Self-adaptive report generation method based on intelligent process driving and storage medium

Abstract

The invention relates to a self-adaptive report generation method based on intelligent process driving and a storage medium. Comprising the following steps that an RPA robot is used for collecting webpage platform data, and the webpage platform data comprises exported data, UI interface content data and structured table data; webpage data processing tasks are executed based on Python, and the data processing tasks comprise data input, data processing and data export and storage; based on the processed data file, automatic image-text generation is achieved, a report is constructed, and the report content comprises a data chart and an analysis abstract; and based on the generated self-adaptive report, storage is carried out, and the storage mode comprises file system storage and archiving storage. According to the method, the content and the structure of the report are automatically adjusted according to the change of the input data based on a dynamic data analysis and processing technology, so that the intelligence degree and the flexibility of report generation are greatly improved, and the data presentation efficiency in the operation and maintenance process is greatly improved.

Description

Technical Field

[0001] The invention relates to an adaptive report generation method based on intelligent process drive and a storage medium. Background Art

[0002] Robotic process automation (RPA) technology completes tasks by simulating and automatically executing human operations on computers. RPA robots can operate between different software and systems like humans, automatically completing a large number of repetitive and highly regular processes. It does not require changing existing systems or adding complex integrations, so it can be quickly deployed on computers to automate processes and improve work efficiency and accuracy.

[0003] In the current field of energy management technology, with the continuous increase of related facilities and the improvement of operation and maintenance requirements, the amount and complexity of data are also increasing. However, in the existing operation and maintenance model, it is usually deeply dependent on manual processing of limited data and simple analysis. This method is not only time-consuming, but also lacks analytical depth, making it difficult to form systematic and comprehensive data insights.

[0004] At present, in the RPA (Robotic Process Automation) application in this field, there is still a lack of mature intelligent process solutions to automatically generate adaptable reports. This limits the ability of enterprises to improve work efficiency and service quality through automation. Summary of the invention

[0005] In view of the problems existing in the prior art, the purpose of the present invention is to provide a technical solution of an adaptive report generation method and storage medium based on intelligent process drive.

[0006] The method for generating an adaptive report based on intelligent process drive is characterized by comprising the following steps: 101) Use RPA robots to collect web platform data, where web platform data includes exportable data, UI interface content data, and structured table data; 102) Perform web page data processing tasks based on Python, where data processing tasks include data input, data processing, and data export and storage; 103) Automatically generate graphics and text based on processed data files and construct reports, where the report content includes data charts and analysis summaries; 104) Storing the generated adaptive report, wherein the storage method includes file system storage and archive storage.

[0007] The method for generating an adaptive report based on intelligent process drive is characterized in that the RPA robot is used to collect web platform data, which specifically includes: 1.1) In the robot process list, select the RPA robot to be executed; 1.2) Use the selected RPA robot process to log in to the corresponding business data platform, where the business data platform includes the photovoltaic data platform, operation and maintenance data platform, and marketing data platform; 1.3) Automatically log in to various business data platforms based on RPA robots to collect web platform data. The web platform data collected includes export data, interface display content data, and data in structured table form.

[0008] The method for generating an adaptive report based on intelligent process drive is characterized in that the web page data processing task is executed based on Python, specifically including: 2.1) Based on the pandas package in the Python library, build a data processing channel to parse and load web page collection data in an integrated environment; 2.2) Process the collected data based on the numpy and pandas packages in the Python library, where data processing includes data cleaning, data screening, data grouping, and data calculation; 2.3) Use the ExcelWriter tool in the Pandas package to work with the underlying engines openpyxl, xlsxwriter, and xlwt to organize and store the collected and processed data in the form of logical groups, and export them as structured files according to the user-defined path and naming rules.

[0009] The method for generating an adaptive report based on intelligent process drive is characterized in that the method realizes automatic image and text generation and constructs a report based on the processed data file, specifically including: 3.1) Use Excel software to load and open the structured file after data processing; 3.2) Use the illustration function of Excel software to insert and standardize custom data visualization charts, where standardizing charts includes standardizing chart content, standardizing chart format, and standardizing chart layout; 3.3) Enable the VBA macro script function and build the VBA code. The VBA code function covers the entire report generation process, including creating documents, inserting graphics and text, and saving documents.

[0010] The method for generating an adaptive report based on intelligent process drive is characterized in that storing the generated adaptive report specifically includes: 4.1) According to the time dimension and usage frequency of report data, the reports are divided into short-term data and long-term data; 4.2) For short-term data, i.e., reports generated in real time and frequently accessed, the file system storage method is used; 4.3) For long-term data, that is, reports that are generated periodically and accessed less frequently but need to be kept for a long time, choose archive storage.

[0011] The computer-readable storage medium stores a computer program, wherein the computer-readable storage medium stores a computer program, wherein the computer program, when executed by a processor, implements the adaptive report generation method according to any one of claims 1 to 5.

[0012] Through intelligent process design and automated execution, the present invention significantly improves the efficiency and accuracy of data processing in energy management, thereby improving operation and maintenance efficiency, reducing labor costs, optimizing data insight capabilities, and providing effective support for the intelligence and systematization of operation and maintenance.

[0013] The core function of the present invention is adaptive report generation driven by intelligent processes. Relying on dynamic data analysis and processing technology, the report content and structure are automatically adjusted according to the changes in input data, thereby greatly improving the intelligence and flexibility of report generation. The present invention not only reduces the dependence on manual operations, but also can adapt to the report requirements in different scenarios, greatly improving the data presentation efficiency in the operation and maintenance process. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0015] The present invention will be further described below in conjunction with the accompanying drawings: The present invention provides an adaptive report generation method based on intelligent process drive, which realizes efficient generation of adaptive reports through intelligent process drive technology. Based on the powerful process operation capabilities of robotic process automation (RPA) software and Python programming, the web page data is accurately captured and intelligently processed, and on this basis, VBA macro scripts are used to generate dynamic reports that can flexibly adapt to various business needs.

[0016] like Figure 1 As shown, the present invention discloses an adaptive report generation method based on intelligent process drive, comprising the following steps: S101, using an RPA robot to collect web platform data, where the web platform data includes exportable data, UI interface content data, and structured table data; S102, executing web page data processing tasks based on Python, wherein the data processing tasks include data input, data processing, and data export and storage; S103, realizing automatic graphic and text generation and constructing a report based on the processed data file, wherein the report content includes data charts and analysis summaries; S104: storing based on the generated adaptive report, wherein the storage method includes file system storage and archive storage.

[0017] It should be noted that, in this embodiment, in the robot process list, the RPA robot to be executed is selected, and the data on the web platform (such as the smart photovoltaic platform) is collected based on the RPA robot. Accordingly, the acquired data includes export data, UI interface content data, and structured table data, so that the data collected from the web page can be further processed based on Python code, covering data input, processing, and export operations, and the processing results are stored as standardized data files, so that based on the processed standardized data files, VBA macro scripts can be used to realize automatic graphic and text generation, and then generate adaptive reports. Finally, the generated reports are divided into two types of data, short-term and long-term, according to storage requirements. Short-term data is stored in the file system, while long-term data is managed in the archive storage mode to ensure the efficiency and security of data storage.

[0018] Use RPA robots to collect web platform data, including: 1.1) In the robot process list, select the RPA robot to be executed; 1.2) Use the selected RPA robot process to log in to the corresponding business data platform, where the business data platform includes the photovoltaic data platform, operation and maintenance data platform, and marketing data platform; 1.3) Automatically log in to various business data platforms based on RPA robots to collect web platform data. The web platform data collected includes export data, interface display content data, and data in structured table form.

[0019] It should be noted that in this embodiment, by selecting the RPA robot process to be executed, the corresponding business data platform is automatically logged in to collect the exported data of the web platform, the interface display content data, and the data in the form of structured tables. This example takes the robot that automatically generates the daily operation report of the photovoltaic power station as an example. Specifically, the RPA robot can automatically log in to the corresponding smart photovoltaic platform system, navigate to the data operation module according to the preset process to export the daily production report, enter the operation and maintenance center to obtain the work order summary interface data, and capture the tabular data of the real-time monitoring of the inverter in the monitoring center. Throughout the process, the RPA robot not only completes data collection efficiently, but also has the ability of intelligent abnormal feedback. In the event of an error, it will capture the error log and interface screenshot in real time, publish it to the console and record it in the system log.

[0020] Perform web data processing tasks based on Python, including: 2.1) Based on the pandas package in the Python library, build a data processing channel to parse and load web page collection data in an integrated environment; 2.2) Process the collected data based on the numpy and pandas packages in the Python library, where data processing includes data cleaning, data screening, data grouping, and data calculation; 2.3) Use the ExcelWriter tool in the Pandas package to work with the underlying engines (openpyxl, xlsxwriter, and xlwt) to organize and store the collected and processed data in the form of logical groups, and export them as structured files according to the user-defined path and naming rules.

[0021] It should be noted that in this embodiment, web page data processing tasks are executed based on Python, including data input, data processing, and data export and storage. This example takes the automatic generation of a daily operation report robot for a photovoltaic power station as an example. Specifically, the daily production report is input into a Python integrated development environment (such as PyCharm, Anaconda) through the pandas package in the Python library. Based on the numpy and pandas packages in the Python library, the specified data is removed, and the data is filtered and grouped according to the local area, so as to perform grouped data calculations, where the data calculation content includes the number of power stations, installed capacity, equivalent operating hours, and power generation, etc., so as to use the ExcelWriter tool in the Pandas package to store the collected and processed data in the same Excel file, and finally, export it as a structured file according to the defined path and naming rules.

[0022] Automatically generate graphics and text based on processed data files and build reports, including: 3.1) Use Microsoft Excel software to load and open the structured file after data processing; 3.2) Use Excel's illustration function to insert and standardize custom data visualization charts, where standardizing charts includes standardizing chart content, standardizing chart format, and standardizing chart layout; 3.3) Enable the VBA macro script function and build the VBA code. The VBA code function covers the entire report generation process, including creating documents, inserting graphics and text, and saving documents.

[0023] It should be noted that in this embodiment, by using Microsoft Excel software to load the structured file after data processing, the illustration function of Excel is used to insert standardized charts, so that VBA macro scripts can be enabled to build report content. This example takes the automatic generation of a daily report on the operation of a photovoltaic power station as an example. Specifically, the structured file after daily data processing is opened, and charts are inserted. The chart content includes a table (chart) of power station operation status in each region, a statistical table of power station access platforms in each region, a statistical table of the top (bottom) 20 power generation efficiency in each region, and a detailed table of newly added power stations connected to the platform each day, so that the VBA macro script can be enabled to enter the code editing window (Visual Basic for Applications). Editor), use the Workbooks.Add method to create a new workbook as the target file for the daily report, set the initial format of the file, such as headers and footers, title style, font size, etc., insert various charts into the target document through VBA code, adjust the layout and format of the chart, including title settings, axis units, colors, legends, etc., use the Range object to add corresponding text descriptions in the target cells, use the Range.Copy and PasteSpecial methods to copy the data table from the original structured file to the target document, and make formatting adjustments, automatically set paging according to the content volume, adjust the page settings through ActiveSheet.PageSetup, use the Workbook.SaveAs method to save the final generated report file, and support multiple format outputs (such as .pdf or .txt).

[0024] Storage based on the generated adaptive reports, including: 4.1) According to the time dimension and usage frequency of report data, the reports are divided into short-term data and long-term data; 4.2) For short-term data, i.e., reports generated in real time and frequently accessed, the file system storage method is used; 4.3) For long-term data, that is, reports that are generated periodically and accessed less frequently but need to be kept for a long time, choose archive storage.

[0025] It should be noted that, in this embodiment, different storage methods are adopted according to the time dimension and usage frequency of the report data. This example takes the robot for automatically generating daily, weekly, monthly, semi-annual and annual reports on the operation of a photovoltaic power station as an example. The daily report is generated frequently and the access demand is concentrated in a short period of time. The file system storage is adopted (an automatic cleanup mechanism is set to retain the daily reports of the last 7 days, and the overdue daily reports are archived to the long-term storage system); the weekly report is generated frequently and the access frequency is concentrated in the current week and the next week. The file system storage is adopted (the weekly reports of the last 4 weeks are retained, and the weekly reports of more than 4 weeks are archived); the monthly report is generated frequently and the access demand is mainly for business analysis and monthly summary. The file system storage is adopted (the monthly reports of the last 12 months are retained, and the overdue monthly reports are archived to long-term storage); the semi-annual report is generated frequently and the data volume is large. The access demand is concentrated on the semi-annual summary and trend analysis, and it is directly archived to the long-term storage system; the annual report is generated frequently at the lowest frequency, but it involves the summary of important data for the whole year and has a high preservation value, so the archiving storage method is adopted.

[0026] The present invention also provides a storage medium, in which a computer program is stored. When the computer program is executed by a processor, it is used to implement the methods provided by the various embodiments described above.

[0027] Through intelligent process design and automated execution, the present invention significantly improves the efficiency and accuracy of data processing in photovoltaic energy management, thereby improving operation and maintenance efficiency, reducing labor costs, optimizing data insight capabilities, and providing effective support for the intelligent and systematic operation and maintenance of photovoltaics.

[0028] The core of the present invention is that the intelligent process-driven adaptive report production method relies on dynamic data analysis and processing technology to automatically adjust the report content and structure according to the changes in input data, thereby greatly improving the intelligence and flexibility of report generation. This invention not only reduces the dependence on manual operation, but also can adapt to the report requirements in different scenarios, greatly improving the data presentation efficiency in the operation and maintenance process.

[0029] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

[0030] Notes:

[0031] 1.RPA: Robotic process automation (RPA) is a business process automation technology based on software robots and artificial intelligence (AI).

[0032] 2. UI: User Interface refers to the overall design of the software's human-computer interaction, operating logic, and interface aesthetics.

[0033] 3. Python: Python is a high-level programming language with a huge standard library and third-party libraries, suitable for various application scenarios such as Web development, data analysis, artificial intelligence, scientific computing, etc.

[0034] 4.numpy: NumPy (Numerical Python) is an open source numerical computing extension of Python.

[0035] 5.pandas: pandas is a tool based on numpy that was created to solve data analysis tasks.

[0036] 6.ExcelWriter: ExcelWriter is a class in the pandas library that is used to write data into Excel files.

[0037] 7.openpyxl: openpyxl is a Python library for reading and writing Excel 2010 xlsx / xlsm / xltx / xltm files. It provides a wealth of functions for operating Excel files, including creating, reading, modifying and saving workbooks and worksheets.

[0038] 8.xlsxwriter: xlsxwriter is a Python library for creating Excel 2007+ XLSX files. It provides a wealth of functions for operating Excel files, including creating workbooks, adding worksheets, writing data, setting cell formats, inserting charts, etc.

[0039] 9.xlwt: An extension tool for writing Excel files in Python language.

[0040] 10. VBA: VBA (Visual Basic for Applications) is a macro language of Visual Basic. It is a programming language for performing common automation (OLE) tasks in its desktop applications. It can be mainly used to expand the application functions of Windows, especially Microsoft Office software. It can also be said to be a basic script for visual application programs.

[0041] 11.PyCharm: PyCharm is a Python IDE (Integrated Development Environment) with a complete set of tools that can help users improve their efficiency when developing in Python language.

[0042] 12.Anaconda: Anaconda, Chinese for "Python", is an open source Python distribution that includes more than 180 scientific packages such as conda and Python and their dependencies.

[0043] 13. Microsoft Excel: Microsoft Excel is a spreadsheet software written by Microsoft for computers using Windows and Apple Macintosh operating systems.

[0044] 14.Workbooks.Add: Workbooks.Add is a method in Excel VBA that is used to create a new blank workbook.

[0045] 15.Range: range is a computer term. In Excel, the Range collection represents a cell, a row, a column, a selected area (which can contain one or more continuous cell areas), or a three-dimensional area. You can use Range(arg) (where arg is the name of the area) to return a Range object representing a single cell or a cell area.

[0046] 16.Range.Copy: Range.Copy is a method in Excel that is used to copy the contents of a specified range of cells to the clipboard.

[0047] 17.PasteSpecial: PasteSpecial is a method in Excel that is used to paste the contents of the clipboard into the target cell in a specific way.

[0048] 18.ActiveSheet.PageSetup: ActiveSheet.PageSetup is a property in Excel VBA that is used to set the page layout and printing options for the active worksheet.

[0049] 19.Workbook.SaveAs: Workbook.SaveAs is a method in Excel VBA that is used to save the current workbook as a new file.

Claims

1. An adaptive report generation method based on intelligent process drive, characterized by The following steps are involved: 101) Use RPA robots to collect web platform data, where web platform data includes exportable data, UI interface content data, and structured table data; 102) Perform web page data processing tasks based on Python, where data processing tasks include data input, data processing, and data export and storage; 103) Automatically generate graphics and text based on processed data files and construct reports, where the report content includes data charts and analysis summaries; 104) Storing the generated adaptive report, wherein the storage method includes file system storage and archive storage.

2. The method for generating an adaptive report based on intelligent process drive according to claim 1 is characterized in that The use of RPA robots to collect web platform data specifically includes: 1.1) In the robot process list, select the RPA robot to be executed; 1.2) Use the selected RPA robot process to log in to the corresponding business data platform, where the business data platform includes the photovoltaic data platform, operation and maintenance data platform, and marketing data platform; 1.3) Automatically log in to various business data platforms based on RPA robots to collect web platform data. The web platform data collected includes export data, interface display content data, and data in structured table form.

3. The method for generating an adaptive report based on intelligent process drive according to claim 1 is characterized in that The web page data processing task based on Python specifically includes: 2.1) Based on the pandas package in the Python library, build a data processing channel to parse and load web page collection data in an integrated environment; 2.2) Process the collected data based on the numpy and pandas packages in the Python library, where data processing includes data cleaning, data screening, data grouping, and data calculation; 2.3) Use the ExcelWriter tool in the Pandas package to work with the underlying engines openpyxl, xlsxwriter, and xlwt to organize and store the collected and processed data in the form of logical groups, and export them as structured files according to the user-defined path and naming rules.

4. The method for generating an adaptive report based on intelligent process drive according to claim 1 is characterized in that The automatic generation of graphics and text and construction of reports based on the processed data files specifically include: 3.1) Use Excel software to load and open the structured file after data processing; 3.2) Use the illustration function of Excel software to insert and standardize custom data visualization charts, where standardizing charts includes standardizing chart content, standardizing chart format, and standardizing chart layout; 3.3) Enable the VBA macro script function and build the VBA code. The VBA code function covers the entire report generation process, including creating documents, inserting graphics and text, and saving documents.

5. The method for generating an adaptive report based on intelligent process drive according to claim 1 is characterized in that The storing based on the generated adaptive report specifically includes: 4.1) According to the time dimension and usage frequency of report data, the reports are divided into short-term data and long-term data; 4.2) For short-term data, i.e., reports generated in real time and frequently accessed, the file system storage method is used; 4.3) For long-term data, that is, reports that are generated periodically and accessed less frequently but need to be preserved for a long time, choose archive storage.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the adaptive report generation method according to any one of claims 1 to 5 is implemented.

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