CN117725122A - An order synchronization method for business management platform - Google Patents

Abstract

An order synchronization method of a service management platform, comprising: s1, establishing a unified data interface standard, wherein the unified data interface standard comprises data field mapping, format conversion rules and interface protocols so as to realize consistency and compatibility of different data sources; s2, inputting the original order data into a conversion module, and carrying out formatting and structuring treatment through unified standards; s3, cleaning, correcting, combining and verifying the formatted data in a data processing module; s4, based on an event-driven data synchronization mechanism, ensuring that data in a service management platform can be updated in real time; and S5, the analysis module carries out deep analysis on the synchronous data and generates a customized report. By integrating unified data interface standards, efficient data conversion, real-time synchronization mechanisms and deep data analysis and report generation, the efficiency and accuracy of data processing are greatly improved, and the quick response capability to market dynamics is enhanced, so that the data management efficiency and market competitiveness of enterprises are enhanced as a whole.

Description

An order synchronization method for business management platform

Technical field

The invention relates to the field of enterprise management, in particular to an order synchronization method of a business management platform.

Background technique

In the current business environment, it is common for businesses to utilize multiple e-commerce platforms to increase their market coverage and sales. These platforms each have independent operating systems and data interfaces, allowing companies to promote and sell their products in a wide range of markets. However, this diversified sales strategy brings complex data management challenges, especially when it comes to order processing and customer data management.

Because data interfaces and formats vary across platforms, companies face significant obstacles when trying to integrate this data to gain a comprehensive view of sales. This data inconsistency leads to inaccuracies in sales records, discrepancies in inventory status, and inefficiencies in customer service. In addition, the existing system's deficiencies in real-time data synchronization further exacerbated these problems, affecting the company's ability to respond quickly to market trends and the accuracy of strategic decisions.

To overcome these challenges, there is an urgent need for a method that can efficiently integrate multi-platform data so that enterprises can effectively manage their sales activities on different e-commerce platforms. This approach should be able to standardize interfaces to different data sources and enable real-time data synchronization to provide accurate and consistent sales and inventory information.

Contents of the invention

In order to overcome the above-mentioned shortcomings of the prior art, the purpose of the present invention is to:

An innovative order synchronization method for business management platform is proposed. The approach includes developing unified data interface standards to seamlessly integrate data from different e-commerce platforms. In addition, the present invention adopts advanced data synchronization technology to ensure that information on all platforms is updated in real time, minimizing information lag and errors. Through this method, enterprises can achieve effective management of multi-platform sales activities, improve decision-making accuracy and market response speed.

The technical solutions adopted by the present invention to solve the technical problems are:

An order synchronization method for a business management platform, including:

S1: Create a unified data interface standard;

S2: Input the order data into the conversion module and convert it into platform data through unified data interface standards;

S3: Input platform data to the processing module for data processing;

S4: Synchronization module performs real-time data update and synchronization;

S5: The analysis module analyzes the precipitation data and inputs it into the report module to generate a report;

The unified data interface standard in S1 includes data field mapping, format conversion rules and interface protocols to achieve consistency and compatibility of data from different sources, simplify the complexity of subsequent data processing and synchronization, and reduce data conversion errors and information loss. reduce risks and improve the efficiency and reliability of data integration.

Input the original order data of each sales platform into the conversion module in the S2, apply the above unified data interface standard to format and structure the sales platform data, and apply the unified data interface standard to format and structure the data. It lays the foundation for subsequent data processing and synchronization, so that order data from different platforms can be efficiently processed in a single system, enhancing data consistency and operability.

The S3 specifically includes cleaning, correcting, merging and verifying the formatted data in the data processing module to improve data quality. The purpose of this step is to eliminate errors and duplications and provide an accurate and reliable basis for data synchronization and analysis, thereby increasing the accuracy and credibility of data analysis.

The S4 implements an event-driven data synchronization mechanism to ensure that data in the business management platform can be updated in real time to reflect the latest status. The main function of real-time synchronization is to provide immediate business insights, reduce operational risks caused by data lag, and improve the ability to respond quickly to market changes.

The S5 performs in-depth analysis of synchronized data in the analysis module and generates customized reports. The core of this process is to support better decision-making through detailed data analysis, enhance the data-driven nature of business decisions, and improve the enterprise's adaptability to market dynamics and competitive advantage.

As a further improvement of the present invention: the development of a unified application programming interface (API) specifically includes: RESTful API design, identifying and defining the main resources that the API will handle, and designing endpoints for each resource, and specifying standards for each endpoint. HTTP methods, clarify the specific operations and expected behaviors of each method, define query parameters and filtering options, allow users to customize requests, specify standard HTTP status codes for responses in different situations, define error handling mechanisms and error response formats; Design GraphQL schema, clarify types, queries and changes, nested queries and multi-resource aggregation.

As a further improvement of the present invention: the specific method for S2 to input order data into the conversion module is to establish an interface connection with the e-commerce platform, automatically collect original order data, and design a data import mechanism to allow manual uploading or regular automatic uploading from Obtain data from each platform; design a conversion module that can process large amounts of data and support parallel processing. Design specific plug-ins or adapters for different e-commerce platforms to process their own unique data formats.

As a further improvement of the present invention: the data interface standard is applied in the conversion module to convert the order data into a unified format, and according to the mapping rules, the fields in the order data are converted into corresponding fields in the general data model. fields; then perform data cleaning operations, including removing duplicate records, correcting erroneous data, filling missing values, verifying the transformed data, and establishing an error handling mechanism to identify and correct anomalies or errors during the transformation process.

As a further improvement of the present invention: the specific method for S3 to input platform data to the processing module for data processing is to perform data cleaning, identify and delete data records that do not meet the criteria, and use average and median values for missing data. Or based on prediction model estimation, detect and remove duplicate data items, ensure that all data follows a unified format, merge data into a unified data set, and check that the data set is complete and includes all necessary fields and records.

As a further improvement of the present invention: the data processing also includes data correction, conducting consistency checks on the data set so that all records follow the same logic and format standards, evaluating data quality, identifying the root causes of data quality problems, and continuously improving the data Processing process; aggregate data, optimize data storage structure and index, ensure efficient data retrieval and access, implement data backup mechanism to ensure data security and recoverability.

As a further improvement of the present invention: the specific method for the S4 synchronization module to perform real-time data update and synchronization is to design a real-time synchronization architecture and adopt an event-driven method. When the source data changes, the synchronization process is triggered and the message queue buffer is used. and transmit data update events, identifying and synchronizing data that has changed since the last update, rather than the entire data set, and implementing predefined resolution strategies when data conflicts arise.

As a further improvement of the present invention: the synchronization module can complete real-time data capture: configure the CDC tool to connect to the e-commerce platform order database, set the database tables and fields that need to capture changes, and configure the data change processing program. Deploy event listeners in the database to monitor data changes. Data changes will trigger the synchronization process and set up a notification mechanism. Once relevant events are monitored, notifications will be sent to the message queue.

Compared with the prior art, the beneficial effects of the present invention are:

The invention is an order synchronization method for a business management platform. It processes large-scale real-time data by creating a unified data interface standard, developing an efficient data conversion module, realizing real-time data synchronization, conducting in-depth data analysis and report generation, and applying a stream processing framework. data flow. These capabilities work together to simplify data processing processes, improve data accuracy and consistency, ensure real-time updates of data, provide decision makers with insightful business information, and enhance the enterprise's ability to respond to complex data processing needs. Thereby improving the data management efficiency and market competitiveness of the enterprise as a whole.

Description of the drawings

Figure 1 is a flow chart of the method of the present invention.

Detailed ways

The present invention will now be further described in conjunction with the accompanying drawings and examples:

Detailed description of the invention:

In order to create a unified data interface standard, S1 can follow the following detailed plan design: first create a unified data interface standard, analyze existing data formats and requirements, research mainstream e-commerce platforms in the market, and collect their data interface documents and format requirements, compare the data formats of different platforms, and identify the main differences and commonalities between them; design a general data model based on the collected information, design a general data model covering all necessary fields, such as order number, product details, prices, customer information, etc., ensure that the data model is flexible enough to accommodate the specific information of different platforms; formulate conversion rules to formulate mapping rules for the unique data formats of each e-commerce platform to convert them into a common data model, formulate Data formatting and validation rules to ensure that the converted data is accurate and compliant with standards; interface protocol development to develop a unified application programming interface (API) so that other systems can easily access and use the converted data, written clearly API documentation, including usage guides and sample codes; compatibility and scalability testing, testing the compatibility and effectiveness of data interface standards on multiple e-commerce platforms to ensure that the designed standards have good scalability and can adapt to the market and Changes in technology; implement a feedback mechanism to collect feedback on data interface standards from end users and developers, adjust and optimize data interface standards based on feedback, and ensure that they continue to meet user needs.

Further, the development of interface specification standards is as follows: RESTful API design, identify and define the main resources that the API will handle (such as orders, customers, products, etc.), and design endpoints for each resource, specifying standard HTTP for each endpoint Methods (GET, POST, PUT, DELETE, etc.), clarify the specific operations and expected behaviors of each method, define query parameters and filtering options, allow users to customize requests (such as sorting, filtering specific fields), and specify standard HTTP status codes Used for responses in different situations, define error handling mechanisms and error response formats; design GraphQL patterns, clarify types, queries, and changes to ensure that GraphQL queries can flexibly handle complex data retrieval requirements, such as nested queries and multi-resource aggregation, and consider how Optimize GraphQL queries to avoid over-fetching data and potential performance issues.

Furthermore, it also includes testing and verification of interface specifications and standards. The method is as follows: Interface test case writing, defining specific test scenarios for each API endpoint and method, including normal processes and edge cases, to ensure that the tests cover different types of Input and verify whether the output meets expectations, including testing the security and access permissions of the API to ensure that only authorized users can access sensitive data; automate test scripts and choose appropriate automated testing tools, such as Postman (mainly for RESTful API) or Selenium (Suitable for more complex end-to-end testing), write automated test scripts based on defined test cases, and integrate automated test scripts into the continuous integration/continuous deployment (CI/CD) process to ensure that tests can be automatically run for each update .

For the step of inputting order data into the conversion module and formatting it, S2 can follow the following detailed plan design: data collection and input, establishing interface connections with various e-commerce platforms to automatically collect original order data, and designing an Data import mechanism allows manual uploading or regular acquisition of data from various platforms through automation; design conversion module and develop an efficient conversion module that can handle large amounts of data and support parallel processing to improve efficiency. Consider designing for different e-commerce platforms Specific plug-ins or adapters are used to process their own unique data formats; apply data interface standards, apply the previously defined unified data interface standards in the conversion module, convert the original data into a unified format, and convert the original data into a unified format according to the mapping rules. Convert the fields into corresponding fields in the common data model; data formatting and cleaning, perform data cleaning operations, including removing duplicate records, correcting erroneous data, filling missing values, etc., to ensure that all data conforms to predetermined format standards, such as dates and Time format, currency unit, etc.; data verification and error handling, verify whether the converted data conforms to the expected structure and content, and establish an error handling mechanism to identify and correct anomalies or errors during the conversion process.

Furthermore, S2 also includes data preprocessing, the method is as follows: format standardization, determine the key fields that need to be standardized, such as date and time, currency unit, size unit, etc., and define unified formatting rules for each type of data. For example, dates and times can be unified to the ISO8601 standard, and scripts can be developed or data processing tools can be used to automatically convert raw data into a unified format.

Furthermore, it also includes the design of data transformation logic. The method is as follows: mapping rule application, defining detailed field mapping rules for each data source, determining how the source fields correspond to the fields of the target model, and implementing these mappings in the data transformation module. Logic, which may require writing specific codes or scripts; dynamic conversion processing, developing conversion logic that can be dynamically adjusted according to specific attributes of the data source (such as platform type, data format), and developing specific plug-ins or adapters for different data sources to process Special data format or structure.

Furthermore, it also includes data verification and formatting: data integrity verification, determining which fields are required and which are optional, writing code or using tools to automatically check whether each record contains all required fields; data format Final confirmation, according to the requirements of the unified data model, clarify the format, type and scope specifications of the final data, and develop post-processing scripts to ensure that all data strictly conforms to these specifications.

S3 inputs the formatted platform data into the data processing module for cleaning, correction, merging and verification. The following is the detailed plan design: data cleaning, identifying and deleting data records that do not meet the standards or obvious errors, and cleaning the missing data. Use appropriate methods to populate, such as using mean, median or prediction model-based estimates, detect and remove duplicate data items to ensure the uniqueness of data; data correction to ensure that all data follows a uniform format, such as date-time format , currency units, etc., to verify whether the data conforms to the business logic and predetermined range, such as checking whether the order amount is a positive value; data merging, merging data from different e-commerce platforms into a unified data set to maintain data integrity and consistency , ensure the alignment of relevant fields during the merge process, such as customer ID, product number, etc.; data verification, check whether the data set is complete, including all necessary fields and records, verify the accuracy of the data, and confirm that there are no Introducing errors; performance optimization, optimizing data processing algorithms and processes to ensure processing speed and efficiency, ensuring that the system can handle large amounts of data without being restricted by performance bottlenecks.

Further, data clarity includes: data cleaning, applying statistical methods and algorithms to identify and process outliers, such as using IQR (interquartile range) to identify and process outliers, and standardizing data, for example, unifying text data. upper and lower case, remove extra spaces and punctuation marks, and use appropriate strategies for missing values, such as using mean, median filling, or applying more complex data imputation techniques such as K-nearest neighbor (K-NN) interpolation Complement; data merging and association, merging data from different sources into a unified data set, handling conflicts and inconsistencies between data sources, and identifying associations between different data sets, such as the association between order data and customer data. , for effective merging.

Furthermore, it also includes data correction, checking the consistency of the data set to ensure that all records follow the same logic and format standards, regularly evaluating data quality, identifying the root causes of data quality problems, and continuously improving the data processing process; data processing and Transformation, aggregation of data as needed, such as aggregation by time period, region or other dimensions, subsequent data analysis and model building to create useful features, such as extracting time and date parts from timestamps; data storage and backup, Optimize data storage structures and indexes to ensure efficient data retrieval and access, and implement data backup mechanisms to ensure data security and recoverability.

S4 implements the synchronization module for real-time data update and synchronization. The following is the specific solution design: design a real-time synchronization architecture and adopt an event-driven approach. When the source data changes (such as new order generation, order status update, etc.), it automatically Trigger the synchronization process and use message queues (such as Kafka, RabbitMQ) to buffer and transmit data update events to ensure the efficiency and reliability of data synchronization; the data update mechanism identifies and synchronizes data that has changed since the last update, not the entire Data sets to improve synchronization efficiency. When data conflicts occur (such as updates of the same data in different sources), predefined resolution strategies are implemented, such as the update of the latest timestamp; performance optimization is performed between the data source and the target system. Achieve load balancing among them to ensure stable operation of the system under high load conditions. Implement caching strategies for frequently accessed data to reduce direct read and write pressure on the database; monitor and log records to establish a real-time monitoring system to monitor the status of data synchronization. and performance, discover and solve problems in a timely manner, record detailed logs of data synchronization, including timestamps, data sources, synchronization status, etc., to facilitate problem tracking and system auditing; security and data consistency, ensure the security of data during the synchronization process , Use encryption technology to protect data transmission, and use appropriate technologies (such as transaction processing, lock mechanisms, etc.) to ensure data consistency and prevent inconsistencies during data synchronization.

Further, the synchronization module can complete real-time data capture: Change Data Capture (CDC). According to the type and structure of the source system, select the appropriate CDC tool, such as Debezium or AWS DMS, configure the CDC tool to connect to the source database, and set the necessary parameters , such as database URLs, credentials, etc., specify the database tables and fields that need to capture changes, and configure how to handle data changes (add, update, delete); listen for events and deploy event listeners in the source system to monitor data changes , define what type of data changes (such as order creation, update, etc.) will trigger the synchronization process, set up a notification mechanism, and send notifications to the message queue once relevant events are monitored.

Furthermore, the synchronization module constructs a data synchronization pipeline: message queue integration, selects a suitable message queue system, such as Apache Kafka or RabbitMQ, installs and configures it, and creates corresponding topics or queues in the message queue system for storage and forward data change events, configure the data source as the message producer, the data target system as the consumer, and establish a connection; set the data buffer and design the buffer to temporarily store a large number of data events to prevent conflicts between the data source and the target system Differences in processing speed lead to data loss. Depending on the data traffic and processing capabilities, implement a suitable buffering strategy such as sliding window or batch processing.

Furthermore, real-time data processing can be carried out: stream processing framework application, select an appropriate stream processing framework, such as Apache Flink or Spark Streaming, configure the stream processing framework, deploy it to an appropriate environment, such as a cloud platform or a local server, and define the data Stream processing logic, including data reception, conversion and output; data enrichment and conversion, implementing data enrichment logic, such as pulling additional information from other systems and combining it with event data, and converting data as needed, such as format adjustment and field mapping , data aggregation, etc.

For S5 to analyze precipitation data through the analysis module and input the results into the report module to generate reports, the following is the specific solution design: build a data analysis module and establish a multi-dimensional data model to support complex data analysis, such as time series analysis , customer behavior analysis, etc., integrating advanced analysis tools and algorithms, such as machine learning models and statistical analysis methods, to reveal trends and patterns in data; data analysis processes, automated processing processes, extract key information and indicators from precipitated data, and combine Historical data and real-time data are used to conduct comprehensive business analysis, including sales trends, customer preferences, inventory optimization, etc.; report module design is to develop a report generation module that allows users to choose different report templates and indicators as needed. During the report generation process, Charts, text parsing and key indicators can be dynamically inserted; visualization and user interface use visual means such as charts, graphs and dashboards to make the report content intuitive and easy to understand, and provide an interactive interface so that users can customize the report content according to their own needs. ; Performance optimization and scalability, optimizing data processing algorithms to ensure that even complex analyzes can be completed quickly, and considering possible future expansion when designing modules, such as adding new analysis methods or report templates; Security and compliance, ensuring The analysis and report generation process complies with data protection regulations, especially when handling sensitive data. Strict access control and rights management are implemented to ensure that only authorized users can access the report generation module.

Furthermore, the selection and construction methods of analysis models are as follows: data set analysis, based on the predetermined analysis goals, select appropriate data sets for analysis, and select applicable statistical models or machine learning algorithms, such as regression analysis, clustering, decision trees, etc. , build a model and use data sets for training and tuning to improve the accuracy and reliability of the model; data visualization, use charting tools (such as Matplotlib, Seaborn) for data visualization, explore data distribution, correlation and trends, and provide data Statistical summaries such as mean, median, standard deviation, etc.

Further, the development method of the report model is as follows: report template design, design the basic framework of the report, including title, summary, body, conclusion and other parts, determine the visual design elements of the report, such as color scheme, font, chart style, etc., use the report Generating tools (such as Microsoft Word templates, LaTeX) to create report templates; dynamic report generation, developing a report generation system that allows automatic extraction of data and analysis results from the analysis module, and implementing dynamic content insertion mechanisms, such as automatically updating charts based on user-selected parameters and text, design the user interface to allow users to easily select different report templates and data views.

Main functions of the invention:

1. Creation of unified data interface standards: define a common standard and format for data from different e-commerce platforms. The beneficial effect of this is to simplify the data processing process, reduce data conversion errors, and improve the compatibility and integration efficiency between different data sources.

2. Efficient data conversion module: Convert the original order data of each e-commerce platform into a unified format, and perform cleaning and standardization processing. Its beneficial effect is to improve the accuracy and consistency of data and provide a reliable foundation for subsequent data analysis and application.

3. Real-time data synchronization module: captures change events in source data and updates them to the business management platform in real time. The beneficial effect of this function is to ensure the real-time and accuracy of data, allowing enterprises to quickly respond to market changes and customer needs.

4. Data analysis and report generation: Conduct in-depth analysis of synchronized data and generate intuitive reports based on the analysis results. Its beneficial effect is to provide decision-makers with insightful business information, support data-based decision-making, and enhance the company's strategic planning and market competitiveness.

5. Application of stream processing framework: process and convert data streams in real time to support complex data processing requirements. The beneficial effect of this function is to improve the ability to process large-scale real-time data and ensure the flexibility and scalability of data processing.

Through these innovative features, the invention provides enterprises with a comprehensive, efficient and reliable solution for managing and analyzing complex data sets from different e-commerce platforms, thereby improving overall business operation efficiency and decision-making quality.

To sum up, after reading the document of the present invention, those of ordinary skill in the art can make various other corresponding transformation schemes based on the technical solutions and technical concepts of the present invention without creative mental effort, which all fall within the scope of protection of the present invention.

Claims (8)

1. An order synchronization method for a business management platform, which is characterized by including the following steps: S1: Create a unified data interface standard; S2: Input the order data into the conversion module and convert it into platform data through unified data interface standards; S3: Input platform data to the processing module for data processing; S4: Synchronization module performs real-time data update and synchronization; S5: The analysis module analyzes the precipitation data and inputs it into the report module to generate a report; The specific method for S1 to create a unified data interface standard is to: analyze the order data format and requirements, collect order data interface documents, identify differences and commonalities in order data formats, and design a common data model containing all necessary fields for Develop mapping rules for different e-commerce order data formats, formulate data formatting and verification rules, and develop a unified application programming interface (API) for the business management platform to use the converted e-commerce data and implement interface standards. Compatibility and validity testing to ensure its applicability in different e-commerce systems. 2. The order synchronization method of a business management platform according to claim 1, characterized in that the development of a unified application programming interface (API) specifically includes: RESTful API design, identifying and defining the main tasks that the API will handle. resources, and design endpoints for each resource, specify standard HTTP methods for each endpoint, clarify the specific operations and expected behaviors of each method, define query parameters and filtering options, allow users to customize requests, and specify standard HTTP status codes Used for responses in different situations, define error handling mechanisms and error response formats; design GraphQL patterns, clarify types, queries and changes, nested queries and multi-resource aggregation. 3. An order synchronization method for a business management platform according to claim 1, characterized in that the specific method for the S2 to input the order data into the conversion module is to establish an interface connection with the e-commerce platform and automatically collect the original orders. Data, design a data import mechanism that allows manual uploading or regular acquisition of data from various platforms through automated means; design a conversion module that can handle large amounts of data and support parallel processing, and design specific plug-ins or adapters for different e-commerce platforms. Handle their own unique data formats. 4. The order synchronization method of a business management platform according to claim 3, characterized in that the data interface standard is applied in the conversion module to convert the order data into a unified format, and according to the mapping Rules to convert the fields in the order data into corresponding fields in the general data model; then perform data cleaning operations, including removing duplicate records, correcting erroneous data, filling in missing values, verifying the converted data, and establishing an error handling mechanism for Identify and correct anomalies or errors in the conversion process. 5. The order synchronization method of a business management platform according to claim 1, characterized in that the specific method for the S3 to input platform data to the processing module for data processing is to perform data cleaning, identify and delete inconsistencies. Target data records, use mean, median or prediction model-based estimation for missing data, detect and remove duplicate data items, ensure that all data follows a unified format, merge data into a unified data set, check The data set is complete, including all necessary fields and records. 6. An order synchronization method for a business management platform according to claim 5, characterized in that the data processing also includes data correction, and a consistency check is performed on the data set so that all records follow the same logic and format. Standards, evaluate data quality, identify the root causes of data quality problems, and continuously improve data processing processes; aggregate data, optimize data storage structures and indexes, ensure the efficiency of data retrieval and access, and implement data backup mechanisms to ensure data security and recoverability. 7. The order synchronization method of a business management platform according to claim 1, characterized in that the specific method for the S4 synchronization module to perform real-time data update and synchronization is to design a real-time synchronization architecture and adopt an event-driven method. , when the source data changes, trigger the synchronization process, use the message queue to buffer and transmit data update events, identify and synchronize the data that has changed since the last update, rather than the entire data set, and implement pre-defined solution strategy. 8. The order synchronization method of a business management platform according to claim 7, characterized in that the synchronization module can complete real-time data capture: configure the CDC tool to connect to the e-commerce platform order database, and set the database that needs to capture changes. Tables and fields, and configure the data change processing program. Deploy event listeners in the e-commerce platform order database to monitor data changes. Data changes will trigger the synchronization process. Set up a notification mechanism. Once relevant events are monitored, the message queue will be sent to the message queue. Send notification.