Cleaning and duplicate removal method for historical offline store input data

Abstract

The invention discloses a cleaning and duplicate removal method for historical offline store input data, which comprises the following steps of: data collection: widely collecting input data related to historical offline stores from various storage media, basic information (store names, detailed addresses, contact numbers and the like), rich sales data (commodity sales details, sales time, sales amount, sales quantity and the like) and inventory change data are covered. According to the method, the problems of missing values, error values and repeated data existing in historical offline store input data are effectively solved through de-duplication and cleaning of the data, the accuracy and availability of the data are remarkably improved, a reliable data basis can be provided for store operation management through accurate data cleaning and efficient de-duplication operation, and the method is suitable for popularization and application. The system is beneficial to accurate sales analysis, inventory management decision, customer behavior research and the like, so that the operation efficiency and competitiveness of the store are improved, and the system has important practical application value and commercial value.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method for cleaning and deduplicating historical offline store input data. Background Art

[0002] With the continuous development of offline store business and the advancement of digital management, a large amount of historical input data has been accumulated. However, these data often have data quality problems, such as missing data, wrong values, and a large amount of duplicate data. Missing data may be caused by negligence of data entry personnel, system failure, etc., and wrong values ​​may include values ​​that do not conform to business logic, wrong data types, etc., while duplicate data may be generated during multiple data entry or data integration. These problems seriously affect the accuracy and availability of data, making it difficult to carry out analysis and decision-making based on these data effectively, and unable to provide reliable support for store operation management. Summary of the invention

[0003] The purpose of the present invention is to provide a method for cleaning and deduplication of historical offline store entry data, so as to solve the quality problems of existing historical offline store entry data raised in the above background technology, improve the accuracy and availability of data, and provide strong data support for store operation management.

[0004] To achieve the above object, the present invention provides the following technical solutions: A method for cleaning and deduplicating historical offline store input data, the cleaning and deduplicating method comprising the following steps: A. Data collection: Collect input data related to historical offline stores from various storage media, including basic store information (store name, detailed address, contact number, etc.), rich sales data (product sales details, sales time, sales amount, sales quantity, etc.), inventory change data, customer consumption records, employee information and other multi-dimensional data to ensure the integrity and comprehensiveness of data collection, laying a solid foundation for subsequent accurate and in-depth analysis; B. Data sorting: Format unification and precision. For date data, according to international common format standards, such as "YYYY-MM-DD", regular expressions or special date processing functions are used to convert and standardize various forms of date expressions. For example, different formats such as "XXXX / XX / XX" and "XX-XX-XXXX" are unified into "XXXX-XX-XX". Phone number format processing: Through a specific string processing algorithm, the numeric part of the phone number is extracted, non-numeric characters (such as brackets, dashes, spaces, etc.) are removed, and the phone number is sorted according to the standard format of the country or region. For example, domestic mobile phone numbers are unified into the format of "1 [3-9]\d {9}"; C. Processing of missing values: Use data scanning algorithms to traverse the entire data set, accurately mark the missing value locations and records in each field, and develop missing value processing strategies to build intelligent decision-making models. Comprehensively consider the distribution characteristics of data, field importance, and business needs. For numeric fields with relatively uniform distribution (such as sales volume), the mean filling method can be used. For date fields with time series characteristics, a filling method based on time series prediction is used, such as ARIMA model prediction filling. For some key categorical fields with a low missing ratio (such as store type), the filling is based on the mode of the field in the data set. For records with a high missing ratio and a greater impact on the overall analysis, they can be deleted after rigorous evaluation. At the same time, the basis for deletion and related information are recorded in detail for subsequent tracing and verification; D. Text standard processing: Using the text normalization algorithm in natural language processing technology, first convert the store names into a specific format (such as all capital letters or first letter capital letters), then build a store name dictionary, map and convert common synonyms, abbreviations and aliases, for example, "XX Supermarket", "XX Supermarket", "XX Shopping Supermarket" and so on are uniformly mapped to the standardized name "XX Supermarket". Standardization of other text information: For other text fields such as product names and customer names, similar text processing technologies are also used to remove special characters, unify uppercase and lowercase letters, and standardize some commonly used abbreviations or aliases to improve data consistency and accuracy; E. Address cleaning: Use the combination of address parsing library and string processing function to deeply clean the address information, remove redundant information in the address (such as repeated expressions such as "province", "city", "district" in "XX province XX city XX district"), redundant spaces and non-standard abbreviations (such as "St." is converted to "Street"), geocoding and standardization: With the help of professional geocoding service API (such as the geocoding interface of Amap and Baidu Map), the cleaned address information is converted into precise latitude and longitude coordinates, and unified and standardized according to the standard address format of the geographic information system (GIS), so as to achieve highly standardized and spatial expression of address information, which is convenient for subsequent data analysis and visualization based on geographic location; F. Remove duplicate data: De-duplicate based on database operations: If the data is stored in a database, you can use the database's built-in de-duplicate function, such as the "DISTINCT" keyword in the SQL statement or the "GROUP BY" clause combined with an aggregate function to remove duplicate records. For example, using the "SELECT DISTINCT * FROM table_name" statement can return a result set that does not contain duplicate rows, where "table_name" is the name of the table where the data is stored, or using the "SELECT column1, column2, MAX (column3) FROM table_name GROUP BY column1, column2" statement, while removing duplicate records, you can also retain specific values ​​of certain fields as needed (such as taking the maximum value); G. Deduplication based on data analysis tools: If the data is in a spreadsheet or other data analysis tools, you can use the deduplication function provided by the tool. For example, in Excel, you can select the data range, and then use the "Remove Duplicates" function in the "Data" tab. Select the column to be deduplicated (i.e. the unique identifier field). The tool will automatically identify and delete duplicate records, and only keep the first record that appears. H. Check uniqueness again: After removing duplicate data, check the uniqueness of the data again to ensure that all duplicate data has been successfully deleted. The same method as before can be used to count the number of occurrences of unique identification field combinations. If all combinations appear only once, the deduplication operation is successful. Data integrity and consistency verification: Check whether the data after deduplication still maintains integrity and consistency, check whether the number of data records is reasonable, whether the logical relationship between the fields is still correct, and whether the data can meet subsequent analysis and application requirements. For example, after deduplication of sales data, the total sales should be consistent with the result obtained by a reasonable calculation method before deduplication, and the correlation between inventory data and sales data should remain normal. If problems are found in the data, further investigation and repair are required. It may be necessary to re-examine the data cleaning and deduplication process to check whether there are any misoperations or omissions.

[0005] Compared with the prior art, the present invention has the following beneficial effects: The present invention effectively solves the problems of missing values, erroneous values ​​and duplicate data in historical offline store entry data by deduplicating and cleaning data, significantly improving the accuracy and availability of data. Through precise data cleaning and efficient deduplication operations, it can provide a reliable data basis for store operation management, and help to conduct accurate sales analysis, inventory management decisions, customer behavior research and other tasks, thereby improving the store's operating efficiency and competitiveness, and has important practical application value and commercial value. DETAILED DESCRIPTION

[0006] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0007] The following technical solutions are provided, which are specifically disclosed: A method for cleaning and deduplicating historical offline store input data, the cleaning and deduplicating method comprising the following steps: A. Data collection: Collect input data related to historical offline stores from various storage media, including basic store information (store name, detailed address, contact number, etc.), rich sales data (product sales details, sales time, sales amount, sales quantity, etc.), inventory change data, customer consumption records, employee information and other multi-dimensional data to ensure the integrity and comprehensiveness of data collection, laying a solid foundation for subsequent accurate and in-depth analysis; B. Data sorting: Format unification and precision. For date data, according to international common format standards, such as "YYYY-MM-DD", regular expressions or special date processing functions are used to convert and standardize various forms of date expressions. For example, different formats such as "XXXX / XX / XX" and "XX-XX-XXXX" are unified into "XXXX-XX-XX". Phone number format processing: Through a specific string processing algorithm, the numeric part of the phone number is extracted, non-numeric characters (such as brackets, dashes, spaces, etc.) are removed, and the phone number is sorted according to the standard format of the country or region. For example, domestic mobile phone numbers are unified into the format of "1 [3-9]\d {9}"; C. Processing of missing values: Use data scanning algorithms to traverse the entire data set, accurately mark the missing value locations and records in each field, and develop missing value processing strategies to build intelligent decision-making models. Comprehensively consider the distribution characteristics of data, field importance, and business needs. For numeric fields with relatively uniform distribution (such as sales volume), the mean filling method can be used. For date fields with time series characteristics, a filling method based on time series prediction is used, such as ARIMA model prediction filling. For some key categorical fields with a low missing ratio (such as store type), the filling is based on the mode of the field in the data set. For records with a high missing ratio and a greater impact on the overall analysis, they can be deleted after rigorous evaluation. At the same time, the basis for deletion and related information are recorded in detail for subsequent tracing and verification; D. Text standard processing: Using the text normalization algorithm in natural language processing technology, first convert the store names into a specific format (such as all capital letters or first letter capital letters), then build a store name dictionary, map and convert common synonyms, abbreviations and aliases, for example, "XX Supermarket", "XX Supermarket", "XX Shopping Supermarket" and so on are uniformly mapped to the standardized name "XX Supermarket". Standardization of other text information: For other text fields such as product names and customer names, similar text processing technologies are also used to remove special characters, unify uppercase and lowercase letters, and standardize some commonly used abbreviations or aliases to improve data consistency and accuracy; E. Address cleaning: Use the combination of address parsing library and string processing function to deeply clean the address information, remove redundant information in the address (such as repeated expressions such as "province", "city", "district" in "XX province XX city XX district"), redundant spaces and non-standard abbreviations (such as "St." is converted to "Street"), geocoding and standardization: With the help of professional geocoding service API (such as the geocoding interface of Amap and Baidu Map), the cleaned address information is converted into precise latitude and longitude coordinates, and unified and standardized according to the standard address format of the geographic information system (GIS), so as to achieve highly standardized and spatial expression of address information, which is convenient for subsequent data analysis and visualization based on geographic location; F. Remove duplicate data: De-duplicate based on database operations: If the data is stored in a database, you can use the database's built-in de-duplicate function, such as the "DISTINCT" keyword in the SQL statement or the "GROUP BY" clause combined with an aggregate function to remove duplicate records. For example, using the "SELECT DISTINCT * FROM table_name" statement can return a result set that does not contain duplicate rows, where "table_name" is the name of the table where the data is stored, or using the "SELECT column1, column2, MAX (column3) FROM table_name GROUP BY column1, column2" statement, while removing duplicate records, you can also retain specific values ​​of certain fields as needed (such as taking the maximum value); G. Deduplication based on data analysis tools: If the data is in a spreadsheet or other data analysis tools, you can use the deduplication function provided by the tool. For example, in Excel, you can select the data range, and then use the "Remove Duplicates" function in the "Data" tab. Select the column to be deduplicated (i.e. the unique identifier field). The tool will automatically identify and delete duplicate records, and only keep the first record that appears. H. Check uniqueness again: After removing duplicate data, check the uniqueness of the data again to ensure that all duplicate data has been successfully deleted. The same method as before can be used to count the number of occurrences of unique identification field combinations. If all combinations appear only once, the deduplication operation is successful. Data integrity and consistency verification: Check whether the data after deduplication still maintains integrity and consistency, check whether the number of data records is reasonable, whether the logical relationship between the fields is still correct, and whether the data can meet subsequent analysis and application requirements. For example, after deduplication of sales data, the total sales should be consistent with the result obtained by a reasonable calculation method before deduplication, and the correlation between inventory data and sales data should remain normal. If problems are found in the data, further investigation and repair are required. It may be necessary to re-examine the data cleaning and deduplication process to check whether there are any misoperations or omissions.

[0008] Importantly, it should be noted that the construction and arrangement of the present application shown in a number of different exemplary embodiments are only exemplary. Although only a few embodiments are described in detail in this disclosure, it should be readily understood by those who refer to this disclosure that many modifications are possible (e.g., the size, scale, structure, shape and proportion of various elements, and parameter values ​​(e.g., temperature, pressure, etc.), mounting arrangements, use of materials, color, changes in orientation, etc.) without substantially departing from the novel teachings and advantages of the subject matter described in the application. For example, an element shown as integrally formed may be composed of multiple parts or elements, the position of the element may be inverted or otherwise changed, and the nature or number or position of the discrete element may be altered or changed. Therefore, all such modifications are intended to be included within the scope of the present invention. The order or sequence of any process or method steps may be changed or reordered according to an alternative embodiment. In the claims, any "device plus function" clause is intended to cover the structure described herein that performs the function, and is not only structurally equivalent but also equivalent structure. Other substitutions, modifications, changes and omissions may be made in the design, operating conditions and arrangement of the exemplary embodiments without departing from the scope of the present invention. Therefore, the invention is not limited to a specific embodiment, but extends to numerous modifications still falling within the scope of the appended claims.

[0009] Additionally, in order to provide a concise description of exemplary embodiments, all features of an actual embodiment may not be described (ie, those features that are not relevant to the best mode presently contemplated for carrying out the invention or those features that are not relevant to implementing the invention).

[0010] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention, rather than to limit the scope of protection of the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the essence and scope of the technical solution of the present invention.

Claims

1. A method for cleaning and deduplication of historical offline store input data, characterized by: The cleaning and deduplication method comprises the following steps: A. Data collection: Collect input data related to historical offline stores from various storage media, including basic store information (store name, detailed address, contact number, etc.), rich sales data (product sales details, sales time, sales amount, sales quantity, etc.), inventory change data, customer consumption records, employee information and other multi-dimensional data to ensure the integrity and comprehensiveness of data collection, laying a solid foundation for subsequent accurate and in-depth analysis; B. Data sorting: Format unification and precision. For date data, according to international common format standards, such as "YYYY-MM-DD", regular expressions or special date processing functions are used to convert and standardize various forms of date expressions. For example, different formats such as "XXXX / XX / XX" and "XX-XX-XXXX" are unified into "XXXX-XX-XX". Phone number format processing: Through a specific string processing algorithm, the numeric part of the phone number is extracted, non-numeric characters (such as brackets, dashes, spaces, etc.) are removed, and the phone number is sorted according to the standard format of the country or region. For example, domestic mobile phone numbers are unified into the format of "1 [3-9]\d {9}"; C. Processing of missing values: Use data scanning algorithms to traverse the entire data set, accurately mark the missing value locations and records in each field, and develop missing value processing strategies to build intelligent decision-making models. Comprehensively consider the distribution characteristics of data, field importance, and business needs. For numeric fields with relatively uniform distribution (such as sales volume), the mean filling method can be used. For date fields with time series characteristics, a filling method based on time series prediction is used, such as ARIMA model prediction filling. For some key classification fields with a low missing ratio (such as store type), the filling is based on the mode of the field in the data set. For records with a high missing ratio and a greater impact on the overall analysis, they can be deleted after rigorous evaluation. At the same time, the basis for deletion and related information are recorded in detail for subsequent tracing and verification; D. Text standard processing: Using the text normalization algorithm in natural language processing technology, first convert the store names into a specific format (such as all capital letters or first letter capital letters), then build a store name dictionary, map and convert common synonyms, abbreviations and aliases, for example, "XX Supermarket", "XX Supermarket", "XX Shopping Supermarket" and so on are uniformly mapped to the standardized name "XX Supermarket", and other text information is standardized: For other text fields such as product names and customer names, similar text processing technologies are also used to remove special characters, unify upper and lower case, and standardize some commonly used abbreviations or aliases to improve data consistency and accuracy; E. Address cleaning: Use the combination of address parsing library and string processing function to deeply clean the address information, remove redundant information in the address (such as repeated expressions such as "province", "city", "district" in "XX province XX city XX district"), redundant spaces and non-standard abbreviations (such as "St." is converted to "Street"). Geocoding and standardization: With the help of professional geocoding service API (such as the geocoding interface of Amap and Baidu Map), the cleaned address information is converted into precise latitude and longitude coordinates, and unified and standardized according to the standard address format of the geographic information system (GIS), so as to achieve highly standardized and spatial expression of address information, which is convenient for subsequent data analysis and visualization based on geographic location; F. Remove duplicate data: De-duplicate based on database operations: If the data is stored in a database, you can use the database's built-in de-duplicate function, such as the "DISTINCT" keyword in the SQL statement or the "GROUP BY" clause combined with aggregate functions to remove duplicate records. For example, using the "SELECT DISTINCT * FROM table_name" statement can return a result set that does not contain duplicate rows, where "table_name" is the name of the table where the data is stored, or using the "SELECT column1, column2, MAX (column3) FROM table_name GROUP BY column1, column2" statement, while removing duplicate records, you can also retain specific values ​​of certain fields as needed (such as taking the maximum value); G. De-duplicate based on data analysis tools: If the data is in a spreadsheet or other data analysis tools, you can use the de-duplicate function provided by the tool. For example, in Excel, you can select the data range, and then use the "Remove Duplicates" function in the "Data" tab. Select the column to be de-duplicated (i.e. the unique identifier field). The tool will automatically identify and delete duplicate records, and only keep the first record that appears. H. Check uniqueness again: After removing duplicate data, check the uniqueness of the data again to ensure that all duplicate data has been successfully deleted. The same method as before can be used to count the number of occurrences of unique identification field combinations. If all combinations appear only once, the deduplication operation is successful. Data integrity and consistency verification: Check whether the data after deduplication still maintains integrity and consistency, check whether the number of data records is reasonable, whether the logical relationship between the fields is still correct, and whether the data can meet subsequent analysis and application requirements. For example, after deduplication of sales data, the total sales should be consistent with the result obtained by a reasonable calculation method before deduplication, and the correlation between inventory data and sales data should remain normal. If problems are found in the data, further investigation and repair are required. It may be necessary to re-examine the data cleaning and deduplication process to check whether there are any misoperations or omissions.