CN110955690A - Self-service data labeling platform and self-service data labeling method based on big data technology - Google Patents
Self-service data labeling platform and self-service data labeling method based on big data technology Download PDFInfo
- Publication number
- CN110955690A CN110955690A CN201910773569.4A CN201910773569A CN110955690A CN 110955690 A CN110955690 A CN 110955690A CN 201910773569 A CN201910773569 A CN 201910773569A CN 110955690 A CN110955690 A CN 110955690A
- Authority
- CN
- China
- Prior art keywords
- data
- label
- calculation
- definition
- hbase
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2457—Query processing with adaptation to user needs
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/248—Presentation of query results
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/25—Integrating or interfacing systems involving database management systems
- G06F16/254—Extract, transform and load [ETL] procedures, e.g. ETL data flows in data warehouses
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
Abstract
The invention discloses a self-service data label platform and a self-service data label method based on big data technology, comprising the following steps: a metadata definition step, namely distinguishing the marking object into attribute data, service data and behavior data; a label definition step, wherein the label definition is carried out on the data; a data acquisition step, wherein data of the service system is acquired and imported into a data warehouse; calculating a basic label; a combined label calculation step, wherein the combined label is calculated and written into Hbase; and an object clustering calculation step, namely calculating a crowd circling result in real time according to the configuration automatic generation calculation code, and writing the result into Hbase. The invention defines the calculation process of the label on the interface in a complete visual mode, and realizes the calculation of the label data of the composite service demand in the PB level data through big data, thereby further providing a data basis for user grouping and user labels.
Description
Technical Field
The invention relates to the technical field of big data processing, in particular to a self-service data label platform based on big data technology.
Background
Big data marketing is a marketing mode applied to the internet advertisement industry based on a large amount of data of various social platforms and relying on big data technology. The core of big data marketing is to make network advertisement be delivered to proper person at proper time and in proper mode through proper carrier. Big data marketing derives from the internet industry and also acts on the internet industry. By means of big data acquisition of a plurality of platforms and the analysis and prediction capabilities of big data technologies, advertisements can be put in more accurately and effectively, and higher investment return rate is brought to brands or enterprises.
When accurate marketing is completed, individualization is emphasized, and multi-platform user data needs to be summarized and labeled according to individualization of people. However, the existing data tag platforms have limited data processing capability and cannot process PB-level data, and the existing data tag platforms all tag fixed business objects and cannot tag any business object. In addition, complex program codes need to be written during data marking, the technical requirement is high, and common business personnel cannot perform data marking work.
Disclosure of Invention
The invention aims to overcome the defects of low data processing capacity, incapability of labeling any service object and high technical requirement in the conventional data label platform, and provides a self-service data label platform based on a big data technology.
In order to achieve the above purpose, the invention provides the following technical scheme:
a big data technology-based self-service data tag platform, comprising:
a metadata definition unit configured to: dividing the marked object into attribute data, service data and behavior data and relations between each data and the marked object through definition of metadata;
a tag definition unit configured to: performing label definition on data, wherein the label definition comprises a fact label, a model label and a subjective label; wherein, the fact label and the model label are generated by defining a calculation rule on the interface;
a data acquisition unit configured to: the data of the business system are collected through the DataX, and the data collection method comprises the following steps: the attribute data, behavior data, associated data and the like of the object are imported into a Hive data warehouse;
a base tag calculation unit configured to: by reading the configuration rules of the tag, the automatic production comprises: computing logics such as Spark SQL, Spark RDD codes and the like, submitting the computing logics to Yarn of Hadoop for execution, and finally writing a computing result into a big data non-relational database Hbase;
a combinatorial label computation unit configured to: calculating a combined label in real time by reading the data of the Hbase and the generated calculation logic code according to the calculated basic label and the configured calculation rule, and writing the calculated combined label into the Hbase;
an object clustering calculation unit configured to: and reading the basic label of the combined label from the Hbase according to a preset crowd circling rule, automatically generating a calculation code according to the configuration, calculating a crowd circling result in real time, and writing the result into the Hbase.
Preferably, the objects defining the marking include users, commodities, stores, and the like.
Preferably, the user defines calculation rules on the interface through the user terminal, wherein the calculation rules comprise fact tags, model tags, source data of subjective tags, data filtering rules and calculation logic.
Preferably, the fact tag is an object own attribute, the model tag comprises user behavior data and a data time range, and the subjective tag is a free definition tag.
A self-service data labeling method based on big data technology comprises the following steps:
the marking method comprises the following steps of defining metadata, namely distinguishing marking objects into attribute data, service data and behavior data and relations between the data and the marking objects through the definition of the metadata;
a label definition step, wherein data is subjected to label definition, and the label definition comprises a fact label, a model label and a subjective label; wherein, the fact label and the model label are generated by defining a calculation rule on the interface;
a data acquisition step, wherein data of the service system is acquired through DataX, and the data acquisition step comprises the following steps: the attribute data, behavior data, associated data and the like of the object are imported into a Hive data warehouse;
and a basic tag calculation step, wherein the automatic production comprises the following steps of reading configuration rules of tags: computing logics such as Spark SQL, Spark RDD codes and the like, submitting the computing logics to Yarn of Hadoop for execution, and finally writing a computing result into a big data non-relational database Hbase;
calculating a combined label, namely calculating the combined label in real time by reading the data of the Hbase and the generated calculation logic code according to the calculated basic label and the configured calculation rule, and writing the calculated combined label into the Hbase;
and an object clustering calculation step, namely reading the basic label of the combined label from the Hbase according to a preset crowd circling rule, automatically generating a calculation code according to configuration, calculating a crowd circling result in real time, and writing the result into the Hbase.
Preferably, the objects defining the marking include users, commodities, stores, and the like.
Preferably, the user defines calculation rules on the interface through the user terminal, wherein the calculation rules comprise fact tags, model tags, source data of subjective tags, data filtering rules and calculation logic.
Preferably, the fact tag is an object own attribute, the model tag comprises user behavior data and a data time range, and the subjective tag is a free definition tag.
Preferably, the user terminal includes, but is not limited to, a mobile phone, a computer, and a notebook.
The invention defines the calculation process of the label on the interface in a complete visual mode, and realizes the calculation of the label data of the composite service requirement in the PB level data by the technologies of big data Spark, Hive, Hbase and the like, thereby further providing a data basis for user grouping and user labels.
Compared with the prior art, the invention has the following beneficial effects:
1. the data processing capacity is improved, and the automatic data note platform can process PB-level data based on a big data technology.
2. The flexibility is improved, the invention defines the calculation process of the label on the interface in a complete visual mode, and can label any business object.
3. The automatic data note platform of the invention can complete the definition and generation of the label by the service personnel without compiling codes, thereby reducing the technical threshold of the operating personnel.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a self-service data tag platform intention based on big data technology, which is provided in embodiment 1 of the present invention;
fig. 2 is a schematic flow chart of a self-service data tagging method based on big data technology, according to embodiment 2 of the present invention;
Detailed Description
Various embodiments of the present disclosure will be described more fully hereinafter. The present disclosure is capable of various embodiments and of modifications and variations therein. However, it should be understood that: there is no intention to limit the various embodiments of the disclosure to the specific embodiments disclosed herein, but rather, the disclosure is to cover all modifications, equivalents, and/or alternatives falling within the spirit and scope of the various embodiments of the disclosure.
Example 1
The embodiment 1 of the invention provides a self-service data label platform based on a big data technology, a business system adopts a mode of a plurality of user touch, and simultaneously has a plurality of product forms such as websites, APPs, small programs and the like, and each product module and product end can generate a large amount of business data and behavior data. This self-service data label platform includes:
a metadata definition unit configured to: dividing the marked object into attribute data, service data and behavior data and relations between each data and the marked object through definition of metadata;
a tag definition unit configured to: performing label definition on data, wherein the label definition comprises a fact label, a model label and a subjective label; wherein, the fact label and the model label are generated by defining a calculation rule on the interface;
a data acquisition unit configured to: the data of the business system are collected through the DataX, and the data collection method comprises the following steps: the attribute data, behavior data, associated data and the like of the object are imported into a Hive data warehouse;
a base tag calculation unit configured to: by reading the configuration rules of the tag, the automatic production comprises: computing logics such as Spark SQL, Spark RDD codes and the like, submitting the computing logics to Yarn of Hadoop for execution, and finally writing a computing result into a big data non-relational database Hbase;
a combinatorial label computation unit configured to: calculating a combined label in real time by reading the data of the Hbase and the generated calculation logic code according to the calculated basic label and the configured calculation rule, and writing the calculated combined label into the Hbase;
an object clustering calculation unit configured to: and reading the basic label of the combined label from the Hbase according to a preset crowd circling rule, automatically generating a calculation code according to the configuration, calculating a crowd circling result in real time, and writing the result into the Hbase.
Preferably, the objects defining the marking include users, commodities, stores, and the like.
Preferably, the user defines calculation rules on the interface through the user terminal, wherein the calculation rules comprise fact tags, model tags, source data of subjective tags, data filtering rules and calculation logic.
Preferably, the fact tag is an object own attribute, the model tag comprises user behavior data and a data time range, and the subjective tag is a free definition tag.
Example 2
The embodiment 2 of the invention provides a self-service data labeling method based on a big data technology, which comprises the following steps:
the marking method comprises the following steps of defining metadata, namely distinguishing marking objects into attribute data, service data and behavior data and relations between the data and the marking objects through the definition of the metadata;
a label definition step, wherein data is subjected to label definition, and the label definition comprises a fact label, a model label and a subjective label; wherein, the fact label and the model label are generated by defining a calculation rule on the interface;
a data acquisition step, wherein data of the service system is acquired through DataX, and the data acquisition step comprises the following steps: the attribute data, behavior data, associated data and the like of the object are imported into a Hive data warehouse;
and a basic tag calculation step, wherein the automatic production comprises the following steps of reading configuration rules of tags: computing logics such as Spark SQL, Spark RDD codes and the like, submitting the computing logics to Yarn of Hadoop for execution, and finally writing a computing result into a big data non-relational database Hbase;
calculating a combined label, namely calculating the combined label in real time by reading the data of the Hbase and the generated calculation logic code according to the calculated basic label and the configured calculation rule, and writing the calculated combined label into the Hbase;
and an object clustering calculation step, namely reading the basic label of the combined label from the Hbase according to a preset crowd circling rule, automatically generating a calculation code according to configuration, calculating a crowd circling result in real time, and writing the result into the Hbase.
Preferably, the objects defining the marking include users, commodities, stores, and the like.
Preferably, the user defines calculation rules on the interface through the user terminal, wherein the calculation rules comprise fact tags, model tags, source data of subjective tags, data filtering rules and calculation logic.
Preferably, the fact tag is an object own attribute, the model tag comprises user behavior data and a data time range, and the subjective tag is a free definition tag.
Preferably, the user terminal includes, but is not limited to, a mobile phone, a computer, and a notebook.
The foregoing is directed to the preferred embodiment of the present invention and is not intended to limit the invention to the specific embodiment described. It will be apparent to those skilled in the art that various modifications, equivalents, improvements and the like can be made without departing from the spirit of the invention, and these are intended to be included within the scope of the invention.
Claims (10)
1. A big data technology-based self-service data tag platform, comprising:
a metadata definition unit configured to: dividing the marked object into attribute data, service data and behavior data and relations between each data and the marked object through definition of metadata;
a tag definition unit configured to: performing label definition on data, wherein the label definition comprises a fact label, a model label and a subjective label; wherein, the fact label and the model label are generated by defining a calculation rule on the interface;
a data acquisition unit configured to: the data of the business system are collected through the DataX, and the data collection method comprises the following steps: the attribute data, behavior data, associated data and the like of the object are imported into a Hive data warehouse;
a base tag calculation unit configured to: by reading the configuration rules of the tag, the automatic production comprises: computing logics such as Spark SQL, Spark RDD codes and the like, submitting the computing logics to Yarn of Hadoop for execution, and finally writing a computing result into a big data non-relational database Hbase;
a combinatorial label computation unit configured to: calculating a combined label in real time by reading the data of the Hbase and the generated calculation logic code according to the calculated basic label and the configured calculation rule, and writing the calculated combined label into the Hbase;
an object clustering calculation unit configured to: and reading the basic label of the combined label from the Hbase according to a preset crowd circling rule, automatically generating a calculation code according to the configuration, calculating a crowd circling result in real time, and writing the result into the Hbase.
2. The big data technology-based self-service data tag platform according to claim 1, wherein the defined branded objects comprise users, goods, stores, and the like.
3. The big data technology-based self-service data tag platform according to claim 1, wherein a user defines calculation rules on an interface through a user terminal, wherein the calculation rules comprise fact tags, model tags, source data of subjective tags, data screening rules and calculation logic.
4. The big data technology-based self-service data tag platform according to claim 1, wherein the fact tags are object-owned attributes, the model tags comprise user behavior data and data time ranges, and the subjective tags are free-definition tags.
5. The big data technology-based self-service data tag platform according to claim 1, wherein the user terminal comprises but is not limited to a mobile phone, a computer, and a notebook.
6. A self-service data labeling method based on big data technology comprises the following steps:
the marking method comprises the following steps of defining metadata, namely distinguishing marking objects into attribute data, service data and behavior data and relations between the data and the marking objects through the definition of the metadata;
a label definition step, wherein data is subjected to label definition, and the label definition comprises a fact label, a model label and a subjective label; wherein, the fact label and the model label are generated by defining a calculation rule on the interface;
a data acquisition step, wherein data of the service system is acquired through DataX, and the data acquisition step comprises the following steps: the attribute data, behavior data, associated data and the like of the object are imported into a Hive data warehouse;
and a basic tag calculation step, wherein the automatic production comprises the following steps of reading configuration rules of tags: computing logics such as Spark SQL, Spark RDD codes and the like, submitting the computing logics to Yarn of Hadoop for execution, and finally writing a computing result into a big data non-relational database Hbase;
calculating a combined label, namely calculating the combined label in real time by reading the data of the Hbase and the generated calculation logic code according to the calculated basic label and the configured calculation rule, and writing the calculated combined label into the Hbase;
and an object clustering calculation step, namely reading the basic label of the combined label from the Hbase according to a preset crowd circling rule, automatically generating a calculation code according to configuration, calculating a crowd circling result in real time, and writing the result into the Hbase.
7. The big data technology-based self-service data labeling method according to claim 5, wherein the object defined for marking comprises a user, a commodity, a shop and the like.
8. The big data technology-based self-service data labeling method according to claim 5, wherein a user defines calculation rules on an interface through a user terminal, wherein the calculation rules comprise fact labels, model labels, source data of subjective labels, data screening rules and calculation logic.
9. The big data technology-based self-service data tagging method according to claim 5, wherein the fact tags are object-owned attributes, the model tags comprise user behavior data and data time ranges, and the subjective tags are free definition tags.
10. The big data technology-based self-service data tagging method according to claim 5, wherein the user terminal comprises but is not limited to a mobile phone, a computer and a notebook.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910773569.4A CN110955690A (en) | 2019-08-21 | 2019-08-21 | Self-service data labeling platform and self-service data labeling method based on big data technology |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910773569.4A CN110955690A (en) | 2019-08-21 | 2019-08-21 | Self-service data labeling platform and self-service data labeling method based on big data technology |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110955690A true CN110955690A (en) | 2020-04-03 |
Family
ID=69976255
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910773569.4A Pending CN110955690A (en) | 2019-08-21 | 2019-08-21 | Self-service data labeling platform and self-service data labeling method based on big data technology |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110955690A (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111787084A (en) * | 2020-06-23 | 2020-10-16 | 杭州数澜科技有限公司 | Method and device for selecting object |
| CN112486710A (en) * | 2020-12-17 | 2021-03-12 | 夏红梅 | Information acquisition method based on big data and artificial intelligence and digital content service platform |
| CN113421133A (en) * | 2021-07-21 | 2021-09-21 | 赛诺数据科技(南京)有限公司 | Network marketing system based on customer matching |
| CN115564356A (en) * | 2022-10-28 | 2023-01-03 | 上海东普信息科技有限公司 | Real-time sharing method and device for relatives and friends logistics order information |
| US11809375B2 (en) | 2021-07-06 | 2023-11-07 | International Business Machines Corporation | Multi-dimensional data labeling |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106548381A (en) * | 2016-12-19 | 2017-03-29 | 武汉理工数字传播工程有限公司 | Intelligent subscriber tag systems and implementation method |
| CN107145586A (en) * | 2017-05-10 | 2017-09-08 | 中国电力科学研究院 | A kind of label output method and apparatus based on power marketing data |
| CN108776686A (en) * | 2018-06-04 | 2018-11-09 | 浪潮软件集团有限公司 | Data tag construction system and method |
| CN109101652A (en) * | 2018-08-27 | 2018-12-28 | 宜人恒业科技发展（北京）有限公司 | A kind of creation of label and management system |
| CN109685579A (en) * | 2018-12-29 | 2019-04-26 | 深圳市酷开网络科技有限公司 | A kind of data processing method based on user tag, smart television and storage medium |
| CN109767255A (en) * | 2018-12-06 | 2019-05-17 | 东莞团贷网互联网科技服务有限公司 | A method of it is modeled by big data and realizes intelligence operation and precision marketing |
| CN109919652A (en) * | 2019-01-17 | 2019-06-21 | 平安城市建设科技(深圳)有限公司 | User group's classification method, device, equipment and storage medium |
-
2019
- 2019-08-21 CN CN201910773569.4A patent/CN110955690A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106548381A (en) * | 2016-12-19 | 2017-03-29 | 武汉理工数字传播工程有限公司 | Intelligent subscriber tag systems and implementation method |
| CN107145586A (en) * | 2017-05-10 | 2017-09-08 | 中国电力科学研究院 | A kind of label output method and apparatus based on power marketing data |
| CN108776686A (en) * | 2018-06-04 | 2018-11-09 | 浪潮软件集团有限公司 | Data tag construction system and method |
| CN109101652A (en) * | 2018-08-27 | 2018-12-28 | 宜人恒业科技发展（北京）有限公司 | A kind of creation of label and management system |
| CN109767255A (en) * | 2018-12-06 | 2019-05-17 | 东莞团贷网互联网科技服务有限公司 | A method of it is modeled by big data and realizes intelligence operation and precision marketing |
| CN109685579A (en) * | 2018-12-29 | 2019-04-26 | 深圳市酷开网络科技有限公司 | A kind of data processing method based on user tag, smart television and storage medium |
| CN109919652A (en) * | 2019-01-17 | 2019-06-21 | 平安城市建设科技(深圳)有限公司 | User group's classification method, device, equipment and storage medium |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111787084A (en) * | 2020-06-23 | 2020-10-16 | 杭州数澜科技有限公司 | Method and device for selecting object |
| CN112486710A (en) * | 2020-12-17 | 2021-03-12 | 夏红梅 | Information acquisition method based on big data and artificial intelligence and digital content service platform |
| US11809375B2 (en) | 2021-07-06 | 2023-11-07 | International Business Machines Corporation | Multi-dimensional data labeling |
| CN113421133A (en) * | 2021-07-21 | 2021-09-21 | 赛诺数据科技(南京)有限公司 | Network marketing system based on customer matching |
| CN115564356A (en) * | 2022-10-28 | 2023-01-03 | 上海东普信息科技有限公司 | Real-time sharing method and device for relatives and friends logistics order information |
| CN115564356B (en) * | 2022-10-28 | 2024-04-12 | 上海东普信息科技有限公司 | Real-time sharing method and device for parent-friend logistics order information |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110955690A (en) | Self-service data labeling platform and self-service data labeling method based on big data technology | |
| CN106296059B (en) | Method and equipment for determining delivery network points | |
| Cai et al. | Multimodal data guided spatial feature fusion and grouping strategy for E-commerce commodity demand forecasting | |
| CN109359244A (en) | A kind of recommendation method for personalized information and device | |
| CN108021929A (en) | Mobile terminal electric business user based on big data, which draws a portrait, to establish and analysis method and system | |
| CN107730389A (en) | Electronic installation, insurance products recommend method and computer-readable recording medium | |
| CN106548381A (en) | Intelligent subscriber tag systems and implementation method | |
| CN108021651B (en) | Network public opinion risk assessment method and device | |
| CN110827112B (en) | Deep learning commodity recommendation method and device, computer equipment and storage medium | |
| CN115002200B (en) | Message pushing method, device, equipment and storage medium based on user portrait | |
| KR102458510B1 (en) | Real-time complementary marketing system | |
| CN108241867B (en) | Classification method and device | |
| CN115391669B (en) | Intelligent recommendation method and device and electronic equipment | |
| CN114663198A (en) | Product recommendation method, device and equipment based on user portrait and storage medium | |
| CN111881901A (en) | Screenshot content detection method and device and computer-readable storage medium | |
| CN107944929A (en) | A kind of user's portrait method for building up being modeled based on user behavior data | |
| CN115712684B (en) | Storage method and system for dynamic information of articles in Internet of things | |
| CN110909050A (en) | Data statistical analysis system | |
| CN111768218A (en) | Method and device for processing user interaction information | |
| CN114996579A (en) | Information pushing method and device, electronic equipment and computer readable medium | |
| CN115641186A (en) | Intelligent analysis method, device and equipment for preference of live broadcast product and storage medium | |
| CN113706207A (en) | Order transaction rate analysis method, device, equipment and medium based on semantic analysis | |
| CN115099680A (en) | Risk management method, device, equipment and storage medium | |
| CN113706019A (en) | Service capability analysis method, device, equipment and medium based on multidimensional data | |
| CN113743721A (en) | Marketing strategy generation method and device, computer equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |