CN114971714A - Accurate customer operation method based on big data label and computer equipment - Google Patents
Accurate customer operation method based on big data label and computer equipmentDownload PDFInfo
- Publication number
- CN114971714A CN114971714ACN202210590769.8ACN202210590769ACN114971714ACN 114971714 ACN114971714 ACN 114971714ACN 202210590769 ACN202210590769 ACN 202210590769ACN 114971714 ACN114971714 ACN 114971714A
- Authority
- CN
- China
- Prior art keywords
- customer
- information
- user
- label
- customer information
- 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 OR CALCULATING; 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
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; 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
- G06F16/22—Indexing; Data structures therefor; Storage structures
- G06F16/2228—Indexing structures
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; 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
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2462—Approximate or statistical queries
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Business, Economics & Management (AREA)
- Accounting & Taxation (AREA)
- Data Mining & Analysis (AREA)
- Development Economics (AREA)
- Finance (AREA)
- Strategic Management (AREA)
- General Physics & Mathematics (AREA)
- Software Systems (AREA)
- Entrepreneurship & Innovation (AREA)
- Databases & Information Systems (AREA)
- General Engineering & Computer Science (AREA)
- Probability & Statistics with Applications (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Economics (AREA)
- Game Theory and Decision Science (AREA)
- Fuzzy Systems (AREA)
- Mathematical Physics (AREA)
- Computational Linguistics (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
Description
Technical Field
The invention relates to the technical field of customer data information processing, in particular to a precise customer operation method based on a big data label and computer equipment.
Background
The traditional data analysis technology based on the database can meet the data analysis requirements under non-mass data, but the technology has limited data capacity and cannot depict the characteristic attributes of users or products by collecting data of various dimensions of social attributes, consumption habits, preference characteristics and the like of the users for a long time, analyze and count the characteristics and mine potential value information, so that the information overview of the users is abstracted.
At present, the situation that the lattice customer data has multiple channels and multiple redundancies and cannot be effectively utilized exists, the sedimentation and the continuous operation of the assets of consumers are difficult to establish, new customers are difficult to obtain, and old customers are seriously impacted by competitors. The invention effectively manages the client assets, and draws images to the clients through labeling, thereby establishing a more accurate channel for enterprises and clients and providing better and better services for the clients.
Disclosure of Invention
Aiming at the problems that the customer data has multiple channels and multiple redundancies and cannot be effectively utilized, and the sedimentation and continuous operation of the consumer assets are difficult to establish, the invention provides an accurate customer operation method and computer equipment based on a big data label, which are used for sorting the data of each channel, unifying the customer data, and performing accurate customer operation through the big data label to achieve the purposes of new customer drainage and old customer activation.
In order to achieve the purpose, the invention adopts the following technical scheme: a precise customer operation method based on big data labels comprises the following steps:
obtaining customer information input by a user, wherein the customer information at least comprises a customer index identification;
judging whether the input customer index identification is repeated with the input customer information;
establishing a mapping relation between the input customer information and a user;
labeling the client information to generate a user portrait;
and executing a marketing strategy on the customer information with the specific label to be marketed.
Preferably, the operating method further comprises:
after the user logs in and verifies, the latest activity condition and/or the customer information statistics of the entered customer information are displayed to the user,
the customer information input by the user comprises single input and/or batch input, the customer information comprises customer basic information, dynamic information and customer index identification, and the customer index identification is contact telephone number information.
Preferably, the operating method further comprises:
receiving input customer information and acquiring a customer index identifier;
comparing the client index identification with the client index identification of the entered client information:
when repeated client index identifications exist, sending whether covering inquiry is carried out to the user;
and when no repeated client index identification exists, creating new client information in the client database, and establishing a mapping relation between the new client information and the user.
Preferably, the operating method further comprises:
user response override query:
when the user selects the overlay processing, newly-entered customer information is added on the basis of the original customer information, wherein the same part of information is not processed;
and when the user selects not to cover the processing, creating new client information in the client database, and establishing a mapping relation between the new client information and the user.
Preferably, the operating method further comprises:
performing labeling management on users, and adding first label information into user information of each user;
and grouping and managing the users with the same first label information.
Preferably, the operating method further comprises:
labeling the customer information, adding second label information into the customer information of each customer, and generating a user portrait according to the customer information;
screening the second label information, and grouping the customer information according to the requirements of the users, wherein the identification of each group takes the second label information as an effective identification;
and summarizing and counting the customer information of each group to construct accurate customer group information.
Preferably, the operating method further comprises:
displaying the customer information, wherein the displaying comprises the following steps:
the customer information is collected and counted by second label information and is displayed through Echart;
and summarizing and counting the customer information by using the second label information and displaying the customer information by using a table.
Preferably, the operating method further comprises:
the customer information carries second label information after being subjected to labeling processing, and specific labels to be marketed are constructed in the second label information;
screening and summarizing the customer information according to the specific label to be marketed to obtain a customer group to be marketed;
and executing marketing strategies of precise customers to a customer group to be marketed, wherein the marketing strategies at least comprise one of telephone marketing strategies and short message marketing strategies.
On the other hand, the invention adopts the following technical scheme: a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements a big data tag-based precision customer operation method as described above when executing the computer program.
On the other hand, the invention adopts the following technical scheme: a computer-readable storage medium, storing a computer program which, when executed by a processor, causes the processor to perform a big data tag based precision customer operation method as described above.
Compared with the prior art, the invention has the following beneficial effects:
the invention arranges the data of each channel and unifies the customer data, constructs an accurate customer operation service system based on big data labels, and achieves the purposes of new customer drainage and old customer activation. And uniformly managing the dispersed data, and clustering the users by using a big data static dynamic label technology to generate the user portrait. The problem that client data are scattered, unified management and analysis are inconvenient to conduct, and more potential value information cannot be mined through data is solved. The invention uses big data label technology to group users, is convenient for operators to describe the characteristics of the users specifically, labellingly and pertinently, and is used as the basis for market analysis, business decision and accurate marketing.
Drawings
In order to more clearly illustrate the technical solution, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.
FIG. 1 is a flow chart illustrating an embodiment of the present invention.
FIG. 2 is a flow chart of another embodiment of the present invention.
Detailed Description
For a clear and complete understanding of the technical solutions, the present invention will now be further described with reference to the embodiments and the accompanying drawings, and it is obvious that the described embodiments are only some embodiments of the present invention, and all other embodiments obtained by those skilled in the art without any inventive work are within the scope of the present invention.
As shown in fig. 1, the present embodiment discloses a precise customer operation method based on a big data tag, which includes:
the method comprises the steps of obtaining customer information input by a user, wherein the customer information input by the user comprises single input and/or batch input, the customer information comprises customer basic surface information, dynamic surface information and customer index identification, the customer index identification is contact telephone number information, and after login verification of the user, recent activity conditions and/or customer information statistics of the input customer information are displayed for the user.
Judging whether the input customer index identification is repeated with the input customer information, and establishing a mapping relation between the input customer information and a user, wherein the mapping relation comprises the following steps:
receiving the input customer information, acquiring a customer index identifier, and comparing the customer index identifier with the customer index identifier of the input customer information:
when repeated client index identifiers exist, whether covering inquiry is sent to a user, when the user selects covering processing, newly-entered client information is added on the basis of original client information, and the same part of information is not processed; when the user selects not to cover the processing, new client information is created in the client database, and a mapping relation is established between the new client information and the user; and when no repeated client index identification exists, creating new client information in the client database, and establishing a mapping relation between the new client information and the user.
And performing labeling management on the users, adding first label information into the user information of each user, and performing grouping management on the users with the same first label information.
And performing labeling processing on the client information, adding second label information into the client information of each client, generating a user figure according to the client information, screening the second label information, performing grouping processing on the client information according to the requirements of the user, taking the second label information as an effective identification identifier for the identifier of each group, summarizing and counting the client information of each group to construct accurate client group information, and executing a marketing strategy on the client information with specific labels to be marketed.
Analyzing and displaying the customer information, wherein the method comprises the following steps: the customer information is collected and counted by second label information and is displayed through Echart; and summarizing and counting the customer information by using the second label information and displaying the customer information by using a table.
And executing a marketing strategy on the customer information with the specific label to be marketed, wherein the customer information carries second label information after being subjected to labeling treatment, the specific label to be marketed is constructed in the second label information, the customer information is screened and summarized according to the specific label to be marketed to obtain a customer group to be marketed, and the marketing strategy of an accurate customer is executed on the customer group to be marketed, and the marketing strategy at least comprises one of a telephone marketing strategy and a short message marketing strategy.
On the other hand, the invention also discloses another implementation case: in order to facilitate understanding of the embodiments of the present application, some terms related to the present application are explained below.
Flume is a distributed, reliable, and highly available system for mass log collection, aggregation, and transmission. Support customization of various data senders in a logging system for collecting data, while flute provides the ability to easily process and write data to various data recipients (e.g., text, HDFS, Hbase, etc.). The flow of flash is always followed by events (events). Events are the basic data unit of the Flume, which carries log data (in the form of byte arrays) and carries header information, these events are generated by sources external to the Agent, and after capturing the Event, the sources perform a specific formatting and then push the Event into the Channel(s).
The ClickHouse is an open-source inline-storage-oriented DBMS for online analytical processing (OLAP), CK for short, and is lightweight compared to Hadoop, Spark. Characteristics of ClickHouse: the open-source column storage database management system supports linear expansion, is simple and convenient, has high reliability, fast fault-tolerant distribution and multiple functions, and is used for analyzing events or log streams with good and clear structures and invariable functions.
Kafka was originally developed by Linkedin corporation, is a distributed, partitioned, multi-replica, multi-subscriber, zookeeper-based coordinated distributed log system (which may also be regarded as MQ system), and can be commonly used for web/nginx logs, access logs, message services, and the like, and the main application scenarios are: a log collection system and a messaging system.
Apache flash is a framework and distributed processing engine for stateful computation of unbounded and bounded data streams. Flink is designed to run in all common clustered environments, performing calculations at memory speed and any scale.
As shown in fig. 2, the processing method includes acquiring a user data file generated by operation through a data front end buried point, where the user data file includes at least one of a login information file, an order information file, and a user behavior information file. And the user data file is processed through the Flume and the Kafka and then is subjected to Flink data processing, the processed data is written into the downstream Kafka and then is analyzed by the Flink in real time, and the result is visualized through real-time analysis. The user data files processed by the Flink data can be written into MySql at the regular time by historical data updating and scheduling to perform original data backup, original data synchronization and historical data analysis are performed through ClickHouse, and the result is visualized after analysis. The implementation case completes the unified management of the scattered data, and on the basis, the accurate user operation based on the big data label is used, so that the delivery of invalid marketing information is reduced, and the accurate touch of the marketing information of the user is realized.
On the other hand, the invention also discloses another embodiment: a computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements a big data tag-based precise customer operation method when executing the computer program. The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In the alternative, the processor and the storage medium may reside as discrete components in a user terminal.
On the other hand, the invention adopts the following technical scheme: a computer-readable storage medium, storing a computer program which, when executed by a processor, causes the processor to perform a method of accurate customer operation based on big data tags as described above. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a computer. By way of example, and not limitation, such computer-readable media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. Any connection is properly termed a computer-readable medium.
The above disclosure is intended to be illustrative of one or more of the preferred embodiments of the present invention and is not intended to limit the invention in any way, which is equivalent or conventional to one skilled in the art and which is intended to cover all modifications, equivalents, and alternatives falling within the scope of the invention as defined by the appended claims.
Claims (10)
1. A precise customer operation method based on big data labels is characterized by comprising the following steps:
obtaining customer information input by a user, wherein the customer information at least comprises a customer index identification;
judging whether the input customer index identification is repeated with the input customer information;
establishing a mapping relation between the input customer information and a user;
labeling the client information to generate a user portrait;
and executing a marketing strategy on the customer information with the specific label to be marketed.
2. The method for precise customer operation based on big data label as claimed in claim 1, wherein the operation method further comprises:
after the user logs in and verifies, the latest activity condition and/or the customer information statistics of the entered customer information are displayed to the user,
the customer information input by the user comprises single input and/or batch input, the customer information comprises customer basic information, dynamic information and customer index identification, and the customer index identification is contact telephone number information.
3. The method for precise customer operation based on big data label as claimed in claim 1, wherein the operation method further comprises:
receiving the input customer information and acquiring a customer index identifier;
comparing the client index identification with the client index identification of the entered client information:
when repeated client index identifications exist, sending whether covering inquiry is carried out to the user;
and when no repeated client index identification exists, creating new client information in the client database, and establishing a mapping relation between the new client information and the user.
4. The method for precise customer operation based on big data label as claimed in claim 3, wherein the operation method further comprises:
user response override query:
when the user selects the overlay processing, newly-entered customer information is added on the basis of the original customer information, wherein the same part of information is not processed;
and when the user selects not to cover the processing, creating new client information in the client database, and establishing a mapping relation between the new client information and the user.
5. The method for precise customer operation based on big data label as claimed in claim 1, wherein the operation method further comprises:
performing labeling management on users, and adding first label information into user information of each user;
and grouping and managing the users with the same first label information.
6. The method for precise customer operation based on big data label as claimed in claim 1, wherein the operation method further comprises:
labeling the customer information, adding second label information into the customer information of each customer, and generating a user portrait according to the customer information;
screening the second label information, and clustering the customer information according to the requirements of the user, wherein the identification of each group takes the second label information as an effective identification;
and summarizing and counting the customer information of each group to construct accurate customer group information.
7. The method for precise customer operation based on big data label as claimed in claim 1, wherein the operation method further comprises:
analyzing and displaying the customer information, wherein the method comprises the following steps:
the customer information is collected and counted by second label information and is displayed through Echart;
and summarizing and counting the customer information by using the second label information and displaying the customer information by using a table.
8. The method for precise customer operation based on big data label as claimed in claim 1, wherein the operation method further comprises:
the customer information carries second label information after being subjected to labeling processing, and specific labels to be marketed are constructed in the second label information;
screening and summarizing the customer information according to the specific label to be marketed to obtain a customer group to be marketed;
and executing marketing strategies of precise customers to a customer group to be marketed, wherein the marketing strategies at least comprise one of telephone marketing strategies and short message marketing strategies.
9. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that: the processor, when executing the computer program, implements a big data tag-based precision customer operation method according to any one of claims 1 to 8.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program, which when executed by a processor causes the processor to execute a big data tag-based precision customer operation method according to any one of claims 1 to 8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210590769.8ACN114971714A (en) | 2022-05-27 | 2022-05-27 | Accurate customer operation method based on big data label and computer equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210590769.8ACN114971714A (en) | 2022-05-27 | 2022-05-27 | Accurate customer operation method based on big data label and computer equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114971714Atrue CN114971714A (en) | 2022-08-30 |
Family
ID=82956867
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210590769.8APendingCN114971714A (en) | 2022-05-27 | 2022-05-27 | Accurate customer operation method based on big data label and computer equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114971714A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115543948A (en)* | 2022-09-27 | 2022-12-30 | 中国建设银行股份有限公司 | SVN heterogeneous file synchronization method and system based on dynamic label |
| CN115879980A (en)* | 2022-12-15 | 2023-03-31 | 中电金信软件有限公司 | Method and device for passenger group circle selection and comparative analysis |
| CN117009453A (en)* | 2023-10-07 | 2023-11-07 | 杭州雅拓信息技术有限公司 | Method and system for inquiring customer group list of customers in real time through digital marketing |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1071030A1 (en)* | 1999-07-21 | 2001-01-24 | Richard Libman | Communication method and apparatus |
| US20120116868A1 (en)* | 2010-11-10 | 2012-05-10 | Wendy Tsyr-Wen Chin | System and method for optimizing marketing effectiveness |
| US8719100B1 (en)* | 2011-05-02 | 2014-05-06 | Stipple Inc. | Interactive delivery of information through images |
| CN106296445A (en)* | 2016-08-01 | 2017-01-04 | 国网浙江省电力公司 | A kind of power customer label construction method |
| CN109327496A (en)* | 2018-07-23 | 2019-02-12 | 平安科技（深圳）有限公司 | Data push method, device, computer equipment and storage medium |
| CN112527920A (en)* | 2020-12-04 | 2021-03-19 | 广州橙行智动汽车科技有限公司 | Data processing method and device |
| CN114036159A (en)* | 2021-11-01 | 2022-02-11 | 上海浦东发展银行股份有限公司 | Banking business information update method and system |
| CN114155004A (en)* | 2021-11-30 | 2022-03-08 | 深圳思为科技有限公司 | Customer management method and device |
| CN114265974A (en)* | 2021-12-06 | 2022-04-01 | 深圳供电局有限公司 | A customer portrait label recommendation system and method |
- 2022
- 2022-05-27CNCN202210590769.8Apatent/CN114971714A/enactivePending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1071030A1 (en)* | 1999-07-21 | 2001-01-24 | Richard Libman | Communication method and apparatus |
| US20120116868A1 (en)* | 2010-11-10 | 2012-05-10 | Wendy Tsyr-Wen Chin | System and method for optimizing marketing effectiveness |
| US8719100B1 (en)* | 2011-05-02 | 2014-05-06 | Stipple Inc. | Interactive delivery of information through images |
| CN106296445A (en)* | 2016-08-01 | 2017-01-04 | 国网浙江省电力公司 | A kind of power customer label construction method |
| CN109327496A (en)* | 2018-07-23 | 2019-02-12 | 平安科技（深圳）有限公司 | Data push method, device, computer equipment and storage medium |
| CN112527920A (en)* | 2020-12-04 | 2021-03-19 | 广州橙行智动汽车科技有限公司 | Data processing method and device |
| CN114036159A (en)* | 2021-11-01 | 2022-02-11 | 上海浦东发展银行股份有限公司 | Banking business information update method and system |
| CN114155004A (en)* | 2021-11-30 | 2022-03-08 | 深圳思为科技有限公司 | Customer management method and device |
| CN114265974A (en)* | 2021-12-06 | 2022-04-01 | 深圳供电局有限公司 | A customer portrait label recommendation system and method |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115543948A (en)* | 2022-09-27 | 2022-12-30 | 中国建设银行股份有限公司 | SVN heterogeneous file synchronization method and system based on dynamic label |
| CN115879980A (en)* | 2022-12-15 | 2023-03-31 | 中电金信软件有限公司 | Method and device for passenger group circle selection and comparative analysis |
| CN117009453A (en)* | 2023-10-07 | 2023-11-07 | 杭州雅拓信息技术有限公司 | Method and system for inquiring customer group list of customers in real time through digital marketing |
| CN117009453B (en)* | 2023-10-07 | 2023-12-26 | 杭州雅拓信息技术有限公司 | Method and system for inquiring customer group list of customers in real time through digital marketing |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US20230015926A1 (en) | Low-latency streaming analytics | |
| CN108416620B (en) | Portrait data intelligent social advertisement putting platform based on big data | |
| US10997192B2 (en) | Data source correlation user interface | |
| CN111666490B (en) | Information pushing method, device, equipment and storage medium based on kafka | |
| CN114971714A (en) | Accurate customer operation method based on big data label and computer equipment | |
| CN112162965B (en) | Log data processing method, device, computer equipment and storage medium | |
| US11269808B1 (en) | Event collector with stateless data ingestion | |
| WO2019099065A1 (en) | Logs to metrics synthesis | |
| CN110909063A (en) | User behavior analysis method and device, application server and storage medium | |
| CN109034993A (en) | Account checking method, equipment, system and computer readable storage medium | |
| CN113360554A (en) | Method and equipment for extracting, converting and loading ETL (extract transform load) data | |
| CN111581054A (en) | ELK-based log point-burying service analysis and alarm system and method | |
| CN103838867A (en) | Log processing method and device | |
| CN102902775B (en) | The method and system that internet calculates in real time | |
| EP3031216A1 (en) | Dynamic collection analysis and reporting of telemetry data | |
| CN104408170A (en) | Business data analysis system | |
| CN103620601A (en) | Joining tables in a mapreduce procedure | |
| CN104182506A (en) | Log management method | |
| CN113010542B (en) | Service data processing method, device, computer equipment and storage medium | |
| JP2021529367A (en) | Dynamic incremental update of data cube | |
| CN112347165A (en) | Log processing method and device, server and computer readable storage medium | |
| CN108228322B (en) | Distributed link tracking and analyzing method, server and global scheduler | |
| US11789950B1 (en) | Dynamic storage and deferred analysis of data stream events | |
| CN112181678A (en) | Service data processing method, device and system, storage medium and electronic device | |
| CN112862598B (en) | Channel information management method, device, electronic equipment and 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 |