem of poor accuracy.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the invention provides a business data management method and device, a storage medium and electronic equipment, which at least solve the technical problem of low business data management efficiency in the prior art.
According to an aspect of an embodiment of the present invention, a method for managing service data is provided, including: a method for managing service data, comprising: acquiring target vehicle service data to be managed; extracting a management dimension label corresponding to the vehicle service attribute from the target vehicle service data; constructing a vehicle business data chain based on the incidence relation among the management dimension labels; respectively counting the target vehicle service data according to different time granularities based on the management dimension label and the vehicle service data chain to obtain statistical result data; and clustering the statistical result data according to different vehicle service themes to obtain a plurality of vehicle service data sets, wherein each vehicle service data corresponds to one vehicle service theme.
According to another aspect of the embodiments of the present invention, there is also provided a service data management apparatus, including: a service data management apparatus, comprising: the system comprises an acquisition unit, a management unit and a management unit, wherein the acquisition unit is used for acquiring target vehicle service data to be managed; the extraction unit is used for extracting a management dimension label corresponding to the vehicle service attribute from the target vehicle service data; the establishing unit is used for establishing a vehicle service data chain based on the incidence relation between the management dimension labels; the statistical unit is used for respectively counting the target vehicle service data according to different time granularities based on the management dimension label and the vehicle service data chain to obtain statistical result data; and the clustering unit is used for clustering the statistical result data according to different vehicle service themes to obtain a plurality of vehicle service data sets, wherein each vehicle service data corresponds to one vehicle service theme.
According to another aspect of the embodiments of the present invention, there is also provided a computer-readable storage medium, in which a computer program is stored, where the computer program is configured to execute the above-mentioned service data management method when running.
According to yet another aspect of embodiments herein, there is provided a computer program product comprising a computer program/instructions stored in a computer readable storage medium. The processor of the computer device reads the computer program/instruction from the computer-readable storage medium, and the processor executes the computer program/instruction, so that the computer device performs the business data management method as above.
According to another aspect of the embodiments of the present invention, there is also provided an electronic device, including a memory and a processor, where the memory stores a computer program, and the processor is configured to execute the service data management method through the computer program.
In the embodiment of the invention, the target vehicle service data to be managed is acquired; extracting a management dimension label corresponding to the vehicle service attribute from the target vehicle service data; constructing a vehicle business data chain based on the incidence relation among the management dimension labels; respectively counting target vehicle service data according to different time granularities based on the management dimension label and the vehicle service data chain to obtain statistical result data; the statistical result data are clustered according to different vehicle service themes to obtain a plurality of vehicle service data sets, and offline integration and management of the service data are realized in advance, so that real-time data statistics and calculation under the condition of data requirements are avoided, the calculation pressure of a service database is reduced, and the technical problem of low management efficiency of the conventional service data is solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
fig. 1 is a schematic diagram of an application environment of an alternative business data management method according to an embodiment of the present invention;
FIG. 2 is a flow chart of an alternative business data management method according to an embodiment of the invention;
fig. 3 is a schematic diagram of another alternative service data management method according to an embodiment of the present invention;
fig. 4 is a schematic diagram of another alternative service data management method according to an embodiment of the present invention;
fig. 5 is a schematic diagram of another alternative service data management method according to an embodiment of the present invention;
fig. 6 is a schematic diagram of another alternative service data management method according to an embodiment of the present invention;
fig. 7 is a schematic structural diagram of an alternative service data management apparatus according to an embodiment of the present invention;
fig. 8 is a schematic structural diagram of an alternative electronic device according to an embodiment of the invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The definitions of abbreviations and key attributes in this application are explained below:
ADP: advisor Data Platform, advisor Data management Platform
CDP: customer Data Platform, Customer Data management Platform
HDFS (Hadoop distributed File System): big data file system
Hive: big data storage and query engine
Dopininescheduler: distributed decentralized and easily-expanded visual DAG workflow task scheduling platform
And (2) TiDB: open source NewSQL database, compatible with MySQL
Kafka: message middleware for transmitting data from different platforms and databases
DataPipline: handling data movement between local data sources and storage services (diverse databases)
ODS: operation Data Store, original Data layer
DIM: dimension, Dimension layer
DWD: data Warehouse Detail, Detail layer
According to an aspect of the embodiments of the present invention, a data collecting method is provided, and optionally, as an optional implementation manner, the data collecting method may be but is not limited to be applied to a service data management system in a hardware environment as shown in fig. 1, where the service data management system may be but is not limited to the terminal device 102 and the server 112. The terminal device 102 may be a mobile terminal (for example, a mobile phone) and may be used to perform the training of the service data management model and the specific operation of the service data management by the server 112 as the object of the hardware performing the service data management operation. The terminal device 102 includes a human-computer interaction screen 104, a processor 106 and a memory 108. The human-computer interaction screen 104 is used for displaying a visual management result of the acquired service data, the processor 106 is used for running a service data management operation process and executing an acquisition operation of the service data, and the memory 108 is used for storing intermediate data and result data of the service data management operation. The server 112 may include a database 114 and a processing engine 116. The database 114 is configured to provide a basic source code for generating the service data management model, and the processing engine 116 is configured to generate a data management model based on the service data and perform a service data management operation, and in particular, the service data management operation may be performed based on a service data set sent by the terminal device 102.
The specific process comprises the following steps: in step S102, the terminal device 102 obtains the service data to be processed. Then, in step S104, the terminal device 102 sends the service data to be processed to the server 112; then the server 112 executes the steps S106-S114 to obtain the target vehicle service data to be managed; extracting a management dimension label corresponding to the vehicle service attribute from the target vehicle service data; constructing a vehicle business data chain based on the incidence relation among the management dimension labels; respectively counting the target vehicle service data according to different time granularities based on the management dimension label and the vehicle service data chain to obtain statistical result data; clustering the statistical result data according to different vehicle service themes to obtain a plurality of vehicle service data sets, wherein each vehicle service data corresponds to one vehicle service theme; the server 112 then executes step S116 to transmit the data processing result to the terminal apparatus 102; finally, step S112 is executed on the terminal device 102, and the data processing result is displayed.
As another alternative, when the terminal device 102 has a relatively high computing processing capability, the steps S106 to S114 may also be performed by the terminal device 102. Here, this is an example, and this is not limited in this embodiment.
Optionally, in this embodiment, the terminal device may be a terminal device for running a service data management service, and may include but is not limited to at least one of the following: mobile phones (such as Android phones, iOS phones, etc.), notebook computers, tablet computers, palm computers, MID (Mobile Internet Devices), PAD, desktop computers, smart televisions, etc. The target video image may be from a target client, and the target client may be a video client, an instant messaging client, a browser client, an education client, or the like, which supports the task of providing a shooting game. Such networks may include, but are not limited to: a wired network, a wireless network, wherein the wired network comprises: a local area network, a metropolitan area network, and a wide area network, the wireless network comprising: bluetooth, WIFI, and other networks that enable wireless communication. The server may be a single server, a server cluster composed of a plurality of servers, or a cloud server. The above is merely an example, and this is not limited in this embodiment.
In the embodiment of the invention, the target vehicle service data to be managed is acquired; extracting a management dimension label corresponding to the vehicle service attribute from the target vehicle service data; constructing a vehicle business data chain based on the incidence relation among the management dimension labels; respectively counting the target vehicle service data according to different time granularities based on the management dimension label and the vehicle service data chain to obtain statistical result data; the statistical result data are clustered according to different vehicle service themes to obtain a plurality of vehicle service data sets, and offline integration and management of the service data are realized in advance, so that real-time data statistics and calculation under the condition of data requirements are avoided, the calculation pressure of a service database is reduced, and the technical problem of low management efficiency of the conventional service data is solved.
As an alternative implementation, as shown in fig. 2, the data acquisition method includes:
s202, acquiring target vehicle service data to be managed;
s204, extracting a management dimension label corresponding to the vehicle service attribute from the target vehicle service data;
s206, constructing a vehicle business data chain based on the incidence relation among the management dimension labels;
s208, respectively counting the target vehicle service data according to different time granularities based on the management dimension label and the vehicle service data chain to obtain statistical result data;
s210, clustering the statistical result data according to different vehicle service themes to obtain a plurality of vehicle service data sets, wherein each vehicle service data corresponds to one vehicle service theme.
It is understood that the data acquisition device for acquiring the target vehicle service data to be managed may include, but is not limited to, at least one of the following: mobile phones (such as Android phones, IOS phones, etc.), notebook computers, tablet computers, palm computers, MID (Mobile Internet Devices), PAD, desktop computers, smart televisions, etc.
In the method, the target vehicle business Data to be managed is all business Data from the same vehicle enterprise with the vehicle business Data Management requirement, and the sources of the business Data may include, but are not limited to SCRM (social Customer relationship Management Platform), CDP (Customer Data Platform, Customer Data Management Platform), ADP (advisor Data Platform, advisor Data Management Platform), SAP (System Application and Products, enterprise Management solution), and other Data platforms, and embedded Data obtained by a embedded point operation using a policy tool for indicating the click behavior of the user at the web/mobile phone end. It is to be understood that the specific source and type of target vehicle traffic data is not limited herein.
The management dimension tag can be used to indicate the attribution type of the vehicle service data, for example, the dimension tag can be a dimension of a dealer, a war zone, a consultant, a train, and the like. Specifically, the vehicle business data can be determined as to the dimension label of the "dealer" according to which dealer the vehicle business data is specifically provided by; the dimension label of the 'war zone' can be determined according to which 'war zone' the vehicle service data are generated specifically; the dimension label of the "advisor" can be determined by which advisor the vehicle service data was generated; the dimension label of the "train" can be determined according to which data the vehicle business is specific to. The above dimension labels are merely exemplary and are not intended to limit the specific type of dimension. It is understood that the same piece of business data may have multiple dimensional tags due to the specific content of its business data.
Further, after the management dimensionality corresponding to the vehicle service attribute is extracted from the vehicle service data, a vehicle service data chain is constructed and obtained based on the incidence relation between the management dimensionality labels. For example, a certain piece of vehicle sales data comes from an A dealer, belongs to the sales business of Wangzhi in the big area of North China, and is sold as an X vehicle series. Through the label, the vehicle sales data can be accurately positioned. Meanwhile, all vehicle service data belonging to the dealership A are correlated, all vehicle service data belonging to the North China district are correlated, all vehicle service data of the consultant Wangzhi are correlated, all vehicle service data of the vehicle department X are correlated, and therefore a vehicle service data chain is obtained. It is understood that the above-mentioned manner for constructing the vehicle service data chain is only an example, and a specific method for constructing the vehicle service data chain is not limited.
As an alternative data source, a vehicle service data platform interface is shown in fig. 3 and 4. In the platform interface, it is displayed that the data platform can acquire business data such as the number of archives, the rate of getting back to the store, the rate of trying to drive to the store, the rate of getting into the store, the order conversion rate, the thread rate and the like in a selected statistical period. Meanwhile, in the case of selecting "number of profiles", a data platform interface as shown in fig. 8 may be displayed to display specific "number of profiles" data of different sales personnel. It is understood that the data set in the data platform can be used as the vehicle service data source in the embodiment. The above data types are only exemplary in this embodiment, and do not limit the selection of a specific data platform and the type of the data source in this embodiment.
And further counting the vehicle data according to different time granularities on the basis of extracting the management dimension label from the service data and constructing a service data chain. It is understood that the time granularity refers to time statistic periods with different sizes, for example, the vehicle service data is counted according to a "year" as a statistic period, the vehicle service data is counted according to a "week" as a statistic period, and the vehicle service data is counted according to a "day" as a statistic period. It can be understood that the finer the time granularity setting indicates that the statistical period is shorter, for example, in the case of performing statistics on the vehicle traffic according to the statistical period of "day", the data of the vehicle traffic per day can be obtained, and this can be regarded as a "pseudo real-time" statistical manner.
It is understood that the above-mentioned manner of clustering the statistical result data to obtain a plurality of sets may be to perform clustering operation according to the theme of the vehicle service data. For example, the theme may be divided according to the business nature of the vehicle business, and may include, but is not limited to, a trial-and-drive order theme, a to-store theme, a sales order theme, a user follow-up theme, a lead theme, and a battle theme (the battle situation includes, but is not limited to, a user loss, a user default, a user rejection, a diversion to another user, etc.). The above theme for dividing the service property is only an exemplary illustration, and the specific theme dividing method is not limited.
In the embodiment of the invention, the target vehicle service data to be managed is acquired; extracting a management dimension label corresponding to the vehicle service attribute from the target vehicle service data; constructing a vehicle business data chain based on the incidence relation among the management dimension labels; respectively counting the target vehicle service data according to different time granularities based on the management dimension label and the vehicle service data chain to obtain statistical result data; the statistical result data are clustered according to different vehicle service themes to obtain a plurality of vehicle service data sets, and offline integration and management of the service data are realized in advance, so that real-time data statistics and calculation under the condition of data requirements are avoided, the calculation pressure of a service database is reduced, and the technical problem of low management efficiency of the conventional service data is solved.
As an optional manner, acquiring the target vehicle service data to be managed includes: and acquiring target vehicle service data meeting the management conditions from an original data layer, wherein the original data layer is used for storing the vehicle service data acquired from a plurality of data channels.
It is to be understood that, in the present mode, the acquired vehicle service data is limited by setting a management condition, which may be setting a data amount threshold value, and no acquisition operation is performed on the service data exceeding the data amount threshold value, thereby relieving the data processing pressure in acquiring the vehicle service data.
Specifically, the plurality of data channels may include, but are not limited to: in the present embodiment, a Data channel is not limited to a specific type, and the Data channel includes a Data Platform such as SCRM (social Customer relationship Management Platform), CDP (Customer Data Platform), ADP (advisor Data Platform), SAP (System Application and Products), and embedded Data obtained by a policy tool through an embedded point operation and used for indicating a click behavior of a user on a web/mobile phone side.
By the method, the management conditions are set to ensure the data processing efficiency in a mode of acquiring the target vehicle service data meeting the management conditions from the original data layer, and the vehicle service data is acquired through a plurality of data sources to realize the diversity and the comprehensiveness of the vehicle data, so that the technical effect of improving the vehicle service data management efficiency is realized.
As an optional implementation manner, before acquiring the target vehicle service data to be managed, the method further includes:
s1, importing the total vehicle historical service data into an original data layer;
s2, synchronizing the vehicle incremental business data to the original data layer by using the incremental log of the database, wherein the incremental log is used for recording the structure change information of the database table and the table data modification information;
s3, acquiring candidate vehicle service data carrying operation mark information, wherein the operation mark information is used for marking the vehicle service data which is subjected to key operation; the candidate vehicle service data is synchronized to the original data layer.
It will be appreciated that vehicle traffic data may also need to be imported in a number of ways before being subjected to management operations. The method comprises the following three operation modes:
the operation of importing the vehicle historical service Data into the original Data layer in total can be importing the vehicle historical service Data into the original Data layer of the Data warehouse in total from the service database by using a datapipeline tool, wherein the source of the vehicle historical service Data can be Data platforms such as SCRM (social Customer relationship Management Platform), CDP (Customer Data Platform), ADP (advisor Data Platform), SAP (System Application and Products, enterprise Management solution), and the like;
the incremental log can be recorded in a binlog form of a business database, and incremental data business data generated at the present stage are synchronized into an original data layer by combining a Flink CDC (data quasi-real-time copy CDC) tool;
the candidate vehicle service data with the operation mark information may be buried point data used for indicating operations such as user sharing operation, important content browsing operation, evaluation or consultation. It can be understood that the partial data is generated in real time and the data amount is huge, so that the Kafka middleware can be adopted to perform peak clipping processing, namely, the Kafka stores the embedded data, and then regularly and quantitatively draws the embedded data meeting the conditions from the Kafka to realize the peak clipping effect.
By the implementation method of the embodiment, the vehicle historical service data is imported into the original data layer in full; synchronizing the vehicle incremental service data to an original data layer by utilizing the incremental log of the database; acquiring candidate vehicle service data carrying operation mark information, wherein the operation mark information is used for marking the vehicle service data subjected to key operation; the candidate vehicle service data are synchronized to the original data layer, so that the data of a plurality of data channels are acquired and preprocessed, the data processing efficiency is ensured by setting management conditions, and the technical effect of improving the vehicle service data management efficiency is realized.
As an optional mode, the building a vehicle service data chain based on the association relationship between the management dimension tags includes:
s1, analyzing the dimension attribute information corresponding to different management dimension labels;
s2, determining the association relationship between the management dimension labels by using the relationship between the dimension attribute information;
and S3, constructing the management dimension labels with the association relationship into a vehicle business data chain.
In the following description with reference to specific embodiments, the vehicle service data link may be a service data detail table (DWS), and the management dimension tag may be, for example: dimension A: dealer, consultant, order volume; dimension B: city, dealer. And then, based on the contact among dealers, the dimensionality can be expanded, and the relation between the city and the dealer as well as between the consultant and the order quantity is established, so that the service detail data (namely, service chain) of respective orders of different cities is obtained, and a huge data detail network is constructed.
By the implementation method, the dimension attribute information corresponding to different management dimension labels is analyzed; determining an association relation between management dimension labels by using a relation between dimension attribute information; the management dimension labels with the incidence relation are constructed into a vehicle service data chain, so that the establishment of a service data detail list (DWS) is realized, namely, a data detail network of the vehicle service is established, the generation of a related detail data list in real time is avoided when the data query requirement is obtained, and the technical effect of improving the vehicle service data management efficiency is realized.
As an optional implementation manner, the respectively counting the target vehicle service data according to different time granularities based on the management dimension tag and the vehicle service data chain to obtain statistical result data includes:
s1, acquiring reference vehicle service data generated under the reference time granularity from the target vehicle service data;
s2, counting the reference vehicle service data according to different management dimension labels based on the vehicle service data chain to obtain a plurality of service index data;
and S3, determining the plurality of service index data as statistical results under the reference time granularity, wherein the statistical result data comprise respective corresponding statistical results of different time granularities.
The reference time granularity may be "year", "month", "week", "day", or even "half day" or "hour", and after the required time granularity is determined, vehicle service data corresponding to different time granularities in the target vehicle service data is obtained. It is understood that the smaller the time granularity is set, the closer the vehicle traffic data is to the "real-time" traffic data. And after the vehicle service data corresponding to different time granularities are obtained and counted, a plurality of service index data are obtained, and then the counting results under different time granularities are determined.
Through the embodiment of the application, the reference vehicle business data generated under the reference time granularity is obtained from the target vehicle business data; counting reference vehicle service data according to different management dimension labels based on a vehicle service data chain to obtain a plurality of service index data; the plurality of service index data are determined as statistical results under the reference time granularity, wherein the statistical result data comprise the mode of the respective corresponding statistical results of different time granularities, and the vehicle service data corresponding to different time granularities are obtained through statistics in advance, so that the condition that the service query requirement is obtained is avoided, the data are queried and counted from a database in real time, the query calculation pressure on the repeated query requirement is reduced, and the vehicle service data management efficiency is improved.
As an optional implementation manner, the clustering the statistical result data according to different vehicle service topics to obtain a plurality of vehicle service data sets includes: and clustering the statistical result data according to a preset vehicle service theme to obtain vehicle service data sets respectively matched with the vehicle service theme, wherein the vehicle service data sets comprise vehicle service data subsets respectively corresponding to different management dimension labels.
Optionally, the vehicle business topics may include, but are not limited to, a test drive order topic, a to store topic, an order topic, a user follow-up topic, a lead topic, a battle-defeat topic (including user loss, user default, user rejection, diversion to other advisors, etc.). Specifically, the obtained statistical results may be clustered according to different topics, for example, data of a uniform topic is migrated to a data table. Note that, here, only data migration and deduplication processing are performed, and other statistical calculations are not performed.
By the above implementation method of this embodiment, clustering the statistical result data by the service theme to obtain a plurality of vehicle service data sets includes: and clustering the statistical result data according to the preset vehicle service theme to obtain vehicle service data sets respectively matched with the vehicle service themes, so that clustering of the vehicle service data with different themes is realized, and the management efficiency of the vehicle service data is improved.
As an optional implementation manner, before acquiring the target vehicle service data to be managed, the method further includes: creating a global scheduling task, wherein the global scheduling task comprises a task sequence with a management time sequence relation, and the task sequence comprises: the system comprises a first task for extracting management dimension labels, a second task for constructing a vehicle service data chain, a third task for obtaining statistical result data and a fourth task for clustering to obtain a vehicle service data set.
It can be understood that, since the above management is an offline operation, in order to ensure that the service data in the same period T is managed, the service data is associated in a task form, so that the processing tasks of the service data in the same period are in the same task flow. Specifically, DophineSchedule can be used for global scheduling, so that data collection, data-oriented-Load (ETL), off-line index calculation and topic-based aggregation indexes are guaranteed to be dependent on and smoothly executed in the same task flow.
In this embodiment, the scheduling management of the overall task of the vehicle service data is realized in a manner of creating a global scheduling task, so that the vehicle service data management efficiency is improved, wherein the global scheduling task includes a task sequence having a management timing relationship.
As an optional implementation manner, after clustering the statistical result data according to different vehicle business topics to obtain a plurality of vehicle business data sets, the method further includes:
s1, acquiring first business data related to a first identity role from a plurality of vehicle business data sets, and generating a first business report matched with the role attribute of the first identity role based on the first business data, wherein the first business report is used for prompting order form transaction information to an object belonging to the first identity role;
and S2, acquiring second service data related to the second identity role from the plurality of vehicle service data sets, and generating a second service report matched with the role attribute of the second identity role based on the second service data, wherein the second service report is used for prompting hot vehicle information to an object belonging to the second identity role.
Alternatively, the first service Data may be acquired ADP (advisor Data Platform) Data, and the second service Data may be acquired CDP (Customer Data Platform) Data. Specifically, the vehicle business data management system acquires data from a database table and displays the data, and the data are divided into two parts, namely ADP and CDP, according to the analysis condition of the vehicle enterprises on the data. Through dimension observation and analysis of the ADP index, the sales conditions (hot car sales system, sales quantity, follow-up condition of the consultant, client viscosity and the like) of a head office/war zone/distributor/consultant can be clearly known, so that the car manager is helped to assign a more reasonable sales strategy for the organization of each level structure; the analysis of the CDP index data is helpful for vehicle enterprises to better know the appeal of the customers to the vehicles, so that the suitable vehicles are more accurately recommended to the customers, and the vehicle purchasing conversion rate is improved.
Through the above embodiment of the application, in a plurality of vehicle service data sets, first service data related to a first identity role is acquired, and a first service report matched with a role attribute of the first identity role is generated based on the first service data; and acquiring second service data related to the second identity role from the plurality of vehicle service data sets, and generating a second service report matched with the role attribute of the second identity role based on the second service data, thereby realizing the technical effect of improving the management efficiency of the vehicle service data.
An embodiment of the present application is described below with reference to fig. 6.
According to the embodiment, a warehouse counting modeling idea is adopted in the automobile enterprise industry, and statistical analysis is performed on target object behavior data and consultant data generated by the automobile enterprise, so that income is further brought to digital marketing of the automobile enterprise.
As shown in fig. 6, the system is mainly divided into three major modules, namely: data source, data warehouse, management system.
First, a data source is introduced: the data source is a storage platform of the vehicle-enterprise data, and business data and buried point data are mainly listed. The business data mainly comprises data such as social customer relationship management (social CRM) SCRM, CDP, ADP, SAP and the like; the embedding data mainly refers to clicking behaviors of a user at a web/mobile phone end and the like, and a strategic tool is used for embedding points;
next, the data warehouse is introduced:
the counting bin mainly comprises two parts: 1. importing data in a data source through a technical tool, which is called an original data layer (ODS) herein; 2. the data is modeled hierarchically in terms of bins (DIM → DWD → DWS → DM).
As shown in fig. 6, for data import, the scheme divides the data into three parts according to the data characteristics, and the three parts are imported in different ways:
a. historical business data: importing the total amount of the data into an original data ODS layer of a data warehouse from a business database by using a Datapipline tool;
b. for the incremental data generated at the present stage: using binlog of a service database, combining with a Flink CDC (data quasi-real-time copy CDC) tool, and incrementally synchronizing to the ODS;
c. for buried point data: this portion is very large and is generated in real time, here using Kafka middleware for peak clipping, synchronized in real time to the ODS.
Then, for the multi-bin layered modeling, the scheme combines the characteristics of the data of the vehicle and the modeling idea of the industry and divides the modeling into four layers of DIM, DWD, DWS and DM:
DIM (data ETL, dimension data): aiming at data in the ODS, extracting relevant flat dimensions (such as dealers, war zones, consultants, vehicle series and the like) of vehicles and enterprises, filtering invalid data, and establishing a multi-bin public dimension table, wherein the multi-bin public dimension table is mainly used for performing the dimension of vehicle enterprise service index statistics (such as the number of inflows/orders of the vehicles and the orders in the current period according to the dealer dimension statistics);
DWD (data ETL, traffic detail data): aiming at data in the ODS, extracting business processes (such as clues, ordering, test driving and the like) interested in digital marketing of the vehicle-enterprise, reserving data with minimum time granularity generated by business, expanding all dimension fields related to the business processes, filtering invalid data, and establishing a business data list;
DWS (off-line calculation of indices): based on a DIM dimension table and a DWD data detail table, a DWS data service layer is constructed according to indexes interested by different dimension offline statistical services with different time granularity (day/month/year and the like), and results are stored in Hive, so that repeated calculation is reduced;
DM (topic aggregation index): based on DWS layer data, indexes are gathered according to themes, indexes (such as arriving stores and orders) related to the same theme of the vehicle and the enterprise are collected into the same table, a DM layer is constructed, and the DM layer is stored in an OLAP database MySQL.
And regarding the scheduling of the integral tasks of the multi-bin, the DophineSchedule global scheduling is used, so that the data acquisition, the data ETL, the index off-line calculation and the sub-topic aggregation indexes are ensured to be dependent and smoothly executed in the same task flow.
The management system is then described: and the management portal system acquires data from the DM database table and displays the data, and the data are divided into two parts, namely ADP and CDP according to the analysis condition of the vehicle and the enterprise on the data. Through dimension observation and analysis of the ADP index, the sales conditions (hot car sales system, sales quantity, follow-up condition of the consultant, client viscosity and the like) of a head office/war zone/distributor/consultant can be clearly known, so that the car manager is helped to assign a more reasonable sales strategy for the organization of each level structure; the analysis of the CDP index data is helpful for vehicle enterprises to better know the appeal of the customers to the vehicles, so that the suitable vehicles are more accurately recommended to the customers, and the vehicle purchasing conversion rate is improved.
In the embodiment of the invention, the vehicle enterprise business data management system is arranged, and the off-line integration and management of the business data are realized in advance through the data storage and the operation processing of the data source, the data warehouse and the management system, so that the real-time data statistics and calculation under the condition of data requirement are avoided, the calculation pressure of a business database is reduced, and the technical problem of low management efficiency of the existing business data is solved.
It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present invention is not limited by the order of acts, as some steps may occur in other orders or concurrently in accordance with the invention. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required by the invention.
According to another aspect of the embodiment of the present invention, a service data management apparatus for implementing the service data management method is also provided. As shown in fig. 7, the apparatus includes:
an obtaining unit 702, configured to obtain target vehicle service data to be managed;
an extracting unit 704, configured to extract a management dimension tag corresponding to a vehicle service attribute from the target vehicle service data;
the establishing unit 706 is used for establishing a vehicle service data chain based on the incidence relation between the management dimension labels;
a statistical unit 708, configured to perform respective statistics on the target vehicle service data according to different time granularities based on the management dimension tag and the vehicle service data chain, so as to obtain statistical result data;
the clustering unit 710 is configured to cluster the statistical result data according to different vehicle service topics to obtain a plurality of vehicle service data sets, where each vehicle service data corresponds to one vehicle service topic.
Optionally, in this embodiment, reference may be made to the above method embodiments for the embodiments to be implemented by the above unit modules, which are not described herein again.
According to another aspect of the embodiment of the present invention, there is also provided an electronic device for implementing the service data management method, where the electronic device may be a terminal device or a server shown in fig. 8. The present embodiment takes the electronic device as a terminal device as an example for explanation. As shown in fig. 8, the electronic device comprises a memory 802 and a processor 804, the memory 802 having a computer program stored therein, the processor 804 being arranged to perform the steps of any of the above-described method embodiments by means of the computer program.
Optionally, in this embodiment, the electronic device may be located in at least one network device of a plurality of network devices of a computer network.
Optionally, in this embodiment, the processor may be configured to execute the following steps by a computer program:
s1, acquiring target vehicle service data to be managed;
s2, extracting management dimension labels corresponding to vehicle service attributes from the target vehicle service data;
s3, constructing a vehicle business data chain based on the incidence relation between the management dimension labels;
s4, respectively counting the target vehicle service data according to different time granularities based on the management dimension label and the vehicle service data chain to obtain statistical result data;
s5, clustering the statistical result data according to different vehicle service themes to obtain a plurality of vehicle service data sets, wherein each vehicle service data corresponds to one vehicle service theme.
Alternatively, it can be understood by those skilled in the art that the structure shown in fig. 8 is only an illustration, and the electronic device may also be a terminal device such as a smart phone (e.g., an Android phone, an iOS phone, etc.), a tablet computer, a palmtop computer, a Mobile Internet Device (MID), a PAD, and the like. Fig. 8 is a diagram illustrating a structure of the electronic device. For example, the electronics may also include more or fewer components (e.g., network interfaces, etc.) than shown in FIG. 8, or have a different configuration than shown in FIG. 8.
The memory 802 may be used to store software programs and modules, such as program instructions/modules corresponding to the business data management method and apparatus in the embodiments of the present invention, and the processor 804 executes various functional applications and image processing by running the software programs and modules stored in the memory 802, so as to implement the business data management method described above. The memory 802 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 802 can further include memory located remotely from the processor 804, which can be connected to the terminal over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof. The memory 802 may be, but not limited to, specifically configured to store various parts in the hardware performance image, service data management information, and other information. As an example, as shown in fig. 8, the memory 802 may include, but is not limited to, an obtaining unit 702, an extracting unit 704, an establishing unit 706, a counting unit 708, and a clustering unit 710 in the service data management apparatus. In addition, the service data management device may further include, but is not limited to, other module units in the service data management device, which is not described in detail in this example.
Optionally, the transmitting device 806 is configured to receive or transmit images via a network. Examples of the network may include a wired network and a wireless network. In one example, the transmission device 806 includes a Network adapter (NIC) that can be connected to a router via a Network cable and other Network devices to communicate with the internet or a local area Network. In one example, the transmission device 806 is a Radio Frequency (RF) module, which is used for communicating with the internet in a wireless manner.
In addition, the electronic device further includes: a display 808 for displaying a vehicle service data management system interface in the interface; and a connection bus 810 for connecting the respective module parts in the above-described electronic apparatus.
In other embodiments, the terminal device or the server may be a node in a distributed system, where the distributed system may be a blockchain system, and the blockchain system may be a distributed system formed by connecting a plurality of nodes through a network communication. Nodes can form a Peer-To-Peer (P2P, Peer To Peer) network, and any type of computing device, such as a server, a terminal, and other electronic devices, can become a node in the blockchain system by joining the Peer-To-Peer network.
According to an aspect of the present application, there is provided a computer program product comprising a computer program/instructions containing program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication section, and/or installed from a removable medium. When executed by the central processing unit, the computer program performs various functions provided by the embodiments of the present application.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
According to an aspect of the present application, there is provided a computer-readable storage medium, a processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the business data manager method.
Alternatively, in the present embodiment, the above-mentioned computer-readable storage medium may be configured to store a computer program for executing the steps of:
s1, acquiring target vehicle service data to be managed;
s2, extracting management dimension labels corresponding to vehicle service attributes from the target vehicle service data;
s3, constructing a vehicle business data chain based on the incidence relation between the management dimension labels;
s4, respectively counting the target vehicle service data according to different time granularities based on the management dimension label and the vehicle service data chain to obtain statistical result data;
s5, clustering the statistical result data according to different vehicle service themes to obtain a plurality of vehicle service data sets, wherein each vehicle service data corresponds to one vehicle service theme.
Alternatively, in this embodiment, a person skilled in the art may understand that all or part of the steps in the methods of the foregoing embodiments may be implemented by a program instructing hardware associated with the terminal device, where the program may be stored in a computer-readable storage medium, and the storage medium may include: flash disks, Read-Only memories (ROMs), Random Access Memories (RAMs), magnetic or optical disks, and the like.
The integrated unit in the above embodiments, if implemented in the form of a software functional unit and sold or used as a separate product, may be stored in the above computer-readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing one or more computer devices (which may be personal computers, servers, network devices, etc.) to execute all or part of the steps of the above methods according to the embodiments of the present invention.
In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the several embodiments provided in the present application, it should be understood that the disclosed client may be implemented in other manners. The above-described embodiments of the apparatus are merely illustrative, and for example, the above-described division of the units is only one type of division of logical functions, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit may be implemented in the form of hardware, or may also be implemented in the form of a software functional unit.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (12)
1. A method for managing service data, comprising:
acquiring target vehicle service data to be managed;
extracting a management dimension label corresponding to the vehicle service attribute from the target vehicle service data;
constructing a vehicle business data chain based on the incidence relation among the management dimension labels;
respectively counting the target vehicle service data according to different time granularities based on the management dimension label and the vehicle service data chain to obtain statistical result data;
and clustering the statistical result data according to different vehicle service themes to obtain a plurality of vehicle service data sets, wherein each vehicle service data corresponds to one vehicle service theme.
2. The method of claim 1, wherein the obtaining target vehicle traffic data to be managed comprises:
and acquiring the target vehicle business data meeting the management conditions from an original data layer, wherein the original data layer is used for storing the vehicle business data acquired from a plurality of data channels.
3. The method of claim 2, further comprising, prior to said obtaining target vehicle traffic data to be managed:
importing the total amount of vehicle historical service data into the original data layer;
synchronizing vehicle incremental business data to the original data layer by utilizing an incremental log of a database, wherein the incremental log is used for recording database table structure change information and table data modification information;
acquiring candidate vehicle service data carrying operation mark information, wherein the operation mark information is used for marking the vehicle service data subjected to key operation; synchronizing the candidate vehicle traffic data to the raw data layer.
4. The method of claim 1, wherein constructing a vehicle business data chain based on the incidence relationships between the management dimension labels comprises:
analyzing the dimension attribute information corresponding to different management dimension labels;
determining the association relationship between the management dimension tags by using the relationship between the dimension attribute information;
constructing the management dimension label with the incidence relation as the vehicle business data chain.
5. The method of claim 1, wherein the respectively counting the target vehicle service data according to different time granularities based on the management dimension label and the vehicle service data chain to obtain statistical result data comprises:
acquiring reference vehicle service data generated under a reference time granularity from the target vehicle service data;
counting the reference vehicle service data according to different management dimension labels based on the vehicle service data chain to obtain a plurality of service index data;
and determining the plurality of service index data as statistical results under the reference time granularity, wherein the statistical result data comprise respective corresponding statistical results of different time granularities.
6. The method of claim 1, wherein the clustering the statistical result data according to different vehicle business topics to obtain a plurality of vehicle business data sets comprises:
and clustering the statistical result data according to the vehicle service theme configured in advance to obtain the vehicle service data sets matched with the vehicle service themes respectively, wherein the vehicle service data sets comprise vehicle service data subsets corresponding to different management dimension labels respectively.
7. The method according to any one of claims 1 to 6, further comprising, prior to said obtaining target vehicle traffic data to be managed:
creating a global scheduling task, wherein the global scheduling task comprises a task sequence with a management time sequence relation, and the task sequence comprises: the vehicle service data processing system comprises a first task for extracting the management dimension labels, a second task for constructing the vehicle service data chains, a third task for obtaining the statistical result data and a fourth task for clustering to obtain the vehicle service data sets.
8. The method according to any one of claims 1 to 6, wherein after clustering the statistical result data according to different vehicle business topics to obtain a plurality of vehicle business data sets, further comprising:
acquiring first service data related to a first identity role from the plurality of vehicle service data sets, and generating a first service report matched with the role attribute of the first identity role based on the first service data, wherein the first service report is used for prompting order form transaction information to an object belonging to the first identity role;
and acquiring second service data related to a second identity role from the plurality of vehicle service data sets, and generating a second service report matched with the role attribute of the second identity role based on the second service data, wherein the second service report is used for prompting hot vehicle information to an object belonging to the second identity role.
9. A service data management apparatus, comprising:
the system comprises an acquisition unit, a management unit and a management unit, wherein the acquisition unit is used for acquiring target vehicle service data to be managed;
the extraction unit is used for extracting a management dimension label corresponding to the vehicle service attribute from the target vehicle service data;
the establishing unit is used for establishing a vehicle service data chain based on the incidence relation between the management dimension labels;
the statistical unit is used for respectively counting the target vehicle service data according to different time granularities based on the management dimension label and the vehicle service data chain to obtain statistical result data;
and the clustering unit is used for clustering the statistical result data according to different vehicle service themes to obtain a plurality of vehicle service data sets, wherein each vehicle service data corresponds to one vehicle service theme.
10. A computer-readable storage medium, comprising a stored program, wherein the program when executed performs the method of any one of claims 1 to 8.
11. A computer program product comprising computer program/instructions, characterized in that the computer program/instructions, when executed by a processor, implement the steps of the method of any of claims 1 to 8.
12. An electronic device comprising a memory and a processor, characterized in that the memory has stored therein a computer program, the processor being arranged to execute the method of any of claims 1 to 8 by means of the computer program.
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| CN114841570A (en) * | 2022-05-07 | 2022-08-02 | 金腾科技信息(深圳)有限公司 | Data processing method, device, equipment and medium for customer relationship management system |
| CN115033646A (en) * | 2022-08-11 | 2022-09-09 | 深圳联友科技有限公司 | Method for constructing real-time warehouse system based on Flink and Doris |
| CN115134383A (en) * | 2022-06-24 | 2022-09-30 | 重庆长安汽车股份有限公司 | Dynamic configuration method, system, equipment and medium for cloud task on Internet of vehicles data |
| CN117171701A (en) * | 2023-08-14 | 2023-12-05 | 陕西天行健车联网信息技术有限公司 | Vehicle running data processing method, device, equipment and medium |
| CN117350520A (en) * | 2023-12-04 | 2024-01-05 | 浙江大学高端装备研究院 | Automobile production optimization method and system |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114841570A (en) * | 2022-05-07 | 2022-08-02 | 金腾科技信息(深圳)有限公司 | Data processing method, device, equipment and medium for customer relationship management system |
| CN115134383A (en) * | 2022-06-24 | 2022-09-30 | 重庆长安汽车股份有限公司 | Dynamic configuration method, system, equipment and medium for cloud task on Internet of vehicles data |
| CN115134383B (en) * | 2022-06-24 | 2023-03-28 | 重庆长安汽车股份有限公司 | Dynamic configuration method, system, equipment and medium for cloud task on Internet of vehicles data |
| CN115033646A (en) * | 2022-08-11 | 2022-09-09 | 深圳联友科技有限公司 | Method for constructing real-time warehouse system based on Flink and Doris |
| CN115033646B (en) * | 2022-08-11 | 2023-01-13 | 深圳联友科技有限公司 | Method for constructing real-time warehouse system based on Flink and Doris |
| CN117171701A (en) * | 2023-08-14 | 2023-12-05 | 陕西天行健车联网信息技术有限公司 | Vehicle running data processing method, device, equipment and medium |
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