CN115392799B - Attribution analysis method and device, computer equipment and storage medium - Google Patents
Attribution analysis method and device, computer equipment and storage medium Download PDFInfo
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Abstract
The application relates to the field of big data, and particularly discloses an attribution analysis method, an attribution analysis device, a computer device and a storage medium, wherein the method comprises the following steps: acquiring service system data, wherein the service system data comprises first dimension information and corresponding first behavior indexes; determining information of a second dimension and/or a second behavior index based on the data expansion rule; generating a data analysis instruction according to the target dimension, the number of the combined dimensions, the first target index and the target analysis mode input by the user; and according to the data analysis instruction, performing data analysis on the information of the first dimension, the first behavior index and the information of the second dimension and/or the second behavior index to obtain an analysis result. Enriching data dimensionality through data expansion; the user sets target dimensions, the number of combined dimensions and the like attributed to analysis as required, and dynamically configures multidimensional combination without presetting a data analysis path. The method can be applied to cloud servers of big data and artificial intelligence platform cloud computing services.
Description
Technical Field
The present application relates to the field of big data technologies, and in particular, to an attribution analysis method, an attribution analysis device, a computer device, and a storage medium.
Background
In the complex data era, a large amount of data is generated every day and a user has a complex behavior path, and for abnormal change of the data (such as sales volume reduction), certain explanation needs to be made so as to cope with the following change, wherein attribution analysis is an analysis method capable of quickly positioning problems. Data analysis is usually performed manually at present, because the business involves information of various dimensions, the analysis path (data dimension) is multi-dimensional and may be combined influence, thus multi-dimensional combination analysis is required, and the current attribution analysis usually depends on the familiarity degree of a user with a business experience model.
Disclosure of Invention
The embodiment of the application provides an attribution analysis method, an attribution analysis device, a computer device and a storage medium, which can solve the problem that a user needs to preset a data analysis path during attribution analysis.
In a first aspect, the present application provides an attribution analysis method, the method comprising:
acquiring service system data, wherein the service system data comprises first dimension information and corresponding first behavior indexes;
determining second dimension information and/or a second behavior index according to the business system data based on a preset data expansion rule;
acquiring a target dimension, a combined dimension number, a first target index and a target analysis mode input by a user on a front-end interactive interface of terminal equipment, wherein the target dimension is selected from the first dimension and/or the second dimension, and the first target index is selected from the first behavior index and/or the second behavior index;
generating a data analysis instruction according to the target dimension, the number of the combined dimensions, the first target index and the target analysis mode based on a preset instruction generation rule;
according to the data analysis instruction, performing data analysis on the information of the first dimension, the first behavior index and the information of the second dimension and/or the second behavior index to obtain an analysis result;
and sending the analysis result to the terminal equipment.
In a second aspect, the present application provides an attribution analysis device, comprising:
the data acquisition module is used for acquiring service system data, wherein the service system data comprises first dimension information and a corresponding first behavior index;
the data expansion module is used for determining second dimension information and/or second behavior indexes according to the service system data based on a preset data expansion rule;
the user setting module is used for acquiring a target dimension, a combined dimension number, a first target index and a target analysis mode which are input by a user on a front-end interactive interface of terminal equipment, wherein the target dimension is selected from the first dimension and/or the second dimension, and the first target index is selected from the first behavior index and/or the second behavior index;
the instruction generating module is used for generating a data analysis instruction according to the target dimension, the number of the combined dimensions, the first target index and the target analysis mode based on a preset instruction generating rule;
the data analysis module is used for carrying out data analysis on the information of the first dimension, the first behavior index and the information of the second dimension and/or the second behavior index according to the data analysis instruction to obtain an analysis result;
and the sending module is used for sending the analysis result to the terminal equipment.
In a third aspect, the present application provides a computer device comprising a memory and a processor; the memory is used for storing a computer program; the processor is configured to execute the computer program and to implement the steps of the attribution analysis method described above when executing the computer program.
In a fourth aspect, the present application provides a computer-readable storage medium storing a computer program which, if executed by a processor, implements the steps of the attribution analysis method described above.
The application discloses an attribution analysis method, an attribution analysis device, attribution analysis equipment and a storage medium, wherein the method comprises the following steps: acquiring service system data, wherein the service system data comprises first dimension information and corresponding first behavior indexes; determining second dimension information and/or a second behavior index according to the business system data based on a preset data expansion rule; acquiring a target dimension, a combined dimension number, a first target index and a target analysis mode input by a user on a front-end interactive interface of terminal equipment, wherein the target dimension is selected from the first dimension and/or the second dimension, and the first target index is selected from the first behavior index and/or the second behavior index; generating a data analysis instruction according to the target dimension, the number of the combined dimensions, the first target index and the target analysis mode based on a preset instruction generation rule; according to the data analysis instruction, carrying out data analysis on the information of the first dimension, the first behavior index and the information of the second dimension and/or the second behavior index to obtain an analysis result; and sending the analysis result to the terminal equipment. The dimensionality of data due to analysis can be enriched by expanding the dimensionality and/or behavior index based on a preset data expansion rule; the user can set the target dimensionality, the combination dimensionality quantity, the first target index and the target analysis mode of attribution analysis through the front-end interactive interface as required, dynamic configuration multi-dimensional combination can be achieved, the user can carry out all-around dimensionality analysis as required, and the problem that a data analysis path needs to be preset is solved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a schematic flow chart of an attribution analysis method according to an embodiment of the present application;
FIG. 2 is a diagram illustrating an application scenario of an attribution analysis method according to an embodiment;
fig. 3 is a schematic structural diagram of an attribute analysis apparatus according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a computer device according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some, but not all, embodiments of the present application. 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 application.
The flow diagrams depicted in the figures are merely illustrative and do not necessarily include all of the elements and operations/steps, nor do they necessarily have to be performed in the order depicted. For example, some operations/steps may be decomposed, combined or partially combined, so that the actual execution sequence may be changed according to the actual situation. In addition, although the division of the functional blocks is made in the device diagram, in some cases, it may be divided in blocks different from those in the device diagram.
The embodiment of the application provides an attribution analysis method, device, equipment and storage medium. Fig. 1 is a schematic flowchart of an attribution analysis method provided in an embodiment of the present application, and fig. 2 is a schematic diagram of an application scenario of the attribution analysis method in an embodiment.
Because business involves data in multiple dimensions, analyzing paths (data dimensions) is multifaceted and may also be a combinatorial impact, thus requiring multidimensional combinatorial analysis. In the related art, the data attribution analysis generally comprises the following steps:
1. establishing an experience model familiar with business to disassemble an analysis thought;
2. according to the analysis thought, a data analysis path is preset, for example, the sales volume is reduced, whether the sales volume is influenced by a product path or a sales channel path is influenced, different paths are required to be summarized, and therefore the root cause influencing the sales volume downslide is found;
3. analyzing multidimensional data (multipath) by a manual data analysis and query platform to find out data with abnormal fluctuation;
4. and (4) screening and filtering the abnormal movement data one by one to obtain the reasons of influence.
Under the condition that the analysis results are more, data of abnormal movement is difficult to obtain through manual searching, and at least one of the following limitations is brought:
1. presetting a data analysis path, which is limited by the familiarity degree of an analyst with a business experience model and indexes of different modules, wherein the analyzed path cannot be reused, so that the path cannot be preset in advance;
2. performing multi-dimensional analysis operation on a data analysis query platform manually, wherein the analysis functions of a back-end UI (User Interface) and a front-end UI (User Interface) of the platform are strong and flexible enough, the required manpower and financial resources are high, and otherwise, the required dimension result data cannot be obtained in time;
3. multi-dimensional analysis is involved, and after the dimensionality reaches multi-dimensionality (even 10 dimensionalities and more), the result of any multi-dimensional dimensionality combination is many; for example, in the 3-dimensional combination analysis, 120 kinds (10 × 9 × 8 ÷ (3 × 2)) of dimension combinations are obtained; when 10 values of one dimension are combined, the total data volume can reach 12 thousands (120 multiplied by 10^ 3), manual screening and filtering cannot be carried out one by one, and the influence factor result of multi-dimensional combination analysis is difficult to obtain.
Based on this, the inventors of the present application have improved the attribution analysis method to solve at least one of the aforementioned technical problems.
Some embodiments of the present application will be described in detail below with reference to the accompanying drawings. The embodiments described below and the features of the embodiments can be combined with each other without conflict.
For example, the attribution analysis method is used for a server, but may be used for a terminal device. The server can be an independent server, a server cluster, a cloud server providing basic cloud computing services such as cloud service, a cloud database, cloud computing, cloud functions, cloud storage, network service, cloud communication, middleware service, domain name service, security service, content Delivery Network (CDN), big data and artificial intelligence platforms and the like; the server is, for example, a web server (or may be referred to as a web server). The terminal device can be an electronic device such as a mobile phone, a tablet computer, a notebook computer, a desktop computer, a personal digital assistant and a wearable device. However, for ease of understanding, the following embodiments will be described in detail mainly with respect to the attribution analysis method applied to the server.
In some embodiments, as shown in fig. 2, the terminal device sends user setting information to the server, the server performs attribution analysis according to the attribution analysis method of the embodiment of the present application, and sends the obtained analysis result to the terminal device, so that the user needs to make certain explanation for abnormal data change (for example, sales volume decrease) so as to cope with the following change.
Referring to fig. 1, the attribution analysis method according to the embodiment of the present application includes the following steps S110 to S160.
Step S110, business system data are obtained, wherein the business system data comprise first dimension information and corresponding first behavior indexes.
In some embodiments, the business system data includes offline data generated by the business system, for example, at 2 am, and the offline data of the previous day is extracted by a data migration tool, such as a Sqoop data migration tool (the pattern is T + 1).
Optionally, the acquired service system data may be stored in a hive library of the Hadoop cluster to form an offline physical table. The Hadoop is a distributed system infrastructure, and a user can develop a distributed program without knowing distributed bottom-layer details, and can fully utilize the power of a cluster to carry out high-speed operation and storage; hive is a data warehouse tool based on Hadoop, which is used for data extraction, transformation and loading, and is a mechanism capable of storing, querying and analyzing large-scale data stored in Hadoop.
Illustratively, the business system data may be preprocessed, such as performing an ETL (Extract-Transform-Load) operation.
Illustratively, the service system data is a wide table mode shown in table 1:
table 1 service system data
| Month(s) | Agent coding | Department coding | Type of diamond | Amount of face interview | FYP | FYC | …… |
| 2022-04 | 64300000678 | 10078678 | Diamond | 500 | 5000 | 300 | …… |
| 2022-04 | 64300000679 | 10078678 | Diamond | 60 | 1000 | 50 | …… |
| …… | …… | …… | …… | …… | …… | …… | …… |
For example, the First dimension includes month, agent code, department code, diamond type, and the First behavioral indicators include interview amount, FYP (First Year commission), FYC (First Year Premium).
And step S120, determining second dimension information and/or a second behavior index according to the business system data based on a preset data expansion rule.
By expanding the dimensionality and/or behavior index based on a preset data expansion rule, for example, the wide table shown in table 1 can be processed into a large wide table, and the data dimensionality of attribution analysis can be enriched; but also reduces the amount of calculation when a cause analysis is subsequently performed.
Optionally, a spark distributed computing engine is used to expand the dimension and/or the behavior index to obtain information of a second dimension and/or a second behavior index. Spark is a general purpose distributed data processing engine that can interface with a distributed storage system. By adopting the spark distributed computing engine, large data volume is processed quickly so as to meet the timeliness requirement.
In some embodiments, the determining, based on the preset data expansion rule, information of a second dimension and/or a second behavior index according to the business system data includes: and converting the first behavior index based on a preset index dimension conversion rule to obtain second dimension information corresponding to the first behavior index. The number of dimensions can be expanded by converting the first behavior index into information of a second dimension, namely, the behavior index is converted into the dimensions.
For example, the converting the first behavior index based on a preset index dimension conversion rule to obtain information of a second dimension corresponding to the first behavior index includes: and converting each first behavior index into an enumeration type field according to the numerical distribution of the plurality of first behavior indexes to obtain the information of the second dimension.
For example, according to a first behavior index, such as the median of the interview volume, each interview volume is converted into an enumeration type field, such as four levels, i.e., low, medium, high and highest, to obtain information of the second dimension, i.e., the interview volume.
In some embodiments, the determining, based on the preset data expansion rule, information of a second dimension and/or a second behavior index according to the business system data includes: and determining a second behavior index according to the information of the first dimension and the first behavior index based on a second behavior index determination rule indicated by a preset analysis mode. By expanding the second behavior index corresponding to the first behavior index according to the preset analysis mode, the second behavior index corresponding to the preset analysis mode can be provided for subsequent attribution analysis, and the attribution analysis efficiency is improved.
For example, the determining, according to the information of the first dimension and the first behavior index, a second behavior index based on a second behavior index determination rule indicated by a preset analysis manner includes: and determining the same-ratio behavior index and/or the ring ratio behavior index corresponding to the first behavior index as the second behavior index according to the information of the time dimension based on the same-ratio behavior index and/or the ring ratio behavior index corresponding to the first behavior index indicated by the same-ratio analysis mode.
The homocyclic analysis mode is a time dimension comparison analysis method. For example, when the time dimension information is 2022 year 04 month and the first behavior index is FYP, the same-ratio behavior index corresponding to the first behavior index may be determined as FYP of 2021 year 4 month, and the ring-ratio behavior index corresponding to the first behavior index may be determined as FYP of 2022 year 03 month.
For example, the index data of 03 and 4 months in 2022 and 2021 are subjected to homonymy ratio slicing to form a fixed slice index, such as the homonymy index of FYP, which may be referred to as FYP _202104.
Illustratively, according to the second dimension information and/or the second behavior index, the width table shown in table 1 is subjected to a widening process to obtain a large width table shown in table 2:
TABLE 2 Wide table
| Month of the year | Agent encoding | …… | …… | Amount of face interview | FYP | FYP_ 202104 | FYP_ 202203 |
| 2022-04 | 64300000678 | …… | …… | 500 | 5000 | 4000 | 2000 |
| …… | …… | …… | …… | …… | …… | …… | …… |
| …… | …… | …… | …… | …… | …… | …… | …… |
It should be noted that the preset analysis manner may also include a linear fitting manner, and based on the linear fitting manner, the second behavior index may also be determined according to the information of the first dimension and the first behavior index, so as to expand the behavior index. For example, the predetermined analysis method may be referred to as a mobile analysis method.
In some embodiments, the method further comprises: and migrating the information of the first dimension and the corresponding first behavior index, and the information of the second dimension and/or the second behavior index to a client/server relational database through a data migration tool.
Optionally, the data migration tool may be a sqoop tool, and the client/server relational database may be a postgresql database. postgresql is a fully featured object-relational database management system that supports most SQL standards and is capable of providing features such as complex queries, foreign keys, triggers, views, transaction integrity, multi-version concurrency control, etc.
For example, a large-width table pre-calculated by a spark distributed computing engine is extracted to a postgresql database through a sqoop tool and provided to an application side of the front end for use. The large-width table includes, for example, a plurality of dimensions of month, agent granularity, and the like, and at least a first behavior index field.
Step S130, obtaining target dimensions, the number of combined dimensions, a first target index and a target analysis mode input by a user on a front-end interactive interface of the terminal equipment.
Wherein the target dimension is selected from the first dimension and/or the second dimension, and the first target indicator is selected from the first behavior indicator and/or the second behavior indicator.
In some embodiments, the front-end interactive interface provides selectable dimensions through which a user may make dimension selections to determine the target dimension. For example, the selectable dimension ranges include the first dimension and the second dimension, such as "secondary enter", whether or not you are a good talent "," virtual agent "," job level category "," type of business member ", and the like. For example, selectable dimensions may be categorized, and a user may first select a category of dimensions and then select the target dimension among the dimensions of the category. For example, the target dimensions include 5 target dimensions of "secondary entry", "whether you", "virtual agent", "job level category". It should be noted that the dimension provided by the front-end interactive interface may not include all of the first dimension and the second dimension; specifically, the dimension addition/deletion/modification can be performed at the backend.
Illustratively, the front-end interactive interface provides an input box or a selection box of the number of the combined dimensions, for example, the user sets the number of the combined dimensions to be 3; it can be determined that the number of dimension combinations to be analyzed needs to be dynamically configured on the basis of the "selected dimension", for example, a combined analysis is performed on any 3 target dimensions of the aforementioned 5 target dimensions, such as "secondary entrance", "excellent talent", "virtual agent", "job level category",8230; \8230;.
For example, the user sets the first target indicator to be FYP, and the target analysis mode is the same-circle-scale analysis mode, but the invention is not limited thereto.
In some embodiments, the target dimension, the number of combined dimensions, the first target index, and the target analysis mode may be referred to as user setting information; it should be noted that the user setting information is not limited to include the target dimension, the number of combined dimensions, the first target index, and the target analysis manner.
And S140, generating a data analysis instruction according to the target dimension, the number of the combined dimensions, the first target index and the target analysis mode based on a preset instruction generation rule.
According to the data analysis instruction, a server can be instructed to perform data analysis on the information of the first dimension, the first behavior index, and the information of the second dimension and/or the second behavior index to obtain an analysis result.
In some embodiments, the step S140 generates the data analysis instruction according to the target dimension, the number of combined dimensions, the first target index, and the target analysis mode based on a preset instruction generation rule, and includes steps S141 to S145.
And step S141, determining a dimension selection instruction according to the target dimension.
For example, dynamically configuring 5 target dimensions, such as "secondary entry", "priority", "virtual agent", "job level category" for analysis, may be dynamically converted into a dimension selection instruction, such as a part of SQL _1 of executable SQL (Structured Query Language), such as: select twist _ flag, excellent _ current, virtual _ flag, rank, rank _ description; wherein the twice _ flag represents "secondary entry", the excellent _ current represents "whether or not best talents are superior", the virtual _ flag represents "virtual agent", the rank represents "job title", and the rank _ description represents "job title category".
And S142, determining a dimension combination instruction according to the number of the target dimensions and the number of the combination dimensions.
For example, the number of target dimensions is 5, the number of combined dimensions is 3, and a multidimensional analysis technique (GROUPING SETS) of postgresql is adopted to determine a dimension combination instruction, such as SQL _2: group by group grouping sets ((1, 2, 3), (1, 2, 4), (1, 2, 5), (2, 3, 4), (2, 3, 5), (3, 4, 5)).
Step S143, determining a second target index and index processing instructions of the first target index and the second target index according to the first target index and the target analysis mode.
For example, if the user sets the first target index to be FYP and the target analysis mode is a homocyclic analysis mode, it is determined that the second target index includes a homocyclic behavior index (which may be referred to as FYP _ lm) and a cyclic behavior index (which may be referred to as FYP _ lym); the metric processing instructions of the first target metric and the second target metric may be expressed as SQL _3: sum (fyp), sum (fyp _ lm), sum (fyp _ lym)).
Step S144, determining a dimension index aggregation instruction according to the target dimension, the first target index and the second target index.
For example, by performing an aggregation (group by) calculation on the dimension selection instruction, the dimension combination instruction, the target dimension designed by the index processing instruction, the first target index, and the second target index, the dimension aggregation calculation is a backend dimension and index matching service; the data amount of multi-dimensional combination dependence can be obviously reduced, and the execution response time is obviously shortened. For example, the dimension index aggregation instruction may be expressed as SQL _4: select twist _ flag, excellent _ current, virtual _ flag, rank, rank _ description, sum (fyp) as fyp, sum (fyp _ lm) as fyp _ lm, sum (fyp _ lym) fyp _ lym from table _ name group by twist _ flag, excellent _ current, virtual _ flag, rank, rank _ description).
Step S145, combining the dimension selection instruction, the dimension combination instruction, the index processing instruction and the index aggregation instruction to obtain the data analysis instruction.
For example, combining and splicing SQL _1, SQL _2, SQL _3, and SQL _4 to obtain the data analysis instruction, for example, it is expressed as: select twist _ flag, excellent _ current, virtual _ flag, rank, rank _ description, sum (fyp) as fyp, sum (fyp _ lm) as fyp _ lm, sum (fyp _ lym) fyp _ lym from table _ name _ by _ group _ grouping set ((1, 2, 3), (1, 2, 4), (1, 2, 5), (2, 3, 4), (2, 3, 5), (3, 4, 5))).
And S150, performing data analysis on the first dimension information, the first behavior index and the second dimension information and/or the second behavior index according to the data analysis instruction to obtain an analysis result.
In some embodiments, the performing, according to the data analysis instruction, data analysis on the information of the first dimension, the first behavior index, and the information of the second dimension and/or the second behavior index to obtain an analysis result includes; and performing multidimensional combination analysis on the information of the first dimension and the corresponding first behavior index, and the information of the second dimension and/or the second behavior index based on the client/server relational database according to the data analysis instruction to obtain a multidimensional combination analysis result.
After the information of the first dimension and the corresponding first behavior index, and the information of the second dimension and/or the second behavior index are migrated to the client/server relational database, the data analysis instruction may be submitted to the client/server relational database, for example, a postgresql database, and executed to obtain the multidimensional combined analysis result.
For example, the analysis results may be in the form of a table, as shown in table 3:
TABLE 3 analytical results
| Dimension one | Dimension two | Dimension three | Dimension four | Dimension five | Index 1 | Index 2 | Index 3 | |
| Secondary entering driver | Whether you are best or not | Virtual agent | Job level | Job level classification | FYP | FYP_lm | FYP_lym | |
| Is that | Is that | Whether or not | Null | Null | 100 | 200 | 21 | |
| Null | Null | Is that | | Advanced | 150 | 180 | 220 | |
| …… | …… | …… | …… | …… | …… | …… | …… |
In some embodiments, the performing, according to the data analysis instruction, data analysis on the information of the first dimension, the first behavior index, and the information of the second dimension and/or the second behavior index to obtain an analysis result includes: according to a dimension index aggregation instruction in the data analysis instruction, performing aggregation processing on the target dimension, the first target index and the second target index; determining a plurality of dimension combinations according to a dimension selection instruction and a dimension combination instruction in the data analysis instruction, wherein each dimension combination comprises a target dimension of the combination dimension number; according to an index processing instruction in the data analysis instruction, performing index processing on a first target index and a second target index corresponding to each dimension combination to obtain an index processing result corresponding to each dimension combination; and determining an analysis result according to the plurality of dimension combinations and the index processing result corresponding to each dimension combination.
According to the dimension index aggregation instruction, the target dimension, the first target index and the second target index are subjected to aggregation processing, and the dimension and the index after the aggregation processing are subjected to analysis processing, so that the data volume of multi-dimensional combination dependence can be remarkably reduced, and the execution response time can be remarkably shortened.
Referring to table 3, according to the dimension selection instruction and the dimension combination instruction in the data analysis instruction, multiple dimension combinations are determined, where a target dimension of one of the dimension combinations is "secondary entry", whether it is a good talent, or a virtual agent ", and a target dimension of another one of the dimension combinations is" virtual agent "," job level ", or" job level category ", which is certainly not limited thereto; according to an index processing instruction in the data analysis instruction, performing index processing on a first target index FYP corresponding to each dimension combination, for example, accumulating FYPs of agents who enter twice, are superior and are not virtual agents in the morning of the month, and obtaining an index processing result FYP corresponding to the dimension combination to be equal to 100; the second target indexes FYP _ lm and FYP _ lym corresponding to each of the dimensional combinations may be subjected to index processing, for example, FYP _ lm of a virtual agent whose role is manager and whose role is classified as high is accumulated to obtain an index processing result FYP _ lm corresponding to the dimensional combination equal to 180, and FYP _ lym of a virtual agent whose role is manager and whose role is classified as high is accumulated to obtain an index processing result FYP _ lym corresponding to the dimensional combination equal to 220. And then, the plurality of dimension combinations and the index processing result corresponding to each dimension combination can be put into a temporary table to obtain an analysis result, or can be called as multi-dimensional combination result data.
In some embodiments, the determining an analysis result according to the plurality of dimension combinations and the corresponding index processing result includes: determining whether index processing results corresponding to the multiple dimension combinations meet preset transaction conditions or not; and determining an analysis result according to the dimension combination meeting the transaction condition and the corresponding index processing result.
Due to the analysis result obtained according to the data analysis instruction, the data volume of the multi-dimensional combination result data is large. For example, there are 10 combinations in any 3-dimensional combination analysis in 5 dimensions, and when there are 10 types of data in each dimension, the data amount can reach 1 ten thousand; there are 120 combinations for any 3-dimensional combination analysis in 10 dimensions, and when there are 10 types of data in each dimension, the data amount reaches 12 thousands, and it is obviously difficult to analyze by human. The embodiment of the application can perform transaction filtering according to the index processing results corresponding to the dimension combinations based on transaction change logic so as to provide the analysis results after the front-end filtering. For example, the abnormal operation conditions input by the user on the front-end interactive interface of the terminal device can be obtained, for example, the FYP same-ratio growth rates can be ranked according to the FYP same-ratio growth rate corresponding to the same-ratio behavior index FYP _ lm in the analysis result, and it is determined that the multiple dimension combinations with higher ranks meet the preset abnormal operation conditions; and determining an analysis result according to the multiple dimension combinations with higher rank and the index processing results corresponding to the multiple dimension combinations.
And step S160, sending the analysis result to the terminal equipment.
The user can timely and laborsavingly obtain the abnormal influence factors according to the analysis result output by the terminal equipment, and the improvement of the working efficiency is facilitated.
The attribution analysis method provided by the embodiment of the application comprises the following steps: acquiring service system data, wherein the service system data comprises first dimension information and corresponding first behavior indexes; determining information of a second dimension and/or a second behavior index according to the service system data based on a preset data expansion rule; acquiring a target dimension, a combined dimension number, a first target index and a target analysis mode input by a user on a front-end interactive interface of terminal equipment, wherein the target dimension is selected from the first dimension and/or the second dimension, and the first target index is selected from the first behavior index and/or the second behavior index; generating a data analysis instruction according to the target dimension, the number of the combined dimensions, the first target index and the target analysis mode based on a preset instruction generation rule; according to the data analysis instruction, performing data analysis on the information of the first dimension, the first behavior index and the information of the second dimension and/or the second behavior index to obtain an analysis result; and sending the analysis result to the terminal equipment. The dimensionality and/or behavior index is expanded based on the preset data expansion rule, so that the data dimensionality of the cause analysis can be enriched; the user can set the target dimensionality, the combination dimensionality quantity, the first target index and the target analysis mode of attribution analysis through the front-end interactive interface as required, dynamic configuration of multi-dimensional combination can be achieved, the user can conduct all-around dimensionality analysis as required, and the difficulty that a data analysis path needs to be preset is solved.
In some embodiments, a set of online real-time multidimensional analysis data architecture is developed based on a spark distributed computing engine and postgresql, and in a batch processing mode, the system periodically performs ETL operation on business system data, and extracts and synchronizes the data from an upstream business system to a Hadoop platform; in an off-line data processing link, index dimension conversion and transaction analysis are effectively fused through a distributed spark calculation engine, fields required by the transaction analysis are fused to a large width table, the large width table is output to a postgresql database, so that real-time multidimensional combination query is carried out by combining a multidimensional analysis algorithm of postgresql, a global analysis result of multidimensional arbitrary combination is obtained, and manual repeated combination of queries with different dimensions is reduced.
In some embodiments, the number of dimension combinations is dynamically configured on the front-end interface, and a multidimensional combination analysis engine of postgresql is combined to perform all-around dimension analysis, so that the difficulty that a data analysis path needs to be preset is solved.
In some embodiments, when there are many global results of any multidimensional combination, by performing uniform configuration management on transaction analysis, transaction data results are filtered in real time according to transaction influence factors selected by a user at the front end, and manual screening and filtering are not needed, so that influence factor results of multidimensional combination analysis can be obtained conveniently.
In some embodiments, the attribution analysis method for dynamically configuring a multidimensional combination according to the embodiments of the present application can implement dynamically configuring a multidimensional combination, implement a transaction analysis method for any multidimensional combination, reduce the degree dependence on the familiarity of a business experience model, do not need to set an analysis path in advance, and support a large amount of influence factor results of multidimensional combination analysis, so that business personnel can obtain transaction influence factors in time and in a labor-saving manner, thereby contributing to improving the work efficiency.
Referring to fig. 3, fig. 3 is a schematic structural diagram of an attribute analysis apparatus according to an embodiment of the present application. Alternatively, the attribution analysis device may be configured in a server for performing the steps of the attribution analysis method described above.
As shown in fig. 3, the attribution analysis apparatus includes:
a data obtaining module 110, configured to obtain service system data, where the service system data includes information of a first dimension and a corresponding first behavior index;
the data expansion module 120 is configured to determine, according to the service system data, information of a second dimension and/or a second behavior index based on a preset data expansion rule;
a user setting module 130, configured to obtain a target dimension, a number of combined dimensions, a first target index, and a target analysis manner, where the target dimension is selected from the first dimension and/or the second dimension, and the first target index is selected from the first behavior index and/or the second behavior index, and the target dimension, the number of combined dimensions, the first target index, and the target analysis manner are input by a user on a front-end interactive interface of a terminal device;
the instruction generating module 140 is configured to generate a data analysis instruction according to the target dimension, the number of combined dimensions, the first target index, and the target analysis mode based on a preset instruction generating rule;
the data analysis module 150 is configured to perform data analysis on the information of the first dimension, the first behavior index, and the information of the second dimension and/or the second behavior index according to the data analysis instruction to obtain an analysis result;
a sending module 160, configured to send the analysis result to the terminal device.
In some embodiments, the data expansion module 120 includes:
the index conversion submodule is used for converting the first behavior index based on a preset index dimension conversion rule to obtain information of a second dimension corresponding to the first behavior index; and/or
And the index expansion submodule is used for determining a second behavior index according to the information of the first dimension and the first behavior index based on a second behavior index determination rule indicated by a preset analysis mode.
In some embodiments, the instruction generation module 140 comprises:
the first instruction submodule is used for determining a dimension selection instruction according to the target dimension;
the second instruction submodule is used for determining a dimension combination instruction according to the number of the target dimensions and the number of the combination dimensions;
a third instruction submodule, configured to determine a second target index and an index processing instruction of the first target index and the second target index according to the first target index and the target analysis manner;
the fourth instruction submodule is used for determining a dimension index aggregation instruction according to the target dimension, the first target index and the second target index;
and the instruction combination sub-module is used for combining the dimension selection instruction, the dimension combination instruction, the index processing instruction and the index aggregation instruction to obtain the data analysis instruction.
In some embodiments, the data analysis module 150 includes:
the aggregation sub-module is used for carrying out aggregation processing on the target dimension, the first target index and the second target index according to a dimension index aggregation instruction in the data analysis instruction;
the dimension combination submodule is used for determining a plurality of dimension combinations according to a dimension selection instruction and a dimension combination instruction in the data analysis instruction, wherein each dimension combination comprises a target dimension of the combination dimension number;
the index processing submodule is used for performing index processing on the first target index and the second target index corresponding to each dimension combination according to an index processing instruction in the data analysis instruction to obtain an index processing result corresponding to each dimension combination;
and the result determining submodule is used for determining an analysis result according to the plurality of dimension combinations and the index processing result corresponding to each dimension combination.
It should be noted that, as will be clear to those skilled in the art, for convenience and brevity of description, the specific working processes of the apparatus, the modules and the units described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The methods, apparatus, and devices of the present application are operational with numerous general purpose or special purpose computing system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet-type devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like.
For example, the method and apparatus described above may be implemented in the form of a computer program that can be run on a computer device as shown in fig. 4.
Referring to fig. 4, fig. 4 is a schematic structural diagram of a computer device according to an embodiment of the present disclosure. The computer device may be a server or a terminal.
Referring to fig. 4, the computer device includes a processor, a memory, and a network interface connected through a system bus, wherein the memory may include a nonvolatile storage medium and an internal memory.
The non-volatile storage medium may store an operating system and a computer program. The computer program includes program instructions that, when executed, cause a processor to perform the steps of any of the attribution analysis methods.
The processor is used for providing calculation and control capability and supporting the operation of the whole computer equipment.
The internal memory provides an environment for the execution of a computer program on a non-volatile storage medium, which when executed by the processor, causes the processor to perform any of the attribution analysis methods.
The network interface is used for network communication, such as sending assigned tasks and the like. It will be appreciated by those skilled in the art that the configuration of the computer apparatus is a block diagram of only a portion of the configuration associated with the present application, and is not intended to limit the computer apparatus to which the present application may be applied, and that a particular computer apparatus may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
It should be understood that the Processor may be a Central Processing Unit (CPU), and the Processor may be other general purpose processors, digital Signal Processors (DSPs), application Specific Integrated Circuits (ASICs), field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, etc. Wherein a general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
Wherein, in one embodiment, the processor is configured to execute a computer program stored in the memory to implement the steps of:
acquiring service system data, wherein the service system data comprises first dimension information and corresponding first behavior indexes;
determining information of a second dimension and/or a second behavior index according to the service system data based on a preset data expansion rule;
acquiring a target dimension, a combined dimension number, a first target index and a target analysis mode input by a user on a front-end interactive interface of terminal equipment, wherein the target dimension is selected from the first dimension and/or the second dimension, and the first target index is selected from the first behavior index and/or the second behavior index;
generating a data analysis instruction according to the target dimension, the number of the combined dimensions, the first target index and the target analysis mode based on a preset instruction generation rule;
according to the data analysis instruction, carrying out data analysis on the information of the first dimension, the first behavior index and the information of the second dimension and/or the second behavior index to obtain an analysis result;
and sending the analysis result to the terminal equipment.
The specific principle and implementation manner of the computer device provided in the embodiment of the present application are similar to those of the attribution analysis method in the foregoing embodiment, and are not described herein again.
From the above description of the embodiments, it is clear to those skilled in the art that the present application can be implemented by software plus necessary general hardware platform. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art in the form of a software product, which may be stored in a storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the embodiments or some parts of the embodiments of the present application, such as:
a computer-readable storage medium, where a computer program is stored, where the computer program includes program instructions, and the processor executes the program instructions to implement any one of the attribution analysis methods provided in the embodiments of the present application.
The computer-readable storage medium may be an internal storage unit of the computer device described in the foregoing embodiment, for example, a hard disk or a memory of the computer device. The computer readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like provided on the computer device.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think of various equivalent modifications or substitutions within the technical scope of the present application, and these modifications or substitutions should be covered within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (9)
1. An attribution analysis method, the method comprising:
acquiring service system data, wherein the service system data comprises first dimension information and corresponding first behavior indexes;
determining information of a second dimension and/or a second behavior index according to the service system data based on a preset data expansion rule;
acquiring a target dimension, a combined dimension number, a first target index and a target analysis mode, wherein the target dimension, the combined dimension number, the first target index and the target analysis mode are input by a user on a front-end interactive interface of terminal equipment, the target dimension is selected from the first dimension and/or the second dimension, and the first target index is selected from the first behavior index and/or the second behavior index;
acquiring abnormal conditions input by a user on the front-end interactive interface;
generating a data analysis instruction according to the target dimension, the number of the combined dimensions, the first target index and the target analysis mode based on a preset instruction generation rule;
according to the data analysis instruction, carrying out data analysis on the information of the first dimension, the first behavior index and the information of the second dimension and/or the second behavior index to obtain an analysis result;
sending the analysis result to the terminal equipment;
wherein, according to the data analysis instruction, performing data analysis on the information of the first dimension, the first behavior index, and the information of the second dimension and/or the second behavior index to obtain an analysis result, and the method includes: determining a plurality of dimension combinations according to any target dimension of the combination dimension number in all the target dimensions according to the data analysis instruction, wherein each dimension combination comprises the target dimension of the combination dimension number; performing index processing on each dimension combination to obtain an index processing result corresponding to each combination dimension; and determining an analysis result according to the dimension combination meeting the transaction condition and the corresponding index processing result.
2. The attribution analysis method according to claim 1, wherein the determining of the second dimension information and/or the second behavior index according to the business system data based on the preset data augmentation rules comprises:
converting the first behavior index based on a preset index dimension conversion rule to obtain second dimension information corresponding to the first behavior index; and/or
And determining a second behavior index according to the information of the first dimension and the first behavior index based on a second behavior index determination rule indicated by a preset analysis mode.
3. The attribution analysis method according to claim 2, wherein the converting the first behavior index based on a preset index dimension conversion rule to obtain information of a second dimension corresponding to the first behavior index comprises:
converting each first behavior index into an enumeration type field according to the numerical distribution of the first behavior indexes to obtain information of the second dimension; and/or
The determining a second behavior index based on a preset analysis mode indication rule according to the information of the first dimension and the first behavior index includes:
and determining the same-ratio behavior index and/or the ring-ratio behavior index corresponding to the first behavior index as the second behavior index according to the information of the time dimension based on the same-ratio behavior index and/or the ring-ratio behavior index corresponding to the first behavior index indicated by the same-ratio analysis mode.
4. The attribution analysis method according to claim 1, wherein generating a data analysis order according to the target dimension, the number of combined dimensions, the first target index and the target analysis manner based on a preset order generation rule comprises:
determining a dimension selection instruction according to the target dimension;
determining a dimension combination instruction according to the number of the target dimensions and the number of the combined dimensions;
determining a second target index and index processing instructions of the first target index and the second target index according to the first target index and the target analysis mode;
determining a dimension index aggregation instruction according to the target dimension, the first target index and the second target index;
and combining the dimension selection instruction, the dimension combination instruction, the index processing instruction and the index aggregation instruction to obtain the data analysis instruction.
5. The attribution analysis method according to claim 4, wherein the performing data analysis on the information of the first dimension, the first behavior index, and the information of the second dimension and/or the second behavior index according to the data analysis instruction to obtain an analysis result comprises:
according to a dimension index aggregation instruction in the data analysis instruction, performing aggregation processing on the target dimension, the first target index and the second target index;
determining a plurality of dimension combinations according to a dimension selection instruction and a dimension combination instruction in the data analysis instruction, wherein each dimension combination comprises a target dimension of the combination dimension number;
according to an index processing instruction in the data analysis instruction, performing index processing on a first target index and a second target index corresponding to each dimension combination to obtain an index processing result corresponding to each dimension combination;
and determining an analysis result according to the plurality of dimension combinations and the index processing result corresponding to each dimension combination.
6. The attribution analysis method of any one of claims 1-5, wherein the method further comprises: migrating the information of the first dimension and the corresponding first behavior index, and the information of the second dimension and/or the second behavior index to a client/server relational database through a data migration tool;
performing data analysis on the information of the first dimension, the first behavior index and the information of the second dimension and/or the second behavior index according to the data analysis instruction to obtain an analysis result, including;
and performing multidimensional combination analysis on the information of the first dimension and the corresponding first behavior index, and the information of the second dimension and/or the second behavior index based on the client/server relational database according to the data analysis instruction to obtain a multidimensional combination analysis result.
7. An attribution analysis device, comprising:
the data acquisition module is used for acquiring service system data, wherein the service system data comprises first dimension information and a corresponding first behavior index;
the data expansion module is used for determining second dimension information and/or second behavior indexes according to the service system data based on a preset data expansion rule;
the system comprises a user setting module, a target dimension, a combined dimension number, a first target index and a target analysis mode, wherein the target dimension, the combined dimension number, the first target index and the target analysis mode are input by a user on a front-end interactive interface of terminal equipment, the target dimension is selected from the first dimension and/or the second dimension, and the first target index is selected from the first behavior index and/or the second behavior index; the user setting module is also used for acquiring the abnormal conditions input by the user on the front-end interactive interface;
the instruction generation module is used for generating a data analysis instruction according to the target dimension, the number of the combined dimensions, the first target index and the target analysis mode based on a preset instruction generation rule;
the data analysis module is used for carrying out data analysis on the information of the first dimension, the first behavior index and the information of the second dimension and/or the second behavior index according to the data analysis instruction to obtain an analysis result;
the sending module is used for sending the analysis result to the terminal equipment;
the data analysis module performs data analysis on the information of the first dimension, the first behavior index, and the information of the second dimension and/or the second behavior index according to the data analysis instruction, and when an analysis result is obtained, the data analysis module is configured to: according to the data analysis instruction, determining a plurality of dimension combinations according to any target dimension of the combination dimension number in all the target dimensions, wherein each dimension combination comprises the target dimension of the combination dimension number; performing index processing on each dimension combination to obtain an index processing result corresponding to each combination dimension; and determining an analysis result according to the dimension combination meeting the transaction condition and the corresponding index processing result.
8. A computer device, wherein the computer device comprises a memory and a processor;
the memory is used for storing a computer program;
the processor for executing the computer program and for implementing the steps of the attribution analysis method of any one of claims 1-6 when executing the computer program.
9. A computer-readable storage medium, in which a computer program is stored which, if executed by a processor, carries out the steps of the attribution analysis method according to any one of claims 1-6.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103488475A (en) * | 2013-09-02 | 2014-01-01 | 用友软件股份有限公司 | Multidimensional data analysis system and multidimensional data analysis method |
| CN110008255A (en) * | 2019-04-03 | 2019-07-12 | 平安信托有限责任公司 | Business datum analysis method, device, computer equipment and storage medium |
| CN110209919A (en) * | 2018-02-23 | 2019-09-06 | 北京国双科技有限公司 | The various dimensions method for decomposing and device of attribution |
| CN113495906A (en) * | 2020-03-20 | 2021-10-12 | 北京京东振世信息技术有限公司 | Data processing method and device, computer readable storage medium and electronic equipment |
| CN113553341A (en) * | 2021-07-27 | 2021-10-26 | 咪咕文化科技有限公司 | Multidimensional data analysis method, multidimensional data analysis device, multidimensional data analysis equipment and computer readable storage medium |
| WO2022160675A1 (en) * | 2021-01-29 | 2022-08-04 | 北京达佳互联信息技术有限公司 | Root factor determination method and apparatus |
Family Cites Families (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120095793A1 (en) * | 2010-10-18 | 2012-04-19 | Panorama Software Inc. | Business intelligence exception analysis by cause and effect |
| CN109800225A (en) * | 2018-12-24 | 2019-05-24 | 北京奇艺世纪科技有限公司 | Acquisition methods, device, server and the computer readable storage medium of operational indicator |
| CN110704751B (en) * | 2019-10-22 | 2023-04-07 | 北京字节跳动网络技术有限公司 | Data processing method and device, electronic equipment and storage medium |
| CN113138771A (en) * | 2020-01-17 | 2021-07-20 | 北京达佳互联信息技术有限公司 | Data processing method, device, equipment and storage medium |
| CN112000747B (en) * | 2020-07-08 | 2022-11-18 | 苏宁云计算有限公司 | Data multidimensional analysis method, device and system |
| CN112286772B (en) * | 2020-10-14 | 2023-03-10 | 北京易观智库网络科技有限公司 | Attribution analysis method and device and electronic equipment |
| CN113781106B (en) * | 2021-08-26 | 2023-11-21 | 唯品会(广州)软件有限公司 | Commodity operation data analysis method, device, equipment and computer readable medium |
| CN113886358A (en) * | 2021-09-23 | 2022-01-04 | 北京明略昭辉科技有限公司 | Multidimensional modeling analysis method, multidimensional modeling analysis system, storage medium and electronic equipment |
-
2022
- 2022-10-27 CN CN202211327905.0A patent/CN115392799B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103488475A (en) * | 2013-09-02 | 2014-01-01 | 用友软件股份有限公司 | Multidimensional data analysis system and multidimensional data analysis method |
| CN110209919A (en) * | 2018-02-23 | 2019-09-06 | 北京国双科技有限公司 | The various dimensions method for decomposing and device of attribution |
| CN110008255A (en) * | 2019-04-03 | 2019-07-12 | 平安信托有限责任公司 | Business datum analysis method, device, computer equipment and storage medium |
| CN113495906A (en) * | 2020-03-20 | 2021-10-12 | 北京京东振世信息技术有限公司 | Data processing method and device, computer readable storage medium and electronic equipment |
| WO2022160675A1 (en) * | 2021-01-29 | 2022-08-04 | 北京达佳互联信息技术有限公司 | Root factor determination method and apparatus |
| CN113553341A (en) * | 2021-07-27 | 2021-10-26 | 咪咕文化科技有限公司 | Multidimensional data analysis method, multidimensional data analysis device, multidimensional data analysis equipment and computer readable storage medium |
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