Method and equipment for realizing advertisement effect analysis based on standardized data processing, and medium

Abstract

The invention discloses a method and equipment for realizing advertisement effect analysis based on standardized data processing, and a medium. The method comprises the following steps: creating a unified media cost table, a burying point data table, a media ID dimension table and a media service relation table; collecting data and recording the data in a corresponding table; storing the media cost table, the burying point data table, the media ID dimension table and the media service relation table in a Mysql database; obtaining user access log data, and storing the user access log data in a local file; obtaining service log data, and storing the service log data on a server program; writing the data on the Mysql database, the local file and the server program into a real-time streaming kafka message queue; the data in the kafka message queue is stored in the ClickHouse in real time; calling data in the ClickHouse to generate a BI report and carrying out statistics; and analyzing an advertisement putting effect according to a statistical result of the BI report. The advertisement putting effect can be tracked in real time.

Description

Technical Field

[0001] The present invention relates to the technical field of advertising effect analysis, and in particular to a method, device and medium for implementing advertising effect analysis based on standardized data processing. Background Art

[0002] In the advertising industry, whether user feedback data is generated after an ad is placed is the basis for judging whether the ad is successfully placed. However, there are still many pain points and challenges in the existing data processing: 1. Real-time problem: The existing solution cannot collect user feedback data for analysis in real time; 2. Different media have different data structure standards when docking, resulting in inconsistent data structures; 3. When the amount of data is too large, it will cause difficulties in data processing and query; 4. The back-end business data cannot be linked to the delivery plan, making it impossible to analyze the delivery and customer acquisition effects. Summary of the invention

[0003] In view of this, the purpose of the present invention is to propose an advertising effect analysis method based on standardized data processing, providing analysis data from a multi-dimensional perspective through ClickHouse; helping to track the effect of advertising delivery in real time, and adjust the delivery strategy in time, so as to accurately and efficiently acquire target users and improve ROI.

[0004] In order to achieve the above technical objectives, the technical solution adopted by the present invention is: The present invention provides a method for analyzing advertising effects based on standardized data processing, comprising the following steps: Step 1: Create a unified media cost table, embedding data table, media ID dimension table, and media business relationship table; Step 2: Collect data from the media end and the business end and record them in the corresponding media cost table, embedding data table, media ID dimension table and media business relationship table; then store the media cost table, embedding data table, media ID dimension table and media business relationship table in a MySQL database; Step 3: Obtain user access log data and store it in a local file; Step 4: Obtain service log data and store it on the server program; Step 5: Write the data in the Mysql database, local files and server program into the real-time streaming Kafka message queue; Step 6: Store the data in the Kafka message queue in ClickHouse in real time; Step 7: Call the data in ClickHouse to generate BI reports and statistics; Step 8: Analyze the effect of advertising according to the statistical results of the BI report.

[0005] Furthermore, the step 1 specifically includes: Step 11: Create a unified media cost table, where multiple data types are set, including data statistics date, data statistics time, advertiser account ID, advertisement ID, book consumption, cash consumption and media channel; Step 12: Create a unified tracking data table, which has multiple data types, including device, advertising product tag, scene group number and event data. The event data is divided into event actor, event array list, event occurrence timestamp and event identifier. Step 13: Create a unified media ID dimension table, where multiple data types are set, including the attributes of the media level to which the current media ID belongs, the media ID, the name corresponding to the media ID, the main investment and media channel to which the media ID belongs; Step 14: Create a unified media business relationship table, which has multiple data types, including media channels, monitoring unique identifier PID used by the business end to monitor the advertising delivery status of the media end, attributes of the media level to which the current media ID belongs, and media ID.

[0006] Furthermore, in step 2, data from the media end and data from the business end are collected and recorded in the corresponding media cost table, embedding data table, media ID dimension table and media business relationship table; specifically including: Step 21: Obtain cost data from each media terminal, the cost data including data statistics date, data statistics time, advertiser account ID, advertisement ID, book consumption, cash consumption and specific content of media channels, and record it under the corresponding data type in the media cost table; Step 22: Obtain the embedding data from each business end, the embedding data including the specific content of the device, advertising product tag, scene group number, event actor, event array list, event occurrence timestamp and event identifier, and record it in the embedding data table under the corresponding data type; Step 23: Obtain media ID data from each media terminal. The media ID data includes the attributes of the media level to which the current media ID belongs, the media ID, the name corresponding to the media ID, the main investment and the specific content of the media channel to which the media ID belongs, and is recorded under the corresponding data type in the media ID dimension table; Step 24: Obtain associated data from each business end, the associated data including media channels, the monitoring unique identifier PID used by the business end to monitor the advertising delivery situation of the media end, the attributes of the media level to which the current media ID belongs, and the specific content of the media ID, and record them under the corresponding data type in the media business relationship table.

[0007] Furthermore, the step 5 specifically includes: Step 51: Enable the Binlog recording function of the MySQL database, use the Flink-CDC tool to capture data changes in the MySQL database in real time, and then write and record them in the topic corresponding to the Kafka message queue; Step 52: Define the result content of the log, and then start a Flume process to monitor the changes of user access log data through the tail command, capture the changes, and then write them to the topic corresponding to the Kafka message queue; Step 53: Start a Flume process to write the service log data directly to the topic corresponding to the Kafka message queue through the server program.

[0008] Furthermore, the step 6 specifically includes: Step 61, create a MergeTree engine table, a ReplacingMergeTree engine table and / or a join engine table of ClickHouse, and store the data in the kafka message queue in real time in the MergeTree engine table, the ReplacingMergeTree engine table and / or the join engine table of ClickHouse; Step 62: Create a business dimension dictionary table and periodically read the data in the ReplacingMergeTree engine table; Step 63. Create a unified ClickHouse cost table, extract the cost data of all media from the ReplacingMergeTree engine table and integrate it into the ClickHouse cost table; Step 64, create multiple flow tables, including an attribution flow table, a click log flow table, and a return log flow table, and extract corresponding data records from the MergeTree engine table to the corresponding attribution flow table, click log flow table, and return log flow table.

[0009] Furthermore, the step 61 specifically includes: Step 611, create a Kafka engine table for ClickHouse and connect it to the corresponding topic in the Kafka message queue; Step 612: Create a MergeTree engine table, a ReplacingMergeTree engine table and / or a ClickHouse join engine table. Step 613: Create a materialized view, read the data on the corresponding topic in the Kafka message queue through the materialized view, and write it to the MergeTree engine table, the ReplacingMergeTree engine table and / or the join engine table of ClickHouse.

[0010] Furthermore, the step 611 is specifically as follows: creating a Kafka engine table for ClickHouse, configuring the corresponding topics, consumer groups and consumption data formats in the associated Kafka message queue for the Kafka engine table, and establishing a connection between the Kafka engine table of ClickHouse and the corresponding topics in the Kafka message queue according to the corresponding topics, consumer groups and consumption data formats in the Kafka message queue.

[0011] Furthermore, the step 7 specifically includes: Step 71. Create an AggregatingMergeTree engine table to monitor data changes on different tables in ClickHouse in real time. Use the AggregatingMergeTree engine table to aggregate data in different tables to obtain different aggregated tables. Step 72: Create advertising application libraries at different levels, query and call corresponding data from different tables aggregated in ClickHouse, and generate corresponding BI reports and statistics under the advertising application library based on the corresponding data.

[0012] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the advertising effect analysis method based on standardized data processing as described above is implemented.

[0013] The present invention also provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the advertising effect analysis method based on standardized data processing as described above is implemented.

[0014] By adopting the above technical solution, the present invention has the following beneficial effects compared with the prior art: 1. In response to the real-time problem, the present invention develops and builds a real-time stream collection and storage system, which writes data into the real-time streaming Kafka message queue, and then stores it from the Kafka message queue into Clickhouse, thereby realizing the real-time streaming processing of Kafka and the real-time data processing and aggregation of Clickhouse.

[0015] 2. In view of the problem of inconsistent data structure in multimedia docking, the present invention designs and formulates standardized data processing procedures and standards, and records different data in different tables according to their types.

[0016] 3. To address the problem of massive data processing and querying, a high-performance OLAP system is built based on ClickHouse, and the data in the Kafka message queue is stored in ClickHouse to facilitate the processing and query of massive data.

[0017] 4. To solve the problem of being unable to analyze the effectiveness of customer acquisition, calling data from different tables can generate multi-dimensional BI reports to support multi-dimensional analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 It is an execution flow chart of an advertising effect analysis method based on standardized data processing provided by an embodiment of the present invention.

[0020] Figure 2 It is a structural framework diagram of an advertising effect analysis based on standardized data processing provided by an embodiment of the present invention.

[0021] Figure 3 It is a schematic diagram of an electronic device provided by an embodiment of the present invention.

[0022] Figure 4 It is a schematic diagram of a computer-readable storage medium provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0023] The present invention will be further described in detail below in conjunction with the accompanying drawings and examples. It is particularly noted that the following examples are only used to illustrate the present invention, but are not intended to limit the scope of the present invention. Similarly, the following examples are only partial embodiments of the present invention rather than all embodiments, and all other embodiments obtained by those of ordinary skill in the art without creative work are within the scope of protection of the present invention.

[0024] See also Figure 1 and Figure 2 The present invention provides a method for analyzing advertising effects based on standardized data processing, comprising the following steps: Step 1: Create a unified media cost table, embedding data table, media ID dimension table, and media business relationship table; In this embodiment, the step 1 specifically includes: Step 11: Create a unified media cost table. The media cost table is set with multiple data types, including data statistics date, data statistics time, advertiser account ID, advertisement ID, book consumption, cash consumption and media channel. The purpose of creating a unified media cost table is to store the cost data crawled back from various media terminals, which can greatly reduce the workload of connecting different media and realize the rapid access of cost data. In addition, the cost data of all media are stored according to the set multiple data types, with a unified and standardized format to facilitate subsequent data processing. Step 12: Create a unified tracking data table. The tracking data table is provided with multiple data types, including equipment, advertising product tags, scene grouping numbers and event data. The event data is divided into event actors, event array lists, event occurrence timestamps and event identifiers. The unified tracking data table is created to store the tracking data obtained from each business end. The tracking data structure uses the widely used JSON structure, which is convenient for each business end to build data without any threshold.

[0025] Step 13: Create a unified media ID dimension table. The media ID dimension table is provided with multiple data types, including the attributes of the media level to which the current media ID belongs, the media ID, the name corresponding to the media ID, the main investment and the media channel to which the media ID belongs. The unified media ID dimension table is created to store the key information corresponding to the media ID crawled back from each media terminal, so that other required information can be quickly associated through the media ID in the intermediate data processing link. Step 14, create a unified media business relationship table, which is set with multiple data types, including media channels, monitoring unique identification PID used by the business end to monitor the advertising delivery situation of the media end, attributes of the media level to which the current media ID belongs, and media ID; creating a unified media business relationship table is used to store the associated information obtained from the PID of each business end. One business end corresponds to one PID, and one PID receives information sent by different media IDs. Through this relationship table, the data of the media end and the business end can be connected in series.

[0026] Step 2: Collect data from the media end and the business end and record them in the corresponding media cost table, embedding data table, media ID dimension table and media business relationship table; then store the media cost table, embedding data table, media ID dimension table and media business relationship table in a MySQL database; In this embodiment, the data of the media end and the data of the service end are collected in step 2 and recorded in the corresponding media cost table, embedding data table, media ID dimension table and media service relationship table; specifically including: Step 21: Obtain cost data from each media terminal, the cost data including data statistics date, data statistics time, advertiser account ID, advertisement ID, book consumption, cash consumption and specific content of media channels, and record it under the corresponding data type in the media cost table; Step 22: Obtain the embedding data from each business end, the embedding data including the specific content of the device, advertising product tag, scene group number, event actor, event array list, event occurrence timestamp and event identifier, and record it in the embedding data table under the corresponding data type; Step 23: Obtain media ID data from each media terminal. The media ID data includes the attributes of the media level to which the current media ID belongs, the media ID, the name corresponding to the media ID, the main investment and the specific content of the media channel to which the media ID belongs, and is recorded under the corresponding data type in the media ID dimension table; Step 24, obtain related data from each business end, the related data includes media channels, monitoring unique identifier PID used by the business end to monitor the advertising situation of the media end, attributes of the media level to which the current media ID belongs, and specific content of the media ID, and record them under the corresponding data type in the media business relationship table. Steps 21 to 24 are to collect data from the media end and the business end respectively and store them in the data type of the corresponding table, attribute and classify the data, and facilitate subsequent data analysis.

[0027] Step 3: Obtain user access log data and store it in a local file; user access log data can be used to obtain user feedback on advertising products, which is convenient for analyzing the effectiveness of advertising; Step 4: Obtain service log data and store it on the server program; the service log data can facilitate system monitoring; Step 5: Write the data in the Mysql database, local files and server program into a real-time streaming Kafka message queue; different storage media use different real-time acquisition technologies and write them into a unified Kafka message queue; In this embodiment, step 5 specifically includes: Step 51: Enable the Binlog recording function of the MySQL database, use the Flink-CDC tool to capture data changes in the MySQL database in real time, and then write and record them in the topic corresponding to the Kafka message queue; the topic can be created and specified independently; Step 52: Define the result content of the log, and then start a Flume process to monitor the changes of user access log data through the tail command, capture the changes, and then write them to the topic corresponding to the Kafka message queue; Step 53: Start a Flume process to write the service log data directly to the topic corresponding to the Kafka message queue through the server program.

[0028] The data on the Mysql database is collected in real time using the Flink-CDC tool and written into the Kafka message queue. The data on local files and server programs is collected in real time using the Flume tool and written into the Kafka message queue. Using appropriate collection methods for different storage media can improve collection efficiency.

[0029] Step 6: Store the data in the kafka message queue in ClickHouse in real time; in ClickHouse, everything is built in the form of tables for easy query; In this embodiment, step 6 specifically includes: Step 61, create ClickHouse's MergeTree engine table, ReplacingMergeTree engine table and / or ClickHouse's join engine table, and store the data in the kafka message queue in real time to ClickHouse's MergeTree engine table, ReplacingMergeTree engine table and / or ClickHouse's join engine table; wherein, the MergeTree engine table is the core table engine in ClickHouse, supports the scenario of massive data analysis, and has the advantages of fast batch data insertion and background concurrent processing. The ReplacingMergeTree engine table can automatically deduplicate data with the same primary key to ensure real-time data update. The join engine table supports quick association of a large amount of data, and can be used like calling a normal function, which is very convenient, such as: SELECT joinGet (join table name, field to be queried, primary key value).

[0030] In this embodiment, step 61 specifically includes: Step 611, create a Kafka engine table for ClickHouse and connect it to the corresponding topic in the Kafka message queue; Step 612: Create a MergeTree engine table, a ReplacingMergeTree engine table and / or a ClickHouse join engine table to store data. Step 613: Create a materialized view, read the data on the corresponding topic in the Kafka message queue through the materialized view and write it to the MergeTree engine table, the ReplacingMergeTree engine table and / or the join engine table of ClickHouse.

[0031] In this embodiment, the step 611 is specifically: creating a Kafka engine table for ClickHouse, configuring the corresponding topics, consumer groups and consumption data formats in the associated Kafka message queue for the Kafka engine table, and establishing a connection between the Kafka engine table of ClickHouse and the corresponding topics in the Kafka message queue according to the corresponding topics, consumer groups and consumption data formats in the Kafka message queue.

[0032] Using unified standards has the following benefits: ① Speed ​​up development efficiency. Consume kafka-topic according to the template and replace the name to complete consumption development.

[0033] ② For the entire Mysql database consumption, it is convenient for downstream to directly parse and use it. New tables can be directly parsed, which is also convenient for data debugging.

[0034] ③When there is a problem with the data, the data can be repaired and refreshed.

[0035] Step 62, create a business dimension dictionary table, and read the data in the ReplacingMergeTree engine table regularly; the business dimension dictionary table can be used in the materialized view, and the business-related dimension information can be associated in real time and streamed to enrich the dimension analysis. The ReplacingMergeTree engine table is used here because it can automatically deduplicate data with the same primary key to ensure real-time data update. The business dimension dictionary table is a memory dictionary table of the Dictionary type, which can read the data in the ReplacingMergeTree engine table regularly and refresh its own data. It can be used just like calling a normal function, which is very convenient, such as: SELECT dictGet (dictionary table name, field to be queried, primary key value).

[0036] Step 63. Create a unified ClickHouse cost table, extract the cost data of all media from the ReplacingMergeTree engine table and integrate it into the ClickHouse cost table; the ClickHouse cost table is used to integrate the cost data of all media, and uses the ReplacingMergeTree engine table to store data. You must first set the primary key of the ReplacingMergeTree engine, and the processing of each primary key value must be unified so that duplicate data does not appear.

[0037] Step 64, create multiple flow tables, including an attribution flow table, a click log flow table, and a return log flow table, and extract corresponding data records from the MergeTree engine table to the corresponding attribution flow table, click log flow table, and return log flow table.

[0038] Step 7: Call the data in ClickHouse to generate BI reports and statistics; the most common data application layer is the BI report.

[0039] In this embodiment, step 7 specifically includes: Step 71. Create an AggregatingMergeTree engine table to monitor data changes on different tables in ClickHouse in real time. Use the AggregatingMergeTree engine table to aggregate data in different tables to obtain different aggregated tables. The AggregatingMergeTree engine table supports the calculation of various types of aggregate functions. Before performing BI report statistics, you can perform aggregation calculations first, which greatly reduces the amount of data scanned during statistics and speeds up query efficiency.

[0040] Step 72: Create advertising application libraries at different levels, query and call corresponding data from different tables aggregated in ClickHouse, generate corresponding BI reports and statistics under the advertising application library based on the corresponding data; meet the needs of different data applications, and be flexible and convenient to expand.

[0041] Step 8: Analyze the effect of advertising according to the statistical results of the BI report.

[0042] The advantages of the present invention are as follows: 1. The system architecture is clear and simple, supports horizontal expansion, and has high availability.

[0043] (1) The core architecture components only require Kafka and ClickHouse.

[0044] (2) Kafka and ClickHouse support unlimited capacity expansion and master-slave backup. The failure of individual machines will not affect the service and have high availability.

[0045] 2. Real-time streaming system architecture design, the fastest latency can be sub-second. After the delivery, you can immediately see the delivery effect data.

[0046] (1) Kafka's real-time streaming processing and Clickhouse's real-time data processing and aggregation allow you to see data on delivery effects within sub-seconds.

[0047] 3. During real-time processing, various business dimension attributes can be extended and associated, supporting multi-dimensional in-depth analysis.

[0048] (1) The memory dictionary table and join engine table technology enable various dimensional information to be conveniently and efficiently associated with real-time data generation, thus meeting the requirements for multi-dimensional in-depth analysis.

[0049] 4. Standardized data model processing solutions make development more efficient.

[0050] (1) The standardized model design of various data in step 1 makes development more efficient.

[0051] 5. Store data throughout the process and support data correction and expansion.

[0052] (1) ClickHouse supports large-scale data storage and computing.

[0053] (2) All data in the process are stored to facilitate correction and expanded application.

[0054] like Figure 3 As shown, an embodiment of the present invention further provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned advertising effect analysis method based on standardized data processing when executing the program.

[0055] like Figure 4 As shown, an embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the advertising effect analysis method based on standardized data processing is implemented.

[0056] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0057] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) or a processor (processor) to perform all or part of the steps of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0058] The above descriptions are only some embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Any equivalent device or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for analyzing advertising effectiveness based on standardized data processing, characterized in that: The steps include: Step 1: Create a unified media cost table, embedding data table, media ID dimension table, and media business relationship table; Step 2: Collect data from the media end and the business end and record them in the corresponding media cost table, embedding data table, media ID dimension table, and media business relationship table; Then, the media cost table, the embedding data table, the media ID dimension table and the media business relationship table are stored in a MySQL database; Step 3: Obtain user access log data and store it in a local file; Step 4: Obtain service log data and store it on the server program; Step 5: Write the data in the Mysql database, local files and server program into the real-time streaming Kafka message queue; Step 6: Store the data in the Kafka message queue in ClickHouse in real time; Step 7: Call the data in ClickHouse to generate BI reports and statistics; Step 8: Analyze the effect of advertising according to the statistical results of the BI report.

2. The method for analyzing advertising effectiveness based on standardized data processing according to claim 1, characterized in that: The step 1 specifically includes: Step 11: Create a unified media cost table, where multiple data types are set, including data statistics date, data statistics time, advertiser account ID, advertisement ID, book consumption, cash consumption and media channel; Step 12: Create a unified tracking data table, which has multiple data types, including device, advertising product tag, scene group number and event data. The event data is divided into event actor, event array list, event occurrence timestamp and event identifier. Step 13: Create a unified media ID dimension table, where multiple data types are set, including the attributes of the media level to which the current media ID belongs, the media ID, the name corresponding to the media ID, the main investment and media channel to which the media ID belongs; Step 14: Create a unified media business relationship table, which has multiple data types, including media channels, monitoring unique identifier PID used by the business end to monitor the advertising delivery status of the media end, attributes of the media level to which the current media ID belongs, and media ID.

3. The method for analyzing advertising effectiveness based on standardized data processing according to claim 2, characterized in that: The step 2 collects data from the media end and the business end and records them in the corresponding media cost table, embedding data table, media ID dimension table and media business relationship table; specifically includes: Step 21: Obtain cost data from each media terminal, the cost data including data statistics date, data statistics time, advertiser account ID, advertisement ID, book consumption, cash consumption and specific content of media channels, and record it under the corresponding data type in the media cost table; Step 22: Obtain the embedding data from each business end, the embedding data including the specific content of the device, advertising product tag, scene group number, event actor, event array list, event occurrence timestamp and event identifier, and record it in the embedding data table under the corresponding data type; Step 23: Obtain media ID data from each media terminal. The media ID data includes the attributes of the media level to which the current media ID belongs, the media ID, the name corresponding to the media ID, the main investment and the specific content of the media channel to which the media ID belongs, and is recorded under the corresponding data type in the media ID dimension table; Step 24: Obtain associated data from each business end, the associated data including media channels, the monitoring unique identifier PID used by the business end to monitor the advertising delivery situation of the media end, the attributes of the media level to which the current media ID belongs, and the specific content of the media ID, and record them under the corresponding data type in the media business relationship table.

4. The method for analyzing advertising effectiveness based on standardized data processing according to claim 1, characterized in that: The step 5 specifically includes: Step 51: Enable the Binlog recording function of the MySQL database, use the Flink-CDC tool to capture data changes in the MySQL database in real time, and then write and record them in the topic corresponding to the Kafka message queue; Step 52: Define the result content of the log, and then start a Flume process to monitor the changes of user access log data through the tail command, capture the changes, and then write them to the topic corresponding to the Kafka message queue; Step 53: Start a Flume process to write the service log data directly to the topic corresponding to the Kafka message queue through the server program.

5. The method for analyzing advertising effectiveness based on standardized data processing according to claim 1, characterized in that: The step 6 specifically includes: Step 61, create a MergeTree engine table, a ReplacingMergeTree engine table and / or a join engine table of ClickHouse, and store the data in the kafka message queue in real time in the MergeTree engine table, the ReplacingMergeTree engine table and / or the join engine table of ClickHouse; Step 62: Create a business dimension dictionary table and periodically read the data in the ReplacingMergeTree engine table; Step 63. Create a unified ClickHouse cost table, extract the cost data of all media from the ReplacingMergeTree engine table and integrate it into the ClickHouse cost table; Step 64, create multiple flow tables, including an attribution flow table, a click log flow table, and a return log flow table, and extract corresponding data records from the MergeTree engine table to the corresponding attribution flow table, click log flow table, and return log flow table.

6. The method for analyzing advertising effectiveness based on standardized data processing according to claim 5, characterized in that: The step 61 specifically includes: Step 611, create a Kafka engine table for ClickHouse and connect it to the corresponding topic in the Kafka message queue; Step 612: Create a MergeTree engine table, a ReplacingMergeTree engine table and / or a ClickHouse join engine table. Step 613: Create a materialized view, read the data on the corresponding topic in the Kafka message queue through the materialized view, and write it to the MergeTree engine table, the ReplacingMergeTree engine table and / or the join engine table of ClickHouse.

7. The method for analyzing advertising effectiveness based on standardized data processing according to claim 6, characterized in that: The step 611 is specifically as follows: creating a Kafka engine table for ClickHouse, configuring the corresponding topics, consumer groups and consumption data formats in the associated Kafka message queue for the Kafka engine table, and establishing a connection between the Kafka engine table for ClickHouse and the corresponding topics in the Kafka message queue according to the corresponding topics, consumer groups and consumption data formats in the Kafka message queue.

8. The method for analyzing advertising effectiveness based on standardized data processing according to claim 1, characterized in that: The step 7 specifically includes: Step 71. Create an AggregatingMergeTree engine table to monitor data changes on different tables in ClickHouse in real time. Use the AggregatingMergeTree engine table to aggregate data in different tables to obtain different aggregated tables. Step 72: Create advertising application libraries at different levels, query and call corresponding data from different tables aggregated in ClickHouse, and generate corresponding BI reports and statistics under the advertising application library based on the corresponding data.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the method for analyzing advertising effectiveness based on standardized data processing as described in any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the advertising effect analysis method based on standardized data processing as described in any one of claims 1 to 8 is implemented.

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