Transaction data statistical method based on networked collection equipment

Abstract

The invention discloses a transaction data statistical method based on networked collection equipment, and relates to the technical field of transaction data statistics. The accuracy and timeliness of transaction data can be guaranteed through real-time data collection, the consistency of the data can also be guaranteed, so that the statistical accuracy can be guaranteed, the collected data is cleaned and processed to guarantee the quality and accuracy of the data, the complexity of subsequent processing can be reduced through data preprocessing, and the data processing efficiency is improved. The statistical efficiency and accuracy are improved; data mining methods and algorithms such as association rule mining and model prediction are adopted to mine and analyze data, user behavior modes and transaction trend related laws are found, data sources and data support are provided for subsequent work, and the processing effect and quality can be improved; by analyzing and displaying the user data, drawing the user portraits and performing behavior analysis on the user portraits, reference is provided for optimization and improvement of networked charging equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of transaction data statistics, and in particular to a transaction data statistics method based on a networked payment collection device. Background Art

[0002] Transaction data refers to static data generated by an enterprise in its daily operations that reflects specific business activities. 1 This type of data has the following characteristics: Static: Once transaction data is created and becomes effective, it typically does not change frequently. For example, it records the status of a completed order or changes in inventory. Business relevance: Each transaction record consists of multiple items, such as product information, transaction time, and transaction amount, which together reflect specific activities within the enterprise or with external organizations. Data comes from a wide range of sources, including POS transaction records, credit card swipe records, ERP system data, sales system data, inventory data, and supply chain data. Application value: In data mining, transaction data can be used for association analysis (such as related product purchases), trend forecasting (such as sales trend analysis), and behavioral analysis (such as customer purchasing behavior patterns).

[0003] Transaction data is often secondary, and its original source is difficult to trace, making it difficult for researchers to verify its validity and authenticity. Some survey data may be inaccurately reported, further weakening the reliability of statistical results. Trading systems can also experience latency, causing sell orders to lag behind actual market conditions and fail to reflect real-time trading activity. Therefore, we propose a transaction data statistics method based on networked payment devices. Summary of the Invention

[0004] The purpose of the present invention is to solve the problems mentioned in the above background technology. The present invention provides a transaction data statistics method based on a networked payment device.

[0005] In order to achieve the above-mentioned purpose, the present invention specifically adopts the following technical solutions:

[0006] A transaction data statistics method based on a networked payment device comprises the following steps:

[0007] Step 1: Data collection: collect user information and transaction record data in real time through the networked payment device, and upload the collected real-time data to the central processor for storage;

[0008] Step 2: Data preprocessing: cleaning and processing the collected data to ensure data quality and accuracy;

[0009] Step 3: Data analysis: Use data mining methods and algorithms, such as association rule mining and predictive models, to mine and analyze data and discover patterns in user behavior and transaction trends.

[0010] Step 4: Profiling and behavior analysis: Use visualization tools to analyze and display user data, draw user profiles, and conduct behavioral analysis to provide reference for the optimization and improvement of networked charging equipment.

[0011] Step 5: Generate decisions and generate reports based on the analysis results to assist merchants in formulating marketing strategies and optimizing inventory management;

[0012] Step 6: Storage backup: upload the generated decisions and reports to the database and cloud via wireless transmission technology for periodic storage.

[0013] Furthermore, the data collected includes transaction amount, transaction time and transaction type.

[0014] Furthermore, the storage method used in the data collection is HBase.

[0015] Furthermore, the data collection center is equipped with data transmission encryption and access permission control technology when storing data.

[0016] Furthermore, the data preprocessing includes the following steps:

[0017] Step 21: Data cleaning, removing duplicate data, processing outliers, and ensuring data accuracy through data verification rules;

[0018] Step 22: Data standardization, unified data format, and transaction data converted into a unified data structure to facilitate subsequent analysis.

[0019] Furthermore, the data analysis includes the following steps:

[0020] Step 31: Data statistics, performing statistical analysis on the data after data preprocessing;

[0021] Step 32: Real-time analysis: Analyze the real-time data stream using the stream processing framework to support dynamic adjustment of statistical rules.

[0022] Furthermore, the data statistics include basic statistics and conditional statistics, wherein the basic statistics include calculating the total transaction volume, average daily transaction amount and transaction peak index, and the conditional statistics include performing multi-dimensional statistical analysis according to preset conditions.

[0023] Furthermore, the visualization tool is based on a dashboard for displaying statistical results and supports multi-dimensional charts and trend analysis, wherein the multi-dimensional charts include bar charts and line charts.

[0024] Furthermore, the user portrait in the portrait and behavior analysis includes basic user information, consumption habits, and preferences, and the behavior analysis includes user activity and churn rate.

[0025] The beneficial effects of the present invention are as follows:

[0026] 1. The present invention collects user information and transaction record data in real time through a networked payment device, and uploads the collected real-time data to a central processing unit for storage. The real-time data collection can ensure the accuracy and timeliness of transaction data, and can also ensure the consistency of data, thereby ensuring the accuracy of statistics. The collected data is cleaned and processed to ensure data quality and accuracy. The preprocessing of the data can reduce the complexity of subsequent processing and improve the efficiency and accuracy of statistics.

[0027] 2. The present invention uses data mining methods and algorithms, such as association rule mining and prediction models, to mine and analyze data, discover user behavior patterns and transaction trend-related laws, provide data sources and data support for subsequent work, and improve processing effects and quality.

[0028] 3. The present invention uses data visualization tools to analyze and display user data, draw user portraits, and conduct behavioral analysis to provide a reference for the optimization and improvement of networked charging equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a workflow diagram of the present invention;

[0030] Figure 2 It is a workflow diagram of data preprocessing in the present invention;

[0031] Figure 3 It is a workflow diagram of data analysis in the present invention. DETAILED DESCRIPTION

[0032] To make the objectives, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0033] See also Figure 1 - Figure 3 The present invention provides a transaction data statistics method based on a networked payment device, comprising the following steps:

[0034] Step 1: Data collection: collect user information and transaction record data in real time through networked payment devices, and upload the collected real-time data to the central processor for storage; the collection of real-time data can ensure the accuracy and timeliness of transaction data, and can also ensure the consistency of data, thereby ensuring the accuracy of statistics.

[0035] Step 2: Data preprocessing: clean and process the collected data to ensure data quality and accuracy. Data preprocessing can reduce the complexity of subsequent processing and improve statistical efficiency and accuracy.

[0036] Step 3: Data analysis: Use data mining methods and algorithms, such as association rule mining and predictive models, to mine and analyze data to discover relevant rules of user behavior patterns and transaction trends; provide data sources and data support for subsequent work, which can improve the effect and quality of processing.

[0037] Step 4: Portrait and behavior analysis: Use visualization tools to analyze and display user data, draw user portraits, and conduct behavior analysis to provide reference for the optimization and improvement of networked charging equipment.

[0038] Step 5: Generate decisions and generate reports based on the analysis results to assist merchants in formulating marketing strategies and optimizing inventory management decisions.

[0039] Step 6: Storage backup: upload the generated decisions and reports to the database and cloud via wireless transmission technology for periodic storage.

[0040] In this embodiment, preferably, the data collected includes transaction amount, transaction time, and transaction type; this can ensure the accuracy of real-time data collection and improve statistical accuracy.

[0041] In this embodiment, preferably, the storage method used in data collection is HBase; HBase is built on HDFS, supports columnar storage and large-scale data query, organizes semi-structured data in tabular form, and supports primary key query and range search.

[0042] In this embodiment, preferably, the data collection center is equipped with data transmission encryption and access permission control technology when storing data; it can ensure data privacy and compliance, and has a good security effect.

[0043] In this embodiment, preferably, data preprocessing includes the following steps:

[0044] Step 21: Data cleaning: remove duplicate data, process outliers such as negative amounts and duplicate transactions, and ensure data accuracy through data validation rules.

[0045] Step 22: Standardize data and unify data formats, such as date format and amount precision, to convert transaction data into a unified data structure for subsequent analysis.

[0046] In this embodiment, preferably, data analysis includes the following steps:

[0047] Step 31: Data statistics, performing statistical analysis on the data after data preprocessing;

[0048] Step 32: Real-time analysis: Analyze the real-time data stream using the stream processing framework to support dynamic adjustment of statistical rules.

[0049] In this embodiment, preferably, data statistics include basic statistics and conditional statistics, wherein basic statistics include calculating the total transaction volume, average daily transaction amount and transaction peak index, and conditional statistics include performing multi-dimensional statistical analysis based on preset conditions, such as transaction type and user attributes.

[0050] In this embodiment, preferably, the visualization tool is based on a dashboard, is used to display statistical results, and supports multi-dimensional charts and trend analysis, wherein the multi-dimensional charts include bar charts and line charts.

[0051] In this embodiment, preferably, the user portrait in the portrait and behavior analysis includes basic user information, consumption habits, and preferences, and the behavior analysis includes user activity and churn rate.

[0052] The working principle and use process of the present invention:

[0053] Step 1: Data collection: User information and transaction record data, including transaction amount, transaction time, and transaction type, are collected in real time through networked payment devices. This ensures the accuracy of real-time data collection and improves statistical accuracy. The collected real-time data is uploaded to the central processing unit for storage. The storage method used in data collection is HBase. HBase is built on HDFS, supports columnar storage and large-scale data queries, organizes semi-structured data in tabular form, supports primary key queries and range searches, and is equipped with data transmission encryption and access permission control technology when storing data. This ensures data privacy and compliance, and has excellent security effects. Real-time data collection can ensure the accuracy and timeliness of transaction data, as well as the consistency of data, thereby ensuring statistical accuracy.

[0054] Step 2: Data preprocessing: clean and process the collected data to ensure data quality and accuracy. Data preprocessing can reduce the complexity of subsequent processing and improve statistical efficiency and accuracy.

[0055] Data preprocessing includes the following steps:

[0056] Step 21: Data cleaning: remove duplicate data, process outliers such as negative amounts and duplicate transactions, and ensure data accuracy through data validation rules.

[0057] Step 22: Standardize data and unify data formats, such as date format and amount precision, to convert transaction data into a unified data structure for subsequent analysis.

[0058] Step 3: Data analysis: Use data mining methods and algorithms, such as association rule mining and predictive models, to mine and analyze data to discover relevant rules of user behavior patterns and transaction trends; provide data sources and data support for subsequent work, which can improve the effect and quality of processing.

[0059] Data analysis includes the following steps:

[0060] Step 31: Data statistics. Statistical analysis is performed on the pre-processed data. Data statistics include basic statistics and conditional statistics. Basic statistics include calculating the total transaction volume, average daily transaction amount, and transaction peak indicators. Conditional statistics include multi-dimensional statistical analysis based on preset conditions, such as transaction type and user attributes.

[0061] Step 32: Real-time analysis: Analyze the real-time data stream using the stream processing framework to support dynamic adjustment of statistical rules.

[0062] Step 4: Profile and behavior analysis. Use visualization tools. The visualization tools are based on dashboards and are used to display statistical results. They also support multi-dimensional charts and trend analysis. Multi-dimensional charts include bar charts and line charts. By analyzing and displaying user data, user profiles are drawn. User profiles include basic user information, consumption habits, and preferences. Behavioral analysis includes user activity and churn rate. Behavioral analysis provides a reference for the optimization and improvement of networked charging equipment.

[0063] Step 5: Generate decisions and generate reports based on the analysis results to assist merchants in formulating marketing strategies and optimizing inventory management decisions.

[0064] Step 6: Storage backup: upload the generated decisions and reports to the database and cloud via wireless transmission technology for periodic storage.

[0065] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A transaction data statistics method based on a networked payment device, characterized in that: The steps include: Step 1: Data collection: collect user information and transaction record data in real time through the networked payment device, and upload the collected real-time data to the central processor for storage; Step 2: Data preprocessing: cleaning and processing the collected data to ensure data quality and accuracy; Step 3: Data analysis: Use data mining methods and algorithms, such as association rule mining and predictive models, to mine and analyze data and discover patterns in user behavior and transaction trends. Step 4: Profiling and behavior analysis: Use visualization tools to analyze and display user data, draw user profiles, and conduct behavioral analysis to provide reference for the optimization and improvement of networked charging equipment; Step 5: Generate decisions and generate reports based on the analysis results to assist merchants in formulating marketing strategies and optimizing inventory management; Step 6: Storage backup: upload the generated decisions and reports to the database and cloud via wireless transmission technology for periodic storage.

2. The transaction data statistics method based on a networked payment device according to claim 1, characterized in that: The data collected includes transaction amount, transaction time and transaction type.

3. The transaction data statistics method based on a networked payment device according to claim 1, characterized in that: The storage method used in the data collection is HBase.

4. The transaction data statistics method based on a networked payment device according to claim 1, characterized in that: When storing data in the data collection center, the data transmission encryption and access permission control technology are provided.

5. The transaction data statistics method based on a networked payment device according to claim 1, characterized in that: The data preprocessing includes the following steps: Step 21: Data cleaning, removing duplicate data, processing outliers, and ensuring data accuracy through data verification rules; Step 22: Data standardization, unified data format, and transaction data converted into a unified data structure to facilitate subsequent analysis.

6. The transaction data statistics method based on a networked payment device according to claim 1, characterized in that: The data analysis includes the following steps: Step 31: Data statistics, performing statistical analysis on the data after data preprocessing; Step 32: Real-time analysis: Analyze the real-time data stream using the stream processing framework to support dynamic adjustment of statistical rules.

7. The transaction data statistics method based on a networked payment device according to claim 7, characterized in that: The data statistics include basic statistics and conditional statistics, wherein the basic statistics include calculating the total transaction volume, average daily transaction amount and transaction peak index, and the conditional statistics include performing multi-dimensional statistical analysis according to preset conditions.

8. The transaction data statistics method based on a networked payment device according to claim 1, characterized in that: The visualization tool is based on a dashboard, is used to display statistical results, and supports multi-dimensional charts and trend analysis, wherein the multi-dimensional charts include bar charts and line charts.

9. The transaction data statistics method based on a networked payment device according to claim 1, characterized in that: The user profile in the portrait and behavior analysis includes basic user information, consumption habits, and preferences, and the behavior analysis includes user activity and churn rate.

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