Method for generating reservoir dam safety monitoring report based on large model

Abstract

The invention discloses a method for generating a reservoir dam safety monitoring report based on a large model. The method comprises the following steps: automatically extracting required data from a database to EXCL by applying DeepSeek large model programming; automatically generating a process chart, a contour map, a characteristic value statistical table and a chart, a table and data required in a statistical value report of the monitoring instrument after data insertion in an EXCL table through programming or functions; a DeepSeek large model is used for modularizing each chapter of a monitoring report, and a complete dam safety monitoring report is automatically formed according to the chapters related to each report; automatically replacing and updating data, characteristic statistical values, charts and the like which need to be changed in templates according to the independent report templates of each reservoir by applying a DeepSeek large model; preliminarily generating an analysis conclusion based on a preset rule; the technical problems of long time, poor accuracy and the like of monitoring report writing of the reservoir dam in the prior art are solved.

Description

Technical Field

[0001] This invention belongs to the field of big data and artificial intelligence technology; in particular, it relates to a method for generating reservoir dam safety monitoring reports based on large models. Background Technology

[0002] The safety monitoring report for a reservoir dam involves numerous monitoring items, each with a variety and quantity of monitoring instruments. Each internal monitoring instrument requires daily measurements over a long period. The report necessitates the classification, combination, and analysis of monitoring data from each instrument, generating numerous process graphs, contour maps, characteristic value statistical tables, and other charts. Manually exporting the required timeframes and pasting them into an Excel spreadsheet is time-consuming and prone to errors. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a method for generating reservoir dam safety monitoring reports based on large models, so as to solve the technical problems of long time and poor accuracy in the preparation of monitoring reports for reservoir dams in the existing technology.

[0004] The technical solution of this invention is:

[0005] A method for generating reservoir dam safety monitoring reports based on a large model, the method comprising:

[0006] Step 1: Use DeepSeek large model programming to automatically extract the required data from the database into EXCL;

[0007] Step 2: After inserting the data, the monitoring instrument automatically generates the required graphs, tables, and data in the EXCL table using programming or functions, including process line graphs, contour maps, characteristic value statistical tables, and statistical value reports.

[0008] Step 3: Apply the DeepSeek large model to modularize the various chapters of the monitoring report, and automatically assemble a complete dam safety monitoring report based on the chapters involved in each report;

[0009] Step 4: Apply the DeepSeek large model and automatically replace and update the data, feature statistics, charts, etc. that need to be changed in the template according to the individual report template for each reservoir; generate preliminary analysis conclusions based on preset rules.

[0010] The method for extracting the required data into EXCL includes: using a Python program, specifying the start and end dates for inserting data, searching for the corresponding monitoring instrument data in the Access database based on the instrument in the EXCL worksheet, finding the date, frequency, and temperature values ​​for that instrument in the database, adding a new row in the EXCL worksheet according to the searched date in chronological order, and filling that row with the date and its corresponding frequency and temperature values; inserting the frequency and temperature values ​​into the column corresponding to the instrument in the EXCL worksheet, and then using the original calculation formula in the EXCL worksheet to fill down the column containing the calculated results to obtain the calculated results for the new time period of that monitoring instrument.

[0011] Step 2 is implemented by manually creating a process curve diagram, adjusting the relevant instrument combinations and parameters, and automatically increasing the data statistical period of the process curve diagram as the monitoring data time increases; the contour map is generated in the same way as the process curve diagram; using DeepSeek large model queries, programming or functions are used to automatically update the data in the characteristic value statistics table; as the data time period increases, the current value, period value, and cumulative value in the characteristic value table change; through function programming, after inserting newly added data, the process curve diagram, characteristic value table, and related statistical values ​​are also automatically updated.

[0012] The method of modularizing the monitoring report using the DeepSeek large model includes: generating a separate Word report template for each reservoir based on its own monitoring items and the types and quantities of monitoring instruments; generating a general template for the dam monitoring report of a new reservoir based on the modules, and then adjusting it according to the project situation to obtain the dam safety monitoring report template for the new reservoir.

[0013] The method of automatically deriving preliminary conclusions from various monitoring data through programming and combining monitoring data includes: applying the DeepSeek large model, and through program execution, replacing each characteristic value statistical table and process line graph that needs to be replaced in the report with the corresponding characteristic value statistical table and process line graph in the EXCL table. The corresponding data and dates that need to be replaced are also replaced through programming; for the analysis conclusions of each monitoring result, several conclusion scenarios are preset through programming, and preliminary automatic judgment is achieved based on the data in the process line graph and characteristic value statistical table. After the report is completed, manual review and changes are made.

[0014] The beneficial effects of this invention are:

[0015] Existing technologies require several hours or days to update data, while this invention achieves automatic data updates in just minutes. Process graphs, characteristic value tables, and related statistical values ​​also update automatically, eliminating the need for manual replacement, saving time, and reducing errors. When opening a report template, simply clicking "Update this report" automatically updates and replaces the report, generating a new report in approximately one minute, with a low risk of replacement errors. Compared to traditional methods of writing dam safety monitoring reports, this invention significantly saves time, improves work efficiency, and eliminates human error.

[0016] This invention utilizes the DeepSeek large-scale model to automatically replace and perform preliminary analysis of monitoring data in reservoir dam monitoring reports, significantly reducing report preparation time and improving work efficiency. The DeepSeek model automatically extracts data from the database of the internal data acquisition system into an Excel spreadsheet. It then automatically calculates the results, generates process curves and contour maps, and creates characteristic value statistical tables. Based on the dam monitoring report template, it automatically replaces the process curves, characteristic value statistical tables, and related analytical data in the monitoring data analysis section. It automatically analyzes the monitoring data and draws preliminary conclusions, achieving intelligent, automated, and standardized monitoring report writing, significantly saving report preparation time and reducing errors caused by human error.

[0017] This solves the technical problems of long processing time and poor accuracy in the preparation of monitoring reports for reservoirs and dams in existing technologies. Detailed Implementation

[0018] A method for generating reservoir dam safety monitoring reports based on a large model includes:

[0019] Step 1: Use DeepSeek large model programming to automatically extract the required data from the database into EXCL.

[0020] The monitoring data from instruments inside the reservoir dam are automatically collected and stored on local devices via automated acquisition systems, and then remotely transmitted to the receiving software system. The collected data is stored in an Access database. This invention uses DeepSeek large-scale model query programming to insert the measured raw data into an Excel spreadsheet. This solves the problem of existing technologies that rely on manually exporting data for the required time period and pasting it into an Excel spreadsheet, which is time-consuming and prone to errors.

[0021] This invention utilizes a Python program. By specifying the start and end dates for data insertion, the program searches for corresponding monitoring instrument data in an Access database based on the instrument's information in the Excel worksheet. It retrieves the date, frequency, and temperature values ​​for that instrument from the database. A new row is then added to the Excel worksheet in chronological order based on the retrieved date, and this row is filled with the date, its corresponding frequency, and temperature values. The frequency and temperature values ​​are then inserted into the column corresponding to the instrument's row in the Excel worksheet. Finally, using the existing calculation formulas in the Excel worksheet, the program calculates the results for the newly added time period for that monitoring instrument, thus obtaining continuous monitoring data from the time of installation to the present.

[0022] This invention uses DeepSeek large model queries and Python software programming to achieve data extraction according to the above path. Data updates can be completed in just a few minutes, whereas previously this step would have taken several hours or days (depending on the number of instruments).

[0023] Step 2: After inserting the data, the monitoring instrument automatically generates the required graphs, tables, and data in the EXCL table through programming or functions, including process line graphs, contour maps, characteristic value statistical tables, and statistical value reports.

[0024] For each reservoir dam, some monitoring instruments of each type need to be combined to generate process curve diagrams, and the number of instruments and parameters for each process curve diagram are different. The approach is to first manually create a process curve diagram, adjust the relevant instrument combinations and parameters, and automatically increase the data statistical period of the process curve diagram as the monitoring data time increases; the generation principle of contour maps is the same as that of process curve diagrams.

[0025] By asking questions using a large DeepSeek model, you can use programming or functions to automatically update the data in the feature value statistics table. As the data time period increases, the current value, period value, and cumulative value in the feature value table will change.

[0026] By using functions, after inserting new data, the process graph, feature table, and related statistical values ​​are automatically updated without the need for manual replacement.

[0027] Step 3: Apply the DeepSeek large model to modularize the chapters of the monitoring report, and automatically assemble a complete dam safety monitoring report based on the chapters involved in each report.

[0028] Each reservoir generates a separate Word report template based on its specific monitoring items and the types and quantities of monitoring instruments. Since the specific monitoring items and the number of monitoring instruments vary from reservoir to reservoir, the data analysis methods also differ. Therefore, this invention uses the DeepSeek large model to modularize the common sections of the monitoring report, while the data analysis and conclusion sections are adjusted and written according to the specific circumstances of each reservoir.

[0029] The dam monitoring report for the new reservoir is generated using a general template based on modules, and then adjusted according to the specific project situation to obtain the dam safety monitoring report template for the new reservoir.

[0030] Step 4: Apply the DeepSeek large model and automatically replace and update the data, feature statistics, charts, etc. that need to be changed in the template according to the individual report template for each reservoir.

[0031] This invention automatically derives preliminary conclusions from various monitoring data through programming and by combining monitoring data.

[0032] This invention utilizes a standard template for reservoir dam safety monitoring reports, employing programming and object linking for precise content replacement to achieve automatic report generation. Simply clicking "Update this document" when opening the Word report template automatically updates and replaces the report, taking only about one minute.

[0033] Because the monitoring items and contents of each reservoir are different, the combination and analysis of monitoring instruments are also different, and the report templates for each reservoir are different.

[0034] Therefore, this invention utilizes the DeepSeek large model. Through program execution, each feature value statistical table and process line graph that needs to be replaced in the report is replaced with the corresponding feature value statistical table and process line graph in the EXCL table. The corresponding data and dates that need to be replaced are also accurately replaced through programming. For the analysis conclusions of each monitoring result, several conclusion scenarios are preset through programming. Based on the data in the process line graph and feature value statistical table, an initial automatic judgment is achieved. After the report is completed, manual review and changes are made.

[0035] This invention automatically extracts data, generates charts and statistical values, and generates reports through a program. This reduces the time required to write a report from several hours to several days to less than an hour, greatly saving report writing time and improving work efficiency.

[0036] The report update process requires only three steps. First, export the raw data from the platform, renaming the Excel files to match the original data, paying attention to the last day date required for the report. Second, run two Python programs; new data will be automatically added, and the process graph and eigenvalue table will be updated simultaneously. Third, open the Word report, select "Yes," and the report will automatically update the necessary content.

Claims

1. A method for generating reservoir dam safety monitoring reports based on large models, characterized in that: The method includes: Step 1: Use DeepSeek large model programming to automatically extract the required data from the database into EXCL; Step 2: After inserting the data, the monitoring instrument automatically generates the required graphs, tables, and data in the EXCL table using programming or functions, including process line graphs, contour maps, characteristic value statistical tables, and statistical value reports. Step 3: Apply the DeepSeek large model to modularize the various chapters of the monitoring report, and automatically assemble a complete dam safety monitoring report based on the chapters involved in each report; Step 4: Apply the DeepSeek large model and automatically replace and update the data, feature statistics, charts, etc. that need to be changed in the template according to the individual report template for each reservoir; generate preliminary analysis conclusions based on preset rules.

2. The method for generating a reservoir dam safety monitoring report based on a large model according to claim 1, characterized in that: The method for extracting the required data into EXCL includes: using a Python program, specifying the start and end dates for inserting data, searching for the corresponding monitoring instrument data in the Access database based on the instrument in the EXCL worksheet, finding the date, frequency, and temperature values ​​for that instrument in the database, adding a new row in the EXCL worksheet according to the searched date in chronological order, and filling that row with the date and its corresponding frequency and temperature values; inserting the frequency and temperature values ​​into the column corresponding to the instrument in the EXCL worksheet, and then using the original calculation formula in the EXCL worksheet to fill down the column containing the calculated results to obtain the calculated results for the new time period of that monitoring instrument.

3. The method for generating a reservoir dam safety monitoring report based on a large model according to claim 1, characterized in that: Step 2 is implemented by manually creating a process curve diagram, adjusting the relevant instrument combinations and parameters, and automatically increasing the data statistical period of the process curve diagram as the monitoring data time increases; the contour map is generated in the same way as the process curve diagram; using DeepSeek large model queries, programming or functions are used to automatically update the data in the characteristic value statistics table; as the data time period increases, the current value, period value, and cumulative value in the characteristic value table change; through function programming, after inserting newly added data, the process curve diagram, characteristic value table, and related statistical values ​​are also automatically updated.

4. The method for generating a reservoir dam safety monitoring report based on a large model according to claim 1, characterized in that: The method of modularizing the monitoring report using the DeepSeek large model includes: generating a separate Word report template for each reservoir based on its own monitoring items and the types and quantities of monitoring instruments; generating a general template for the dam monitoring report of a new reservoir based on the modules, and then adjusting it according to the project situation to obtain the dam safety monitoring report template for the new reservoir.

5. The method for generating a reservoir dam safety monitoring report based on a large model according to claim 1, characterized in that: The method for generating preliminary analysis conclusions based on preset rules includes: applying the DeepSeek large model, and through program execution, replacing each feature value statistical table and process line graph that needs to be replaced in the report with the corresponding feature value statistical table and process line graph in the .excl table. The corresponding data and dates that need to be replaced are also replaced through program programming; for the analysis conclusions of each monitoring result, several conclusion scenarios are preset through programming, and the data of the process line graph and feature value statistical table are used to initially achieve automatic judgment, and then manual review and modification are performed after the report is completed.