CN114528452A - Data tag implementation method and system based on tobacco and wine sales - Google Patents
Data tag implementation method and system based on tobacco and wine sales Download PDFInfo
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Abstract
The invention discloses a method and a system for realizing a data label based on tobacco and wine sales, which belong to the technical field of big data processing, and aim to solve the technical problems of quickly and accurately labeling data, liberating manpower and improving productivity, and the technical scheme is as follows: the method completes the preprocessing of data characteristics by classifying the characteristics in the business main body of the tobacco and wine selling industry and using different processing modes, thereby supporting the quick display of data labels; the method comprises the following specific steps: defining a characteristic rule: defining a feature type, a calculation rule corresponding to the feature and a feature name; the feature types comprise linear features or convergent features; a linear feature processing engine: extracting data of each linear feature, acquiring a corresponding feature value and writing the feature value into a feature library; a convergent label engine; defining a label rule; defining a tag group; drawing: and displaying all the labels of the business body by formulating any business body to finish the portrait display.
Description
Technical Field
The invention relates to the technical field of big data processing, in particular to a method and a system for realizing a data label based on tobacco and wine sales.
Background
In recent years, data has been growing explosively with the development of big data technology, and portrait and tag systems based on big data have become a relatively popular topic.
And (3) labeling the main business object, wherein the current popular mode is a pure manual writing sql mode for data analysis, and data labeling is completed. The work task is complicated, repetitive, and cannot respond quickly. Therefore, how to rapidly and accurately label data becomes a current problem of liberation of manpower and improvement of productivity.
Disclosure of Invention
The technical task of the invention is to provide a method and a system for realizing data labels based on tobacco and wine sales, so as to solve the problems of how to quickly and accurately label data, liberate manpower and improve productivity.
The technical task of the invention is realized in the following way, the method for realizing the data label based on the tobacco and wine sales is characterized in that the method completes the pretreatment of the data characteristics by using different processing ways through the classification of the characteristics in the business main body of the tobacco and wine sales industry, thereby supporting the rapid display of the data label; the method comprises the following specific steps:
defining a characteristic rule: defining a feature type, a calculation rule corresponding to the feature and a feature name; the feature type comprises a linear feature or a convergent feature;
a linear feature processing engine: extracting data of each linear feature, acquiring a corresponding feature value and writing the feature value into a feature library;
convergent label engine: aiming at each convergent label, independently executing a spark calculation task, calculating the sales volume of each dealer in one year and the characteristic value corresponding to the sales volume of each dealer in each day through sum, count and avg aggregation functions, and writing the characteristic values into a characteristic library;
defining a label rule: configuring a characteristic value range corresponding to the label and the relation between the characteristic values and the characteristics according to the defined characteristic value, and defining a label; defining the users to be self-defined, for example, defining that the sales amount of the users in one year is more than 1kw yuan and the sales amount of the users in one day is more than 1k pieces as high-quality dealers; the method comprises the steps that a user is supported to define the name of a label and the limiting condition of the label in a page;
defining a tag group: defining a label group through the combination of labels, and selecting a group of service bodies through the label group or the labels;
drawing: and displaying all the labels of the business body by formulating any business body to finish the portrait display. The system comprises a dealer, a tobacco and wine (the characteristics of the tobacco and wine include degree, sales amount and the like, and the labels of the tobacco and wine include hot sales products, cold products, high-degree white spirit, low-degree white spirit and the like), and medium and small enterprises (the characteristics of the tobacco and wine include scale and registered fund, and the labels of the tobacco and wine can include small enterprises, fund-strained enterprises, high-risk enterprises and the like).
Preferably, the linear feature refers to a feature type with a data relationship of a single table or an association relationship of a one-to-one relationship; the convergent feature is a feature with one-to-many, many-to-one or convergent function.
Preferably, when the rule definition is calculated, the following conditions are included:
firstly, linear characteristics need to specify a data table, an association relation of the data table and a characteristic field;
writing sql for the convergent features to calculate corresponding feature data; when writing sql (spark ksql), defining corresponding sql based on data of a specific service scene, wherein each sql query result corresponds to a feature;
for the sales volume of each dealer in one year, written sql is specifically: select $ { dealer id }, sum ($ { per sales amount }) from order table where $ { time-in-year }.
Preferably, the linear feature processing engine is specifically as follows:
for each linear feature, extracting a designated business main body table through a data extraction program unit, analyzing a label rule, extracting data in a related association table, marking the data in the extracted association table with an identifier of a main body object, and sending the identifier to a feature analysis engine;
calculating a corresponding characteristic value by characteristic analysis, and writing the characteristic value into a characteristic library, namely an elastic search; the feature library is a large-width table, the main key is a service object main body identifier and sequentially comprises feature columns, and each column of features records a feature value of the object.
Preferably, the feature analysis engine is an engine for specifically operating a definition rule in the linear features, and is configured to analyze the data extracted in real time item by item, convert the data into corresponding features according to the defined feature rule, and send the corresponding features to the feature library for storage;
the specific implementation method of the stored characteristics is as follows:
scanning each piece of data and all defined features once, and judging whether corresponding features are compounded or not:
if the characters are matched, the corresponding characters are assembled into a character library stored in the form of < id, character >;
if not, the processing is not carried out.
Preferably, the data extraction program unit is a program for reading the data of the service library and sending the data to the linear feature engine; the data extraction past program unit is used for reading all data related to the table by the characteristic rule definition when the linear characteristic processing engine is started, monitoring data change of the corresponding table in the service library and extracting the changed data;
the working process of the data extraction program unit is as follows:
when the linear characteristic processing engine is started, reading data from the whole table, and when the data changes: and setting a listener for the service library with the listener, monitoring data change and sending the data change, adding a log for any addition and deletion change of specified data without the listener, and extracting message content as data and sending the data to the linear characteristic processing engine when monitoring the log change by using a flash component.
A data tag system based on tobacco and wine sales, the system comprises,
the characteristic rule definition unit is used for defining the characteristic type, the calculation rule corresponding to the characteristic and the characteristic name; the feature type comprises a linear feature or a convergent feature;
the linear characteristic processing engine unit is used for extracting data of each linear characteristic, acquiring a corresponding characteristic value and writing the characteristic value into a characteristic library;
the convergent label engine unit is used for independently executing a spark calculation task aiming at each convergent label, calculating a sales amount of each dealer in one year and a characteristic value corresponding to the sales amount of each dealer in each day through sum, count and avg aggregation functions, and writing the characteristic values into a characteristic library;
the label rule defining unit is used for configuring a characteristic value range corresponding to the label and the relation between the characteristic values and the label rule according to the defined characteristic value and defining a label; defining the users to be self-defined, for example, defining that the sales amount of the users in one year is more than 1kw yuan and the sales amount of the users in one day is more than 1k pieces as high-quality dealers; the method comprises the steps that a user is supported to define the name of a label and the limiting condition of the label in a page;
the tag group definition unit is used for defining a tag group through tag combination and selecting a group of service bodies through the tag group or tags;
and the portrait unit is used for displaying all the labels of any business body by formulating the business body to finish portrait display. The system comprises a dealer, a tobacco and wine (the characteristics of the tobacco and wine include degree, sales amount and the like, and the labels of the tobacco and wine include hot sales products, cold products, high-degree white spirit, low-degree white spirit and the like), and medium and small enterprises (the characteristics of the tobacco and wine include scale and registered fund, and the labels of the tobacco and wine can include small enterprises, fund-strained enterprises, high-risk enterprises and the like).
Preferably, the linear feature processing engine specifically operates as follows:
(1) for each linear feature, extracting a designated business main body table through a data extraction program unit, analyzing a label rule, extracting data in a related association table, marking the data in the extracted association table with an identifier of a main body object, and sending the identifier to a feature analysis engine;
(2) calculating a corresponding characteristic value by characteristic analysis, and writing the characteristic value into a characteristic library, namely an elastic search; the feature library is a large-width table, the main key is a service object main body identifier and sequentially comprises feature columns, and each column of features records a feature value of the object.
An electronic device, comprising: a memory and at least one processor;
wherein the memory has stored thereon a computer program;
the at least one processor executes the memory-stored computer program causing the at least one processor to execute the smoking-wine sales based data tag implementation method as described above.
A computer readable storage medium having stored therein a computer program executable by a processor to implement a data tag implementation method of a tobacco-based wine sales as described above.
The method and the system for realizing the data label based on the tobacco and wine sales have the following advantages that:
the invention carries out data analysis on the business theme from the big data, defines the label rule and easily completes the quick and automatic generation of the data label;
the invention faces to big data, supports the real-time calculation of linear characteristics and supports the calculation of convergent characteristics;
the invention supports data labeling and portrait display;
(IV) the invention supports real-time update of linear characteristics;
the invention supports the calculation method of the convergent label;
the invention supports the quick query of the label of a specific certain business body;
and (seventh) the invention completes the preprocessing mode of the characteristic value to support the rapid display of the label.
Drawings
The invention is further described below with reference to the accompanying drawings.
FIG. 1 is a schematic diagram of a data tag implementation method based on tobacco and wine sales.
Detailed Description
The method and system for realizing the data label based on the tobacco and wine sales of the invention are explained in detail in the following with reference to the attached drawings and specific embodiments of the specification.
Example 1:
as shown in the attached figure 1, the data label realization method based on the tobacco and wine sales of the invention is to finish the preprocessing of data characteristics by classifying the characteristics in the business main body of the tobacco and wine sales industry and using different processing modes, thereby supporting the rapid display of the data label; the method comprises the following specific steps:
s1, defining a feature rule: defining a feature type, a calculation rule corresponding to the feature and a feature name; the feature type comprises a linear feature or a convergent feature;
s2, linear feature processing engine: extracting data of each linear feature, acquiring a corresponding feature value and writing the feature value into a feature library;
s3, convergent label engine: aiming at each convergent label, independently executing a spark calculation task, calculating the sales volume of each dealer in one year and the characteristic value corresponding to the sales volume of each dealer in each day through sum, count and avg aggregation functions, and writing the characteristic values into a characteristic library;
s4, defining a label rule: configuring a characteristic value range corresponding to the label and the relation between the characteristic values and the characteristics according to the defined characteristic value, and defining a label; defining the users to be self-defined, for example, defining that the sales amount of the users in one year is more than 1kw yuan and the sales amount of the users in one day is more than 1k pieces as high-quality dealers; the method comprises the steps that a user is supported to define the name of a label and the limiting condition of the label in a page;
s5, defining a tag group: defining a label group through the combination of labels, and selecting a group of service bodies through the label group or the labels;
s6, image: and displaying all the labels of the business body by formulating any business body to finish the portrait display. The system comprises a dealer, a tobacco and wine (the characteristics of the dealer comprise degree, sales amount and the like, and the labels comprise hot sales products, cold products, high-degree liquor, low-degree liquor and the like), and medium-sized and small-sized enterprises (the characteristics of the dealer and the wine comprise scale and registered fund, and the labels can comprise small enterprises, fund-strained enterprises, high-risk enterprises and the like).
The linear feature in step S1 in this embodiment refers to a feature type with a data relationship of a single table or an association relationship of a one-to-one relationship; the convergent feature is a feature with one-to-many, many-to-one or convergent function.
When the calculation rule in this embodiment is defined, the following conditions are included:
firstly, linear characteristics need to specify a data table, an association relation of the data table and a characteristic field;
writing sql for the convergent features to calculate corresponding feature data; when writing sql (spark ksql), defining corresponding sql based on data of a specific service scene, wherein each sql query result corresponds to a feature;
for the sales volume of each dealer in one year, written sql is specifically: select $ { dealer id }, sum ($ { per sales amount }) from order table where $ { time-in-year }.
The linear feature processing engine in step S2 in this embodiment is specifically as follows:
s201, aiming at each linear feature, extracting a designated business main body table through a data extraction program unit, analyzing a label rule, extracting data in a related association table, marking the data in the extracted association table with an identifier of a main body object, and sending the identifier to a feature analysis engine;
s202, calculating a corresponding characteristic value through characteristic analysis, and writing the characteristic value into a characteristic library, namely an elastic search; the feature library is a large-width table, the main key is a service object main body identifier and sequentially comprises feature columns, and each column of features records a feature value of the object.
The feature analysis engine in step S201 of this embodiment is an engine that specifically runs a definition rule in linear features, and is configured to analyze data extracted in real time item by item, convert the data into corresponding features according to the defined feature rule, and send the corresponding features to a feature library for storage;
the specific implementation method of the stored characteristics is as follows:
scanning each piece of data and all defined features once, and judging whether corresponding features are compounded or not:
if the characters are matched, the corresponding characters are assembled into a character library stored in the form of < id, character >;
if not, the processing is not carried out.
In this embodiment, the data extraction program unit in step S201 is a program that reads data of the service library and sends the data to the linear feature engine; the data extraction past program unit is used for reading all data related to the table by the characteristic rule definition when the linear characteristic processing engine is started, monitoring data change of the corresponding table in the service library and extracting the changed data;
the working process of the data extraction program unit is as follows:
when the linear characteristic processing engine is started, reading data from the whole table, and when the data changes: and setting a listener for the service library with the listener, monitoring data change and sending the data change, adding a log for any addition and deletion change of specified data without the listener, and extracting message content as data and sending the data to the linear characteristic processing engine when monitoring the log change by using a flash component.
Example 2:
the invention relates to a data label system based on tobacco and wine sales, which comprises,
the characteristic rule definition unit is used for defining the characteristic type, the calculation rule corresponding to the characteristic and the characteristic name; the feature type comprises a linear feature or a convergent feature;
the linear characteristic processing engine unit is used for extracting data of each linear characteristic, acquiring a corresponding characteristic value and writing the characteristic value into a characteristic library;
the convergent label engine unit is used for independently executing a spark calculation task aiming at each convergent label, calculating a sales amount of each dealer in one year and a characteristic value corresponding to the sales amount of each dealer in each day through sum, count and avg aggregation functions, and writing the characteristic values into a characteristic library;
the label rule defining unit is used for configuring a characteristic value range corresponding to the label and the relation between the characteristic values and the label rule according to the defined characteristic value and defining a label; defining the users to be self-defined, for example, defining that the sales amount of the users in one year is more than 1kw yuan and the sales amount of the users in one day is more than 1k pieces as high-quality dealers; the method comprises the steps that a user is supported to define the name of a label and the limiting condition of the label in a page;
the tag group definition unit is used for defining a tag group through tag combination and selecting a group of service bodies through the tag group or tags;
and the portrait unit is used for displaying all the labels of any business body by formulating the business body to finish portrait display. The system comprises a dealer, a tobacco and wine (the characteristics of the tobacco and wine include degree, sales amount and the like, and the labels of the tobacco and wine include hot sales products, cold products, high-degree white spirit, low-degree white spirit and the like), and medium and small enterprises (the characteristics of the tobacco and wine include scale and registered fund, and the labels of the tobacco and wine can include small enterprises, fund-strained enterprises, high-risk enterprises and the like).
The working process of the linear feature processing engine in the embodiment is specifically as follows:
(1) for each linear feature, extracting a designated business main body table through a data extraction program unit, analyzing a label rule, extracting data in a related association table, marking the data in the extracted association table with an identifier of a main body object, and sending the identifier to a feature analysis engine;
(2) calculating a corresponding characteristic value by characteristic analysis, and writing the characteristic value into a characteristic library, namely an elastic search; the feature library is a large-width table, the main key is a service object main body identifier and sequentially comprises feature columns, and each column of features records a feature value of the object.
Example 3:
an embodiment of the present invention further provides an electronic device, including: a memory and at least one processor;
wherein the memory stores computer-executable instructions;
the at least one processor executing the computer-executable instructions stored by the memory causes the at least one processor to perform a method for data tag implementation based on smoking-wine sales in any embodiment of the present invention.
Example 4:
the embodiment of the invention also provides a computer-readable storage medium, wherein a plurality of instructions are stored, and the instructions are loaded by a processor to enable the processor to execute the data tag implementation method based on the tobacco and wine sales in any embodiment of the invention. Specifically, a system or an apparatus equipped with a storage medium on which software program codes that realize the functions of any of the above-described embodiments are stored may be provided, and a computer (or a CPU or MPU) of the system or the apparatus is caused to read out and execute the program codes stored in the storage medium.
In this case, the program code itself read from the storage medium can realize the functions of any of the above-described embodiments, and thus the program code and the storage medium storing the program code constitute a part of the present invention.
Examples of the storage medium for supplying the program code include a floppy disk, a hard disk, a magneto-optical disk, an optical disk (e.g., CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RYM, DVD-RW, DVD + RW), a magnetic tape, a nonvolatile memory card, and a ROM. Alternatively, the program code may be downloaded from a server computer by a communications network.
Further, it should be clear that the functions of any one of the above-described embodiments may be implemented not only by executing the program code read out by the computer, but also by causing an operating system or the like operating on the computer to perform a part or all of the actual operations based on instructions of the program code.
Further, it is to be understood that the program code read out from the storage medium is written to a memory provided in an expansion board inserted into the computer or to a memory provided in an expansion unit connected to the computer, and then causes a CPU or the like mounted on the expansion board or the expansion unit to perform part or all of the actual operations based on instructions of the program code, thereby realizing the functions of any of the above-described embodiments.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A data label implementation method based on tobacco and wine sales is characterized in that the method completes the preprocessing of data characteristics by classifying the characteristics in the business main body of the tobacco and wine sales industry and using different processing modes, thereby supporting the quick display of the data label; the method comprises the following specific steps:
defining a characteristic rule: defining a feature type, a calculation rule corresponding to the feature and a feature name; the feature type comprises a linear feature or a convergent feature;
a linear feature processing engine: extracting data of each linear feature, acquiring a corresponding feature value and writing the feature value into a feature library;
convergent label engine: aiming at each convergent label, independently executing a spark calculation task, calculating the sales volume of each dealer in one year and the characteristic value corresponding to the sales volume of each dealer in each day through sum, count and avg aggregation functions, and writing the characteristic values into a characteristic library;
defining a tag rule: configuring a characteristic value range corresponding to the label and the relation between the characteristic values and the characteristics according to the defined characteristic value, and defining a label;
defining a tag group: defining a label group through the combination of labels, and selecting a group of service bodies through the label group or the labels;
drawing: and displaying all the labels of the business body by formulating any business body to finish the portrait display.
2. The method for realizing the data label based on the tobacco and wine sales of claim 1, wherein the linear characteristic refers to a characteristic type with a data relationship of a single table or an association relationship of a one-to-one relationship; the convergent feature is a feature with one-to-many, many-to-one or convergent function.
3. A method for implementing a data tag based on the sale of tobacco and wine according to claim 1 or 2, characterized in that the calculation rule definition includes the following cases:
firstly, linear characteristics need to specify a data table, an association relation of the data table and a characteristic field;
writing sql for the convergent features to calculate corresponding feature data; when writing sql, defining corresponding sql based on data of a specific service scene, wherein each sql query result corresponds to a feature;
for the sales volume of each dealer in one year, written sql is specifically: select $ { dealer id }, sum ($ { per sales amount }) from order table where $ { time-in-year }.
4. The method for implementing a data tag based on tobacco and wine sales of claim 1, wherein the linear feature processing engine is specifically as follows:
for each linear feature, extracting a designated business main body table through a data extraction program unit, analyzing a label rule, extracting data in a related association table, marking the data in the extracted association table with an identifier of a main body object, and sending the identifier to a feature analysis engine;
calculating a corresponding characteristic value by characteristic analysis, and writing the characteristic value into a characteristic library, namely an elastic search; the feature library is a large-width table, the main key is a service object main body identifier and sequentially comprises feature columns, and each column of features records a feature value of the object.
5. The method for realizing the data tag based on the tobacco and wine sales of claim 4, wherein the feature analysis engine is an engine for specifically operating a definition rule in linear features, and is used for analyzing the data extracted in real time one by one, converting the data into corresponding features according to the defined feature rule, and sending the corresponding features to a feature library for storage;
the specific implementation method of the stored characteristics is as follows:
scanning each piece of data and all defined features once, and judging whether corresponding features are compounded or not:
if the characters are matched, the corresponding characters are assembled into a character library stored in the form of < id, character >;
if not, the processing is not carried out.
6. The method for realizing the data tag based on the tobacco and wine sales according to the claim 4 or 5, characterized in that the data extraction program unit is a program for reading the data of the service library and sending the data to the linear feature engine; the data extraction past program unit is used for reading all data related to the table by the characteristic rule definition when the linear characteristic processing engine is started, monitoring data change of the corresponding table in the service library and extracting the changed data;
the working process of the data extraction program unit is as follows:
when the linear characteristic processing engine is started, reading data from the whole table, and when the data changes: and setting a listener for the service library with the listener, monitoring data change and sending the data change, adding a log for any addition and deletion change of specified data without the listener, and extracting message content as data and sending the data to the linear characteristic processing engine when monitoring the log change by using a flash component.
7. A data label system based on tobacco and wine sales is characterized in that the system comprises,
the characteristic rule definition unit is used for defining the characteristic type, the calculation rule corresponding to the characteristic and the characteristic name; the feature type comprises a linear feature or a convergent feature;
the linear characteristic processing engine unit is used for extracting data of each linear characteristic, acquiring a corresponding characteristic value and writing the characteristic value into a characteristic library;
the convergent label engine unit is used for independently executing a spark calculation task aiming at each convergent label, calculating a sales amount of each dealer in one year and a characteristic value corresponding to the sales amount of each dealer in each day through sum, count and avg aggregation functions, and writing the characteristic values into a characteristic library;
the label rule defining unit is used for configuring a characteristic value range corresponding to the label and the relation between the characteristic values and the label rule according to the defined characteristic value and defining a label;
the tag group definition unit is used for defining a tag group through tag combination and selecting a group of service bodies through the tag group or tags;
and the portrait unit is used for displaying all labels of the business main body by formulating any business main body to finish portrait display.
8. The smoking-wine-sales-based data tagging system of claim 7, wherein the linear feature processing engine operates as follows:
(1) for each linear feature, extracting a designated business main body table through a data extraction program unit, analyzing a label rule, extracting data in a related association table, marking the data in the extracted association table with an identifier of a main body object, and sending the identifier to a feature analysis engine;
(2) calculating a corresponding characteristic value by characteristic analysis, and writing the characteristic value into a characteristic library, namely an elastic search; the feature library is a large-width table, the main key is a service object main body identifier and sequentially comprises feature columns, and each column of features records a feature value of the object.
9. An electronic device, comprising: a memory and at least one processor;
wherein the memory has stored thereon a computer program;
the at least one processor executing the memory-stored computer program causes the at least one processor to perform the method of any one of claims 1 to 6 for data tag implementation based on smoking-wine sales.
10. A computer-readable storage medium, in which a computer program is stored, which computer program is executable by a processor to implement a method of data tag implementation based on the sale of tobacco and wine according to any one of claims 1 to 6.
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