AppStore keyword popularity search scale processing method

Abstract

The invention discloses an AppStore keyword popularity search scale processing method which comprises the following steps: S1, connecting to a database, and executing query to obtain popularity and search volume data; s2, preprocessing the acquired data, wherein the preprocessing comprises data traversal and normalization processing; s3, acquiring a relationship between popularity and exposure by using the data acquired in the S2; s4, generating popularity data according to the relationship obtained in S3, and calculating corresponding exposure; s5, adjusting coefficients of popularity data of different intervals, and correcting a calculation result; s6, checking the monotonicity of the exposure, outputting a result, and inserting the result into a database; and S7, closing the database. Compared with the prior art, the AppStore keyword popularity search scale processing method has the advantages that direct and accurate information is provided in a selected word putting scene according to key indexes, and powerful data support is provided for word selection.

Description

Technical Field

[0001] The present invention relates to the technical field of keyword popularity search volume statistics, and in particular to a method for processing an AppStore keyword popularity search volume table. Background Art

[0002] When placing ads on the AppStore, you need to rely on relevant data indicators of search terms to choose which words may have better effects. The important reference data here is the popularity of the words. Relatively popular words can be better displayed and downloaded.

[0003] The current situation usually takes popularity and heat as a reference, and selects the interval range of popularity and the interval range of heat to comprehensively judge the search situation of the word.

[0004] When judging the popularity of a word based on current common sense and existing data, there may be situations where the popularity is low but the heat is high, or the popularity is high but the heat is low. This results in abnormal interference in the selection process, making it difficult to select a word. Summary of the invention

[0005] The technical problem to be solved by the present invention is to overcome the above technical defects and provide an AppStore keyword popularity search table processing method that gives direct and accurate information in the word selection and delivery scenario according to key indicators, providing strong data support for word selection.

[0006] To solve the above technical problems, the technical solution provided by the present invention is: a method for processing the AppStore keyword popularity search volume table, comprising the following steps: S1: connecting to a database, executing a query to obtain popularity and search volume data; S2: preprocessing the obtained data, including data traversal and normalization processing; S3: using the data obtained in S2 to obtain the relationship between popularity and exposure; S4: generating popularity data based on the relationship obtained in S3, and calculating the corresponding exposure; S5: adjusting the coefficients of popularity data in different intervals, and correcting the calculation results; S6: checking the monotonicity of the exposure and outputting the results, and inserting the results into the database; S7: closing the database.

[0007] Preferably, the database is a MySQL database, and a cursor object is obtained to execute an SQL statement, wherein the obtained data is JSON data.

[0008] Preferably, S2 includes defining a power function and performing curve fitting, wherein the power function is y=a*x**b, wherein x is popularity and y is exposure, and coefficients a and b are determined by fitting known data.

[0009] Preferably, in the generation of popularity data, a generation range is 5 to 100, and the generated data is substituted into a power function to calculate the exposure.

[0010] Preferably, S6 includes denormalizing the calculated exposure to restore it to its original range, and ensuring that the exposure is non-negative and monotonically increasing by comparing the values ​​of adjacent data points.

[0011] The advantages of the present invention over the prior art are: the method provided in the present invention has good scalability, can be functionally expanded and optimized according to actual needs, can add more data processing steps, optimize algorithm performance, support more database types, and avoid the problem of increased error costs caused by the occurrence of information interference through indicator information;

[0012] In the present invention, the results output to the console can help developers quickly understand the data processing situation, discover and solve problems in a timely manner, and through the analysis and fitting of a large amount of actual data, the most suitable power function form can be found, so that the calculated exposure is more consistent with the actual situation, thereby improving the accuracy and reliability of data processing. By generating new data points, the changing trend between popularity and exposure can be more comprehensively understood, providing stronger support for decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 It is a structural diagram of the method for processing the keyword popularity search table of AppStore. DETAILED DESCRIPTION

[0014] The present invention is further described in detail below in conjunction with the accompanying drawings.

[0015] Combined with Figure 1 As shown, the method for processing the AppStore keyword popularity search volume table includes the following steps: S1: connecting to the database, executing queries to obtain popularity and search volume data; S2: preprocessing the obtained data, including data traversal and normalization; S3: using the data obtained in S2 to obtain the relationship between popularity and exposure; S4: generating popularity data based on the relationship obtained in S3, and calculating the corresponding exposure; S5: adjusting the coefficients of popularity data in different intervals, and correcting the calculation results; S6: checking the monotonicity of the exposure and outputting the results, and inserting the results into the database; S7: closing the database.

[0016] In specific use, the database is a MySQL database, and a cursor object is obtained to execute SQL statements, where the data obtained is JSON data. S2 includes defining a power function and performing curve fitting. The power function is y=a*x**b, where x is popularity and y is exposure. The coefficients a and b are determined by fitting known data.

[0017] In generating popularity data, the generation range is 5 to 100, and the generated data is substituted into the power function to calculate the exposure. S6 includes denormalizing the calculated exposure to restore it to the original range, and by comparing the values ​​of adjacent data points, it is ensured that the exposure is non-negative and monotonically increasing.

[0018] More specifically:

[0019] Establish a connection with the MySQL database and create a database cursor to execute SQL queries, which provides a basis for subsequent data query and processing and ensures that the required data can be obtained from the database efficiently;

[0020] In one embodiment:

[0021] For example, use Python's database connection library (such as pymysql) to establish a connection and obtain a cursor object to execute various SQL statements to obtain popularity and search volume data from the aso_search_volume_formula table. Through carefully designed SQL query statements, the required data can be accurately extracted to provide raw data support for subsequent data processing steps, such as using the SELECT popularity, search_volume FROM aso_search_volume_formula statement to obtain data from the database table for the popularity and search volume fields.

[0022] Traverse the query results: traverse the query results, parse the JSON data and normalize it. This step helps convert the original data into a unified format for subsequent processing and analysis. For example, for fields containing JSON format data, use Python's JSON parsing library to convert it into a processable object and normalize it to make the data comparable within a specific range.

[0023] Curve fitting is performed to establish the relationship between popularity and exposure. By defining a power function and substituting data into the function for calculation, the mathematical relationship between popularity and exposure is found, providing a basis for subsequent data generation and adjustment. For example, a power function y=a*x**b is defined, where x is popularity and y is exposure. By fitting known data, the values ​​of coefficients a and b are determined.

[0024] When generating new popularity data, generate popularity data ranging from 5 to 100 and calculate the corresponding exposure. Based on the established relationship between popularity and exposure, generate a series of new popularity data, and calculate the corresponding exposure through functions, providing more data points for data expansion and analysis. Use a loop to generate popularity data from 5 to 100, and substitute it into the power function to calculate the corresponding exposure.

[0025] The adjustment coefficient includes adjusting the coefficient of popularity data in different intervals. Taking into account the actual business needs and data characteristics, the coefficient of popularity data in different intervals is adjusted to ensure that the calculated exposure more accurately reflects the actual situation. For example, for data with popularity within a specific interval, an adjustment coefficient is multiplied to correct the calculation result.

[0026] Denormalization and checking monotonicity include denormalizing the calculated exposure and ensuring that it is non-negative and monotonically increasing. After completing the data processing, denormalization is performed to restore the data to the original value range and check whether the exposure is non-negative and monotonically increasing to ensure the rationality and usability of the data. Specifically, the calculated exposure is restored to the original range using the denormalization formula and the exposure is ensured to be monotonically increasing by comparing the values ​​of adjacent data points.

[0027] When inserting and outputting results, the calculated exposure results are inserted into the database and output to the console. The processed exposure results are inserted into the database for subsequent query and analysis, and the results are output to the console to facilitate users to view and verify the results of data processing. For example, use SQL INSERT or UPDATE statements to insert the exposure results into the database table, and use the print function to output the results to the console.

[0028] The advantage of the present invention is that data is directly extracted from a specific table (aso_search_volume_formula) in the MySQL database, which ensures the authority and reliability of the data and avoids the errors and uncertainties that may be caused by obtaining data from unreliable sources. When conducting market trend analysis, accurate popularity and search volume data can provide enterprises with real market feedback and help them make more informed decisions.

[0029] Parsing and normalizing the JSON data in the query results allows data in different formats to be processed with a unified standard, greatly improving the consistency and comparability of the data and reducing errors caused by differences in data formats.

[0030] Defining a power function for curve fitting can accurately establish the relationship between popularity and exposure, providing strong support for subsequent analysis and decision-making. Adjusting the coefficients of popularity data in different intervals can further optimize the calculation results to make them more in line with actual conditions, reflecting the fine processing of data and improving the accuracy and practicality of the data. In the formulation of advertising strategies, the accurate relationship between popularity and exposure can more accurately determine the target audience and intensity of advertising, thereby improving advertising effectiveness and return on investment.

[0031] Provides efficient data traversal: In real-time data analysis scenarios, fast traversal processing can provide the latest data analysis results in a timely manner, helping companies make decisions quickly. In data modeling and prediction, more data samples can improve the accuracy and generalization ability of the model, and efficient data processing strategies can speed up the model training and optimization process, saving time and computing resources.

[0032] Denormalization and monotonicity checking make it easier to understand and apply the results, and increase the reliability and rationality of the results.

[0033] The contents not described in detail in this specification belong to the prior art known to professional and technical personnel in this field.

[0034] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited by the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

[0035] The present invention and its embodiments are described above, and such description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if ordinary technicians in the field are inspired by it, without departing from the purpose of the invention, they can design a structure and embodiment similar to the technical solution without creativity, which should belong to the protection scope of the present invention.

Claims

1. The method for processing the AppStore keyword popularity search table is characterized by: The steps include: S1: Connect to the database and execute queries to obtain popularity and search volume data; S2: preprocess the acquired data, including data traversal and normalization; S3: Use the data obtained in S2 to obtain the relationship between popularity and exposure; S4: Generate popularity data based on the relationship obtained in S3 and calculate the corresponding exposure; S5: Adjust the coefficients of popularity data in different intervals to correct the calculation results; S6: Check the monotonicity of the exposure and output the result, and insert the result into the database; S7: Close the database.

2. The method for processing the AppStore keyword popularity search table according to claim 1, characterized in that: The database is a MySQL database, and a cursor object is obtained to execute an SQL statement, wherein the obtained data is JSON data.

3. The method for processing the AppStore keyword popularity search table according to claim 1, characterized in that: The S2 includes defining a power function and performing curve fitting, wherein the power function is y=a*x**b, where x is popularity and y is exposure, and coefficients a and b are determined by fitting known data.

4. The method for processing the AppStore keyword popularity search table according to claim 3, characterized in that: In the generation of popularity data, the generation range is 5 to 100, and the generated data is substituted into the power function to calculate the exposure.

5. The method for processing the AppStore keyword popularity search table according to claim 1, characterized in that: The step S6 includes denormalizing the calculated exposure to restore it to its original range, and ensuring that the exposure is non-negative and monotonically increasing by comparing the values ​​of adjacent data points.

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